The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

By Sam Charrington

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Subscribers: 1348
Reviews: 3


 Dec 25, 2018

Elvis Alive
 Jul 20, 2018
Great podcast covering both business and technical aspects of ML and AI

A Podcast Republic user
 Jul 9, 2018

Description

Machine learning and artificial intelligence are dramatically changing the way businesses operate and people live. The TWIML AI Podcast brings the top minds and ideas from the world of ML and AI to a broad and influential community of ML/AI researchers, data scientists, engineers and tech-savvy business and IT leaders. Hosted by Sam Charrington, a sought after industry analyst, speaker, commentator and thought leader. Technologies covered include machine learning, artificial intelligence, deep learning, natural language processing, neural networks, analytics, deep learning, computer science, data science and more.

Episode Date
Computer Vision for Remote AR with Flora Tasse - #390
40:54

Today we conclude our CVPR coverage joined by Flora Tasse, Head of Computer Vision & AI Research at Streem. 

Flora, a keynote speaker at the AR/VR workshop at CVPR, walks us through some of the interesting use cases at the intersection of AI, computer vision, and augmented reality technology. In our conversation, we discuss how Flora’s interest in a career in AR/VR developed, the origin of her company Selerio, which was eventually acquired by Streem, and her current research.

We also spend time exploring the difficulties associated with building 3D mesh environments, extracting metadata from those environments, the challenges of pose estimation, and other papers that caught Flora’s eye from the conference.

The complete show notes for this episode can be found at twimlai.com/talk/390. For our complete CVPR series, head to twimlai.com/cvpr20.

Jul 09, 2020
Deep Learning for Automatic Basketball Video Production with Julian Quiroga - #389
42:15

Today we return to our coverage of the 2020 CVPR conference with a conversation with Julian Quiroga, a Computer Vision Team Lead at Genius Sports.

Julian presented his recent paper “As Seen on TV: Automatic Basketball Video Production using Gaussian-based Actionness and Game States Recognition” at the CVSports workshop. We jump right into the paper, discussing details like camera setups and angles, detection and localization of the figures on the court (players, refs, and of course, the ball), and the role that deep learning plays in the process. We also break down how this work applies to different sports, and the ways that Julian is looking to improve on this work for better accuracy. 

The complete show notes for this episode can be found at twimlai.com/talk/389. To follow along with our entire CVPR series, visit twimlai.com/cvpr20.

Thanks again to our friends at Qualcomm for their support of the podcast and sponsorship of this series!

Jul 06, 2020
How External Auditing is Changing the Facial Recognition Landscape with Deb Raji - #388
01:21:47

Today we’re taking a break from our CVPR coverage to bring you this interview with Deb Raji, a Technology Fellow at the AI Now Institute at New York University. 

Over the past week or two, there have been quite a few major news stories in the AI community, including the self-imposed moratorium on facial recognition technology from Amazon, IBM and Microsoft.There was also the release of PULSE, a controversial computer vision model that ultimately sparked a Twitter firestorm involving Yann Lecun and AI ethics researchers, including friend of the show, Timnit Gebru. The controversy echoed into the broader AI community, eventually leading to the former’s departure from Twitter. 

In our conversation with Deb, we dig into these stories in depth, discussing the origins of Deb’s work on the Gender Shades project, how subsequent work put a spotlight on the potential harms of facial recognition technology, and who holds responsibility for dealing with underlying bias issues in datasets.

The complete show notes for this episode can be found at twimlai.com/talk/388.

Jul 02, 2020
AI for High-Stakes Decision Making with Hima Lakkaraju - #387
45:54

Today we’re joined by Hima Lakkaraju, an Assistant Professor at Harvard University with appointments in both the Business School and Department of Computer Science. 

At CVPR, Hima was a keynote speaker at the Fair, Data-Efficient and Trusted Computer Vision Workshop, where she spoke on Understanding the Perils of Black Box Explanations. Hima talks us through her presentation, which focuses on the unreliability of explainability techniques that center perturbations, such as LIME or SHAP, as well as how attacks on these models can be carried out, and what these attacks look like. We also discuss people’s tendency to trust computer systems and their outputs, her thoughts on collaborator (and former TWIML guest) Cynthia Rudin’s theory that we shouldn’t use black-box algorithms, and much more.

For the complete show notes, visit twimlai.com/talk/387. For our continuing CVPR Coverage, visit twimlai.com/cvpr20.

Jun 29, 2020
Invariance, Geometry and Deep Neural Networks with Pavan Turaga - #386
47:14

We continue our CVPR coverage with today’s guest, Pavan Turaga, Associate Professor at Arizona State University, with dual appointments as the Director of the Geometric Media Lab, and Interim Director of the School of Arts, Media, and Engineering.

Pavan gave a keynote presentation at the Differential Geometry in CV and ML Workshop, speaking on Revisiting Invariants with Geometry and Deep Learning. In our conversation, we go in-depth on Pavan’s research integrating physics-based principles into computer vision. We also discuss the context of the term “invariant,” and the role of architectural, loss function, and data constraints on models. Pavan also contextualizes this work in relation to Hinton’s similar Capsule Network research.

Check out the complete show notes for this episode at twimlai.com/talk/386.

Jun 25, 2020
Channel Gating for Cheaper and More Accurate Neural Nets with Babak Ehteshami Bejnordi - #385
55:58

Today we’re joined by Babak Ehteshami Bejnordi, a Research Scientist at Qualcomm.

Babak works closely with former guest Max Welling and is currently focused on conditional computation, which is the main driver for today’s conversation. We dig into a few papers in great detail including one from this year’s CVPR conference, Conditional Channel Gated Networks for Task-Aware Continual Learning

We also discuss the paper TimeGate: Conditional Gating of Segments in Long-range Activities, and another paper from this year’s ICLR conference, Batch-Shaping for Learning Conditional Channel Gated Networks. We cover how gates are used to drive efficiency and accuracy, while decreasing model size, how this research manifests into actual products, and more! 

For more information on the episode, visit twimlai.com/talk/385. To follow along with the CVPR 2020 Series, visit twimlai.com/cvpr20

Thanks to Qualcomm for sponsoring today’s episode and the CVPR 2020 Series!

Jun 22, 2020
Machine Learning Commerce at Square with Marsal Gavalda - #384
51:53

Today we’re joined by Marsal Gavalda, head of machine learning for the Commerce platform at Square. 

Marsal, who hails from Barcelona, Catalonia, kicks off our conversation by indulging Sam in their shared love for language, which is what put him on the path to a career in machine learning. At Square, Marsal manages the development of machine learning for various tools and platforms, including marketing, appointments, and above all, risk management. 

We explore how they manage this vast portfolio of projects, and how having an ML and technology focus at the outset of the company has contributed to their success. We also discuss some of Marsal’s tips and best practices for internal democratization of ML, their approach to developing ML-driven features, the techniques deployed in the development of those features, and much more!

The complete show notes for this episode can be found at twimlai.com/talk/384.

Jun 18, 2020
Cell Exploration with ML at the Allen Institute w/ Jianxu Chen - #383
43:21

Today we’re joined by Jianxu Chen, a scientist in the Assay Development group at the Allen Institute for Cell Science. 

At the latest GTC conference, Jianxu presented his work on the Allen Cell Explorer Toolkit, an open-source project that allows users to do 3D segmentation of intracellular structures in fluorescence microscope images at high resolutions, making the images more accessible for data analysis. 

In our conversation, we discuss three of the major components of the toolkit: the cell image analyzer, the image generator, and the image visualizer. We also explore Jianxu’s transition from computer science into computational biology. More broadly, we cover how the use of GPUs has fundamentally changed this research, and the goals his team had in mind when they began the project.

Check out the complete show notes at twimlai.com/talk/383.

Jun 15, 2020
Neural Arithmetic Units & Experiences as an Independent ML Researcher with Andreas Madsen - #382
30:54

Today we’re joined by Andreas Madsen, an independent researcher based in Denmark whose research focuses on developing interpretable machine learning models. 

While we caught up with Andreas to discuss his ICLR spotlight paper, “Neural Arithmetic Units,” we also spend time exploring his experience as an independent researcher. We discuss the difficulties of working with limited resources, the importance of finding peers to collaborate with, and tempering expectations of getting papers accepted to conferences -- something that might take a few tries to get right.

In his paper, Andreas notes that Neural Networks struggle to perform exact arithmetic operations over real numbers, but this can be helped with the addition of two NN components: the Neural Addition Unit (NAU), which can learn exact addition and subtraction; and the Neural Multiplication Unit (NMU) that can multiply subsets of a vector.

The complete show notes can be found at twimlai.com/talk/382.

Jun 11, 2020
2020: A Critical Inflection Point for Responsible AI with Rumman Chowdhury - #381
01:01:58

Today we’re joined by Rumman Chowdhury, Managing Director and Global Lead of Responsible Artificial Intelligence at Accenture. In our conversation with Rumman, we explored questions like: 

  • Why is now such a critical inflection point in the application of responsible AI?
  • How should engineers and practitioners think about AI ethics and responsible AI?
  • Why is AI ethics inherently personal and how can you define your own personal approach?
  • Is the implementation of AI governance necessarily authoritarian?
  • How do we balance idealism and pragmatism in the application of AI ethics?

We also cover practical topics like how and where you should implement responsible AI in your organization, and building the teams and processes capable of taking on critical ethics and governance questions.

The complete show notes for this episode can be found at twimlai.com/talk/381.

Jun 08, 2020
Panel: Advancing Your Data Science Career During the Pandemic - #380
01:07:16

Today we’re joined by Ana Maria Echeverri, Caroline Chavier, Hilary Mason, and Jacqueline Nolis, our guests for the recent Advancing Your Data Science Career During the Pandemic panel.

In this conversation, we explore ways that Data Scientists and ML/AI practitioners can continue to advance their careers despite current challenges. Our panelists provide concrete tips, advice, and direction for those just starting out, those affected by layoffs, and those just wanting to move forward in their careers.

Topics we cover include:

  • Guerilla Job Hunting
  • Portfolio Building
  • Navigating Hiring Freezes
  • Acing the Technical Interview
  • Presenting the Best Candidate

For more information about our guests, or for slinks to the resources mentioned, visit the show notes page at twimlai.com/talk/380.

Jun 04, 2020
On George Floyd, Empathy, and the Road Ahead
06:20

Visit twimlai.com/blacklivesmatter for resources to support organizations pushing for social equity like Black Lives Matter, and groups offering relief for those jailed for exercising their rights to peaceful protest. 

Jun 02, 2020
Engineering a Less Artificial Intelligence with Andreas Tolias - #379
46:41

Today we’re joined by Andreas Tolias, Professor of Neuroscience at Baylor College of Medicine and Principal Investigator of the Neuroscience-Inspired Networks for Artificial Intelligence organization.

We caught up with Andreas to discuss his recent perspective piece, “Engineering a Less Artificial Intelligence,” which explores the shortcomings of state-of-the-art learning algorithms in comparison to the brain. The paper also offers several ideas about how neuroscience can lead the quest for better inductive biases by providing useful constraints on representations and network architecture. We discuss the promise of deep neural networks, the differences between inductive bias and model bias, the role of interpretability, and the exciting future of biological systems and deep learning. 

The complete show notes can be found at twimali.com/talk/379.

May 28, 2020
Rethinking Model Size: Train Large, Then Compress with Joseph Gonzalez - #378
52:39

Today we’re joined by Joseph Gonzalez, Assistant Professor in the EECS department at UC Berkeley. 

Our main focus in the conversation is Joseph’s paper “Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers,” which explores compute-efficient training strategies, based on model size.

We discuss the two main problems being solved; 1) How can we rapidly iterate on variations in architecture? And 2) If we make models bigger, is it really improving any efficiency? We also discuss the parallels between computer vision and NLP tasks, how he characterizes both “larger” and “faster” in the paper.

Check out the complete show notes for this episode at twimlai.com/talk/378.

May 25, 2020
The Physics of Data with Alpha Lee - #377
34:29

Today we’re joined by Alpha Lee, Winton Advanced Fellow in the Department of Physics at the University of Cambridge, and Co-Founder of data-driven drug discovery startup, PostEra. Our conversation centers around Alpha’s research which can be broken down into three main categories: data-driven drug discovery, material discovery, and physical analysis of machine learning. 

We discuss the similarities and differences between drug discovery and material science, including the parallels in the design test cycle, and the major differences in cost. We also explore the goals associated with uncertainty estimation, why deep networks are easier to optimize than shallow networks, the concept of energy landscape, and how it all fits into his research. We also talk about his startup, PostEra which offers medicinal chemistry as a service powered by machine learning.

The complete show notes for this episode can be found at twimlai.com/talk/377.

May 21, 2020
Is Linguistics Missing from NLP Research? w/ Emily M. Bender - #376
52:34

Today we’re joined by Emily M. Bender, Professor of Linguistics at the University of Washington. 

Our discussion covers a lot of ground, but centers on the question, "Is Linguistics Missing from NLP Research?" We explore if we would be making more progress, on more solid foundations, if more linguists were involved in NLP research, or is the progress we're making (e.g. with deep learning models like Transformers) just fine?

Later this afternoon (3pm PT) we’ll be hosting a viewing party with Emily over on our YouTube channel. Sam and Emily will be in the live chat answering your questions from the conversation. Register at twimlai.com/376viewing!

Check out the complete show notes for this conversation at twimlai.com/talk/376.

May 18, 2020
Disrupting DeepFakes: Adversarial Attacks Against Conditional Image Translation Networks with Nataniel Ruiz - #375
42:42

Today we’re joined by Nataniel Ruiz, a PhD Student in the Image & Video Computing group at Boston University. 

We caught up with Nataniel to discuss his paper “Disrupting DeepFakes: Adversarial Attacks Against Conditional Image Translation Networks and Facial Manipulation Systems,” which will be presented at the upcoming CVPR conference. In our conversation, we discuss the concept of this work, which essentially injects noise into an image to disrupt a generative model’s ability to manipulate said image. We also explore some of the challenging parts of implementing this work, a few potential scenarios in which this could be deployed, and the broader contributions that went into this work. 

The complete show notes for this episode can be found at twimlai.com/talk/375.

May 14, 2020
Understanding the COVID-19 Data Quality Problem with Sherri Rose - #374
44:30

Today we’re joined by Sherri Rose, Associate Professor at Harvard Medical School. 

Sherri’s research centers around developing and integrating statistical machine learning approaches to improve human health. We cover a lot of ground in our conversation, including the intersection of her research with the current COVID-19 pandemic, the importance of quality in datasets and rigor when publishing papers, and the pitfalls of using causal inference.

We also touch on Sherri’s work in algorithmic fairness, including the necessary emphasis being put on studying issues of fairness, the shift she’s seen in fairness conferences covering these issues in relation to healthcare research, and her paper “Fair Regression for Health Care Spending.”

Check out the complete show notes for this episode at twimlai.com/talk/374.

May 11, 2020
The Whys and Hows of Managing Machine Learning Artifacts with Lukas Biewald - #373
53:30

Today we’re joined by Lukas Biewald, founder and CEO of Weights & Biases, to discuss their new tool Artifacts, an end to end pipeline tracker. You might remember Lukas from his original interview with us towards the end of last year, for more background on Lukas and W&B we encourage you to check that out here .

In this conversation, we explore Artifacts’ place in the broader machine learning tooling ecosystem through the lens of our eBook “The definitive guide to ML Platforms” and how it fits with the W&B model management platform. We discuss also discuss what exactly “Artifacts” are, what the tool is tracking, and take a look at the onboarding process for users. 

Check out the complete show notes for this episode at twimlai.com/talk/373.

May 07, 2020
Language Modeling and Protein Generation at Salesforce with Richard Socher - #372
42:36

Today we’re joined Richard Socher, Chief Scientist and Executive VP at Salesforce.

Richard, who has been at the forefront of Salesforce’s AI Research since they acquired his startup Metamind in 2016, and his team have been publishing a ton of great projects as of late, including CTRL: A Conditional Transformer Language Model for Controllable Generation, and ProGen, an AI Protein Generator, both of which we cover in-depth in this conversation. We explore the balancing act between investments, product requirement research and otherwise at a large product-focused company like Salesforce, the evolution of his language modeling research since being acquired, and how it ties in with Protein Generation.

The complete show notes for this episode can be found at twimlai.com/talk/372.  

May 04, 2020
AI Research at JPMorgan Chase with Manuela Veloso - #371
45:25

Today we’re joined by Manuela Veloso, Head of AI Research at JPMorgan Chase and Professor at Carnegie Mellon University. Since moving from CMU to JPMorgan Chase, Manuela and her team established a set of seven lofty research goals. In this conversation we focus on the first three: building AI systems to eradicate financial crime, safely liberate data, and perfect client experience. 

We also explore Manuela’s background, including her time as a PhD student at CMU, or as she describes it, the “mecca of AI,” with some of the most influential figures in AI like Geoff Hinton, and Herb Simon on the faculty at the time. We also cover Manuela’s founding role with RoboCup, an annual international competition centered on autonomous robots playing soccer.

The complete show notes for this episode can be found at twimlai.com/talk/371.

Apr 30, 2020
Panel: Responsible Data Science in the Fight Against COVID-19 - #370
57:03

Since the beginning of the coronavirus pandemic, we’ve seen an outpouring of interest on the part of data scientists and AI practitioners wanting to make a contribution. At the same time, some of the resulting efforts have been criticized for promoting the spread of misinformation or being disconnected from the applicable domain knowledge.

In this discussion, we explore how data scientists and ML/AI practitioners can responsibly contribute to the fight against coronavirus and COVID-19. Four experts: Rex Douglass, Rob Munro, Lea Shanley, and Gigi Yuen-Reed shared a ton of valuable insight on the best ways to get involved.

We've gathered all the resources that our panelists discussed during the conversation, you can find those at twimlai.com/talk/370.

Apr 29, 2020
Adversarial Examples Are Not Bugs, They Are Features with Aleksander Madry - #369
41:03

Today we’re joined by Aleksander Madry, Faculty in the MIT EECS Department, a member of CSAIL and of the Theory of Computation group. Aleksander, whose work is more on the theoretical side of machine learning research, walks us through his paper “Adversarial Examples Are Not Bugs, They Are Features,” which was published previously presented at last year’s NeurIPS conference. 

In our conversation, we explore the idea of adversarial examples in machine learning systems being features, with results that might be undesirable, but still working as designed. We talk through what we expect these systems to do, vs what they’re actually doing, if we’re able to characterize these patterns, and what makes them compelling, and if the insights from the paper will inform opinions on either side of the deep learning debate.

The complete show notes for this can be found at twimlai.com/talk/369.

Apr 27, 2020
AI for Social Good: Why "Good" isn't Enough with Ben Green - #368
40:34

Today we’re joined by Ben Green, PhD Candidate at Harvard, Affiliate at the Berkman Klein Center for Internet & Society at Harvard, Research Fellow at the AI Now Institute at NYU. 

Ben’s research is focused on social and policy impacts of data science, with a focus on algorithmic fairness, municipal governments, and the criminal justice system. In our conversation, we discuss his paper ‘Good' Isn't Good Enough,’ which explores the 2 things he feels are missing from data science and machine learning projects, papers and research; A grounded definition of what “good” actually means, and the absence of a “theory of change.” We also talk through how he thinks about the unintended consequence associated with the application of technology to social good, and his theory for the relationship between technology and social impact. 

The complete show notes for this episode can be found at twimlai.com/talk/368.

Apr 23, 2020
The Evolution of Evolutionary AI with Risto Miikkulainen - #367
38:13

Today we’re joined by Risto Miikkulainen, Associate VP of Evolutionary AI at Cognizant AI, and Professor of Computer Science at the UT Austin.

Risto joined us back on episode #47 to discuss evolutionary algorithms, and today we do an update of sorts on what is the latest we should know on the topic. In our conversation, we discuss various use cases for evolutionary AI, the relationship between evolutionary algorithms and reinforcement learning, some of the latest approaches to deploying evolutionary models. We also explore his paper “Better Future through AI: Avoiding Pitfalls and Guiding AI Towards its Full Potential,” which details the historical evolution of AI, discussing where things currently stand, and where they might go in the future. 

The complete show notes for this episode can be found at twimlai.com/talk/367.

Apr 20, 2020
Neural Architecture Search and Google’s New AutoML Zero with Quoc Le - #366
53:43

Today we’re super excited to share our recent conversation with Quoc Le, a research scientist at Google, on the Brain team. Quoc has been very busy recently with his work on Google’s AutoML Zero, which details significant advances in automated machine learning that can  “automatically discover complete machine learning algorithms just using basic mathematical operations as building blocks.”

Another major theme of this conversation is semi-supervised learning, discussing his work on the paper “Self-training with Noisy Student improves ImageNet classification.” Finally, we discuss how his interest in sequence to sequence learning, and a chance encounter, led to the development of Meena, Google’s recent multi-turn conversational chatbot. 

This was a really fun conversation, so much so that we decided to release the video! April 16th at 12 pm PT, Quoc and Sam will premiere the video version of this interview, and answer your questions in the chat. We’ll see you there!

The complete show notes for this episode can be found at twimlai.com/talk/366.

Apr 16, 2020
Automating Electronic Circuit Design with Deep RL w/ Karim Beguir - #365
35:23

Today we’re joined by return guest Karim Beguir, Co-Founder and CEO of InstaDeep. We originally spoke with Karim about InstaDeep’s work back on episode 302, check that episode out for a full brief of Karim’s background.

In today’s conversation, we chat with Karim about InstaDeep’s new offering, DeepPCB, an end-to-end platform for automated circuit board design. We discuss challenges and problems with some of the original iterations of auto-routers, how Karim defines circuit board “complexity,” the differences between reinforcement learning being used for games and in this use case, and their spotlight paper from NeurIPS, co-authored with a team from Deepmind. 

Check out the complete show notes at twimlai.com/talk/365.

Apr 13, 2020
Neural Ordinary Differential Equations with David Duvenaud - #364
48:49

Today we’re joined by David Duvenaud, Assistant Professor at the University of Toronto. David, who joined us back on episode #96 back in January ‘18, is back to talk about the various papers that have come out of his lab over the last year and change, focused on Neural Ordinary Differential Equations, a type of continuous-depth neural network.

In our conversation, we talk through quite a few of David’s papers on the topic, which you can find below on the show notes page. We discuss the problem that David is trying to solve with this research, the potential that ODEs have to replace “the backbone” of the neural networks that are used to train today, and David’s approach to engineering. 

The complete show notes for this episode can be found at twimlai.com/talk/364.

Apr 09, 2020
The Measure and Mismeasure of Fairness with Sharad Goel - #363
47:33

Today we’re joined by Sharad Goel, Assistant Professor in the management science & engineering department at Stanford. Sharad, who also has appointments in the computer science, sociology, and law departments, has spent the recent years focused on applying machine learning to better understand and improve public policy. 

In our conversation, we dive into Sharad’s non-traditional path to academia, which includes extensive work on discriminatory policing, including practices like stop-and-frisk, leading up to his work on The Stanford Open Policing Project, which uses data from over 200 million traffic stops nationwide to “help researchers, journalists, and policymakers investigate and improve interactions between police and the public.” Finally, we discuss Sharad’s paper “The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning,” which identifies three formal definitions of fairness in algorithms, the statistical limitations of each, and details how mathematical formalizations of fairness could be introduced into algorithms.

Check out the complete show notes for this episode at twimlai.com/talk/363.

Apr 06, 2020
Simulating the Future of Traffic with RL w/ Cathy Wu - #362
34:16

Today we’re joined by Cathy Wu, Gilbert W. Winslow Career Development Assistant Professor in the department of Civil and Environmental Engineering at MIT. We had the pleasure of catching up with Cathy at NeurIPS to discuss her talk “Mixed Autonomy Traffic: A Reinforcement Learning Perspective.” 

In our conversation, we discuss Cathy’s transition to applying machine learning to civil engineering, specifically, understanding the potential impact autonomous vehicles would have on traffic once deployed. To better understand this, Cathy built multiple reinforcement learning simulations, including a track and intersection scenarios. We talk through how each scenario is set up, how human drivers are modeled for this simulation, and the results of the experiments.

Check out the complete show notes for this episode at twimlai.com/talk/362.

Apr 02, 2020
Consciousness and COVID-19 with Yoshua Bengio - #361
48:19

Today we’re joined by one of, if not the most cited computer scientist in the world, Yoshua Bengio. Yoshua is a Professor in the Department of Computer Science and Operations Research at the University of Montreal and the Founder and Scientific Director of MILA. We caught up with Yoshua just a few weeks into the coronavirus pandemic, so we spend a bit of time discussing his work both broadly on the impact of AI in society, as well as his current endeavor in building a COVID-19 tracing application, and the use of ML to propose experimental candidate drugs.

We also explore his work on consciousness, including how Yoshua defines consciousness, his paper “The Consciousness Prior,” the relationship between consciousness and intelligence, how attention could be used to train consciousness, the current state of consciousness research, and how he sees it evolving. 

Check out the complete show notes page at twimlai.com/talk/361.

Mar 30, 2020
Geometry-Aware Neural Rendering with Josh Tobin - #360
24:58

Today we’re joined by Josh Tobin, Co-Organizer of the machine learning training program Full Stack Deep Learning, and more recently, the founder of a stealth startup. We had the pleasure of sitting down with Josh prior to his presentation of his paper Geometry-Aware Neural Rendering at NeurIPS.

This work looks to build upon DeepMind’s “Neural scene representation and rendering,” with the goal of developing implicit scene understanding. We discuss challenges, the various datasets used to train his model, and the similarities between variational autoencoder training and his process. 

The complete show notes for this episode can be found at twimlai.com/talk/360.

Mar 26, 2020
The Third Wave of Robotic Learning with Ken Goldberg - #359
01:00:37

Today we’re joined by Ken Goldberg, professor of engineering and William S. Floyd Jr. distinguished chair in engineering at UC Berkeley. Ken, who is also an accomplished artist, and collaborator on projects such as DexNet and The Telegarden, has recently been focusing on robotic learning for grasping.

In our conversation with Ken, we chat about some of the challenges that arise when working on robotic grasping, including uncertainty in perception, control, and physics. We also discuss his view on the role of physics in robotic learning, citing co-contributors Sergey Levine and Pieter Abbeel along the way. Finally, we discuss some of his thoughts on potential robot use cases, from the use of robots in assisting in telemedicine, and agriculture, and even robotic Covid-19 testing.

The complete show notes for this episode can be found at twimlai.com/talk/359.

Mar 23, 2020
Learning Visiolinguistic Representations with ViLBERT w/ Stefan Lee - #358
27:36

Today we’re joined by Stefan Lee, assistant professor at the school of electrical engineering and computer science at Oregon State University. Stefan, who we sat down with at NeurIPS this past winter, is focused on the development of agents that can perceive their environment and communicate their understanding with humans in order to coordinate their actions to achieve mutual goals. 

In our conversation, we focus on his paper ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks, a model for learning joint representations of image content and natural language. We talk through the development and training process for this model, the adaptation of the training process to incorporate additional visual information to BERT models, where this research leads from the perspective of integration between visual and language tasks and finally, we discuss the importance of visual grounding.

Check out the complete show notes page at twimlai.com/talk/358.

Mar 18, 2020
Upside-Down Reinforcement Learning with Jürgen Schmidhuber - #357
33:19

Today we’re joined by Jürgen Schmidhuber, Co-Founder and Chief Scientist of NNAISENSE, the Scientific Director at IDSIA, as well as a Professor of AI at USI and SUPSI in Switzerland.

Jürgen’s lab is well known for creating the Long Short-Term Memory (LSTM) network which has become a prevalent neural network, used commonly devices such as smartphones, which we discuss in detail in our first conversation with Jürgen back in 2017.

In this conversation, we dive into some of Jürgen’s more recent work, including his recent paper, Reinforcement Learning Upside Down: Don’t Predict Rewards — Just Map Them to Actions.

Check out the show notes page at twimlai.com/talk/357.

Mar 16, 2020
SLIDE: Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning with Beidi Chen - #356
31:21
Today we're joined by Beidi Chen, PhD student at Rice University. Beidi is part of the team that developed a cheaper, algorithmic, CPU alternative to state-of-the-art GPU machines. They presented their findings at NeurIPS 2019 and have since gained a lot of attention for their paper, SLIDE: In Defense of Smart Algorithms Over Hardware Acceleration for Large-Scale Deep Learning Systems. In this interview, Beidi shares how the team took a new look at deep learning with the case of extreme classification by turning it into a search problem and using locality-sensitive hashing.
 
Check out the complete show notes at twimlai.com/talk/356. 
Mar 12, 2020
Advancements in Machine Learning with Sergey Levine - #355
42:13

Today we're joined by Sergey Levine, an Assistant Professor in the Department of Electrical Engineering and Computer Science at UC Berkeley. We last heard from Sergey back in 2017, where we explored Deep Robotic Learning. We caught up with Sergey at NeurIPS 2019, where Sergey and his team presented 12 different papers -- which means a lot of ground to cover!

Sergey and his lab’s recent efforts have been focused on contributing to a future where machines can be “out there in the real world, learning continuously through their own experience.” Sergey shares how many of the papers presented at the most recent NeurIPS conference are working to make that happen. Some of the major developments have been in the research fields of model-free reinforcement learning, causality and imitation learning, and offline reinforcement learning.

Check out the complete show notes page at twimlai.com/talk/355.

Mar 09, 2020
Secrets of a Kaggle Grandmaster with David Odaibo - #354
41:15

Imagine spending years learning ML from the ground up, from its theoretical foundations, but still feeling like you didn’t really know how to apply it. That’s where David Odaibo found himself in 2015, after the second year of his PhD. David’s solution was Kaggle, a popular platform for data science competitions.

Fast forward four years, and David is now a Kaggle Grandmaster, the highest designation, with particular accomplishment in computer vision competitions. Having completed his degree last year, he is currently co-founder and CTO of Analytical AI, a company that grew out of one of his recent Kaggle successes.

David has a background in deep learning and medical imaging–something he shares with his brother, Stephen Odaibo, who we interviewed last year about his work in Retinal Image Generation for Disease Discovery.

Check out the full article and interview at twimlai.com/talk/354

Mar 05, 2020
NLP for Mapping Physics Research with Matteo Chinazzi - #353
34:12

Predicting the future of science, particularly physics, is the task that Matteo Chinazzi, an associate research scientist at Northeastern University focused on in his paper Mapping the Physics Research Space: a Machine Learning Approach, along with co-authors including former TWIML AI Podcast guest Bruno Gonçalves.

In addition to predicting the trajectory of physics research, Matteo is also active in the computational epidemiology field. His work in that area involves building simulators that can model the spread of diseases like Zika or the seasonal flu at a global scale. 

Check out our full article on this episode at twimlai.com/talk/353.

Mar 02, 2020
Metric Elicitation and Robust Distributed Learning with Sanmi Koyejo - #352
55:11

The unfortunate reality is that many of the most commonly used machine learning metrics don't account for the complex trade-offs that come with real-world decision making. This is one of the challenges that today’s guest, Sanmi Koyejo has dedicated his research to address.

Sanmi is an assistant professor at the Department of Computer Science at the University of Illinois, where he applies his background in cognitive science, probabilistic modeling, and Bayesian inference to pursue his research which focuses broadly on “adaptive and robust machine learning.”

Check out the full episode write-up at twimlai.com/talk/352.

Feb 27, 2020
High-Dimensional Robust Statistics with Ilias Diakonikolas - #351
34:48

Today we’re joined by Ilias Diakonikolas, faculty in the CS department at the University of Wisconsin-Madison, and author of the paper Distribution-Independent PAC Learning of Halfspaces with Massart Noise, which was the recipient of the NeurIPS 2019 Outstanding Paper award. The paper, which focuses on high-dimensional robust learning, is regarded as the first progress made around distribution-independent learning with noise since the 80s. In our conversation, we explore robustness in machine learning, problems with corrupt data in high-dimensional settings, and of course, a deep dive into the paper. 

Check out our full write up on the paper and the interview at twimlai.com/talk/351.

Feb 24, 2020
How AI Predicted the Coronavirus Outbreak with Kamran Khan - #350
50:05

Today we’re joined by Kamran Khan, founder & CEO of BlueDot, and professor of medicine and public health at the University of Toronto. BlueDot, a digital health company with a focus on surveilling global infectious disease outbreaks, has been the recipient of a lot of attention for being the first to publicly warn about the coronavirus that started in Wuhan. How did the company’s system of algorithms and data processing techniques help flag the potential dangers of the disease? In this interview, Kamran talks us through how the technology works, its limits, and the motivation behind the work. 

Check out our new and improved show notes article at twimlai.com/talk/350.

Feb 19, 2020
Turning Ideas into ML Powered Products with Emmanuel Ameisen - #349
42:53

Today we’re joined by Emmanuel Ameisen, machine learning engineer at Stripe, and author of the recently published book “Building Machine Learning Powered Applications; Going from Idea to Product.” In our conversation, we discuss structuring end-to-end machine learning projects, debugging and explainability in the context of models, the various types of models covered in the book, and the importance of post-deployment monitoring. 

Check out our full show notes article at twimlai.com/talk/349.

Feb 17, 2020
Algorithmic Injustices and Relational Ethics with Abeba Birhane - #348
41:19

Today we’re joined by Abeba Birhane, PhD Student at University College Dublin and author of the recent paper Algorithmic Injustices: Towards a Relational Ethics. We caught up with Abeba, whose aforementioned paper was the recipient of the Best Paper award at the most recent Black in AI Workshop at NeurIPS, to go in-depth on the paper and the thought process around AI ethics.

In our conversation, we discuss the “harm of categorization”, and how the thinking around these categorizations should be discussed, how ML generally doesn’t account for the ethics of various scenarios and how relational ethics could solve this issue, her most recent paper “Robot Rights? Let’s Talk about Human Welfare Instead,” and much more.

Check out our complete write-up and resource page at twimlai.com/talk/348. 

Feb 13, 2020
AI for Agriculture and Global Food Security with Nemo Semret - #347
01:06:38

Today we’re excited to kick off our annual Black in AI Series joined by Nemo Semret, CTO of Gro Intelligence. Gro provides an agricultural data platform dedicated to improving global food security, focused on applying AI at macro scale. In our conversation with Nemo, we discuss Gro’s approach to data acquisition, how they apply machine learning to various problems, and their approach to modeling. 

 

Check out the full interview and show notes at twimlai.com/talk/347.

Feb 10, 2020
Practical Differential Privacy at LinkedIn with Ryan Rogers - #346
33:31

Today we’re joined by Ryan Rogers, Senior Software Engineer at LinkedIn. We caught up with Ryan at NeurIPS, where he presented the paper “Practical Differentially Private Top-k Selection with Pay-what-you-get Composition” as a spotlight talk. In our conversation, we discuss how LinkedIn allows its data scientists to access aggregate user data for exploratory analytics while maintaining its users’ privacy with differential privacy, and the major components of the paper. We also talk through one of the big innovations in the paper, which is discovering the connection between a common algorithm for implementing differential privacy, the exponential mechanism, and Gumbel noise, which is commonly used in machine learning.

 

The complete show notes for this episode can be found at twimlai.com/talk/346

Feb 07, 2020
Networking Optimizations for Multi-Node Deep Learning on Kubernetes with Erez Cohen - #345
34:00

Today we conclude our KubeCon ‘19 Series joined by Erez Cohen, VP of CloudX & AI at Mellanox. In our conversation, we discuss:

  • Erez’s talk “Networking Optimizations for Multi-Node Deep Learning on Kubernetes.” where he discusses problems and solutions related to networking discovered during the journey to reduce training time. 
  • NVIDIA’s recent acquisition of Mellanox, and what fruits that relationship hopes to bear. 
  • The evolution of technologies like RDMA, GPU Direct, and Sharp, Mellanox’s solution to improve the performance of MPI operations, which can be found in NVIDIA’s NCCL collective communications library.
  • How Mellanox is enabling Kubernetes and other platforms to take advantage of the various technologies mentioned above. 
  • Why we should care about networking in Deep Learning, which is inherently a compute-bound process. 

The complete show notes for this episode can be found at twimlai.com/talk/345.

Feb 05, 2020
Managing Research Needs at the University of Michigan using Kubernetes w/ Bob Killen - #344
24:40

Today we’re joined by Bob Killen, Research Cloud Administrator at the University of Michigan. In our conversation, we discuss:

  • How his group is deploying Kubernetes at UM.
  • The user experience of his broad user base, including those using KubeFlow environments.
  • How users are taking advantage of distributed computing.
  • Should ML/AI focused Kubernetes users should fear that the larger non-ML/AI user base will negatively impact their feature needs?
  • Where do the largest gaps currently exist in trying to support ML/AI users’ workloads?
  • Where Bob sees things going from a user perspective, and what are the things those users are asking about most? 

The complete show notes for this episode can be found at twimlai.com/talk/344.

Feb 03, 2020
Scalable and Maintainable Workflows at Lyft with Flyte w/ Haytham AbuelFutuh and Ketan Umare - #343
45:24

Today we kick off our KubeCon ‘19 series joined by Haytham AbuelFutuh and Ketan Umare, a pair of software engineers at Lyft. In our conversation, we discuss: 

  • Their newly open-sourced, cloud-native ML and data processing platform, Flyte.
  • What prompted Ketan to undertake this project and his experience building Flyte.
  • The core value proposition of Flyte.
  • What type-systems mean for the user experience.
  • How Flyte relates to Kubeflow. 
  • How Flyte is used across Lyft.

The complete show notes for this episode can be found at twimlai.com/talk/343

Jan 30, 2020
Causality 101 with Robert Osazuwa Ness - #342
43:14

Today we’re accompanied by Robert Osazuwa Ness, Machine Learning Research Engineer at ML Startup Gamalon and Instructor at Northeastern University. Robert, who we had the pleasure of meeting at the Black in AI Workshop at NeurIPS last month, joins us to discuss:

  • Causality, what it means, and how that meaning changes across domains and users.
  • Benefits of causal models vs non-causal models.
  • Real-world applications of causality. 
  • Various tools and packages for causality, 
  • Areas where it is effectively being deployed, like ML in production.
  • Our upcoming study group based around his new course sequence, “Causal Modeling in Machine Learning,” for which you can find details at twimlai.com/community.

The complete show notes for this episode can be found at twimlai.com/talk/342.

Jan 27, 2020
PaccMann^RL: Designing Anticancer Drugs with Reinforcement Learning w/ Jannis Born - #341
43:13

Today we’re joined by Jannis Born, Ph.D. student at ETH & IBM Research Zurich. We caught up with Jannis a few weeks back at NeurIPS, to discuss: 

  • His research paper “PaccMann^RL: Designing anticancer drugs from transcriptomic data via reinforcement learning,” a framework built to accelerate new anticancer drug discovery. 
  • How his background in cognitive science and computational neuroscience applies to his current ML research.
  • How reinforcement learning fits into the goal of cancer drug discovery, and how deep learning has changed this research.
  • Jannis describes a few interesting observations made during the training of their DRL learner. 
  • And of course, Jannis offers us a step-by-step walkthrough of how the framework works to predict the sensitivity of cancer drugs on a cell and subsequently discover new anticancer drugs. 

Check out the complete show notes for this episode at twimlai.com/talk/341

Jan 23, 2020
Social Intelligence with Blaise Aguera y Arcas - #340
46:56

Today we’re joined by Blaise Aguera y Arcas, a distinguished scientist at Google. We had the pleasure of catching up with Blaise at NeurIPS last month, where he was invited to speak on “Social Intelligence.” In our conversation, we discuss:

  • Blaise’s role at Google, where he leads the Cerebra team. 
  • Their approach to machine learning at the company, and how they differ from the more forward-facing Google Brain team. 
  • Blaise gives us a look into his presentation, discussing today’s ML landscape.
  • The gap between AI and ML/DS research, what it means and why it exists.
  • The difference between intelligent systems and what we would deem to be “actual intelligence.” 
  • What does optimizing truly mean when training models?

Check out the complete show notes for this episode at twimlai.com/talk/340.

Jan 20, 2020
Music & AI Plus a Geometric Perspective on Reinforcement Learning with Pablo Samuel Castro - #339
43:49

Today we’re joined by Pablo Samuel Castro, Staff Research Software Developer at Google. Pablo, whose research is mainly focused on reinforcement learning, and I caught up at NeurIPS last month. We cover a lot of ground in our conversation, including his love for music, and how that has guided his work on the Lyric AI project, and a few of his other NeurIPS submissions, including “A Geometric Perspective on Optimal Representations for Reinforcement Learning” and “Estimating Policy Functions in Payments Systems using Deep Reinforcement Learning.” 

Check out the complete show notes at twimlai.com/talk/339.

Jan 16, 2020
Trends in Computer Vision with Amir Zamir - #338
01:30:18

Today we close out AI Rewind 2019 joined by Amir Zamir, who recently began his tenure as an Assistant Professor of Computer Science at the Swiss Federal Institute of Technology.

Amir joined us back in 2018 to discuss his CVPR Best Paper winner, and in today’s conversation, we continue with the thread of Computer Vision. In our conversation, we discuss quite a few topics, including Vision-for-Robotics, the expansion of the field of 3D Vision, Self-Supervised Learning for CV Tasks, and much more! Check out the rest of the series at twimlai.com/rewind19.

The complete show notes for this episode can be found at twimlai.com/talk/338.

 

Jan 13, 2020
Trends in Natural Language Processing with Nasrin Mostafazadeh - #337
01:12:17

Today we continue the AI Rewind 2019 joined by friend-of-the-show Nasrin Mostafazadeh, Senior AI Research Scientist at Elemental Cognition. We caught up with Nasrin to discuss the latest and greatest developments and trends in Natural Language Processing, including Interpretability, Ethics, and Bias in NLP, how large pre-trained models have transformed NLP research, and top tools and frameworks in the space.

The complete show notes can be found at twimlai.com/talk/337

Check out the rest of the series at twimlai.com/rewind19!

Jan 09, 2020
Trends in Fairness and AI Ethics with Timnit Gebru - #336
49:45

Today we keep the 2019 AI Rewind series rolling with friend-of-the-show Timnit Gebru, a research scientist on the Ethical AI team at Google. A few weeks ago at NeurIPS, Timnit joined us to discuss the ethics and fairness landscape in 2019. In our conversation, we discuss diversification of NeurIPS, with groups like Black in AI, WiML and others taking huge steps forward, trends in the fairness community, quite a few papers, and much more.

We want to hear from you! Send your thoughts on the year that was 2019 below in the comments, or via twitter @samcharrington or @twimlai.

The complete show notes for this episode can be found at twimlai.com/talk/336.

Check out the rest of the series at twimlai.com/rewind19!

Jan 06, 2020
Trends in Reinforcement Learning with Chelsea Finn - #335
01:06:57

Today we continue to review the year that was 2019 via our AI Rewind series, and do so with friend of the show Chelsea Finn, Assistant Professor in the Computer Science Department at Stanford University. Chelsea’s research focuses on Reinforcement Learning, so we couldn’t think of a better person to join us to discuss the topic. In this conversation, we cover topics like Model-based RL, solving hard exploration problems, along with RL libraries and environments that Chelsea thought moved the needle last year. 

We want to hear from you! Send your thoughts on the year that was 2019 below in the comments, or via twitter @samcharrington or @twimlai.

The complete show notes for this episode can be found at twimlai.com/talk/335.

Check out the rest of the series at twimlai.com/rewind19!

Jan 02, 2020
Trends in Machine Learning & Deep Learning with Zack Lipton - #334
01:19:42

Today we kick off our 2019 AI Rewind Series joined by Zack Lipton, a jointly appointed Professor in the Tepper School of Business and the Machine Learning Department at CMU.

You might remember Zack from our conversation earlier this year, “Fairwashing” and the Folly of ML Solutionism, which you can find at twimlai.com/talk/285. In our conversation, Zack recaps advancements across the vast fields of Machine Learning and Deep Learning, including trends, tools, research papers and more.

We want to hear from you! Send your thoughts on the year that was 2019 below in the comments, or via Twitter @samcharrington or @twimlai.

To get the complete show notes for this episode, head over to twimlai.com/talk/334. 

 

Dec 30, 2019
FaciesNet & Machine Learning Applications in Energy with Mohamed Sidahmed - #333
40:31

Today we close out our 2019 NeurIPS series with Mohamed Sidahmed, Machine Learning and Artificial Intelligence R&D Manager at Shell. In our conversation, we discuss: 

  • The papers Mohamed and his team submitted to the conference this year, in particular: 
    • Accelerating Least Squares Imaging Using Deep Learning Techniques, which details how researchers can computationally efficiently reconstruct imaging using a deep learning framework approach.

    • FaciesNet: Machine Learning Applications for Facies Classification in Well Logs, which Mohamed describes as “A novel way of designing a new architecture for how we use sequence modeling and recurrent networks to be able to break out of the benchmark for classifying the different types of rock.” 

The full show notes for this episode can be found at twimlai.com/talk/333. Make sure you head over to twimlai.com/neurips2019 to follow along with this series!

Dec 27, 2019
Machine Learning: A New Approach to Drug Discovery with Daphne Koller - #332
43:40

Today we continue our 2019 NeurIPS coverage joined by Daphne Koller, co-Founder and former co-CEO of Coursera and Founder and CEO of Insitro. We caught up with Daphne to discuss: 

  • Her background in machine learning, beginning in ‘93, and her work with the Stanford online machine learning courses, and eventually her work at Coursera.
  • The current landscape of pharmaceutical drug discovery, including the current pricing of drugs and misnomers with why drugs are so expensive, 
  • Her work at Insitro, a company looking to advance drug discovery and development with machine learning. 
  • An overview of Insitro’s goal of using ML as a “compass” in drug discovery. 
  • How Insitro functions as a company in this space, including their focus on the biology of drug discovery and the landscape of ML techniques being used
  • Daphne’s thoughts on AutoML, and much more!

The full show notes for this episode can be found at twimlai.com/talk/332. Make sure you head over to twimlai.com/neurips2019 to follow along with this series!

Dec 26, 2019
Sensory Prediction Error Signals in the Neocortex with Blake Richards - #331
41:05

Today we continue our 2019 NeurIPS coverage, this time around joined by Blake Richards, Assistant Professor at McGill University and a Core Faculty Member at Mila. In our conversation, we discuss:

  • His invited talk at the Neuro-AI Workshop “Sensory Prediction Error Signals in the Neocortex.” 
  • His recent studies on two-photon calcium imaging, predictive coding, and hierarchical inference.
  • Blake’s recent work on memory systems for reinforcement learning. 

The complete show notes for this episode can be found at twimlai.com/talk/331.

Make sure you head over to twimlai.com/neurips2019 to follow along with this series!

Dec 24, 2019
How to Know with Celeste Kidd - #330
54:03

Today we begin our coverage of the 2019 NeurIPS conference with Celeste Kidd, Assistant Professor of Psychology at UC Berkeley. In our conversation, we discuss:

  • The research at the Kidd Lab, which is focused on understanding “how people come to know what they know.”
  • Her invited talk “How to Know,” which details the core cognitive systems people use to guide their learning about the world.
  • Why people are curious about some things but not others.
  • How our past experiences and existing knowledge shape our future interests.
  • Why people believe what they believe, and how these beliefs are influenced in one direction or another.
  • How machine learning figures into this equation.

Check out the complete show notes for this episode at twimlai.com/talk/330. You can also follow along with this series at twimlai.com/neurips2019.

Dec 23, 2019
Using Deep Learning to Predict Wildfires with Feng Yan - #329
49:49

Today we’re joined by Feng Yan, Assistant Professor at the University of Nevada, Reno. In our conversation, we discuss:

  • ALERTWildfire, a camera-based network infrastructure that captures satellite imagery of wildfires.
  • The many purposes of ALERTWildfire, including the discovery of wildfires, the ability to scale resources accordingly, and a few others
  • The development of the machine learning models and surrounding infrastructure used in ALERTWildfire. 
  • Problem formulation and challenges with using camera and satellite data in this use case.
  • How they have combined the use of Infra-as-a-Service and Function-as-a-Service tools for cost-effectiveness and scalability. 

Check out the complete show notes at twimlai.com/talk/329.

Dec 20, 2019
Advancing Machine Learning at Capital One with Dave Castillo - #328
33:26

Today we’re joined by Dave Castillo, Managing Vice President for ML at Capital One and head of their Center for Machine Learning. We caught up with David at re:Invent to discuss the aforementioned Center for Machine Learning, and what has changed since our last discussing with Capital One, which you can find at twimlai.com/talk/147. In our conversation we explore:

  • Capital One’s transition from “lab-based” machine learning to “enterprise-wide” adoption and support of ML.
  • Surprising machine learning use cases like granting employee access privileges via an automated system.
  • Their current platform ecosystem, including their design vision in building this into a larger, all-encompassing platform, pain points in building this platform, and more. 

Check out the complete show notes for this episode at twimlai.com/talk/328.

Dec 19, 2019
Helping Fish Farmers Feed the World with Deep Learning w/ Bryton Shang - #327
38:06

Today we’re joined by Bryton Shang, Founder & CEO at Aquabyte. We caught up with Bryton after his talk at re:Invent’s ML Summit to discuss:

  • Aquabyte, a company focused on the application of computer vision fish farming.
  • How Bryton identified the various problems associated with mass fish farming and how he eventually moved to Norway to develop the solution.
  • The challenges with developing machine learning solutions that can measure the height and weight of fish,
  • How they use computer vision algorithms to asses issues like sea lice, which can be up to 25% of the cost associated with running farms.
  • Cool new features currently in the works like facial recognition for fish!

The complete show notes for this episode can be found at twimlai.com/talk/327.

Dec 17, 2019
Metaflow, a Human-Centric Framework for Data Science with Ville Tuulos - #326
56:17

Today we kick off our re:Invent 2019 series with Ville Tuulos, Machine Learning Infrastructure Manager at Netflix. At re:Invent, Netflix announced the open-sourcing of Metaflow, their “human-centric framework for data science.” In our conversation, we discuss all things Metaflow, including:

  • The problem Metaflow is trying to solve
  • Why it was important for Netflix to open-source Metaflow
  • Core Features
  • The user experience accessing and managing data, experimentation, training and model development
  • The various supported tools and libraries


If you’re interested in checking out a Metaflow democast with Villa, reach out at twimlai.com/contact! 

Dec 13, 2019
Single Headed Attention RNN: Stop Thinking With Your Head with Stephen Merity - #325
59:04

Today we’re joined by Stephen Merity, startup founder and independent researcher, with  a focus on NLP and Deep Learning. In our conversation, we discuss:

  • Stephen’s newest paper, Single Headed Attention RNN: Stop Thinking With Your Head.
  • His motivations behind writing the paper; the fact that NLP research has been recently dominated by the use of transformer models, and the fact that these models are not the most accessible/trainable for broad use.
  • The architecture of transformers models.
  • How Stephen decided to use SHA-RNNs for this research.
  • How Stephen built and trained the model, for which the code is available on Github.
  • His approach to benchmarking this project.
  • Stephen’s goals for this research in the broader NLP research community. 

The complete show notes for this episode can be found at twimlai.com/talk/325. There you’ll find links to both the paper referenced in this interview, and the code. Enjoy!

Dec 12, 2019
Automated Model Tuning with SigOpt - #324
46:10

In this TWIML Democast, we're joined by SigOpt Co-Founder and CEO Scott Clark. Scott details the SigOpt platform, and gives us a live demo!

This episode is best consumed by watching the corresponding video demo, which you can find at twimlai.com/talk/324

 

 

Dec 09, 2019
Automated Machine Learning with Erez Barak - #323
43:25

In the final episode of our Azure ML series, we’re joined by Erez Barak, Partner Group Manager of Azure ML at Microsoft. In our conversation, we discuss:

  • Erez’s AutoML philosophy, including how he defines “true AutoML” and his take on the AutoML space, its role and its importance.
  • We also discuss in great detail the application of AutoML as a contributor to the end-to-end data science process, which Erez breaks down into 3 key areas; Featurization, Learner/Model Selection, and Tuning/Optimizing Hyperparameters.
  • Finally, we discuss post-deployment AutoML use cases and other areas under the AutoML umbrella that are currently generating excitement.

Check out the complete show notes at twimlai.com/talk/323!

Dec 06, 2019
Responsible AI in Practice with Sarah Bird - #322
38:41

Today we continue our Azure ML at Microsoft Ignite series joined by Sarah Bird, Principal Program Manager at Microsoft. In our conversation, we discuss:

  • Sarah’s work in machine learning systems, with a focus on bringing machine learning research into production through Azure ML, with an emphasis on responsible AI.

  • A set of newly released tools focused on responsible machine learning, Azure Machine Learning 'Machine Learning Interpretability Toolkit’
  • Moving from “Black-Box” models to “Glass-Box Models”
  • Sarah’s recent work in differential privacy, including risks and benefits
  • Her work in the broader ML community, including being a founding member of the MLSys conference and workshops.

Check out the complete show notes at twimlai.com/talk/322.

Dec 04, 2019
Enterprise Readiness, MLOps and Lifecycle Management with Jordan Edwards - #321
39:43

Today we’re joined by Jordan Edwards, Principal Program Manager for MLOps on Azure ML at Microsoft. In our conversation, Jordan details:

  • How Azure ML accelerates model lifecycle management with MLOps, enabling data scientists to collaborate with IT teams to increase the pace of model development and deployment.
  • Problems associated with generalizing ML at scale at Microsoft, and how those problems are prioritized, 
  • What is MLOps, and the role of testing is in an MLOps environment, and experiences working with customers to implement these tests. 
  • The “four phases” along the journey of customer implementation of MLOps, how companies should look at hiring ML Engineers vs DevOps Engineers, and other aspects of managing model life cycles that Jordan finds important for us to think about. 

The complete show notes can be found at twimlai.com/talk/321. 

Dec 02, 2019
DevOps for ML with Dotscience - #320
47:04

Today we’re joined by Luke Marsden, Founder and CEO of Dotscience. Luke walks us through the Dotscience platform and their manifesto on DevOps for ML.

Thanks to Luke and Dotscience for their sponsorship of this Democast and their continued support of TWIML.  

Head to https://twimlai.com/democast/dotscience to watch the full democast!

Nov 26, 2019
Building an Autonomous Knowledge Graph with Mike Tung - #319
44:47

Today we’re joined by Mike Tung, Founder, and CEO of Diffbot. In our conversation, we discuss: 

  • Their various tools, including their Knowledge Graph, Extraction API, and CrawlBot.
  • How Knowledge Graph was inspired by Imagenet, how it was built, and how it differs from other, more mainstream knowledge graphs like Google Search and MSFT Bing.
  • How they balance being a research company that is also commercially viable.
  • The developer experience with their tools, and challenges faced.

The complete show notes can be found at twimlai.com/talk/319.

Nov 21, 2019
The Next Generation of Self-Driving Engineers with Aaron Ma - Talk #318
47:53

Today we’re joined by our youngest guest ever (by far), Aaron Ma, an 11-year-old middle school student and machine learning engineer in training. Aaron has completed over 80(!) Coursera courses and is the recipient of 3 Udacity nano-degrees. In our conversation, we discuss:

  • Aaron’s research interests, reinforcement learning, and self-driving cars,
  • His experiences participating in over 35 kaggle competitions
  • How he balances his passion for machine learning with things like chores and homework.

This was a really fun interview! 

The complete show notes for this episode can be found at twimlai.com/talk/318.

Nov 18, 2019
Spiking Neural Networks: A Primer with Terrence Sejnowski - #317
49:34

On today’s episode, we’re joined by Dr. Terrence Sejnowski, Francis Crick Chair, head of the Computational Neurobiology Laboratory at the Salk Institute for Biological Studies and faculty member at UC San Diego. In our conversation with Terry, we discuss:

  • His role as a founding researcher in the field of computational neuroscience, and as a founder of the annual Telluride Neuromorphic Cognition Engineering Workshop. 
  • We dive deep into the world of spiking neural networks and brain architecture,
  • the relationship of neuroscience to machine learning, and ways to make NN’s more efficient through spiking. 
  • Terry also gives us some insight into hardware used in this field, characterizes the major research problems currently being undertaken, and the future of spiking networks. 

Check out the complete show notes at twimlai.com/talk/317.

 

Nov 14, 2019
Bridging the Patient-Physician Gap with ML and Expert Systems w/ Xavier Amatriain - #316
39:01

Today we’re joined by return guest Xavier Amatriain, Co-founder and CTO of Curai. In our conversation, we discuss

  • Curai’s goal of providing the world’s best primary care to patients via their smartphone, and how ML & AI will bring down costs healthcare accessible and scaleable. 
  • The shortcomings of traditional primary care, and how Curai fills that role, 
  • Some of the unique challenges his team faces in applying this use case in the healthcare space. 
  • Their use of expert systems, how they develop and train their models with synthetic data through noise injection
  • How NLP projects like BERT, Transformer, and GPT-2 fit into what Curai is building. 

Check out the complete show notes page at twimlai.com/talk/316

Nov 11, 2019
What Does it Mean for a Machine to "Understand"? with Thomas Dietterich - #315
38:09

Today we’re joined by Tom Dietterich, Distinguished Professor Emeritus at Oregon State University. We had the pleasure of discussing Tom’s recent blog post, “What does it mean for a machine to “understand,” in which he discusses:

  • Tom’s position on what qualifies as machine “understanding”, including a few examples of systems that he believes exhibit understanding.
  • The role of deep learning in achieving artificial general intelligence.
  • The current “Hype Engine” that exists around AI Research, and SOOO much more.  

Make sure you check out the show notes at twimlai.com/talk/315, where you’ll find links to Tom’s blog post, as well as a ton of other references. 

Nov 07, 2019
Scaling TensorFlow at LinkedIn with Jonathan Hung - #314
35:07

Today we’re joined by Jonathan Hung, Sr. Software Engineer at LinkedIn, who we caught up with at TensorFlow World last week. In our conversation, we discuss: 

  • Jonathan’s presentation at the event focused on LinkedIn’s efforts scaling Tensorflow.
  • Jonathan’s work as part of the Hadoop infrastructure team, including experimenting on Hadoop with various frameworks, and their motivation for using TensorFlow on their pre-existing Hadoop clusters infrastructure. 
  • TonY, or TensorFlow on Yard, LinkedIn’s framework that natively runs deep learning jobs on Hadoop, and its relationship with Pro-ML, LinkedIn’s internal AI Platform, which we’ve discussed on earlier episodes of the podcast (Link).
  • Finally, we discuss how far LinkedIn’s Hadoop infrastructure has come since 2017, and their foray into using Kubernetes for research. 

The complete show notes can be found at twimlai.com/talk/314.

Nov 04, 2019
Machine Learning at GitHub with Omoju Miller - #313
43:41

Today we’re joined by Omoju Miller, a Sr. machine learning engineer at GitHub. In our conversation, we discuss:

  • Her dissertation, Hiphopathy, A Socio-Curricular Study of Introductory Computer Science, 
  • Her work as an inaugural member of the Github machine learning team
  • Her two presentations at Tensorflow World, “Why is machine learning seeing exponential growth in its communities” and “Automating your developer workflow on GitHub with Tensorflow.”

The complete show notes for this episode can be found at twimlai.com/talk/313. 

Oct 31, 2019
Using AI to Diagnose and Treat Neurological Disorders with Archana Venkataraman - #312
47:48

Today we’re joined by Archana Venkataraman, John C. Malone Assistant Professor of Electrical and Computer Engineering at Johns Hopkins University, and MIT 35 innovators under 35 recipient.

Archana’s research at the Neural Systems Analysis Laboratory focuses on developing tools, frameworks, and algorithms to better understand, and treat neurological and psychiatric disorders, including autism, epilepsy, and others. In our conversation, we explore her lab’s work in applying machine learning to these problems, including biomarker discovery, disorder severity prediction, as well as some of the various techniques and frameworks used.

The complete show notes for this episode can be found at twimlai.com/talk/312.

Oct 28, 2019
Deep Learning for Earthquake Aftershock Patterns with Phoebe DeVries & Brendan Meade - #311
35:44

Today we are joined by Phoebe DeVries, Postdoctoral Fellow in the Department of Earth and Planetary Sciences at Harvard and assistant faculty at the University of Connecticut and Brendan Meade, Professor of Earth and Planetary Sciences and affiliate faculty in computers sciences at Harvard. In this episode, we discuss:

  • Phoebe and Brendan’s work is focused on discovering as much as possible about earthquakes before they happen, and through measuring how the earth’s surface moves, predicting future movement location
  • Their recent paper, ‘Deep learning of aftershock patterns following large earthquakes’, and 
  • The preliminary steps that guided them to using machine learning in the earth sciences
  • Their current research involving calculating stress changes in the crust and upper mantle after a large earthquake and using a neural network to map those changes to predict aftershock locations
  • The complex systems that encompass earth science studies, including the approaches, challenges, surprises, and results that come with incorporating machine learning models and data sets into a new field of study

The complete show notes for this episode can be found at twimlai.com/talk/311.

Oct 25, 2019
Live from TWIMLcon! Operationalizing Responsible AI - #310
30:33

An often forgotten about topic garnered high praise at TWIMLcon this month: operationalizing responsible and ethical AI. This important topic was combined with an impressive panel of speakers, including: Rachel Thomas, Director, Center for Applied Data Ethics at the USF Data Institute, Guillaume Saint-Jacques, Head of Computational Science at LinkedIn, and Parinaz Sobahni, Director of Machine Learning at Georgian Partners, moderated by Khari Johnson, Senior AI Staff Writer at VentureBeat. This episode covers:

  • The basics of operationalizing AI ethics in a range of orgs and insight into an array of tools, approaches, and methods that have been found useful for teams to use
  • The biggest concerns, like focusing more on harm as opposed to algorithmic bias and encouraging specific responsibility for systems
  • Educating the general public on the realities and misconceptions of probabilistic methods and putting into place preventative guardrails has become imperative for any operation
  • The long-term benefits of ethical decision-making and the challenges of established versus startup companies
  • Questions from the TWIMLcon audience, some common examples of power dynamics in AI ethics, and what we as a community can be doing to push the needle in the very powerful world of responsible AI

The complete show notes can be found at twimlai.com/talk/310

Oct 22, 2019
Live from TWIMLcon! Scaling ML in the Traditional Enterprise - #309
33:37

In this episode from a stellar TWIMLcon panel, the state and future of larger, more established brands is analyzed and discussed. Hear from Amr Awadallah, Founder and Global CTO of Cloudera, Pallav Agrawal, Director of Data Science at Levi Strauss & Co., and Jürgen Weichenberger, Data Science Senior Principal & Global AI Lead at Accenture, moderated by Josh Bloom Professor at UC Berkeley. In this episode we discuss:

  • For an ML/AI initiative to be successful, a conscious and noticeable shift is now required in how things used to be managed while educating cross-functional teams in data science terms and methodologies 
  • It can be tempting and exciting to constantly be trying out the latest technologies, but brand consistency and sustainability is imperative to success
  • How the real business value - the money - can be found by putting your big ML/AI goals and projects in the core competencies of the company.  
  • Are traditional enterprises fundamentally changing their business through ML/AI, and if so, why? 
  • Real-world examples and thought-provoking ideas for scaling ML/AI in the traditional enterprise

The complete show notes can be found at twimlai.com/talk/309.

Oct 18, 2019
Live from TWIMLcon! Culture & Organization for Effective ML at Scale (Panel) - #308
27:59

TWIMLcon brought together so many in the ML/AI community to discuss the unique challenges to building and scaling machine learning platforms. In this episode, hear from a diverse set of panelists including: Pardis Noorzad, Data Science Manager at Twitter, Eric Colson, Chief Algorithms Officer Emeritus at Stitch Fix, and Jennifer Prendki, Founder & CEO at Alectio, moderated by Maribel Lopez, Founder & Principal Analyst at Lopez Research:

  • How to approach changing the way companies think about machine learning
  • Engaging different groups to work together effectively - i.e. c-suite, marketing, sales, engineering, etc. 
  • The importance of clear communication about ML lifecycle management
  • How full stack roles can provide immense value
  • Tips and tricks to work faster, more efficiently, and create an org-wide culture that holds machine learning as a valued priority

The complete show notes can be found at twimlai.com/talk/308.

Oct 15, 2019
Live from TWIMLcon! Use-Case Driven ML Platforms with Franziska Bell - #307
32:14

Franziska Bell, Ph.D., is the Director of Data Science Platforms at Uber, and joined Sam on stage at TWIMLcon last week to discuss all things platform at Uber. With the goal of providing cutting edge data science company-wide at the push of a button, Fran has developed a portfolio of platforms, ranging from forecasting to anomaly detection to conversational AI. In this episode, we discuss:

  • Through strategic use cases, Fran’s team of data scientists works closely with teams across the organization at every stage to solve problems and build infrastructure
  • The evolving working relationship between her team and Michelangelo (Uber’s ML Platform), including the challenges and benefits that such a platform provides
  • Insight into Uber’s development methodology and how the data science team is organized from start to finish to create a culture of learning and expertise that results in fast results and reduced risk
  • Fran’s take on the future of ML platforms and more!

Check out the complete show notes at twimlai.com/talk/307

Oct 10, 2019
Live from TWIMLcon! Operationalizing ML at Scale with Hussein Mehanna - #306
33:39

The live interviews from TWIMLcon continue with Hussein Mehanna, Head of Machine Learning and Artificial Intelligence at Cruise. From his start at Facebook and then Google and now to Cruise, leading the trend of autonomous vehicles, Hussein has seen first hand what it takes to scale and sustain machine learning programs. In this episode, hear him and Sam discuss:

  • At Facebook, a few early wins in the realm of infrastructure building set the stage for scaling via faster algorithms and soon the entire Facebook organization could achieve a new level of ML scaling with all workflows shareable, reusable and discoverable through a search interface
  • Cruise’s unique focus on the interplay between applied research problems and the underlying tools and platforms
  • The immense capacity that the industry of autonomous vehicles has to push ML and AI to new limits of depth and scale
  • The challenges (and joys) of working in the industry and his insight into analyzing scale when innovation is happening in parallel with the development
  • Hussein’s experiences at Facebook, Google, and Cruise, along with his thoughts on productivity being a "usability" vs "modeling" challenge and his prediction for the future of ML platforms!

The complete show notes can be found at twimlai.com/talk/306.

Oct 08, 2019
Live from TWIMLcon! Encoding Company Culture in Applied AI Systems - #305
32:22

In this episode, Sam is joined by Deepak Agarwal, VP of Engineering at LinkedIn, who graced the stage at TWIMLcon: AI Platforms for a keynote interview. In this episode Deepak shares:

  • The incredible impact that standardizing processes and tools have on a company’s culture and overall productivity levels
  • Insight into the best way to increase ML ROI and how to sell ML programs to the C-Suite (two things that often go hand in hand)
  • The Pro-ML initiative for delivering machine learning systems at scale, specifically looking at aligning improvement of tooling and infrastructure with the pace of innovation and more!

Check out the complete show notes at twimlai.com/talk/305.

Oct 04, 2019
Live from TWIMLcon! Overcoming the Barriers to Deep Learning in Production with Andrew Ng - #304
33:59

Earlier today, Andrew Ng joined us onstage at TWIMLcon to share some of his immense knowledge. As the Founder and CEO of Landing AI, Co-Chairman and Co-Founder of Coursera, and founding lead of Google Brain, Andrew is no stranger to knowing what it takes for AI and machine learning to be successful.

In this episode, hear about:

  • The work that Landing AI is doing to help organizations adopt modern AI
  • His experiences in overcoming the challenges that large companies face
  • Insight into how enterprises can get the most value for their ML investment
  • The ‘essential complexity’ of software engineering and more! 

The complete show notes can be found at twimlai.com/talk/304.

Oct 01, 2019
The Future of Mixed-Autonomy Traffic with Alexandre Bayen - #303
43:44

Today we are joined by Alexandre Bayen, Director of the Institute for Transportation Studies and Professor at UC Berkeley.In this episode, we discuss Alex’s background in machine learning, his current research in mixed-autonomy traffic, and the idea of swarming in terms of the impact just a few self-driving cars can have on traffic mobility. In the AWS re:Invent conference last year, Alex presented on the future of mixed-autonomy traffic and the two major revolutions he predicts will take place in the next 10-15 years. This includes model-free deep reinforcement learning techniques and end-to-end pixel learning. Looking ahead, Alex shares his take on the future of transportation systems and the potential for varying levels of automation in sub-communities.

The complete show notes can be found at twimlai.com/talk/303.

Sep 27, 2019
Deep Reinforcement Learning for Logistics at Instadeep with Karim Beguir - #302
43:45

Today we are joined by Karim Beguir, Co-Founder and CEO of InstaDeep, a company in Tunisia, Africa focusing on building advanced decision-making systems for the enterprise. In this episode, we discuss where his and InstaDeep’s journey began in Tunisia, Africa (00:27), the challenges that enterprise companies are seeing in logistics that can be solved by deep learning and machine learning (05:43), how InstaDeep is applying DL and RL to real world problems (09:45), and what are the data sets used to train these models and the application of transfer learning between similar data sets (13:00). Additionally, we go over ‘Rank Rewards’, a paper Karim published last year, in which adversarial self-play in two-player games has delivered impressive results when used with reinforcement learning algorithms (22:40), the overall efficiency of RL for logistical problems (23:05), and details on the InstaDeep process (35:37).

The complete show notes for this episode can be found at twimlai.com/talk/302. 

Sep 25, 2019
Deep Learning with Structured Data w/ Mark Ryan - #301
39:30

Today we are joined by Mark Ryan, author of Deep Learning with Structured Data, currently in the Manning Early Access Program (MEAP), due for publication in Spring 2020. While working on the Support team at IBM Data and AI, he saw that there was a lack of general structured data sets that people could apply their models to. Using the streetcar network in his hometown of Toronto, Mark created a deep learning model to predict delays, but more importantly, gathered an open data set that was the perfect size and variety, and jump started the research for his latest book. In this episode, Mark shares the benefits of applying deep learning to structured data (and recent reduced barriers to entry), details of his experience with a range of data sets, the everlasting appreciation he and Sam shares for the Fast.ai course by Jeremy Howard, and the contents of his new book, aimed to help set up and maintain deep learning models with structured data.

With just two weeks left, time is running out for you to register for TWIMLcon: AI Platforms. Don't be left out! Register NOW at twimlcon.com/register

Sep 19, 2019
Time Series Clustering for Monitoring Fueling Infrastructure Performance with Kalai Ramea - #300
30:04

Today we are joined by Kalai Ramea, Data Scientist at PARC, a Xerox Company. With a background in transportation, energy efficiency, art, and machine learning, Kalai has been fortunate enough to follow her passions through her work. In this episode we discuss:

  • Her environmentally efficient pursuit that lead to the purchase of a hydrogen car, and the subsequent journey and paper that followed assessing fueling stations 
  • Kalai’s next paper, looking at fuel consumption at hydrogen stations using temporal clustering to identify signatures of usage over time, grouping the stations into categories 
  • With the construction of fueling stations is planned to increase dramatically in the next 5 years, building reliability on their performance is crucial
  • A sneak peek into how Kalai incorporates her love of art into her work!

Check out the show notes, and the refresh, at twimlai.com

Sep 18, 2019
Swarm AI for Event Outcome Prediction with Gregg Willcox - TWIML Talk #299
42:35

Today we are joined by Gregg Willcox, Director of Research and Development at Unanimous AI. Inspired by the natural phenomenon called 'swarming', which uses the collective intelligence of a group to produce more accurate results than an individual alone, ‘Swarm AI’ was born. A game-like platform that channels the convictions of individuals to come to a consensus and using a behavioral neural network trained on people’s behavior called ‘Conviction’, to further amplify the results. 

The complete show notes for this episode can be found at twimlai.com/talk/299.

We're just over two weeks out from TWIMLcon: AI Platforms! You definitely want to be there. Visit twimlcon.com for more info, or to register. 

Sep 13, 2019
Rebooting AI: What's Missing, What's Next with Gary Marcus - TWIML Talk #298
47:49

Today we are joined by Gary Marcus, CEO and Founder at Robust.AI, former CEO and Founder of Geometric Intelligence (acquired by Uber) and well-known scientist, bestselling author, professor and entrepreneur. In this episode hear Gary discuss:

  • His latest book, ‘Rebooting AI: Building Artificial Intelligence We Can Trust’, an extensive look into the current gaps, pitfalls and areas for improvement in the field of machine learning and AI 
  • A break down of the difference between reinforcement learning and real learning 
  • Why we need machines with both automation and autonomy to be truly usable in the world today 
  • Examples from his book, including Teslas driving into tow trucks and Microsoft’s SQuAD reading test results
  • Insight into what we should be talking and thinking about to make even greater (and safer) strides in AI

The complete show notes for this episode can be found at twimlai.com/talk/298.

Only 3 weeks left to register for TWIMLcon: AI Platforms! Visit twimlcon.com/register now!

 

Sep 10, 2019
DeepQB: Deep Learning to Quantify Quarterback Decision-Making with Brian Burke - TWIML Talk #297
51:15

Today we are joined by Brian Burke, Analytics Specialist with the Stats & Information Group at ESPN. A former Navy pilot and lifelong football fan, Brian saw the correlation between fighter pilots and quarterbacks in the quick, pressure-filled decisions both roles have to make on a regular basis. In this episode, we discuss:

  • Brian’s self-taught modeling techniques and his journey finding and handling vast amounts of sports data 
  • His findings in the paper, “DeepQB: Deep Learning with Player Tracking to Quantify Quarterback Decision-Making & Performance”
  • Brian talks through the making of his model, with geometry, algebra and a self-proclaimed ‘vanilla’ neural network
  • His excitement for the future of machine learning in sports and more!

The complete show notes for this episode can be found at twimlai.com/talk/297.

Sep 05, 2019
Measuring Performance Under Pressure Using ML with Lotte Bransen - TWIML Talk #296
34:57

Today we are joined by Lotte Bransen, Scientific Researcher at SciSports. With a background in mathematics, econometrics and soccer, Lotte has honed her research on analytics of the game and its players. More specifically, using trained models to understand the impact of mental pressure on a player’s performance. In this episode, Lotte discusses:

  • Her latest paper, ‘Choke or Shine? Quantifying Soccer Players' Abilities to Perform Under Mental Pressure’ and shares 
  • The basis of the models through two aspects of mental pressure: pre-game and in-game, and three performance metrics: the chance of a goal with every action a player takes (contribution), the quality of that decision and the quality of the execution
  • The implications of her research in the world of sports
  • Just a few of the exponential applications for her work - check it out!

Check out the full show notes at twimlai.com/talk/296.

Sep 03, 2019
Managing Deep Learning Experiments with Lukas Biewald - TWIML Talk #295
43:39

Today we are joined by Lukas Biewald, CEO and Co-Founder of Weights & Biases. Lukas, previously CEO and Founder of Figure Eight (CrowdFlower), has a straightforward goal: provide researchers with SaaS that is easy to install, simple to operate, and always accessible. Seeing a need for reproducibility in deep learning experiments, Lukas founded Weights & Biases. In this episode we discuss:

  • The experiment tracking tool, how it works, and the components that make it unique in the ML marketplace
  • The open, collaborative culture that Lukas promotes
  • How Lukas got his start in deep learning experiments, what his experiment tracking used to look like, 
  • The current Weights & Biases business success strategy and what his team is working on today

The complete show notes for this episode can be found at twimlai.com/talk/295

Thanks to our friends at Weights & Biases for their support of the show, their sponsorship of this episode, and our upcoming event, TWIMLcon: AI Platforms. 

Registration for TWIMLcon is still open! Visit twimlcon.com/register today! 

Aug 29, 2019
Re-Architecting Data Science at iRobot with Angela Bassa - TWIML Talk #294
49:27

Today we’re joined by Angela Bassa, Director of Data Science at iRobot. In our conversation, Angela and I discuss:

• iRobot's re-architecture, and a look at the evolution of iRobot.

• Where iRobot gets its data from and how they taxonomize data science.

• The platforms and processes that have been put into place to support delivering models in production.

•The role of DevOps in bringing these various platforms together, and much more!

The complete show notes can be found at twimlai.com/talk/294.

Check out the recently released speaker list for TWIMLcon: AI Platforms now! twimlcon.com/speakers.

Aug 26, 2019
Disentangled Representations & Google Research Football with Olivier Bachem - TWIML Talk #293
43:29

Today we’re joined by Olivier Bachem, a research scientist at Google AI on the Brain team.

Initially, Olivier joined us to discuss his work on Google’s research football project, their foray into building a novel reinforcement learning environment, but we spent a fair amount of time exploring his research in disentangled representations. Olivier and Sam also discuss what makes the football environment different than other available reinforcement learning environments like OpenAI Gym and PyGame, what other techniques they explored while using this environment, and what’s on the horizon for their team and Football RLE.

Check out the full show notes at twimlai.com/talk/293

Aug 22, 2019
Neural Network Quantization and Compression with Tijmen Blankevoort - TWIML Talk #292
51:09

Today we’re joined by Tijmen Blankevoort, a staff engineer at Qualcomm, who leads their compression and quantization research teams. Tijmen is also co-founder of ML startup Scyfer, along with Qualcomm colleague Max Welling, who we spoke with back on episode 267. In our conversation with Tijmen we discuss: 

• The ins and outs of compression and quantization of ML models, specifically NNs,

• How much models can actually be compressed, and the best way to achieve compression, 

• We also look at a few recent papers including “Lottery Hypothesis."  

Check out the full show notes at twimlai.com/talk/292.

 

Aug 19, 2019
Identifying New Materials with NLP with Anubhav Jain - TWIML Talk #291
39:54

Today we are joined by Anubhav Jain, Staff Scientist & Chemist at Lawrence Berkeley National Lab. Anubhav leads the Hacker Materials Research Group, where his research focuses on applying computing to accelerate the process of finding new materials for functional applications. With the immense amount of published scientific research out there, it can be difficult to understand how that information can be applied to future studies, let alone find a way to read it all. In this episode we discuss:

- His latest paper, ‘Unsupervised word embeddings capture latent knowledge from materials science literature’

- The design of a system that takes the literature and uses natural language processing to analyze, synthesize and then conceptualize complex material science concepts

- How the method is shown to recommend materials for functional applications in the future - scientific literature mining at its best.

Check out the complete show notes at twimlai.com/talk/291.

Aug 15, 2019
The Problem with Black Boxes with Cynthia Rudin - TWIML Talk #290
48:25

You asked, we listened! Today, by listener request, we are joined by Cynthia Rudin, Professor of Computer Science, Electrical and Computer Engineering, and Statistical Science at Duke University. Cynthia is passionate about machine learning and social justice, with extensive work and leadership in both areas. In this episode we discuss:

  • Her paper, ‘Please Stop Explaining Black Box Models for High Stakes Decisions’
  • How interpretable models make for less error-prone and more comprehensible decisions - and why we should care
  • A break down of black box and interpretable models, including their development, sample use cases, and more!

Check out the complete show notes at https://twimlai.com/talk/290

Aug 14, 2019
Human-Robot Interaction and Empathy with Kate Darling - TWIML Talk #289
43:56

Today we’re joined by Dr. Kate Darling, Research Specialist at the MIT Media Lab. Kate’s focus is on robot ethics and interaction, namely the social implication of how people treat robots and the purposeful design of robots in our daily lives. This episode is a fascinating look into the intersection of psychology and how we are using technology. We cover topics like:

  • How to measure empathy
  • The impact of robot treatment on kids behavior
  • The correlation between animals and robots 
  • Why ‘successful’ robots aren’t always humanoid and so much more!
Aug 08, 2019
Automated ML for RNA Design with Danny Stoll - TWIML Talk #288
36:29

Today we’re joined by Danny Stoll, Research Assistant at the University of Freiburg. Since high school, Danny has been fascinated by Deep Learning which has grown into a desire to make machine learning available to anyone with interest. Danny’s current research can be encapsulated in his latest paper, ‘Learning to Design RNA’. Designing RNA molecules has become increasingly popular as RNA is responsible for regulating biological process, even connected to diseases like Alzheimers and Epilepsy. In this episode, Danny discusses:

  • The RNA design process through reverse engineering
  • How his team’s deep learning algorithm is applied to train and design sequences
  • Transfer learning & multitask learning
  • Ablation studies, hyperparameter optimization, the difference between chemical and statistical based approaches and more!
Aug 05, 2019
Developing a brain atlas using deep learning with Theofanis Karayannis - TWIML Talk #287
38:37

Today we’re joined by Theofanis Karayannis, Assistant Professor at the Brain Research Institute of the University of Zurich. Theo’s research is currently focused on understanding how circuits in the brain are formed during development and modified by experiences. Working with animal models, Theo segments and classifies the brain regions, then detects cellular signals that make connections throughout and between each region. How? The answer is (relatively) simple: Deep Learning. In this episode we discuss:

  • Adapting DL methods to fit the biological scope of work
  • The distribution of connections that makes neurological decisions in both animals and humans every day
  • The way images of the brain are collected
  • Genetic trackability, and more!
Aug 01, 2019
Environmental Impact of Large-Scale NLP Model Training with Emma Strubell - TWIML Talk #286
38:36

Today we’re joined by Emma Strubell, currently a visiting scientist at Facebook AI Research. Emma’s focus is on NLP and bringing state of the art NLP systems to practitioners by developing efficient and robust machine learning models. Her paper, Energy and Policy Considerations for Deep Learning in NLP, hones in on one of the biggest topics of the generation: environmental impact. In this episode we discuss:

  • How training neural networks have resulted in an increase in accuracy, however the computational resources required to train these models is staggering - and carbon footprints are only getting bigger
  • Emma’s research methods for determining carbon emissions
  • How companies are reacting to environmental concerns
  • What we, as an industry, can be doing better
Jul 29, 2019
“Fairwashing” and the Folly of ML Solutionism with Zachary Lipton - TWIML Talk #285
01:15:39

Today we’re joined by Zachary Lipton, Assistant Professor in the Tepper School of Business. With an overarching theme of data quality and interpretation, Zachary's research and work is focused on machine learning in healthcare, with the goal of not replacing doctors, but to assist through an understanding of the diagnosis and treatment process. Zachary is also working on the broader question of fairness and ethics in machine learning systems across multiple industries. We delve into these topics today, discussing: 

  • Supervised learning in the medical field, 
  • Guaranteed robustness under distribution shifts, 
  • The concept of ‘fairwashing’,
  • How there is insufficient language in machine learning to encompass abstract ethical behavior, and much, much more
Jul 25, 2019
Retinal Image Generation for Disease Discovery with Stephen Odaibo - TWIML Talk #284
41:39

Today we’re joined by Dr. Stephen Odaibo, Founder and CEO of RETINA-AI Health Inc. Stephen’s unique journey to machine learning and AI includes degrees in math, medicine and computer science, which led him to an ophthalmology practice before taking on the ultimate challenge as an entrepreneur. In this episode we discuss:

  • How RETINA-AI Health harnesses the power of machine learning to build autonomous systems that diagnose and treat retinal diseases 
  • The importance of domain experience and how Stephen’s expertise in ophthalmology and engineering along with the current state of both industries that led to the founding of his company
  • His work with GANs to create artificial retinal images and why more data isn’t always better!
Jul 22, 2019
Real world model explainability with Rayid Ghani - TWiML Talk #283
50:58

Today we’re joined by Rayid Ghani, Director of the Center for Data Science and Public Policy at the University of Chicago. Rayid’s goal is to combine his skills in machine learning and data with his desire to improve public policy and the social sector. Drawing on his range of experience from the corporate world to Chief Scientist for the 2012 Obama Campaign, we delve into the world of automated predictions and explainability methods. Here we discuss:

  • How automated predictions can be helpful, but they don’t always paint a full picture 
  • When dealing with public policy and the social sector, the key to an effective explainability method is the correct context
  • Machine feedback loops that help humans override the wrong predictions and reinforce the right ones
  • Supporting proactive intervention through complex explanability tools
Jul 18, 2019
Inspiring New Machine Learning Platforms w/ Bioelectric Computation with Michael Levin - TWiML Talk #282
25:55

Today we’re joined by Michael Levin, Director of the Allen Discovery Institute at Tufts University. Michael joined us back at NeurIPS to discuss his invited talk “What Bodies Think About: Bioelectric Computation Beyond the Nervous System as Inspiration for New Machine Learning Platforms.” In our conversation, we talk about:

  • Synthetic living machines, novel AI architectures and brain-body plasticity
  • How our DNA doesn’t control everything like we thought and how the behavior of cells in living organisms can be modified and adapted
  • Biological systems dynamic remodeling in the future of developmental biology and regenerative medicine...and more!

The complete show notes for this episode can be found at twimlai.com/talk/282

Register for TWIMLcon: AI Platforms now at twimlcon.com!

Jul 15, 2019
Simulation and Synthetic Data for Computer Vision with Batu Arisoy - TWiML Talk #281
41:36

Today we’re joined by Batu Arisoy, Research Manager with the Vision Technologies & Solutions team at Siemens Corporate Technology. Currently, Batu’s research focus is solving limited data computer vision problems, providing R&D for many of the business units throughout the company. In our conversation we discuss:

  • An emulation of a teacher teaching students information without the use of memorization
  • Discerning which parts of our neural network are required to make decisions
  • An activity recognition project with the Office of Naval Research that keeps ‘humans in the loop’ and more.

 The complete show notes for this episode can be found at twimlai.com/talk/281

Register for TWIMLcon: AI Platforms now at twimlcon.com!

Thanks to Siemens for their sponsorship of today's episode! Check out what they’re up to, visit twimlai.com/siemens.

Jul 09, 2019
Spiking Neural Nets and ML as a Systems Challenge with Jeff Gehlhaar - TWIML Talk #280
54:08

Today we’re joined by Jeff Gehlhaar, VP of Technology and Head of AI Software Platforms at Qualcomm. As we’ve explored in our conversations with both Gary Brotman and Max Welling, Qualcomm has a hand in tons of machine learning research and hardware, and our conversation with Jeff is no different. We discuss:

• How the various training frameworks fit into the developer experience when working with their chipsets.

• Examples of federated learning in the wild.

• The role inference will play in data center devices and more.

The complete show notes for this episode can be found at twimlai.com/talk/280

Register for TWIMLcon now at twimlcon.com.

Thanks to Qualcomm for their sponsorship of today's episode! Check out what they're up to at twimlai.com/qualcomm.

Jul 08, 2019
Transforming Oil & Gas with AI with Adi Bhashyam and Daniel Jeavons - TWIML Talk #279
46:27

Today we’re joined by return guest Daniel Jeavons, GM of Data Science at Shell, and Adi Bhashyam, GM of Data Science at C3, who we had the pleasure of speaking to at this years C3 Transform Conference. In our conversation, we discuss:

• The progress that Dan and his team has made since our last conversation, including an overview of their data platform.

• We explore the various types of users of the platform, and how those users informed the decision to use C3’s out-of-the-box platform solution instead of building their own internal platform.

• Adi gives us an overview of the evolution of C3 and their platform, along with a breakdown of a few Shell-specific use cases. 

The complete show notes can be found at twimlai.com/talk/279.

Visit twimlcon.com to learn more about the TWIMLcon: AI Platforms conference! Early-bird registration has been extended until this Wednesday, 7/3, register today for the lowest possible price!!

Jul 01, 2019
Fast Radio Burst Pulse Detection with Gerry Zhang - TWIML Talk #278
38:04

Today we’re joined by Yunfan Gerry Zhang, a PhD student in the Department of Astrophysics at UC Berkely, and an affiliate of Berkeley’s SETI research center. In our conversation, we discuss: 

• Gerry's research on applying machine learning techniques to astrophysics and astronomy.

• His paper “Fast Radio Burst 121102 Pulse Detection and Periodicity: A Machine Learning Approach”.

• We explore the types of data sources used for this project, challenges Gerry encountered along the way, the role of GANs and much more.

The complete show notes can be found at twimlai.com/talk/278.

Visit twimlcon.com to learn more about the TWIMLcon: AI Platforms conference! Early-bird registration ends TOMORROW 6/28! Register now!

Jun 27, 2019
Tracking CO2 Emissions with Machine Learning with Laurence Watson - TWIML Talk #277
41:08

Today we’re joined by Laurence Watson, Co-Founder and CTO of Plentiful Energy and a former data scientist at Carbon Tracker. In our conversation, we discuss:

• Carbon Tracker's goals, and their report “Nowhere to hide: Using satellite imagery to estimate the utilisation of fossil fuel power plants”.

• How they're using computer vision to process satellite images of coal plants, including how the images are labeled

•Various challenges with the scope and scale of this project, including dealing with varied time zones and imbalanced training classes.

The complete show notes can be found at twimlai.com/talk/277.

Visit twimlcon.com to learn more about the TWIMLcon: AI Platforms conference! Early-bird registration ends on 6/28!

Jun 24, 2019
Topic Modeling for Customer Insights at USAA with William Fehlman - TWIML Talk #276
44:27

Today we’re joined by William Fehlman, director of data science at USAA. We caught up with William a while back to discuss:

  • His work on topic modeling, which USAA uses in various scenarios, including chat channels with members via mobile and desktop interfaces.
  • How their datasets are generated.
  • Explored methodologies of topic modeling, including latent semantic indexing, latent Dirichlet allocation, and non-negative matrix factorization.
  • We also explore how terms are represented via a document-term matrix, and how they are scored based on coherence.

The complete show notes can be found at twimlai.com/talk/276.

Visit twimlcon.com to learn more about the TWIMLcon: AI Platforms conference! Early-bird registration ends on 6/28!

Jun 20, 2019
Phronesis of AI in Radiology with Judy Gichoya - TWIML Talk #275
43:04

Today we’re joined by Judy Gichoya an interventional radiology fellow at the Dotter Institute at Oregon Health and Science University. In our conversation, we discuss:

• Judy's research in “Phronesis of AI in Radiology: Superhuman meets Natural Stupidy,” reviewing the claims of “superhuman” AI performance in radiology.

• We explore potential roles in which AI can have success in radiology, along with some of the different types of biases that can manifest themselves across multiple use cases.

• We look at the CheXNet paper, which details how human and AI performance can complement and improve each other's performance for detecting pneumonia in chest X-rays.

The complete show notes can be found at twimlai.com/talk/275.

Visit twimlcon.com to learn more about the TWIMLcon: AI Platforms conference! 

Jun 18, 2019
The Ethics of AI-Enabled Surveillance with Karen Levy - TWIML Talk #274
42:34

Today we’re joined by Karen Levy, assistant professor in the department of information science at Cornell University. Karen’s research focuses on how rules and technologies interact to regulate behavior, especially the legal, organizational, and social aspects of surveillance and monitoring. In our conversation we discuss:

• Examples of how data tracking and surveillance can be used in ways that can be abusive to various marginalized groups, including detailing her extensive research into truck driver surveillance.

• Her thoughts on how the broader society will react to the increase in surveillance,

• The unintended consequences of surveillant systems, questions surrounding hybridization of jobs and systems, and more!

The complete show notes can be found at twimlai.com/talk/274.

Visit twimlcon.com to learn more about the TWIMLcon: AI Platforms conference! 

Jun 14, 2019
Supporting Rapid Model Development at Two Sigma with Matt Adereth & Scott Clark - TWIML Talk #273
49:09

Today we’re joined by Matt Adereth, managing director of investments at Two Sigma, and return guest Scott Clark, co-founder and CEO of SigOpt, to discuss:

• The end to end modeling platform at Two Sigma, who it serves, and challenges faced in production and modeling.

• How Two Sigma has attacked the experimentation challenge with their platform.

• The relationship between the optimization and infrastructure teams at SigOpt.

• What motivates companies that aren’t already heavily invested in platforms, optimization or automation, to do so.

The complete show notes can be found at twimlai.com/talk/273.

Visit twimlcon.com to learn more about the TWIMLcon: AI Platforms conference! The first 10 listeners who register get their ticket for 75% off using the discount code TWIMLFIRST!

Follow along with the entire AI Platforms Vol 2 series at twimlai.com/aiplatforms2.

Thanks to SigOpt for their continued support of the podcast, and their sponsorship of this episode! Check out their machine learning experimentation and optimization suite, and get a free trial at twimlai.com/sigopt.

Jun 11, 2019
Scaling Model Training with Kubernetes at Stripe with Kelley Rivoire - TWIML Talk #272
45:07

Today we’re joined by Kelley Rivoire, engineering manager working on machine learning infrastructure at Stripe. Kelley and I caught up at a recent Strata Data conference to discuss:

• Her talk "Scaling model training: From flexible training APIs to resource management with Kubernetes."

• Stripe’s machine learning infrastructure journey, including their start from a production focus.

• Internal tools used at Stripe, including Railyard, an API built to manage model training at scale & more!

The complete show notes can be found at twimlai.com/talk/272.

Visit twimlcon.com to learn more about the TWIMLcon: AI Platforms conference! The first 10 listeners who register get their ticket for 75% off using the discount code TWIMLFIRST!

Follow along with the entire AI Platforms Vol 2 series at twimlai.com/aiplatforms2.

Thanks to SigOpt for their continued support of the podcast, and their sponsorship of this episode! Check out their machine learning experimentation and optimization suite, and get a free trial at twimlai.com/sigopt.

Jun 06, 2019
Productizing ML at Scale at Twitter with Yi Zhuang - TWIML Talk #271
49:20

Today we continue our AI Platforms series joined by Yi Zhuang, Senior Staff Engineer at Twitter & Tech Lead for Machine Learning Core Environment at Twitter Cortex. In our conversation, we cover: 

• The machine learning landscape at Twitter, including with the history of the Cortex team

• Deepbird v2, which is used for model training and evaluation solutions, and it's integration with Tensorflow 2.0.

• The newly assembled “Meta” team, that is tasked with exploring the bias, fairness, and accountability of their machine learning models, and much more!

The complete show notes can be found at twimlai.com/talk/271.

Visit twimlcon.com to learn more about the TWIMLcon: AI Platforms conference! The first 10 listeners who register get their ticket for 75% off using the discount code TWIMLFIRST!

Follow along with the entire AI Platforms Vol 2 series at twimlai.com/aiplatforms2.

Thanks to SigOpt for their continued support of the podcast, and their sponsorship of this episode! Check out their machine learning experimentation and optimization suite, and get a free trial at twimlai.com/sigopt.

Finally, visit twimlai.com/3bday to help us celebrate TWiML's 3rd Birthday!

Jun 03, 2019
Snorkel: A System for Fast Training Data Creation with Alex Ratner - TWiML Talk #270
45:42

Today we’re joined by Alex Ratner, Ph.D. student at Stanford. In our conversation, we discuss:

• Snorkel, the open source framework that is the successor to Stanford's Deep Dive project.

• How Snorkel is used as a framework for creating training data with weak supervised learning techniques.

• Multiple use cases for Snorkel, including how it is used by large companies like Google. 

The complete show notes can be found at twimlai.com/talk/270.

Follow along with the entire AI Platforms Vol 2 series at twimlai.com/aiplatforms2.

Thanks to SigOpt for their continued support of the podcast, and their sponsorship of this episode! Check out their machine learning experimentation and optimization suite, and get a free trial at twimlai.com/sigopt.

Finally, visit twimlai.com/3bday to help us celebrate TWiML's 3rd Birthday!

May 30, 2019
Advancing Autonomous Vehicle Development Using Distributed Deep Learning with Adrien Gaidon - TWiML Talk #269
50:04

In this, the kickoff episode of AI Platforms Vol. 2, we're joined by Adrien Gaidon, Machine Learning Lead at Toyota Research Institute. Adrien and I caught up to discuss his team’s work on deploying distributed deep learning in the cloud, at scale. In our conversation, we discuss: 

• The beginning and gradual scaling up of TRI's platform.

• Their distributed deep learning methods, including their use of stock Pytorch.

• Applying devops to their research infrastructure, and much more!

The complete show notes for this episode can be found at twimlai.com/talk/269.

Thanks to SigOpt for their continued support of the podcast, and their sponsorship of this episode! Check out their machine learning experimentation and optimization suite, and get a free trial at twimlai.com/sigopt.

Finally, visit twimlai.com/3bday to help us celebrate TWiML's 3rd Birthday!

May 28, 2019
Are We Being Honest About How Difficult AI Really Is? w/ David Ferrucci - TWiML Talk #268
52:36

Today we’re joined by David Ferrucci, Founder, CEO, and Chief Scientist at Elemental Cognition, a company focused on building natural learning systems that understand the world the way people do. In our conversation, we discuss: 

• His experience leading the team that built the IBM Watson system that won on Jeopardy.


• The role of “understanding” in the context of AI systems, and the types of commitments and investments needed to achieve even modest levels of understanding in these systems.

• His thoughts on the power of deep learning, what the path to AGI looks like, and the need for hybrid systems to get there.

The complete show notes for this episode can be found at twimlai.com/talk/268.

Visit twimlai.com/3bday to help us celebrate TWiML's 3rd Birthday!

 

May 23, 2019
Gauge Equivariant CNNs, Generative Models, and the Future of AI with Max Welling - TWiML Talk #267
01:04:26

Today we’re joined by Max Welling, research chair in machine learning at the University of Amsterdam, as well as VP of technologies at Qualcomm, and Fellow at the Canadian Institute for Advanced Research, or CIFAR. In our conversation, we discuss: 

• Max’s research at Qualcomm AI Research and the University of Amsterdam, including his work on Bayesian deep learning, Graph CNNs and Gauge Equivariant CNNs, and in power efficiency for AI via compression, quantization, and compilation.

• Max’s thoughts on the future of the AI industry, in particular, the relative importance of models, data and compute.

The complete show notes for this episode can be found at twimlai.com/talk/267.

Thanks to Qualcomm for sponsoring today's episode! Check out what they're up to at twimlai.com/qualcomm.

 

May 20, 2019
Can We Trust Scientific Discoveries Made Using Machine Learning? with Genevera Allen - TWiML Talk #266
41:54

Today we’re joined by Genevera Allen, associate professor of statistics in the EECS Department at Rice University, Founder and Director of the Rice Center for Transforming Data to Knowledge and Investigator with the Neurological Research Institute with the Baylor College of Medicine.

Genevera caused quite the stir at the American Association for the Advancement of Science meeting earlier this year with her presentation “Can We Trust Data-Driven Discoveries?" In our conversation we cover:

• The goal of Genevera's talk, and what was lost in translation.

• Use cases outlining the shortcomings of current machine learning techniques.

• Reproducibility, including inference vs discovery, and the lack of terminology for many of the various reproducibility issues, & much more!

The complete show notes for this episode can be found at twimlai.com/talk/266.

 

May 16, 2019
Creative Adversarial Networks for Art Generation with Ahmed Elgammal - TWiML Talk #265
37:13

Today we’re joined by Ahmed Elgammal, a professor in the department of computer science at Rutgers, and director of The Art and Artificial Intelligence Lab. In my conversation with Ahmed, we discuss:

• His work on AICAN, a creative adversarial network that produces original portraits, trained with over 500 years of European canonical art.

• How complex the computational representations of the art actually are, and how he simplifies them.

• Specifics of the training process, including the various types of artwork used, and the constraints applied to the model.

The complete show notes for this episode can be found at twimlai.com/talk/265.

May 13, 2019
Diagnostic Visualization for Machine Learning with YellowBrick w/ Rebecca Bilbro - TWiML Talk #264
42:34

Today we close out our PyDataSci series joined by Rebecca Bilbro, head of data science at ICX media and co-creator of the popular open-source visualization library YellowBrick.

In our conversation, Rebecca details:

• Her relationship with toolmaking, which led to the eventual creation of Yellowbrick.

• Popular tools within YellowBrick, including a summary of their unit testing approach.

• Interesting use cases that she’s seen over time.

• The growth she’s seen in the community of contributors and examples of their contributions as they approach the release of YellowBrick 1.0.

The complete show notes for this episode can be found at twimlai.com/talk/264. Check out the rest of the PyDataSci series at twimlai.com/pydatasci.

We want to better understand your views on the importance of open source and the projects and players in this space. To access the survey visit twimlai.com/pythonsurvey.

Thanks to this weeks sponsor, IBM, for their support of the podcast! Visit twimlai.com/ibm to learn more about the IBM Data Science Community.

May 10, 2019
Librosa: Audio and Music Processing in Python with Brian McFee - TWiML Talk #263
39:10

Today we continue our PyDataSci series joined by Brian McFee, assistant professor of music technology and data science at NYU, and creator of LibROSA, a python package for music and audio analysis.

Brian walks us through his experience building LibROSA, including:

• Detailing the core functions provided in the library,

• His experience working within Jupyter Notebook,

• We explore a typical LibROSA workflow & more!

The complete show notes for this episode can be found at twimlai.com/talk/263.

Check out the rest of the PyDataSci series at twimlai.com/pydatasci.

We want to better understand your views on the importance of open source and the projects and players in this space. To access the survey visit twimlai.com/pythonsurvey.

Thanks to this weeks sponsor, IBM, for their support of the podcast! Visit twimlai.com/ibm to learn more about the IBM Data Science Community.

May 09, 2019
Practical Natural Language Processing with spaCy and Prodigy w/ Ines Montani - TWiML Talk #262
49:39

In this episode of PyDataSci, we’re joined by Ines Montani, Cofounder of Explosion, Co-developer of SpaCy and lead developer of Prodigy.

Ines and I caught up to discuss her various projects, including the aforementioned SpaCy, an open-source NLP library built with a focus on industry and production use cases.

The complete show notes for this episode can be found at twimlai.com/talk/262. Check out the rest of the PyDataSci series at twimlai.com/pydatasci.

We want to better understand your views on the importance of open source and the projects and players in this space. To access the survey visit twimlai.com/pythonsurvey.

Thanks to this weeks sponsor, IBM, for their support of the podcast! Visit twimlai.com/ibm to learn more about the IBM Data Science Community. 

May 07, 2019
Scaling Jupyter Notebooks with Luciano Resende - TWiML Talk #261
34:28

Today we kick off PyDataSci with Luciano Resende, an Open Source AI Platform Architect at IBM and part of the Center for Open Source Data and AI Technology.

Luciano and I caught up to discuss his work on Jupyter Enterprise Gateway, a scalable way to share Jupyter notebooks and other resources in an enterprise environment. In our conversation, we discuss some of the challenges that arise using Jupyter Notebooks at scale, the role of open source projects like Jupyter Hub and Enterprise Gateway, and some potential reasons for investing in and building custom notebooks. We also explore some common requests from the community, such as tighter integration with git repositories, as well as the python-centricity of the vast Jupyter ecosystem.

The complete show notes for this episode can be found at twimlai.com/talk/261. Check out the rest of the PyDataSci series at twimlai.com/pydatasci.

Thanks to this weeks sponsor, IBM, for their support of the podcast! Visit twimlai.com/ibm to learn more about the IBM Data Science Community. 

 

May 06, 2019
Fighting Fake News and Deep Fakes with Machine Learning w/ Delip Rao - TWiML Talk #260
58:40

Today we’re joined by Delip Rao, vice president of research at the AI Foundation, co-author of the book Natural Language Processing with PyTorch, and creator of the Fake News Challenge.

Our conversation begins with the origin story of the Fake News Challenge, including Delip’s initial motivations for the project, and what some of his key takeaways were from that experience. We then dive into a discussion about the generation and detection of artificial content, including “fake news” and “deep fakes.” We discuss the state of generation and detection for text, video, and audio, the key challenges in each of these modalities, the role of GANs on both sides of the equation, and other potential solutions. Finally, we discuss Delip’s new book, Natural Language Processing with PyTorch and his philosophy behind writing it.

The complete show notes for this episode can be found at https://twimlai.com/talk/260.

For more from the AI Conference NY series, visit twimlai.com/nyai19.

Thanks to our friends at HPE for sponsoring this week's series of shows from the O’Reilly AI Conference in New York City! For more information on HPE InfoSight, visit twimlai.com/hpe.

 

May 03, 2019
Maintaining Human Control of Artificial Intelligence with Joanna Bryson - TWiML Talk #259
38:11

Today we’re joined by Joanna Bryson, Reader at the University of Bath.

I was fortunate to catch up with Joanna at the AI Conference where she presented on “Maintaining Human Control of Artificial Intelligence,“ focusing on technological and policy mechanisms that could be used to achieve that goal. In our conversation, we explore our current understanding of “natural intelligence” and how it can inform the development of AI, the context in which she uses the term “human control” and its implications, and the meaning of and need to apply “DevOps” principles when developing AI systems. This was a fun one!

The complete show notes for this episode can be found at https://twimlai.com/talk/259.

For more from the AI Conference NY series, visit twimlai.com/nyai19.

Thanks to our friends at HPE for sponsoring this week's series of shows from the O’Reilly AI Conference in New York City! For more information on HPE InfoSight, visit twimlai.com/hpe.

May 01, 2019
Intelligent Infrastructure Management with Pankaj Goyal & Rochna Dhand - TWiML Talk #258
44:49

Today we kick off our AI conference NY series with Pankaj Goyal, VP for AI & HPC product management at HPE, and Rochna Dhand, director of product management for HPE InfoSight.


Today we get things kicked off with Pankaj Goyal, VP for AI & HPC product management at HPE, and Rochna Dhand, director of product management for HPE InfoSight. In our conversation, Pankaj shares some examples of the kind of AI projects HPE is working with customers on And Rochna details hows HPE’s Infosight helps IT organizations better manage and ensure the health of an enterprise’s IT infrastructure using machine learning. We discuss the key use cases addressed by InfoSight, the types of models it uses for its analysis and some of the results seen in real-world deployments.

The complete show notes for this episode can be found at https://twimlai.com/talk/258.

For more from the AI Conference NY series, visit twimlai.com/nyai19.

Thanks to our friends at HPE for sponsoring this week's series of shows from the O’Reilly AI Conference in New York City! For more information on HPE InfoSight, visit twimlai.com/hpe.

Apr 29, 2019
Organizing for Successful Data Science at Stitch Fix with Eric Colson - TWiML Talk #257
52:38

For the final episode of our Strata Data series, we’re joined by Eric Colson, Chief Algorithms Officer at Stitch Fix, whose presentation at the conference explored “How to make fewer bad decisions.”

Our discussion focuses in on the three key organizational principles for data science teams that he’s developed at Stitch Fix. Along the way, we also talk through the various roles data science plays at the company, explore a few of the 800+ algorithms in use at the company spanning recommendations, inventory management, demand forecasting, and clothing design. We discuss the roles of Stitch Fix’splatforms team in supporting the data science organization, and his unique perspective on how to identify platform features.

The complete show notes for this episode can be found at https://twimlai.com/talk/257.

For more from the Strata Data conference series, visit twimlai.com/stratasf19.

I want to send a quick thanks to our friends at Cloudera for their sponsorship of this series of podcasts from the Strata Data Conference, which they present along with O’Reilly Media. Cloudera’s long been a supporter of the podcast; in fact, they sponsored the very first episode of TWiML Talk, recorded back in 2016. Since that time Cloudera has continued to invest in and build out its platform, which already securely hosts huge volumes of enterprise data, to provide enterprise customers with a modern environment for machine learning and analytics that works both in the cloud as well as the data center. In addition, Cloudera Fast Forward Labs provides research and expert guidance that helps enterprises understand the realities of building with AI technologies without needing to hire an in-house research team. To learn more about what the company is up to and how they can help, visit Cloudera’s Machine Learning resource center at cloudera.com/ml.

Apr 26, 2019
End-to-End Data Science to Drive Business Decisions at LinkedIn with Burcu Baran - TWiML Talk #256
49:51

In this episode of our Strata Data conference series, we’re joined by Burcu Baran, Senior Data Scientist at LinkedIn.

At Strata, Burcu, along with a few members of her team, delivered the presentation “Using the full spectrum of data science to drive business decisions,” which outlines how LinkedIn manages their entire machine learning production process. In our conversation, Burcu details each phase of the process, including problem formulation, monitoring features, A/B testing and more. We also discuss how her “horizontal” team works with other more “vertical” teams within LinkedIn, various challenges that arise when training and modeling such as data leakage and interpretability, best practices when trying to deal with data partitioning at scale, and of course, the need for a platform that reduces the manual pieces of this process, promoting efficiency.

The complete show notes for this episode can be found at https://twimlai.com/talk/256.

For more from the Strata Data conference series, visit twimlai.com/stratasf19.

I want to send a quick thanks to our friends at Cloudera for their sponsorship of this series of podcasts from the Strata Data Conference, which they present along with O’Reilly Media. Cloudera’s long been a supporter of the podcast; in fact, they sponsored the very first episode of TWiML Talk, recorded back in 2016. Since that time Cloudera has continued to invest in and build out its platform, which already securely hosts huge volumes of enterprise data, to provide enterprise customers with a modern environment for machine learning and analytics that works both in the cloud as well as the data center. In addition, Cloudera Fast Forward Labs provides research and expert guidance that helps enterprises understand the realities of building with AI technologies without needing to hire an in-house research team. To learn more about what the company is up to and how they can help, visit Cloudera’s Machine Learning resource center at cloudera.com/ml.

I’d also like to send a huge thanks to LinkedIn for their continued support and sponsorship of the show! Now that I’ve had a chance to interview several of the folks on LinkedIn’s Data Science and Engineering teams, it’s really put into context the complexity and scale of the problems that they get to work on in their efforts to create enhanced economic opportunities for every member of the global workforce. AI and ML are integral aspects of almost every product LinkedIn builds for its members and customers and their massive, highly structured dataset gives their data scientists and researchers the ability to conduct applied research to improve member experiences. To learn more about the work of LinkedIn Engineering, please visit engineering.linkedin.com/blog.

Apr 24, 2019
Learning with Limited Labeled Data with Shioulin Sam - TWiML Talk #255
44:36

Today, in the first episode of our Strata Data conference series, we’re joined by Shioulin Sam, Research Engineer with Cloudera Fast Forward Labs.

Shioulin and I caught up to discuss the newest report to come out of CFFL, “Learning with Limited Label Data,” which explores active learning as a means to build applications requiring only a relatively small set of labeled data. We start our conversation with a review of active learning and some of the reasons why it’s recently become an interesting technology for folks building systems based on deep learning. We then discuss some of the differences between active learning approaches or implementations, and some of the common requirements of an active learning system. Finally, we touch on some packaged offerings in the marketplace that include active learning, including Amazon’s SageMaker Ground Truth, and review Shoulin’s tips for getting started with the technology.

The complete show notes for this episode can be found at https://twimlai.com/talk/255.

For more from the Strata Data conference series, visit twimlai.com/stratasf19.

I want to send a quick thanks to our friends at Cloudera for their sponsorship of this series of podcasts from the Strata Data Conference, which they present along with O’Reilly Media. Cloudera’s long been a supporter of the podcast; in fact, they sponsored the very first episode of TWiML Talk, recorded back in 2016. Since that time Cloudera has continued to invest in and build out its platform, which already securely hosts huge volumes of enterprise data, to provide enterprise customers with a modern environment for machine learning and analytics that works both in the cloud as well as the data center. In addition, Cloudera Fast Forward Labs provides research and expert guidance that helps enterprises understand the realities of building with AI technologies without needing to hire an in-house research team. To learn more about what the company is up to and how they can help, visit Cloudera’s Machine Learning resource center at cloudera.com/ml.

Apr 22, 2019
cuDF, cuML & RAPIDS: GPU Accelerated Data Science with Paul Mahler - TWiML Talk #254
38:13

Today we're joined by Paul Mahler, senior data scientist and technical product manager for machine learning at NVIDIA.

In our conversation, Paul and I discuss NVIDIA's RAPIDS open source project, which aims to bring GPU acceleration to traditional data science workflows and machine learning tasks. We dig into the various subprojects like cuDF and cuML that make up the RAPIDS ecosystem, as well as the role of lower-level libraries like mlprims and the relationship to other open-source projects like Scikit-learn, XGBoost and Dask.

The complete show notes for this episode can be found at https://twimlai.com/talk/254.

Visit twimlai.com/gtc19 for more from our GTC 2019 series.

To learn more about Dell Precision workstations, and some of the ways they’re being used by customers in industries like Media and Entertainment, Engineering and Manufacturing, Healthcare and Life Sciences, Oil and Gas, and Financial services, visit Dellemc.com/Precision.

Apr 19, 2019
Edge AI for Smart Manufacturing with Trista Chen - TWiML Talk #253
38:39

Today we’re joined by Trista Chen, chief scientist of machine learning at Inventec.

At GTC, Trista spoke on “Edge AI in Smart Manufacturing: Defect Detection and Beyond.” In our conversation, we discuss a few of the challenges that Industry 4.0 initiatives aim to address and dig into a few of the various use cases she’s worked on, such as the deployment of machine learning in an industrial setting to perform defect detection, safety improvement, demand forecasting, and more. We also dig into the role of edge, cloud, and what she calls hybrid AI, which is inference happening both in the cloud and on the edge concurrently. Finally, we discuss the challenges associated with estimating the ROI of industrial AI projects and the need that often arises to redefine the problem to understand the ultimate impact of the solution.

The complete show notes for this episode can be found at https://twimlai.com/talk/253.

Visit twimlai.com/gtc19 for more from our GTC 2019 series.

To learn more about Dell Precision workstations, and some of the ways they’re being used by customers in industries like Media and Entertainment, Engineering and Manufacturing, Healthcare and Life Sciences, Oil and Gas, and Financial services, visit Dellemc.com/Precision.

Apr 18, 2019
Machine Learning for Security and Security for Machine Learning with Nicole Nichols - TWiML Talk #252
41:56

Today we’re joined by Nicole Nichols, a senior research scientist at the Pacific Northwest National Lab.

Nicole joined me to discuss her recent presentation at GTC, which was titled “Machine Learning for Security, and Security for Machine Learning.” Our conversation explores the two use cases she presented, insider threat detection, and software fuzz testing. We discuss the effectiveness of standard and bidirectional RNN language models for detecting malicious activity within the Los Alamos National Laboratory cybersecurity dataset, the augmentation of software fuzzing techniques using deep learning, and light-based adversarial attacks on image classification systems. I’d love to hear your thoughts on these use cases!

The complete show notes for this episode can be found at https://twimlai.com/talk/252.

Visit twimlai.com/gtc19 for more from our GTC 2019 series.

To learn more about Dell Precision workstations, and some of the ways they’re being used by customers in industries like Media and Entertainment, Engineering and Manufacturing, Healthcare and Life Sciences, Oil and Gas, and Financial services, visit Dellemc.com/Precision.

Apr 16, 2019
Domain Adaptation and Generative Models for Single Cell Genomics with Gerald Quon - TWiML Talk #251
32:24

Today we’re joined by Gerald Quon, assistant professor in the Molecular and Cellular Biology department at UC Davis.

Gerald presented his work on Deep Domain Adaptation and Generative Models for Single Cell Genomics at GTC this year, which explores single cell genomics as a means of disease identification for treatment. In our conversation, we discuss how Gerald and his team use deep learning to generate novel insights across diseases, the different types of data that was used, and the development of ‘nested’ Generative Models for single cell measurement.

The complete show notes for this episode can be found at https://twimlai.com/talk/251.

Visit twimlai.com/gtc19 for more from our GTC 2019 series.

To learn more about Dell Precision workstations, and some of the ways they’re being used by customers in industries like Media and Entertainment, Engineering and Manufacturing, Healthcare and Life Sciences, Oil and Gas, and Financial services, visit Dellemc.com/Precision.

Apr 15, 2019
Mapping Dark Matter with Bayesian Neural Networks w/ Yashar Hezaveh - TWiML Talk #250
36:04

You might have seen the news yesterday that MIT researcher Katie Bouman produced the first image of a black hole. What’s been less reported is that the algorithm she developed to accomplish this is based on machine learning. Machine learning is having a huge impact in the fields of astronomy and astrophysics, and I’m excited to bring you interviews with some of the people innovating in this area.

Today we’re joined by Yashar Hezaveh, Assistant Professor at the University of Montreal, and Research Fellow at the Center for Computational Astrophysics at Flatiron Institute.

Yashar and I caught up to discuss his work on gravitational lensing, which is the bending of light from distant sources due to the effects of gravity. In our conversation, Yashar and I discuss how machine learning can be applied to undistort images, including some of the various techniques used and how the data is prepared to get the best results. We also discuss the intertwined roles of simulation and machine learning in generating images, incorporating other techniques such as domain transfer or GANs, and how he assesses the results of this project.

For even more on this topic, I’d also suggest checking out the following interviews, TWiML Talk #117 with Chris Shallue, where we discuss the discovery of exoplanets, TWiML Talk #184, with Viviana Acquaviva, where we explore dark energy and star formation, and if you want to go way back, TWiML Talk #5 with Joshua Bloom which provides a great overview of the application of ML in astronomy.

Thanks to Pegasystems for sponsoring today's show! I'd like to invite you to join me at PegaWorld, the company’s annual digital transformation conference, which takes place this June in Las Vegas. To learn more about the conference or to register, visit pegaworld.com and use TWIML19 in the promo code field when you get there for $200 off.

The complete show notes for this episode can be found at https://twimlai.com/talk/250.

Apr 11, 2019
Deep Learning for Population Genetic Inference with Dan Schrider - TWiML Talk #249
49:53

Today we’re joined by Dan Schrider, assistant professor in the department of genetics at The University of North Carolina at Chapel Hill.

My discussion with Dan starts with an overview of population genomics and from there digs into his application of machine learning in the field, allowing us to, for example, better understand population size changes and gene flow from DNA sequences. We then dig into Dan’s paper “The Unreasonable Effectiveness of Convolutional Neural Networks in Population Genetic Inference,” which was published in the Molecular Biology and Evolution journal, which examines the idea that CNNs are capable of outperforming expert-derived statistical methods for some key problems in the field.

Thanks to Pegasystems for sponsoring today's show! I'd like to invite you to join me at PegaWorld, the company’s annual digital transformation conference, which takes place this June in Las Vegas. To learn more about the conference or to register, visit pegaworld.com and use TWIML19 in the promo code field when you get there for $200 off.

The complete show notes for this episode can be found at https://twimlai.com/talk/249.

Apr 09, 2019
Empathy in AI with Rob Walker - TWiML Talk #248
41:26

Today we’re joined by Rob Walker, Vice President of Decision Management at Pegasystems.

Rob joined us back in episode 127 to discuss “Hyperpersonalizing the customer experience.” Today, he’s back for a discussion about the role of empathy in AI systems. In our conversation, we dig into the role empathy plays in consumer-facing human-AI interactions, the differences between empathy and ethics, and a few examples of ways empathy should be considered when building enterprise AI systems.

What do you think? Should empathy be a consideration in AI systems? If so, do any examples jump out for you of where and how it should be applied? I’d love to hear your thoughts on the topic! Feel free to shoot me a tweet at @samcharrington or leave a comment on the show notes page with your thoughts.

Thanks to Pegasystems for sponsoring today's show! I'd like to invite you to join me at PegaWorld, the company’s annual digital transformation conference, which takes place this June in Las Vegas. To learn more about the conference or to register, visit pegaworld.com and use TWIML19 in the promo code field when you get there for $200 off.

The complete show notes for this episode can be found at https://twimlai.com/talk/248.

Apr 05, 2019
Benchmarking Custom Computer Vision Services at Urban Outfitters with Tom Szumowski - TWiML Talk #247
50:38

Today we’re joined by Tom Szumowski, Data Scientist at URBN, the parent company of Urban Outfitters, Anthropologie, and other consumer fashion brands.

Tom and I caught up recently to discuss his project “Exploring Custom Vision Services for Automated Fashion Product Attribution.” We start our discussion with a definition of the product attribution problem in retail and fashion, and a discussion of the challenges it offers to data scientists. We then look at the process Tom and his team took to building custom attribution models, and the results of their evaluation of various custom vision APIs for this purpose, with a focus on the various roadblocks and lessons he and his team encountered along the way.

Thanks to Pegasystems for sponsoring today's show! I'd like to invite you to join me at PegaWorld, the company’s annual digital transformation conference, which takes place this June in Las Vegas. To learn more about the conference or to register, visit pegaworld.com and use TWIML19 in the promo code field when you get there for $200 off.

The complete show notes for this episode can be found at https://twimlai.com/talk/247.

Apr 03, 2019
Pragmatic Quantum Machine Learning with Peter Wittek - TWiML Talk #245
01:06:59

Today we’re joined by Peter Wittek, Assistant Professor at the University of Toronto working on quantum-enhanced machine learning and the application of high-performance learning algorithms in quantum physics.

Peter and I caught up back in November to discuss a presentation he gave at re:Invent, “Pragmatic Quantum Machine Learning Today.” In our conversation, we start with a bit of background including the current state of quantum computing, a look ahead to what the next 20 years of quantum computing might hold, and how current quantum computers are flawed. We then dive into our discussion on quantum machine learning, and Peter’s new course on the topic, which debuted in February. I’ll link to that in the show notes. Finally, we briefly discuss the work of Ewin Tang, a PhD student from the University of Washington, who’s undergrad thesis “A quantum-inspired classical algorithm for recommendation systems,” made quite a stir last summer. As a special treat for those interested, I’m also sharing my interview with Ewin as a bonus episode alongside this one. I’d love to hear your thoughts on how you think quantum computing will impact machine learning in the next 20 years! Send me a tweet or leave a comment on the show notes page.

Thanks to Pegasystems for sponsoring today's show! I'd like to invite you to join me at PegaWorld, the company’s annual digital transformation conference, which takes place this June in Las Vegas. To learn more about the conference or to register, visit pegaworld.com and use TWIML19 in the promo code field when you get there for $200 off.

The complete show notes for this episode can be found at https://twimlai.com/talk/245.

Apr 01, 2019
*Bonus Episode* A Quantum Machine Learning Algorithm Takedown with Ewin Tang - TWiML Talk #246
40:03

In this special bonus episode of the podcast, I’m joined by Ewin Tang, a PhD student in the Theoretical Computer Science group at the University of Washington.

In our conversation, Ewin and I dig into her paper “A quantum-inspired classical algorithm for recommendation systems,” which took the quantum computing community by storm last summer. We haven’t called out a Nerd-Alert interview in a long time, but this interview inspired us to dust off that designation, so get your notepad ready!

The complete show notes for this episode can be found at https://twimlai.com/talk/246.

Apr 01, 2019
Supporting TensorFlow at Airbnb with Alfredo Luque - TWiML Talk #244
40:57

This interview features my conversation with Alfredo Luque, a software engineer on the machine infrastructure team at Airbnb.

If you’re among the many TWiML fans interested in AI Platforms and ML infrastructure, you probably remember my interview with Airbnb’s Atul Kale, in which we discussed their Bighead platform. In my conversation with Alfredo, we dig a bit deeper into Bighead’s support for TensorFlow, discuss a recent image categorization challenge they solved with the framework, and explore what the new 2.0 release means for their users. The complete show notes for this episode can be found at https://twimlai.com/talk/244

I’d like to send a huge thanks to the TensorFlow team for helping us bring you this podcast series and giveaway. With all the great announcements coming out of the TensorFlow Dev Summit, including the 2.0 alpha, you should definitely check out the latest and greatest at https://tensorflow.org where you can also download and start building with the framework.

In conjunction with the TensorFlow 2.0 alpha release, and our TensorFlow Dev Summit series, we invite you to enter our TensorFlow Edge Kit Giveaway. Winners will receive a gift box from Google that includes some fun toys including the new Coral Edge TPU device and the SparkFun Edge development board powered by TensorFlow. Find out more at https://twimlai.com/tfgiveaway.

Mar 28, 2019
Mining the Vatican Secret Archives with TensorFlow w/ Elena Nieddu - TWiML Talk #243
44:06

Today we’re joined by Elena Nieddu, PhD Student at Roma Tre University, who presented on her project “In Codice Ratio” at the TF Dev Summit.

In our conversation, Elena provides an overview of the project, which aims to annotate and transcribe Vatican secret archive documents via machine learning. We discuss the many challenges associated with transcribing this vast archive of handwritten documents, including overcoming the high cost of data annotation. I think you’ll agree that her team’s approach to that challenge was particularly creative. The complete show notes for this episode can be found at https://twimlai.com/talk/243

I’d like to send a huge thanks to the TensorFlow team for helping us bring you this podcast series and giveaway. With all the great announcements coming out of the TensorFlow Dev Summit, including the 2.0 alpha, you should definitely check out the latest and greatest at https://tensorflow.org where you can also download and start building with the framework.

In conjunction with the TensorFlow 2.0 alpha release, and our TensorFlow Dev Summit series, we invite you to enter our TensorFlow Edge Kit Giveaway. Winners will receive a gift box from Google that includes some fun toys including the new Coral Edge TPU device and the SparkFun Edge development board powered by TensorFlow. Find out more at https://twimlai.com/tfgiveaway.

Mar 27, 2019
Exploring TensorFlow 2.0 with Paige Bailey - TWiML Talk #242
41:17

Today we're joined by Paige Bailey, a TensorFlow developer advocate at Google to discuss the TensorFlow 2.0 alpha release.

Paige and I sat down to talk through the latest TensorFlow updates, and we cover a lot of ground, including the evolution of the TensorFlow APIs and the role of eager mode, tf.keras and tf.function, the evolution of TensorFlow for Swift and its inclusion in the new fast.ai course, new updates to TFX (or TensorFlow Extended), Google’s end-to-end machine learning platform, the emphasis on community collaboration with TF 2.0, and a bunch more. The complete show notes for this episode can be found at https://twimlai.com/talk/242

I’d like to send a huge thanks to the TensorFlow team for helping us bring you this podcast series and giveaway. With all the great announcements coming out of the TensorFlow Dev Summit, including the 2.0 alpha, you should definitely check out the latest and greatest at https://tensorflow.org where you can also download and start building with the framework.

In conjunction with the TensorFlow 2.0 alpha release, and our TensorFlow Dev Summit series, we invite you to enter our TensorFlow Edge Kit Giveaway. Winners will receive a gift box from Google that includes some fun toys including the new Coral Edge TPU device and the SparkFun Edge development board powered by TensorFlow. Find out more at https://twimlai.com/tfgiveaway.

 

 

Mar 25, 2019
Privacy-Preserving Decentralized Data Science with Andrew Trask - TWiML Talk #241
32:40

Today we’re joined by Andrew Trask, PhD student at the University of Oxford and Leader of the OpenMined Project.

OpenMined is an open-source community focused on researching, developing, and promoting tools for secure, privacy-preserving, value-aligned artificial intelligence. Andrew and I caught up back at NeurIPS to dig into why OpenMined is important and explore some of the basic research and technologies supporting Private, Decentralized Data Science. We touch on ideas such as Differential Privacy, and Secure Multi-Party Computation, and how these ideas come into play in, for example, federated learning.

Thanks to Pegasystems for sponsoring today's show! I'd like to invite you to join me at PegaWorld, the company’s annual digital transformation conference, which takes place this June in Las Vegas. To learn more about the conference or to register, visit pegaworld.com and use TWIML19 in the promo code field when you get there for $200 off.

The complete show notes for this episode can be found at https://twimlai.com/talk/241.

Mar 21, 2019
The Unreasonable Effectiveness of the Forget Gate with Jos Van Der Westhuizen - TWiML Talk #240
33:32

Today we’re joined by Jos Van Der Westhuizen, PhD student in Engineering at Cambridge University.

Jos’ research focuses on applying LSTMs, or Long Short-Term Memory neural networks, to biological data for various tasks. In our conversation, we discuss his paper The unreasonable effectiveness of the forget gate, in which he explores the various “gates” that make up an LSTM module and the general impact of getting rid of gates on the computational intensity of training the networks. Jos eventually determines that leaving only the forget-gate results in an unreasonably effective network, and we discuss why. Jos also gives us some great LSTM related resources, including references to Jurgen Schmidhuber, whose research group invented the LSTM, and who I spoke to back in Talk #44.

Thanks to Pegasystems for sponsoring today's show! I'd like to invite you to join me at PegaWorld, the company’s annual digital transformation conference, which takes place this June in Las Vegas. To learn more about the conference or to register, visit pegaworld.com and use TWIML19 in the promo code field when you get there for $200 off.

The complete show notes for this episode can be found at https://twimlai.com/talk/240.

Mar 18, 2019
Building a Recommendation Agent for The North Face with Andrew Guldman - TWiML Talk #239
48:42

Today we’re joined by Andrew Guldman, VP of Product Engineering and Research and Development at Fluid.

Andrew and I caught up a while back to discuss Fluid XPS, a user experience built to help the casual shopper decide on the best product choices during online retail interactions. While XPS has expanded since we recorded this interview, we specifically discuss its origins as a product to assist outerwear retailer The North Face. In our conversation, we discuss their use of heat-sink algorithms and graph databases, and their use of chat and other interfaces, and the challenges associated with staying on top of a constantly changing technology landscape. This was a fun interview!

Thanks to Pegasystems for sponsoring today's show! I'd like to invite you to join me at PegaWorld, the company’s annual digital transformation conference, which takes place this June in Las Vegas. To learn more about the conference or to register, visit pegaworld.com and use TWIML19 in the promo code field when you get there.

The complete show notes for this episode can be found at https://twimlai.com/talk/239.

Mar 14, 2019
Active Learning for Materials Design with Kevin Tran - TWiML Talk #238
34:55

Today we’re joined by Kevin Tran, PhD student in the department of chemical engineering at Carnegie Mellon University.

Kevin’s research focuses on creating and using automated, active learning workflows to perform density functional theory, or DFT, simulations, which are used to screen for new catalysts for a myriad of materials applications. In our conversation, we explore the challenges surrounding one such application—the creation of renewable energy fuel cells, which is discussed in his recent Nature paper “Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution.” We dig into the role and need for good catalysts in this application, the role that quantum mechanics plays in finding them, and how Kevin uses machine learning and optimization to predict electrocatalyst performance.

The complete show notes for this episode can be found at twimlai.com/talk/238.

The Artificial Intelligence Conference is returning to New York in April and we have one FREE conference pass for a lucky listener! Visit twimlai.com/ainygiveaway to enter!

Mar 11, 2019
Deep Learning in Optics with Aydogan Ozcan - TWiML Talk #237
42:07

Today, we’re joined by Aydogan Ozcan, Professor of Electrical and Computer Engineering at UCLA, where his research group focuses on photonics and its applications to nano- and biotechnology.

In our conversation, we explore his group's research into the intersection of deep learning and optics, holography and computational imaging. We specifically look at a really interesting project to create all-optical neural networks which work based on diffraction, where the printed pixels of the network are analogous to neurons. We also explore some of the practical applications for their research and other areas of interest for their group.

The complete show notes for this episode can be found at twimlai.com/talk/237

Be sure to subscribe to our weekly newsletter at twimlai.com/newsletter!

Mar 07, 2019
Scaling Machine Learning on Graphs at LinkedIn with Hema Raghavan and Scott Meyer - TWiML Talk #236
47:01

Today we’re joined by Hema Raghavan and Scott Meyer of LinkedIn.

Hema is an Engineering Director Responsible for AI for Growth and Notifications, while Scott serves as a Principal Software Engineer. In this conversation, Hema, Scott and I dig into the graph database and machine learning systems that power LinkedIn features such as “People You May Know” and second-degree connections. Hema shares her insight into the motivations for LinkedIn’s use of graph-based models and some of the challenges surrounding using graphical models at LinkedIn’s scale, while Scott details his work on the software used at the company to support its biggest graph databases.

We'd like to send a huge thanks to LinkedIn for sponsoring today’s show! LinkedIn Engineering solves complex problems at scale to create economic opportunity for every member of the global workforce. AI and ML are integral aspects of almost every product the company builds for its members and customers. LinkedIn’s highly structured dataset gives their data scientists and researchers the ability to conduct applied research to improve member experiences. To learn more about the work of LinkedIn Engineering, please visit engineering.linkedin.com/blog.

For the complete show notes, visit https:/twimlai.com/talk/236. 

Mar 04, 2019
Safer Exploration in Deep Reinforcement Learning using Action Priors with Sicelukwanda Zwane - TWiML Talk #235
54:01

Today we conclude our Black in AI series with Sicelukwanda Zwane, a masters student at the University of Witwatersrand and graduate research assistant at the CSIR.

At the workshop, he presented on “Safer Exploration in Deep Reinforcement Learning using Action Priors,” which explores transferring action priors between robotic tasks to reduce the exploration space in reinforcement learning, which in turn reduces sample complexity. In our conversation, we discuss what “safer exploration” means in this sense, the difference between this work and other techniques like imitation learning, and how this fits in with the goal of “lifelong learning.”

The complete show notes for this episode can be found at https://twimlai.com/talk/235. To follow along with the Black in AI series, visit https://twimlai.com/blackinai19.

Mar 01, 2019
Dissecting the Controversy around OpenAI's New Language Model - TWiML Talk #234
01:06:22

If you’re listening to this podcast, you’ve likely seen some of the press coverage and discussion surrounding the release, or lack thereof, of OpenAI’s new GPT-2 Language Model. The announcement caused quite a stir, with reactions spanning confusion, frustration, concern, and many points in between. Several days later, many open questions remained about the model and the way the release was handled.

Seeing the continued robust discourse, and wanting to offer the community a forum for exploring this topic with more nuance than Twitter’s 280 characters allow, we convened the inaugural “TWiML Live” panel. I was joined on the panel by Amanda Askell and Miles Brundage of OpenAI, Anima Anandkumar of NVIDIA and CalTech, Robert Munro of Lilt, and Stephen Merity, the latter being some of the most outspoken voices in the online discussion of this issue.

Our discussion thoroughly explored the many issues surrounding the GPT-2 release controversy. We cover the basics like what language models are and why they’re important, and why this announcement caused such a stir, and dig deep into why the lack of a full release of the model raised concerns for so many.

The discussion initially aired via Youtube Live and we’re happy to share it with you via the podcast as well. To be clear, both the panel discussion and live stream format were a bit of an experiment for us and we’d love to hear your thoughts on it. Would you like to see, or hear, more of these TWiML Live conversations? If so, what issues would you like us to take on?

If you have feedback for us on the format or if you’d like to join the discussion around OpenAI’s GPT-2 model, head to the show notes page for this show at twimlai.com/talk/234 and leave us a comment.

Feb 25, 2019
Human-Centered Design with Mira Lane - TWiML Talk #233
47:04

Today we present the final episode in our AI for the Benefit of Society series, in which we’re joined by Mira Lane, Partner Director for Ethics and Society at Microsoft.

Mira and I focus our conversation on the role of culture and human-centered design in AI. We discuss how Mira defines human-centered design, its connections to culture and responsible innovation, and how these ideas can be scalably implemented across large engineering organizations.

We’d like to thank Microsoft for their support and their sponsorship of this series. Microsoft is committed to ensuring the responsible development and use of AI and is empowering people around the world with intelligent technology to help solve previously intractable societal challenges spanning sustainability, accessibility and humanitarian action. Learn more at Microsoft.ai.

The complete show notes for this episode can be found at twimlai.com/talk/233. For more information on the AI for the Benefit of Society series, visit twimlai.com/ai4society.

Feb 22, 2019
Fairness in Machine Learning with Hanna Wallach - TWiML Talk #232
49:04

Today we’re joined by Hanna Wallach, a Principal Researcher at Microsoft Research.

Hanna and I really dig into how bias and a lack of interpretability and transparency show up across machine learning. We discuss the role that human biases, even those that are inadvertent, play in tainting data, and whether deployment of “fair” ML models can actually be achieved in practice, and much more. Along the way, Hanna points us to a TON of papers and resources to further explore the topic of fairness in ML. You’ll definitely want to check out the notes page for this episode, which you’ll find at twimlai.com/talk/232.

We’d like to thank Microsoft for their support and their sponsorship of this series. Microsoft is committed to ensuring the responsible development and use of AI and is empowering people around the world with intelligent technology to help solve previously intractable societal challenges spanning sustainability, accessibility and humanitarian action. Learn more at Microsoft.ai.

Feb 18, 2019
AI for Healthcare with Peter Lee - TWiML Talk #231
57:19

In this episode, we’re joined by Peter Lee, Corporate Vice President at Microsoft Research responsible for the company’s healthcare initiatives.

Peter and I met a few months ago at the Microsoft Ignite conference, where he gave me some really interesting takes on AI development in China. You can find more on that topic in the show notes. This conversation centers the three impact areas Peter sees for AI in healthcare, namely diagnostics and therapeutics, tools, and the future of precision medicine. We dig into some examples in each area, and Peter details the realities of applying machine learning and some of the impediments to rapid scale.

We’d like to thank Microsoft for their support and their sponsorship of this series. Microsoft is committed to ensuring the responsible development and use of AI and is empowering people around the world with intelligent technology to help solve previously intractable societal challenges spanning sustainability, accessibility and humanitarian action. Learn more at Microsoft.ai.

The complete show notes for this episode can be found at twimlai.com/talk/231.

Feb 18, 2019
An Optimized Recurrent Unit for Ultra-Low Power Acoustic Event Detection with Justice Amoh Jr. - TWiML Talk #230
45:51

Today, we're joined by Justice Amoh Jr., a Ph.D. student at Dartmouth’s Thayer School of Engineering.

Justice presented his work on “An Optimized Recurrent Unit for Ultra-Low Power Acoustic Event Detection.” In our conversation, we discuss his goal of bringing low cost, high-efficiency wearables to market for monitoring asthma. We explore the many challenges of using classical machine learning models on microcontrollers, and how he went about developing models optimized for constrained hardware environments. We’d also like to wish Justice the best of luck as he should be defending his Ph.D. any day now!

The complete show notes for this episode can be found at https://twimlai.com/talk/230. To follow along with the Black in AI series, visit https://twimlai.com/blackinai19.

 

Feb 11, 2019
Pathologies of Neural Models and Interpretability with Alvin Grissom II - TWiML Talk #229
32:28

Today, we're excited to continue our Black in AI series with Alvin Grissom II, Assistant Professor of Computer Science at Ursinus College.

Alvin’s research is focused on computational linguistics, and we begin with a brief chat about some of his prior work on verb prediction using reinforcement learning. We then dive into the paper he presented at the workshop, “Pathologies of Neural Models Make Interpretations Difficult.” We talk through some of the “pathological behaviors” he identified in the paper, how we can better understand the overconfidence of trained deep learning models in certain settings, and how we can improve model training with entropy regularization. We also touch on the parallel between his work and the work being done on adversarial examples by Ian Goodfellow and others.

For the complete show notes, visit https://twimlai.com/talk/229. To follow along with our Black in AI series, visit https://twimlai.com/blackinai19.

 

Feb 11, 2019
AI for Earth with Lucas Joppa - TWiML Talk #228
57:07

In this episode of our AI For the Benefit of Society with Microsoft series, we’re joined by Lucas Joppa and Zach Parisa.

Lucas is the Chief Environmental Officer at Microsoft, spearheading their 5 year, $50 million AI for Earth commitment, which seeks to apply machine learning and AI across four key environmental areas: agriculture, water, biodiversity, and climate change. Zack is Co-founder and president of SilviaTerra, a Microsoft AI for Earth grantee whose mission is to help people use modern data sources to better manage forest habitats and ecosystems.

In our conversation we discuss the ways that machine learning and AI can be used to advance our understanding of forests and other ecosystems and support conservation efforts. We discuss how SilviaTerra uses computer vision and data from a wide array of sensors like LIDAR, combined with AI, to yield more detailed small-area estimates of the various species in our forests. We also briefly discuss another AI for Earth project, WildMe, a computer vision based wildlife conservation project we discussed with Jason Holmberg back on episode 166.

The complete show notes for this episode can be found at https://twimlai.com/talk/288. To follow along with the entire AI for the Benefit of Society series, visit https://twimlai.com/ai4society.

We’d like to thank Microsoft for their support and their sponsorship of this series. Microsoft is committed to ensuring the responsible development and use of AI and is empowering people around the world with intelligent technology to help solve previously intractable societal challenges spanning sustainability, accessibility and humanitarian action. Learn more at https://Microsoft.ai.

 

 

Feb 08, 2019
AI for Accessibility with Wendy Chisholm - TWiML Talk #227
51:12

Today we’re joined by Wendy Chisholm, Lois Brady, and Matthew Guggemos. Wendy is a principal accessibility architect at Microsoft, and one of the chief proponents of the AI for Accessibility program, which extends grants to AI-powered accessibility projects the areas of Employment, Daily Life, and Communication & Connection. Lois and Matthew are Co-Founders and CEO and CTO, respectively, of iTherapy, an AI for Accessibility grantee and creator of the Inner Voice app, which utilizes visual language to strengthen communication in children on the autism scale.

In our conversation, we discuss the intersection of AI and accessibility, the lasting impact that innovation in AI can have for people with disabilities and society as a whole, and the importance of programs like AI for Accessibility in bringing projects in this area to fruition. 

For the complete show notes, visit https://twimlai.com/talk/226.

The transcript for this interview can be found at https://twimlai.com/talk/206/tx.

To follow along with the AI for the Benefit of Society series, visit https://twimlai.com/ai4society.

Thanks to Microsoft for their support and their sponsorship of this series. Microsoft is committed to ensuring the responsible development and use of AI and is empowering people around the world with intelligent technology to help solve previously intractable societal challenges spanning sustainability, accessibility and humanitarian action. Learn more at https://microsoft.ai.

 

Feb 06, 2019
AI for Humanitarian Action with Justin Spelhaug - TWiML Talk #226
59:21

Today we're joined by Justin Spelhaug, General Manager of Technology for Social Impact at Microsoft.

In our conversation, Justin and I discuss the company’s efforts in AI for Humanitarian Action, a program which extends grants to fund AI-powered projects focused on disaster response, the needs of children, protecting refugees, and promoting respect for human rights. We cover Microsoft’s overall approach to technology for social impact, how his group helps mission-driven organizations best leverage technologies like AI, and how AI is being used at places like the World Bank, Operation Smile, and Mission Measurement to create greater impact.

The complete show notes for this episode can be found at https://twimlai.com/talk/226. Follow along with the entire AI for the Benefit of Society series, visit https://twimlai.com/ai4society.

We’d like to thank Microsoft for their support of the show, and their sponsorship of this series.  Microsoft is committed to ensuring the responsible development and use of AI and is empowering people around the world with this intelligent technology to help solve previously intractable societal challenges spanning sustainability, accessibility and humanitarian action. Learn more about their plan at Microsoft.ai

Feb 04, 2019
Teaching AI to Preschoolers with Randi Williams - TWiML Talk #225
44:32

Today, in the first episode of our Black in AI series, we’re joined by Randi Williams, PhD student at the MIT Media Lab.

At the Black in AI workshop Randi presented her research on Popbots: A Early Childhood AI Curriculum, which is geared towards teaching preschoolers the fundamentals of artificial intelligence. In our conversation, we discuss the origins of the project, the three AI concepts that are taught in the program, and the goals that Randi hopes to accomplish with her work. This was a fun conversation!

The complete show notes for this episode can be found at twimlai.com/talk/225.

Follow along with our Black in AI series at twimlai.com/blackinai19.

Jan 31, 2019
Holistic Optimization of the LinkedIn News Feed - TWiML Talk #224
48:24

Today we’re joined by Tim Jurka, Head of Feed AI at LinkedIn.

As you can imagine Feed AI is responsible for curating all the content you see daily on the LinkedIn site. What’s less apparent to those that don’t work on this type of product is the wide variety of opposing factors that need to be considered in organizing the feed. As you’ll learn in our conversation, Tim calls this the holistic optimization of the feed and we discuss some of the interesting technical and business challenges associated with trying to do this. We talk through some of the specific techniques used at LinkedIn like Multi-arm Bandits and Content Embeddings, and also jump into a really interesting discussion about organizing for machine learning at scale.

We’d like to send a huge thanks to LinkedIn for sponsoring today’s show! LinkedIn Engineering solves complex problems at scale to create economic opportunity for every member of the global workforce. AI and ML are integral aspects of almost every product the company builds for its members and customers. LinkedIn’s highly structured dataset gives their data scientists and researchers the ability to conduct applied research to improve member experiences. To learn more about the work of LinkedIn Engineering, please visit https://engineering.linkedin.com/blog.

The complete show notes can be found at https://twimlai.com/talk/224.

Jan 28, 2019
AI at the Edge at Qualcomm with Gary Brotman - TWiML Talk #223
51:54

Today we’re joined by Gary Brotman, Senior Director of Product Management at Qualcomm Technologies, Inc.

Gary, who got his start in AI through music, now leads strategy and product planning for the company’s Artificial Intelligence and Machine Learning technologies, including those that make up the Qualcomm Snapdragon mobile platforms. In our conversation, we discuss AI on mobile devices and at the edge, including popular use cases, and explore some of the various acceleration technologies offered by Qualcomm and others that enable them. We also dig into the state of AI on devices from the application developer’s perspective, and how various acceleration technologies fit together to help developers bring new products to market.

Thanks to our friends at Qualcomm for sponsoring today’s show! As you’ll hear in the conversation with Gary, Qualcomm has been in the AI space for well over a decade now, powering some of the latest and greatest Android devices with their Snapdragon chipset. With their strong footing in the mobile chipset space, Qualcomm now has the goal of making AI at the edge ubiquitous, beyond mobile devices. To find out more about what they’re up to, and how they plan to get there, visit twimlai.com/qualcomm.

The complete show notes for this episode can be found at twimlai.com/talk/223.

Jan 24, 2019
AI Innovation at CES - TWiML Talk #222
02:01

A few weeks ago, I made the trek to Las Vegas for the world’s biggest electronics conference, CES.

CES is one of those things that’s hard to fully understand without having seen, so I thought it’d be fun to give you a look at it from my vantage point. In this special visual episode, we’re going to check out some of the interesting examples of machine learning and AI that I found at the event. We cover a bunch of different categories, including several that don’t really target consumers at all, like John Deere’s gigantic, combine harvester, a company building a drone that stops bullets, and a startup that wants to do away with something we all despise, traffic.

Check out the video at https://twimlai.com/ces2019, and be sure to hit the like and subscribe buttons and let us know how you like the show via a comment!

For the show notes, visit https://twimlai.com/talk/222.

 

Jan 21, 2019
Self-Tuning Services via Real-Time Machine Learning with Vladimir Bychkovsky - TWiML Talk #221
46:06

Today we’re joined by Vladimir Bychkovsky, Engineering Manager at Facebook, to discuss Spiral.

Spiral is a system they’ve developed for self-tuning high-performance infrastructure services at scale, using real-time machine learning. In our conversation, we explore the ins and outs of Spiral, including how the system works, how it was developed, and how infrastructure teams at Facebook can use it to replace hand-tuned parameters set using heuristics with services that automatically optimize themselves in minutes rather than in weeks. We also discuss the challenges of implementing these kinds of systems, overcoming user skepticism, and achieving an appropriate level of explainability.

The complete show notes for this episode can be found at twimlai.com/talk/221

 

Jan 17, 2019
Building a Recommender System from Scratch at 20th Century Fox with JJ Espinoza - TWiML Talk #220
35:08

Today we’re joined by JJ Espinoza, former Director of Data Science at 20th Century Fox.

In this talk we start out with a discussion JJ’s transition from econometrician to data scientist, and then dig into his and his team’s experience building and deploying a content recommendation system from the ground up. In our conversation, we explore the design of a couple of key components of their system, the first of which processes movie scripts to make recommendations about which movies the studio should make, and the second processes trailers to determine which should be recommended to users. We discuss the challenges they’ve encountered fielding these systems, some of the tools that were used along the way, and a few of the upcoming projects that could be layered on top of the platform they’ve built.

For the complete show notes for this episode, visit twimlai.com/talk/220.

If this talk piqued your interest, you should also check out Talk #201, where Leemay Nassery of Comcast breaks down how she led the rebuild of the Comcast Xfinity X1 recommender platform.

 

Jan 14, 2019
Legal and Policy Implications of Model Interpretability with Solon Barocas - TWiML Talk #219
47:00

Today we’re joined by Solon Barocas, Assistant Professor of Information Science at Cornell University.

Solon is also the co-founder of the Fairness, Accountability, and Transparency in Machine Learning workshop that is hosted annually at conferences like ICML. Solon and I caught up to discuss his work on model interpretability and the legal and policy implications of the use of machine learning models. In our conversation, we discuss the gap between law, policy, and ML, and how to build the bridge between them, including formalizing ethical frameworks for machine learning. We also look at his paper ”The Intuitive Appeal of Explainable Machines,” which proposes that explainability is really two problems, inscrutability and non-intuitiveness, and that disentangling the two allows us to better reason about the kind of explainability that’s really needed in any given situation.

The complete show notes for this episode can be found at https://twimlai.com/talk/219.

And be sure to sign up for our weekly newsletter at https://twimlai.com/newsletter! 

 

Jan 10, 2019
Trends in Computer Vision with Siddha Ganju - TWiML Talk #218
01:11:01

In the final episode of our AI Rewind series, we’re excited to have Siddha Ganju back on the show.

Siddha, who is now an autonomous vehicles solutions architect at Nvidia shares her thoughts on trends in Computer Vision in 2018 and beyond. We cover her favorite CV papers of the year in areas such as neural architecture search, learning from simulation, application of CV to augmented reality, and more, as well as a bevy of tools and open source projects.

The complete show notes for this episode can be found at https://twimlai.com/talk/218

For more information on our AI Rewind series, visit https://twimlai.com/rewind18.

Jan 07, 2019
Trends in Reinforcement Learning with Simon Osindero - TWiML Talk #217
52:46

In this episode of our AI Rewind series, we introduce a new friend of the show, Simon Osindero, Staff Research Scientist at DeepMind.

We discuss trends in Deep Reinforcement Learning in 2018 and beyond. We’ve packed a bunch into this show, as Simon walks us through many of the important papers and developments seen last year in areas like Imitation Learning, Unsupervised RL, Meta-learning, and more.

The complete show notes for this episode can be found at https://twimlai.com/talk/217.

For more information on our 2018 AI Rewind series, visit https://twimlai.com/rewind2018.

 

 

Jan 03, 2019
Trends in Natural Language Processing with Sebastian Ruder - TWiML Talk #216
53:32

In this episode of our AI Rewind series, we’ve brought back recent guest Sebastian Ruder, PhD Student at the National University of Ireland and Research Scientist at Aylien, to discuss trends in Natural Language Processing in 2018 and beyond.

In our conversation we cover a bunch of interesting papers spanning topics such as pre-trained language models, common sense inference datasets and large document reasoning and more, and talk through Sebastian’s predictions for the new year.

The complete show notes for this episode can be found at twimlai.com/talk/216.

For more information on the AI Rewind 2018 series, visit twimlai.com/rewind18.

Dec 31, 2018
Trends in Machine Learning with Anima Anandkumar - TWiML Talk #215
51:54

In this episode of our AI Rewind series, we’re back with Anima Anandkumar, Bren Professor at Caltech and now Director of Machine Learning Research at NVIDIA.

Anima joins us to discuss her take on trends in the broader Machine Learning field in 2018 and beyond. In our conversation, we cover not only technical breakthroughs in the field but also those around inclusivity and diversity.

For this episode's complete show notes, visit twimlai.com/talk/215.

For more information on the AI Rewind series, visit twimlai.com/rewind18.

Dec 27, 2018
Trends in Deep Learning with Jeremy Howard - TWiML Talk #214
01:08:47

In this episode of our AI Rewind series, we’re bringing back one of your favorite guests of the year, Jeremy Howard, founder and researcher at Fast.ai.

Jeremy joins us to discuss trends in Deep Learning in 2018 and beyond. We cover many of the papers, tools and techniques that have contributed to making deep learning more accessible than ever to so many developers and data scientists.

The complete show notes for this episode can be found at https://twimlai.com/talk/214.

Follow along with our AI Rewind 2018 series visit https://twimlai.com/rewind18

Dec 24, 2018
Training Large-Scale Deep Nets with RL with Nando de Freitas - TWiML Talk #213
55:17

Today we close out both our NeurIPS series and our 2018 conference coverage with this interview with Nando de Freitas, Team Lead & Principal Scientist at Deepmind and Fellow at the Canadian Institute for Advanced Research.

In our conversation, we explore his interest in understanding the brain and working towards artificial general intelligence through techniques like meta-learning, few-shot learning and imitation learning. In particular, we dig into a couple of his team’s NeurIPS papers: “Playing hard exploration games by watching YouTube,” and “One-Shot high-fidelity imitation: Training large-scale deep nets with RL.”

The complete show notes for this episode can be found at https://twimlai.com/talk/213.

For more information on the NeurIPS series, visit https://twimlai.com/neurips2018.

 

Dec 20, 2018
Making Algorithms Trustworthy with David Spiegelhalter - TWiML Talk #212
23:26

In this, the second episode of our NeurIPS series, we’re joined by David Spiegelhalter, Chair of Winton Center for Risk and Evidence Communication at Cambridge University and President of the Royal Statistical Society.

David, an invited speaker at NeurIPS, presented on “Making Algorithms Trustworthy: What Can Statistical Science Contribute to Transparency, Explanation and Validation?”. In our conversation, we explore the nuanced difference between being trusted and being trustworthy, and its implications for those building AI systems. We also dig into how we can evaluate trustworthiness, which David breaks into four phases, the inspiration for which he drew from British philosopher Onora O'Neill's ideas around 'intelligent transparency’.

The complete show notes for this episode can be found at twimlai.com/talk/212.

For more information on the NeurIPS series, visit twimlai.com/neurips2018.

Dec 20, 2018
Designing Computer Systems for Software with Kunle Olukotun - TWiML Talk #211
56:32

Today we’re joined by Kunle Olukotun, Professor in the department of Electrical Engineering and Computer Science at Stanford University, and Chief Technologist at Sambanova Systems.

Kunle was an invited speaker at NeurIPS this year, presenting on “Designing Computer Systems for Software 2.0.” In our conversation, we discuss various aspects of designing hardware systems for machine and deep learning, touching on multicore processor design, domain specific languages, and graph-based hardware. We cover the limitations of the current hardware such as GPUs, and peer a bit into the future as well. This was a fun one!

The complete show notes for this episode can be found at twimlai.com/talk/211

For more information on this series, visit twimlai.com/neurips2018.

Dec 18, 2018
Operationalizing Ethical AI with Kathryn Hume - TWiML Talk #210
54:28

Today we conclude our Trust in AI series with this conversation with Kathryn Hume, VP of Strategy at Integrate AI.

You might remember Kathryn from our interview last year on “Selling AI to the Enterprise,” which was TWiML Talk #20. This time around, we discuss her newly released white paper “Responsible AI in the Consumer Enterprise,” which details a framework for ethical AI deployment in e-commerce companies and other consumer-facing enterprises. We look at the structure of the ethical framework she proposes, and some of the many questions that need to be considered when deploying AI in an ethical manner.

For the complete show notes for this episode, visit twimlai.com/talk/210.

 

Dec 14, 2018
Approaches to Fairness in Machine Learning with Richard Zemel - TWiML Talk #209
46:12

Today we continue our exploration of Trust in AI with this interview with Richard Zemel, Professor in the department of Computer Science at the University of Toronto and Research Director at Vector Institute.

In our conversation, Rich describes some of his work on fairness in machine learning algorithms, including how he defines both group and individual fairness and his group’s recent NeurIPS poster, “Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer.”

This week’s series is sponsored by our friends at Georgian Partners. Georgian recently published Building Conversational AI Teams, a comprehensive guide to lead you through sourcing, acquiring and nurturing a successful conversational AI team. Download at: https://gptrs.vc/convoai

For this episode's complete show notes, visit twimlai.com/talk/209.

Dec 12, 2018
Trust and AI with Parinaz Sobhani - TWiML Talk #208
46:42

In today’s episode we’re joined by Parinaz Sobhani, Director of Machine Learning at Georgian Partners.

In our conversation, Parinaz and I discuss some of the main issues falling under the “trust” umbrella, such as transparency, fairness and accountability. We also explore some of the trust-related projects she and her team at Georgian are working on, as well as some of the interesting trust and privacy papers coming out of the NeurIPS conference.

This week’s series is sponsored by our friends at Georgian Partners. Georgian recently published Building Conversational AI Teams, a comprehensive guide to lead you through sourcing, acquiring and nurturing a successful conversational AI team. Download at: https://gptrs.vc/convoai

For this episode's complete show notes, visit twimlai.com/talk/208.

Dec 11, 2018
Unbiased Learning from Biased User Feedback with Thorsten Joachims - TWiML Talk #207
40:43

In the final episode of our re:Invent series, we're joined by Thorsten Joachims, Professor in the Department of Computer Science at Cornell University.

Thorsten participated at the conference’s AI Summit, presenting his research on “Unbiased Learning from Biased User Feedback.” In our conversation, we take a look at some of the inherent and introduced biases in recommender systems, and the ways to avoid them. We also discuss how inference techniques can be used to make learning algorithms more robust to bias, and how these can be enabled with the correct type of logging policies.

The complete show notes for this episode can be found at https://twimlai.com/talk/207

For more information on our AWS re:Invent series, visit https://twimlai.com/reinvent2018.

 

 

Dec 07, 2018
Language Parsing and Character Mining with Jinho Choi - TWiML Talk #206
47:48

Today, in the second episode of our re:Invent series, we’re joined by Jinho Choi, assistant professor of computer science at Emory University.

Jinho presented at the conference on ELIT — a cloud-based NLP platform — which is short for Evolution of Language and Information Technology. In our conversation, we discuss some of the key NLP challenges that Jinho and his group are tackling, including language parsing and character mining. We also discuss their vision for ELIT, which is to make it easy for researchers to develop, access, and deploying cutting-edge NLP tools models on the cloud.

The complete show notes can be found at https://twimlai.com/talk/206

For more info on our re:Invent series, visit https://twimlai.com/reinvent2018

Dec 05, 2018
re:Invent Roundup Roundtable 2018 with Dave McCrory and Val Bercovici - TWiML Talk #205
01:08:35

For today’s show, I’m excited to present our second annual re:Invent Roundtable Roundup. This year I’m joined by my friends Dave McCrory, VP of Software Engineering at Wise.io at GE Digital, and Val Bercovici, Founder and CEO of Pencil Data.

If you missed the news coming out of re:Invent, or you want to know more about what one of the biggest AI platform providers is up to, you’ll want to say tuned, because we’ll discuss many of their new offerings in this episode. We cover all of AWS’ most important ML and AI announcements, including SageMaker Ground Truth, Reinforcement Learning and New, DeepRacer, Inferentia and Elastic Inference, ML Marketplace, Personalize, Forecast and Textract, and more.

For the complete show notes for this episode, visit https://twimlai.com/talk/205.

Dec 03, 2018
Knowledge Graphs and Expert Augmentation with Marisa Boston - TWiML Talk #204
47:40

Today we’re joined by Marisa Boston, Director of Cognitive Technology in KPMG’s Cognitive Automation Lab.

Marisa and I caught up to discuss some of the ways that they’re using AI to build tools that help augment the knowledge of KPMG’s teams of professionals. We start out with a discussion of knowledge graphs, and how they can be used to map out and relate various concepts. We then explore how they use these in conjunction with NLP tools to create insight engines, tools that curate and contextualize news and other text-based data sources to produce a series of content recommendations that help their users work more effectively. Finally, Marisa shares some general principles for using AI to augment experts.

The complete show notes for this episode can be found at twimlai.com/talk/204.

Nov 29, 2018
ML/DL for Non-Stationary Time Series Analysis in Financial Markets and Beyond with Stuart Reid - TWiML Talk #203
59:36

Today, we’re joined by Stuart Reid, Chief Scientist at NMRQL Research.

NMRQL, based in Stellenbosch, South Africa, is an investment management firm that uses machine learning algorithms to make adaptive, unbiased, scalable, and testable trading decisions for its funds. In our conversation, Stuart and I dig into the way NMRQL uses machine learning and deep learning models to support the firm’s investment decisions. In particular, we focus on techniques for modeling non-stationary time-series, of which financial markets are just one example. We start from first principles and look at stationary vs non-stationary time-series, discuss some of the challenges of building models using financial data, explore issues like model interpretability, and much more. This was a very insightful conversation, which I expect will be very useful not just for those in the fintech space.

Check out the complete show notes for this episode at twimlai.com/talk/203

Nov 26, 2018
Industrializing Machine Learning at Shell with Daniel Jeavons - TWiML Talk #202
46:46

In this episode of our AI Platforms series, we’re joined by Daniel Jeavons, General Manager of Data Science at Shell.

In our conversation, Daniel and I explore the evolution of analytics and data science at Shell, and cover a ton of interesting machine learning use cases that the company is pursuing, such as well drilling and charging smart cars. A good bit of our conversation centers around IoT-related applications and issues, such as inference at the edge, federated machine learning, and digital twins, all key considerations for the way they apply ML. We also talk about the data science process at Shell and the importance of platform technologies to Daniel’s organization and the company as a whole and we discuss some of the technologies he and his team are excited about introducing to the company.

For the complete show notes for this episode, visit twimlai.com/talk/202.

For more information on the AI Platforms series, visit twimlai.com/aiplatforms.

Be sure to sign up for our weekly newsletter at twimlai.com/newsletter!

Nov 21, 2018
Resurrecting a Recommendations Platform at Comcast with Leemay Nassery - TWiML Talk #201
48:47

In this episode of our AI Platforms series, we’re joined by Leemay Nassery, Senior Engineering Manager and head of the recommendations team at Comcast.

Leemay spoke at the Strange Loop conference a few months ago on “Resurrecting a recommendations platform.” In our conversation, Leemay and I discuss just how she and her team resurrected the Xfinity X1 recommendations platform, including rebuilding the data pipeline, the machine learning process, and the deployment and training of their updated models. We also touch on the importance of A-B testing and maintaining their rebuilt infrastructure. 

For the complete show notes for this episode, visit twimlai.com/talk/201

For more information on our upcoming eBook series or the AI Platforms series, visit twimlai.com/aiplatforms.

Make sure you sign up for our newsletter at twimlai.com/newsletter!

Nov 19, 2018
Productive Machine Learning at LinkedIn with Bee-Chung Chen - TWiML Talk #200
49:02

In this episode of our AI Platforms series, we’re joined by Bee-Chung Chen, Principal Staff Engineer and Applied Researcher at LinkedIn.

Bee-Chung and I caught up to discuss LinkedIn’s internal AI automation platform, Pro-ML, which was built with the hopes of providing a single platform for the entire lifecycle of developing, training, deploying, and testing machine learning models. In our conversation, Bee-Chung details Pro-ML, breaking down some of the major pieces of the pipeline including their feature marketplace, model creation tooling, and training management system to name a few. We also discuss LinkedIn’s experience bringing Pro-ML to the company's developers and the role the LinkedIn AI Academy plays in helping them get up to speed.

For the complete show notes, visit https://twimlai.com/talk/200.

For more information about the AI Platforms series, visit https://twimlai.com/aiplatforms.

Be sure to sign up for our newsletter at https://twimlai.com/newsletter.

Nov 15, 2018
Scaling Deep Learning on Kubernetes at OpenAI with Christopher Berner - TWiML Talk #199
51:04

In this episode of our AI Platforms series we’re joined by OpenAI’s Head of Infrastructure, Christopher Berner.

Chris has played a key role in overhauling OpenAI’s deep learning infrastructure of the course of his two years with the company. In our conversation, we discuss the evolution of OpenAI’s deep learning platform, the core principles which have guided that evolution, and its current architecture. We dig deep into their use of Kubernetes and discuss various ecosystem players and projects that support running deep learning at scale on the open source project.

For the complete show notes for this episode, visit twimlai.com/talk/199.

For more information on the AI Platforms Series, or to sign up for our eBooks, visit twimlai.com/aiplatforms.

Nov 12, 2018
Bighead: Airbnb's Machine Learning Platform with Atul Kale - TWiML Talk #198
51:15

In this episode of our AI Platforms series, we’re joined by Atul Kale, Engineering Manager on the machine learning infrastructure team at Airbnb.

Atul and I met at the Strata Data conference a while back to discuss Airbnb’s internal machine learning platform, Bighead. In our conversation, Atul outlines the ML lifecycle at Airbnb and how the various components of Bighead support it. We then dig into the major components of Bighead, which include Redspot, their supercharged Jupyter notebook service, Deep Thought, their real-time inference environment, Zipline, their data management platform, and quite a few others. We also take a look at some of Atul’s best practices for scaling machine learning, and discuss a special announcement that Atul and his team made at Strata.

For the complete show notes, visit twimlai.com/talk/198

For more information on the AI Platforms series, visit twimlai.com/aiplatforms.

Nov 08, 2018
Facebook's FBLearner Platform with Aditya Kalro - TWiML Talk #197
40:44

In this, the kickoff episode of our AI Platforms series, we’re joined by Aditya Kalro, Engineering Manager at Facebook, to discuss their internal machine learning platform FBLearner Flow.

Introduced in May of 2016, FBLearner Flow is the workflow management platform at the heart of the Facebook ML engineering ecosystem. In our conversation, Aditya and I discuss the history and development of the platform, as well as its functionality and its evolution from an initial focus on model training to supporting the entire ML lifecycle at Facebook. Aditya also walks us through the data science tech stack at Facebook, and shares his advice for supporting ML development at scale.

For the complete show notes, visit twimlai.com/talk/197.

To learn more about our AI Platforms series, or to download our upcoming ebooks, visit twimlai.com/aiplatforms.

Nov 06, 2018
Geometric Statistics in Machine Learning w/ geomstats with Nina Miolane - TWiML Talk #196
44:45

In this episode we’re joined by Nina Miolane, researcher and lecturer at Stanford University.

Nina and I recently spoke about her work in the field of geometric statistics in machine learning. Specifically, we discuss the application of Riemannian geometry, which is the study of curved surfaces, to ML. Riemannian geometry can be helpful in building machine learning models in a number of situations including in computational anatomy and medicine where it helps Nina create models of organs like the brain and heart. In our discussion we review the differences between Riemannian and Euclidean geometry in theory and practice, and discuss several examples from Nina’s research. We also discuss her new Geomstats project, which is a python package that simplifies computations and statistics on manifolds with geometric structures.

The full show notes for this episode can be found at twimlai.com/talk/196.

Nov 01, 2018
Milestones in Neural Natural Language Processing with Sebastian Ruder - TWiML Talk #195
01:01:40

In this episode, we’re joined by Sebastian Ruder, a PhD student studying natural language processing at the National University of Ireland and a Research Scientist at text analysis startup Aylien.

In our conversation, Sebastian and I discuss recent milestones in neural NLP, including multi-task learning and pretrained language models. We also discuss the use of attention-based models, Tree RNNs and LSTMs, and memory-based networks. Finally, Sebastian walks us through his recent ULMFit paper, short for “Universal Language Model Fine-tuning for Text Classification,” which he co-authored with Jeremy Howard of fast.ai who I interviewed in episode 186.

For the complete show notes for this episode, visit https://twimlai.com/talk/195.

Oct 29, 2018
Natural Language Processing at StockTwits with Garrett Hoffman - TWiML Talk #194
51:38

In this episode, we’re joined by Garrett Hoffman, Director of Data Science at Stocktwits.

Garrett and I caught up at last month’s Strata Data conference, where he presented a tutorial on “Deep Learning Methods for NLP with Emphasis on Financial Services.” Stocktwits is a social network for the investing community which has its roots in the use of the $cashtag on Twitter. In our conversation, we discuss applications such as Stocktwits’ own use of “social sentiment graphs” built on multilayer LSTM networks to gauge community sentiment about certain stocks in real time, as well as the more general use of natural language processing for generating trading ideas.

I’d also like to send a huge thanks to our friends at IBM for their sponsorship of this episode. Are you interested in exploring code patterns leveraging multiple technologies, including ML and AI? Then check out IBM Developer. With more than 100 open source programs, a library of knowledge resources, developer advocates ready to help, and a global community of developers, what in the world will you create? Dive in at https://ibm.biz/mlaipodcast, and be sure to let them know that TWiML sent you!

For the complete show notes for this episode, visit https://twimlai.com/talk/194.

Oct 25, 2018
Advanced Reinforcement Learning & Data Science for Social Impact with Vukosi Marivate - TWiML Talk #193
47:14

In this, the final show of our Deep Learning Indaba Series, we speak with Vukosi Marivate, Chair of Data Science at the University of Pretoria and a co-organizer of the Indaba.

My conversation with Vukosi fell into two distinct parts. The first part focused on his PhD research in the area of reinforcement learning, discussing several advanced RL scenarios including inverse RL, multiple agent RL, and using RL when we have incomplete knowledge of the environment. We then moved on to discuss his current research, which broadly falls under the banner of data science with social impact. Specifically, we review several of the applications he and his students are currently exploring in areas such as public safety and energy.

The complete show notes for this episode can be found at https://twimlai.com/talk/193.

For more information on our Deep Learning Indaba Series, visit https://twimlai.com/indaba2018

Oct 23, 2018
AI Ethics, Strategic Decisioning and Game Theory with Osonde Osoba - TWiML Talk #192
47:26

In this episode of our Deep Learning Indaba Series, we’re joined by Osonde Osoba, Engineer at RAND Corporation and Professor at the Pardee RAND Graduate School.

Osonde and I spoke on the heels of the Indaba, where he presented on AI Ethics and Policy. We discuss his framework-based approach for evaluating ethical issues, such as applying the ethical principles laid out in the Belmont Report, and how to build an intuition for where ethical flashpoints may exist in these discussions. We then shift gears to Osonde’s own model development research and end up in a really interesting discussion about the application of machine learning to strategic decisions and game theory, including the use of fuzzy cognitive map models.

The complete show notes for this episode can be found at twimlai.com/talk/192.

For more info on the Deep Learning Indaba series, visit twimlai.com/indaba2018.

Oct 18, 2018
Acoustic Word Embeddings for Low Resource Speech Processing with Herman Kamper - TWiML Talk #191
01:02:00

In this episode of our Deep Learning Indaba Series, we’re joined by Herman Kamper, Lecturer in the electrical and electronics engineering department at Stellenbosch University in SA and a co-organizer of the Indaba.

Herman and I discuss his work on limited- and zero-resource speech recognition, how those differ from regular speech recognition, and the tension between linguistic and statistical methods in this space. We dive into the specifics of the methods being used and developed in Herman’s lab as well, including how phoneme data is used for segmenting and processing speech data.

The full show notes for this episode can be found at https://twimlai.com/talk/191.

For more on the Deep Learning Indaba series, visit https://twimlai.com/indaba2018.

 

Oct 16, 2018
Learning Representations for Visual Search with Naila Murray - TWiML Talk #190
41:54

In this episode of our Deep Learning Indaba series, we’re joined by Naila Murray, Senior Research Scientist and Group Lead in the computer vision group at Naver Labs Europe.

Naila presented at the Indaba on computer vision, and in this discussion we explore her work on visual attention, including why visual attention is important and the trajectory of work in the field over time. We also discuss her paper “Generalized Max Pooling,” and her recent research interest in learning representations with deep learning.

For the complete show notes, visit twimlai.com/talk/190.

Oct 12, 2018
Evaluating Model Explainability Methods with Sara Hooker - TWiML Talk #189
01:05:02

In this, the first episode of the Deep Learning Indaba series, we’re joined by Sara Hooker, AI Resident at Google Brain.

I had the pleasure of speaking with Sara in the run-up to the Indaba about her work on interpretability in deep neural networks. We discuss what interpretability means and when it’s important, and explore some nuances like the distinction between interpreting model decisions vs model function. We also dig into her paper Evaluating Feature Importance Estimates and look at the relationship between this work and interpretability approaches like LIME.

We also talk a bit about Google, in particular, the relationship between Brain and the rest of the Google AI landscape and the significance of the recently announced Google AI Lab in Accra, Ghana, being led by friend of the show Moustapha Cisse. And, of course, we chat a bit about the Indaba as well.

For the complete show notes for this episode, visit twimlai.com/talk/189.

For more information on the Deep Learning Indaba series, visit twimlai.com/indaba2018

Oct 10, 2018
Graph Analytic Systems with Zachary Hanif - TWiML Talk #188
55:29

In this, the final episode of our Strata Data Conference series, we’re joined by Zachary Hanif, Director of Machine Learning at Capital One’s Center for Machine Learning.

Zach led a session at Strata called “Network effects: Working with modern graph analytic systems,” which we had a great chat about back in New York. We start our discussion with a look at the role of graph analytics in the machine learning toolkit, including some important application areas for graph-based systems. We continue with an overview of the different ways to implement graph analytics, with a particular emphasis on the emerging role of what he calls graphical processing engines which excel at handling large datasets. We also discuss the relationship between these kinds of systems and probabilistic graphical models, graphical embedding models, and graph convolutional networks in deep learning.

The complete show notes for this episode can be found at twimlai.com/talk/188.

For more information on the Strata Data Conference series, visit twimlai.com/stratany2018.

Oct 08, 2018
Diversification in Recommender Systems with Ahsan Ashraf - TWiML Talk #187
45:43

In this episode of our Strata Data conference series, we’re joined by Ahsan Ashraf, data scientist at Pinterest.

In our conversation, Ahsan and I discuss his presentation from the conference, “Diversification in recommender systems: Using topical variety to increase user satisfaction.” We cover the experiments his team ran to explore the impact of diversification in user’s boards, the methodology his team used to incorporate variety into the Pinterest recommendation system, the metrics they monitored through the process, and how they performed sensitivity sanity testing.

The show notes for this episode can be found at https://twimlai.com/talk/187.

Oct 04, 2018
The Fastai v1 Deep Learning Framework with Jeremy Howard - TWiML Talk #186
01:11:19

In today's episode we’ll be taking a break from our Strata Data conference series and presenting a special conversation with Jeremy Howard, founder and researcher at Fast.ai.

Fast.ai is a company many of our listeners are quite familiar with due to their popular deep learning course. This episode is being released today in conjunction with the company’s announcement of version 1.0 of their fastai library at the inaugural Pytorch Devcon in San Francisco.

Jeremy and I cover a ton of ground in this conversation. Of course, we dive into the new library and explore why it’s important and what’s changed. We also explore the unique way in which it was developed and what it means for the future of the fast.ai courses. Jeremy shares a ton of great insights and lessons learned in this conversation, not to mention mentions a bunch of really interesting-sounding papers.

The complete show notes, and links to the fastai library can be found here.

Oct 02, 2018
Federated ML for Edge Applications with Justin Norman - TWiML Talk #185
48:25

In this episode of our Strata Data conference series, we’re joined by Justin Norman, Director of Research and Data Science Services at Cloudera Fast Forward Labs.

Fast Forward Labs was an Applied AI research firm and consultancy founded by Hilary Mason, who’s TWiML Talk episode remains an all-time fan favorite. My chat with Justin took place on the 1 year anniversary of Fast Forward Labs’ acquisition by Cloudera, so we start with an update on the company before diving into a look at some of recent and upcoming research projects. Specifically, we discuss their recent report on Multi-Task Learning and their upcoming research into Federated Machine Learning for AI at the edge.

To learn more about Cloudera and CFFL, visit Cloudera's Machine Learning resource center at cloudera.com/ml.

For the complete show notes, visit https://twimlai.com/talk/185.

 

Sep 27, 2018
Exploring Dark Energy & Star Formation w/ ML with Viviana Acquaviva - TWiML Talk #184
41:22

In today’s episode of our Strata Data series, we’re joined by Viviana Acquaviva, Associate Professor at City Tech, the New York City College of Technology.

Viviana led a tutorial at the conference, titled “Learning Machine Learning using Astronomy data sets.” In our conversation, we begin by discussing an ongoing project she’s a part of called the “Hobby-Eberly Telescope Dark Energy eXperiment,” or HETDEX. In this project, Viviana tackles the challenge of understanding of how and why the expansion of the universe is accelerating, which is directly contrary to the principles of gravity. We discuss her motivation for undertaking this project, how she gets her data, the models she uses, and how she evaluates their performance.

The complete show notes can be found at https://twimlai.com/talk/184

Sep 26, 2018
Document Vectors in the Wild with James Dreiss - TWiML Talk #183
42:07

In this episode of our Strata Data series we’re joined by James Dreiss, Senior Data Scientist at international news syndicate Reuters.

James and I sat down to discuss his talk from the conference “Document vectors in the wild, building a content recommendation system,” in which he details how Reuters implemented document vectors to recommend content to users of their new “infinite scroll” page layout. In our conversation we take a look at what document vectors are and how they’re created, how they tested the accuracy of their models, and the future of embeddings for natural language processing.

The complete show notes for this episode can be found at https://twimlai.com/talk/183.

For more info on the Strata Data Conference Series, visit https://twimlai.com/stratany2018.

Sep 24, 2018
Applied Machine Learning for Publishers with Naveed Ahmad - TWiML Talk #182
39:34

In today’s episode we’re joined by Naveed Ahmad, Senior Director of data engineering and machine learning at Hearst Newspapers.

A few months ago, Naveed gave a talk at the Google Cloud Next Conference on “How Publishers Can Take Advantage of Machine Learning.” In our conversation, we discuss into the role of ML at Hearst, including their motivations for implementing it and some of their early projects, the challenges of data acquisition within a large organization, and the benefits they enjoy from using Google’s BigQuery as their data warehouse.

For the complete show notes for this episode, visit https://twimlai.com/talk/182.

Sep 20, 2018
Anticipating Superintelligence with Nick Bostrom - TWiML Talk #181
45:29

In this episode, we’re joined by Nick Bostrom, professor in the faculty of philosophy at the University of Oxford, where he also heads the Future of Humanity Institute, a multidisciplinary institute focused on answering big-picture questions for humanity with regards to AI safety and ethics.

Nick is of course also author of the book “Superintelligence: Paths, Dangers, Strategies.” In our conversation, we discuss the risks associated with Artificial General Intelligence and the more advanced AI systems Nick refers to as superintelligence. We also discuss Nick’s writings on the topic of openness in AI development, and the advantages and costs of open and closed development on the part of nations and AI research organizations. Finally, we take a look at what good safety precautions might look like, and how we can create an effective ethics framework for superintelligent systems.

The notes for this episode can be found at https://twimlai.com/talk/181.

Sep 17, 2018
Can We Train an AI to Understand Body Language? with Hanbyul Joo - TWIML Talk #180
51:53

In this episode, we’re joined by Hanbyul Joo, a PhD student in the Robotics Institute at Carnegie Mellon University.

Han, who is on track to complete his thesis at the end of the year, is working on what is called the “Panoptic Studio,” a multi-dimension motion capture studio with over 500 camera sensors that are used to capture human body behavior and body language. While robotic and other artificially intelligent systems can interact with humans, Han’s work focuses on understanding how humans interact and behave so that we can teach AI-based systems to react to humans more naturally. In our conversation, we discuss his CVPR best student paper award winner “Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies.” Han also shares a complete overview of the Panoptic studio, and we dig into the creation and performance of the models, and much more.

For the complete show notes for this episode, visit https://twimlai.com/talk/180.

Sep 13, 2018
Biological Particle Identification and Tracking with Jay Newby - TWiML Talk #179
45:57

In today’s episode we’re joined by Jay Newby, Assistant Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta.

Jay joins us to discuss his work applying deep learning to biology, including his paper “Deep neural networks automate detection for tracking of submicron scale particles in 2D and 3D.” In our conversation, Jay gives us an overview of particle tracking and a look at how he combines neural networks with physics-based particle filter models. We also touch on some of the unique challenges to working at the micron level in biology, how he evaluated the success of his experiments, and the next steps for his research.

The complete show notes for this episode can be found at https://twimlai.com/talk/179.

 

Sep 10, 2018
AI for Content Creation with Debajyoti Ray - TWiML Talk #178
55:58

In today’s episode we’re joined by Debajyoti Ray, Founder and CEO of RivetAI, a startup producing AI-powered tools for storytellers and filmmakers.

Rivet’s tools are inspired in part by the founders’ collaboration with the team that created Sunspring, a short, AI-written film starring Silicon Valley’s Thomas Middleditch, which you may have seen when it was making the rounds a while back. Deb and I discuss some of what he’s learned in the journey to apply AI to content creation, including how Rivet approaches the use of machine learning to automate creative processes, the company’s use hierarchical LSTM models and autoencoders, and the tech stack that they’ve put in place to support the business.

For the complete show notes for this episode, visit twimlai.com/talk/178.

Sep 06, 2018
Deep Reinforcement Learning Primer and Research Frontiers with Kamyar Azizzadenesheli - TWiML Talk #177
01:35:25

Today we’re joined by Kamyar Azizzadenesheli, PhD student at the University of California, Irvine, and visiting researcher at Caltech where he works with Anima Anandkumar, who you might remember from TWiML Talk 142.

We begin with a reinforcement learning primer of sorts, in which we review the core elements of RL, along with quite a few examples to help get you up to speed. We then discuss a pair of Kamyar’s RL-related papers: “Efficient Exploration through Bayesian Deep Q-Networks” and “Sample-Efficient Deep RL with Generative Adversarial Tree Search.” In addition to discussing Kamyar’s work, we also chat a bit of the general landscape of RL research today. So whether you’re new to the field or want to dive into cutting-edge reinforcement learning research with us, this podcast is here for you!


If you'd like to skip the Deep Reinforcement Learning primer portion of this and jump to the research discussion, skip ahead to the 34:30 mark of the episode.

Aug 30, 2018
OpenAI Five with Christy Dennison - TWiML Talk #176
48:21

Today we’re joined by Christy Dennison, Machine Learning Engineer at OpenAI.

Since joining OpenAI earlier this year, Christy has been working on OpenAI’s efforts to build an AI-powered agent to play the DOTA 2 video game. Our conversation begins with an overview of DOTA 2 gameplay and the recent OpenAI Five benchmark which put the OpenAI agent up against a team of professional human players. We then dig into the underlying technology used to create OpenAI Five, including their use of deep reinforcement learning and LSTM recurrent neural networks, and their liberal use of entity embeddings, plus some of the tricks and techniques they use to train the model on 256 GPUs and 128,000 CPU cores.

The complete show notes for this episode can be found at twimlai.com/talk/176.

 

Aug 27, 2018
How ML Keeps Shelves Stocked at Home Depot with Pat Woowong - TWiML Talk #175
45:00

Today we’re joined by Pat Woowong, principal engineer in the applied machine intelligence group at The Home Depot.

We discuss a project that Pat recently presented at the Google Cloud Next conference which used machine learning to predict shelf-out scenarios within stores. We dig into the motivation for this system and how the team went about building it, including what type of models ended up working best, how they collected their data, their use of kubernetes to support future growth in the platform, and much more.

For the complete show notes, visit twimlai.com/talk/175.

Aug 23, 2018
Contextual Modeling for Language and Vision with Nasrin Mostafazadeh - TWiML Talk #174
49:12

Today we’re joined by Nasrin Mostafazadeh, Senior AI Research Scientist at New York-based Elemental Cognition.

Our conversation focuses on Nasrin’s work in event-centric contextual modeling in language and vision, which she sees as a means of giving AI systems a bit of “common sense.” We discuss Nasrin’s work on the Story Cloze Test, which is a reasoning framework for evaluating story understanding and generation. We explore the details of this task--including what constitutes a “story”--and some of the challenges it presents and approaches for solving it. We also discuss how you model what a computer understands, building semantic representation algorithms, different ways to approach “explainability,” and multimodal extensions to her contextual modeling work.

The notes for this episode can be found at https://twimlai.com/talk/174.

Aug 20, 2018
ML for Understanding Satellite Imagery at Scale with Kyle Story - TWiML Talk #173
56:05

Today we’re joined by Kyle Story, computer vision engineer at Descartes Labs.

Kyle and I caught up after his recent talk at the Google Cloud Next Conference titled “How Computers See the Earth: A Machine Learning Approach to Understanding Satellite Imagery at Scale.” We discuss some of the interesting computer vision problems he’s worked on at Descartes, including custom object detectors and the company’s geovisual search engine, covering everything from the models they’ve developed and platform they’ve built, to the key challenges they’ve had to overcome in scaling them.

For the complete show notes, visit twimlai.com/talk/173.

Aug 16, 2018
Generating Ground-Level Images From Overhead Imagery Using GANs with Yi Zhu - TWiML Talk #172
38:38

Today we’re joined by Yi Zhu, a PhD candidate at UC Merced focused on geospatial image analysis.

In our conversation, Yi and I take a look at his recent paper “What Is It Like Down There? Generating Dense Ground-Level Views and Image Features From Overhead Imagery Using Conditional Generative Adversarial Networks.” Yi and I discuss the goal of this research, which is to train effective land-use classifiers on proximate, or ground-level, images, and how he uses conditional GANs along with images sourced from social media to generate artificial ground-level images for this task. We also explore future research directions such as the use of reversible generative networks as proposed in the recently released OpenAI Glow paper to producing higher resolution images.

The notes for this episode can be found at https://twimlai.com/talk/172.

Aug 13, 2018
Vision Systems for Planetary Landers and Drones with Larry Matthies - TWiML Talk #171
43:12

Today we’re joined by Larry Matthies, Sr. Research Scientist and head of computer vision in the mobility and robotics division at JPL.

Larry joins us on the heels of two presentations at this year’s CVPR conference, the first on Onboard Stereo Vision for Drone Pursuit or Sense and Avoid and another on Vision Systems for Planetary Landers. In our conversation, we touch on both of these talks, his work on vision systems for the first iteration of Mars rovers in 2004 and the future of planetary landing projects.

For the complete show notes, visit https://twimlai.com/talk/171.

 

Aug 09, 2018
Learning Semantically Meaningful and Actionable Representations with Ashutosh Saxena - TWiML Talk #170
45:35

In this episode I'm joined by Ashutosh Saxena, a veteran of Andrew Ng’s Stanford Machine Learning Group, and co-founder and CEO of Caspar.ai.

Ashutosh and I discuss his RoboBrain project, a computational system that creates semantically meaningful and actionable representations of the objects, actions and observations that a robot experiences in its environment, and allows these to be shared and queried by other robots to learn new actions. We also discuss his startup Caspar, which applies these principles to the challenge of creating smart homes.

For complete show notes, visit https://twimlai.com/talk/170.

Aug 06, 2018
AI Innovation for Clinical Decision Support with Joe Connor - TWiML Talk #169
42:41

In this episode I speak with Joe Connor, Founder of Experto Crede.

Joe’s been listening to the podcast for a while and he and I connected after he reached out to discuss an article I wrote regarding AI in the healthcare space. In this conversation, we explore his experiences bringing AI powered healthcare projects to market in collaboration with the UK National Health Service and its clinicians. We take a look at some of various challenges he’s run into when applying ML and AI in healthcare, as well as some of his successes, such as tackling effective triage of mental health patients using emotion recognition within a chatbot environment. We also discuss data protections, especially GDPR, and the challenges that come along with building systems that are dependent on using patient data under these restrictions. Finally we take a look at potential ways to include clinicians in the building of these applications.

The complete show notes can be found at https://twimlai.com/talk/169

Aug 02, 2018
Dynamic Visual Localization and Segmentation with Laura Leal-Taixé -TWiML Talk #168
45:33

In this episode I'm joined by Laura Leal-Taixé, Professor at the Technical University of Munich where she leads the Dynamic Vision and Learning Group, and 2017 recipient of prestigious Sofja Kovalevskaja Award.

In our conversation, we discuss several of her recent projects including work on image-based localization techniques that fuse traditional model-based computer vision approaches with a data-driven approach based on deep learning. We also discuss her paper on one-shot video object segmentation and the broader vision for her research, which aims to create tools for allowing individuals to better navigate cities using systems constructed from visual data.

The show notes for this page can be found at twimlai.com/talk/168.

Jul 30, 2018
Conversational AI for the Intelligent Workplace with Gillian McCann - TWiML Talk #167
38:05

In this episode I'm joined by Gillian McCann, Head of Cloud Engineering and AI at Workgrid Software.

 

Workgrid offers an intelligent workplace assistant that integrates with a variety of business tools and systems. In our conversation, which focuses on Workgrid’s use of cloud-based AI services, Gillian details some of the underlying systems that make Workgrid tick, including a breakdown of its conversational interface. We also take a look their engineering pipeline and how they build high quality systems that incorporate external APIs. Finally, Gillian shares her view on some of the factors that contribute to misunderstandings and impatience on the part of users of AI-based products.

 

The show notes for this episode can be found at twimlai.com/talk/167.

Jul 26, 2018
Computer Vision and Intelligent Agents for Wildlife Conservation with Jason Holmberg - TWiML Talk #166
49:42

In this episode, I'm joined by Jason Holmberg, Executive Director and Director of Engineering at WildMe.

Wildme’s Wildbook and Whaleshark.org are both open source computer vision based conservation projects, that have been compared to a facebook for wildlife. Jason kicks us off with the interesting story of how Wildbook came to be, and the eventual expansion of the project from a focus on whale sharks to include Giant Manta Rays, Humpback Whales, Zebras and Giraffes. Jason and I explore the evolution of these projects’ use of computer vision and deep learning, the unique characteristics of the models they’re building, and how they’re ultimately enabling a new kind of citizen science. Finally, we take a look at a cool new “intelligent agent” project that Jason is working on, which mines YouTube for wildlife sightings and automatically engages with the relevant individuals and scientists on Wildbook’s behalf.

For the complete show notes, visit twimlai.com/talk/166

Jul 22, 2018
Pragmatic Deep Learning for Medical Imagery with Prashant Warier - TWiML Talk #165
37:13

In this episode I'm joined by Prashant Warier, CEO and Co-Founder of Qure.ai, a company building AI-powered software for radiology.

In our conversation, Prashant and I discuss the company’s work building products for interpreting head CT scans and chest x-rays. Prashant shares with us some great insights into some of the things he and his team have learned in bringing a commercial product to market in this space, including what the gap between academic research papers and commercially viable software, the challenge of data acquisition and how to best capitalize on what data you have access, and much more. We also touch on the application of transfer learning in this space, and the algorithms and annotation pipelines they’ve developed to support 3D scans.

For the complete show notes, visit https://twimlai.com/talk/165.

Jul 19, 2018
Taskonomy: Disentangling Transfer Learning for Perception (CVPR 2018 Best Paper Winner) with Amir Zamir - TWiML Talk #164
49:14

In this episode I'm joined by Amir Zamir, Postdoctoral researcher at both Stanford & UC Berkeley.

Amir joins us fresh off of winning the 2018 CVPR Best Paper Award for co-authoring "Taskonomy: Disentangling Task Transfer Learning." In this work, Amir and his coauthors explore the relationships between different types of visual tasks and use this structure to better understand the types of transfer learning that will be most effective for each, resulting in what they call a “computational taxonomic map for task transfer learning.”

In our conversation, we discuss the nature and consequences of the relationships that Amir and his team discovered, and how they can be used to build more effective visual systems with machine learning. Along the way Amir provides a ton of great examples and explains the various tools his team has created to illustrate these concepts.

Jul 16, 2018
Predicting Metabolic Pathway Dynamics w/ Machine Learning with Zak Costello - TWiML Talk #163
39:49

In today’s episode I’m joined by Zak Costello, post-doctoral fellow at the Joint BioEnergy Institute.

Zak joins me to discuss his recent paper, “A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data.” In our conversation, we start with an overview of synthetic biology, and from there dig into Zak’s particular application, which is the use of ML techniques to optimize metabolic reactions for engineering biofuels at scale. We get into an interesting chat about BioHacking at the end of our interview; it turns out that it’s pretty easy for hobbyists to get started on this type of work, which is pretty wild.

For the complete show notes, visit twimlai.com/talk/163.

To nominate us for Best Podcast and Best Technology Podcast at this years People's Choice Podcast Awards, visit twimlai.com/nominate!

Jul 11, 2018
Machine Learning to Discover Physics and Engineering Principles with Nathan Kutz - TWiML Talk #162
43:26

In this episode, I’m joined by Nathan Kutz, Professor of applied mathematics, electrical engineering and physics at the University of Washington.

Nathan and I met a few months ago at the Prepare.AI conference in St. Louis where he gave a talk on “Machine Learning to Discover Physics and Engineering Principles.” Our conversation is laser-focused on his research into the use of machine learning to help discover the fundamental governing equations for physical and engineering systems from time series measurements. We explore the application of his work to self-tuning fiber-optic lasers as well as to biological systems and other complex multi-scale systems.

The notes for this show can be found at twimlai.com/talk/162.

We’re also in the running for this year’s People's Choice podcast awards, in both the People’s Choice and Technology categories and we’d really appreciate your support! Head over to twimlai.com/nominate to find out how to vote for us!!! Thanks in advance!

Jul 09, 2018
Automating Complex Internal Processes w/ AI with Alexander Chukovski - TWiML Talk #161
41:16

In this episode, I'm joined by Alexander Chukovski, Director of Data Services at Munich, Germany based career platform, Experteer.

In our conversation, we explore Alex’s journey to implement machine learning at Experteer. Alex and I discuss the Experteer NLP pipeline and how it’s evolved over time to address the company’s need for greater automation in the way it processes jobs on its platform. We also discuss Alex’s work with deep learning based ML models, including models like VDCNN and Facebook’s FastText offering, which he’s particularly excited about. Finally, we briefly discuss recent papers that look at transfer learning for NLP, how Alex keeps up with recent academic papers, and a few tips for people looking to inject ML/DL in their products or projects.

We’ve got some great news to share and also favor to ask! We’re in the running for this year’s People's Choice podcast awards, in both the People’s Choice and Technology categories and we’d really appreciate your support! Head over to twimlai.com/nominate to vote for us!!! Thanks in advance!

The complete notes for this show can be found at twimlai.com/talk/161.

Jul 05, 2018
Designing Better Sequence Models with RNNs with Adji Bousso Dieng - TWiML Talk #160
40:08

In this episode, I'm joined by Adji Bousso Dieng, PhD Student in the Department of Statistics at Columbia University.

In this interview, Adji and I discuss two of her recent papers, the first, an accepted paper from this year’s ICML conference titled “Noisin: Unbiased Regularization for Recurrent Neural Networks,” which, as the name implies, presents a new way to regularize RNNs using noise injection. The second paper, an ICLR submission from last year titled “TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency,” debuts an RNN-based language model designed to capture the global semantic meaning relating words in a document via latent topics. We dive into the details behind both of these papers and I learn a ton along the way.

For complete show notes, visit twimlai.com/talk/160. 

Jul 02, 2018
Love Love: AI and ML in Tennis with Stephanie Kovalchik - TWiML Talk #159
46:48

In this, the final show in our AI in Sports series, I’m joined by Stephanie Kovalchik, Research Fellow at Victoria University and Senior Sports Scientist at Tennis Australia.

Stephanie and I had a great conversation about a few of the many interesting projects underway at Tennis Australia. We look at their use of data to develop a player rating system based on ability and probability, as opposed to the current official one which is based on points scored and match results. We then get into some of the interesting products her Game Insight Group is developing, including a win forecasting algorithm, and a statistic that measures a given player’s workload during a match. Stephanie details her paper “Is there a pythagorean theorem for winning in tennis?”, which explores the development and application of a pythagorean theorem for win expectation in tennis. We also take a look at her project to develop a system for classifying “ending” shots, and an emotion tracking system that help shows the link between emotion and performance in tennis.

For the complete show notes, visit twimlai.com/talk/159.

For details on our AI in Sports series, visit twimlai.com/aiinsports. 

Jun 29, 2018
Growth Hacking Sports w/ Machine Learning with Noah Gift - TWiML Talk #158
50:40

In this episode of our AI in Sports series I'm joined by Noah Gift, Founder and Consulting CTO at Pragmatic Labs and professor at UC Davis.

Noah previously worked for a startup called Score Sports, which used machine learning to uncover athlete influence on social media and internet platforms. We look into some of his findings in that role, including how to predict the impact of athletes’ social media engagement. We also discuss some of his more recent work in using social media to predict which players hold the most on-court value, and how this work could lead to more complete approaches to player valuation. Finally, we spend some time discussing some areas that Noah sees as ripe for new research and experimentation across sports, and we take a look at his upcoming book Pragmatic AI, An Introduction to Cloud-Based Machine Learning. For those interested in pre-ordering the book, be sure to check out the link in the show notes for a nice discount code.

 

The notes for this show can be found at twimlai.com/talk/158.

For more on our AI in Sports series visit twimlai.com/aiinsports.

Jun 28, 2018
Fine-Grained Player Prediction in Sports with Jennifer Hobbs - TWiML Talk #157
43:26

In this episode of the series, I'm joined by Jennifer Hobbs, Senior Data Scientist at STATS, a collector and distributor of sports data, covering sports like basketball, soccer, American football and rugby.

Jennifer and I explore the STATS data pipeline and how they collect and store different types of data for easy consumption and application. We dig into a paper she co-authored, Mythbusting Set-Pieces in Soccer, which takes a look at the data surrounding free kicks and corner kicks in soccer in an effort to debunk some long-standing myths around various situations. If you’re using machine learning to predict World Cup winners, you’ll definitely want to check this segment out. Finally, Jennifer and I chat about potential projects and applications of machine learning to sports, and the accessibility of sports-specific datasets for hobbyists.

For the complete show notes, visit twimlai.com/talk/157.

For more information on the AI in Sports Series, visit twimlai.com/aiinsports.

Jun 27, 2018
Targeted Ticket Sales Using Azure ML with the Trail Blazers w/ Mike Schumacher & Chenhui Hu - TWiML Talk #156
37:16

In today’s episode of our AI in Sports series I'm joined by Mike Schumacher, director of business analytics for the Portland Trail Blazers, and Chenhui Hu, a data scientist at Microsoft.

In our conversation, Mike, Chenhui and I discuss how the Blazers are using machine learning to produce better-targeted sales campaigns, for both single-game and season-ticket buyers. Mike describes some of the early use cases the Trail Blazers explored in their drive to apply analytics to the process of increasing ticket sales. Chenhui elaborates on some of the unique challenges of the Trail Blazers’ dataset and the modeling techniques the team applied to solve their problem.

 

For the complete show notes, visit twimlai.com/talk/156.

For more on our AI in Sports series, visit twimlai.com/aiinsports2018.

Jun 26, 2018
AI for Athlete Optimization with Sinead Flahive - TWiML Talk #155
41:25

Perhaps especially appropriate given that much of the globe is glued to the World Cup at the moment, this week we’re excited to kick off a series of shows on AI in sports. While I'm not personally the biggest sports fan, my producer Imari is a huge sports follower, and this series has been something he’s wanted to see since we started working together. So, if you like these shows, be sure to hit him up on Twitter at @twiml_imari.

In this episode I'm joined by Sinead Flahive, data scientist at Dublin, Ireland based Kitman Labs. Sinead joined me to discuss Kitman’s Athlete Optimization System, which allows sports trainers and coaches to collect and analyze data for player performance optimization and injury reduction. In our conversation, we take a look at the different ways this data is collected and analyzed, and the various modeling techniques Sinead and her team use to create player insights for coaches and trainers. Sinead also shares her view of the data-driven sports landscape and how it’s evolving. Enjoy!

 

For the complete show notes, visit twimlai.com/talk/155.

For more on our AI in Sports series, visit twimlai.com/aiinsports2018.

Jun 25, 2018
Omni-Channel Customer Experiences with Vince Jeffs - TWiML Talk #154
43:39
In this, the final episode of our PegaWorld series I’m joined by Vince Jeffs, Senior Director of Product Strategy for AI and Decisioning at Pegasystems. Vince and I had a great talk about the role AI and advanced analytics will play in defining future customer experiences. We do this in the context provided by one of his presentations from the conference, which explores four technology scenarios from Pegasystems’ innovation labs. These look at a connected car experience, the use of deep learning for diagnostics, dynamic notifications, and continuously optimized marketing. We also get into an interesting discussion about how much is too much when it comes to hyperpersonalized experiences, and how businesses can manage this challenge. The notes for this show can be found at twimlai.com/talk/154. For more information on the Pegaworld series, visit twimlai.com/pegaworld2018.
Jun 21, 2018
Workforce Intelligence for Automation & Productivity with Michael Kempe - TWiML Talk #153
36:52
In this episode of our PegaWorld series, I’m joined by Michael Kempe, chief operating officer at global share registry and financial services provider Link Market Services. In the interview, Michael and I dig into Link’s use of workforce intelligence software to allow it to track and analyze the performance of its workforce and business processes. Michael and I discuss some of the initial challenges associated with implementing this type of system, including skepticism amongst employees, and how it ultimately sets the stage for the Link’s broader use of machine learning, AI and so called “robotic process automation” to increase workforce productivity. The notes for this show can be found at twimlai.com/talk/153. For more information on our PegaWorld series, visit twimlai.com/pegaworld2018.
Jun 20, 2018
Data Platforms for Decision Automation at Scotiabank with Jim Saleh - TWiML Talk #152
33:19
In this show, part of our PegaWorld 18 series, I'm joined by Jim Saleh, Senior Director of process and decision automation at Scotiabank. Jim is tasked with helping the bank transition from a world where customer interactions are based on historical analytics to one where they’re based on real-time decisioning and automation. In our conversation we discuss what’s required to deliver real-time decisioning, starting from the ground up with the data platform. In this vein we explore topics like data lakes, data warehouses, integration, and more, and the effort required to take advantage of these. The notes for this show can be found at twimlai.com/talk/152. For more info on our PegaWorld 2018 series, visit twimlai.com/pegaworld2018.
Jun 19, 2018
Towards the Self-Driving Enterprise with Kirk Borne - TWiML Talk #151
42:09
In this show, the first of our PegaWorld 18 series, I'm joined by Kirk Borne, Principal Data Scientist at management consulting firm Booz Allen Hamilton. In our conversation, Kirk shares his views on automation as it applies to enterprises and their customers. We discuss his experiences evangelizing data science within the context of a large organization, and the role of AI in helping organizations achieve automation. Along the way Kirk, shares a great analogy for intelligent automation, comparing it to an autonomous vehicle . We covered a ton of ground in this chat, which I think you’ll get a kick out of. The notes for this show can be found at twimlai.com/talk/151. For more info about our PegaWorld 2018 Series, visit twimlai.com/pegaworld2018.
Jun 18, 2018
How a Global Energy Company Adopts ML & AI with Nicholas Osborn - TWiML Talk #150
47:37
On today’s show I’m excited to share this interview with Nick Osborn, a longtime listener of the show and Leader of the Global Machine Learning Project Management Office at AES Corporation, a Fortune 200 power company. Nick and I met at my AI Summit a few weeks back, and after a brief chat about some of the things he was up to at AES, I knew I needed to get him on the show! In this interview, Nick and I explore how AES is implementing machine learning across multiple domains at the company. We dig into several examples falling under the Natural Language, Computer Vision, and Cognitive Assets categories he’s established for his projects. Along the way we cover some of the key podcast episodes that helped Nick discover potentially applicable ML techniques, and how those are helping his team broaden the use of machine learning at AES. This was a fun and informative conversation that has a lot to offer. Thanks, Nick! The notes for this episode can be found at twimlai.com/talk/150.
Jun 14, 2018
Problem Formulation for Machine Learning with Romer Rosales - TWiML Talk #149
51:26
In this episode, i'm joined by Romer Rosales, Director of AI at LinkedIn. We begin with a discussion of graphical models and approximate probability inference, and he helps me make an important connection in the way I think about that topic. We then review some of the applications of machine learning at LinkedIn, and how what Romer calls their ‘holistic approach’ guides the evolution of ML projects at LinkedIn. This leads us into a really interesting discussion about problem formulation and selecting the right objective function for a given problem. We then talk through some of the tools they’ve built to scale their data science efforts, including large-scale constrained optimization solvers, online hyperparameter optimization and more. This was a really fun conversation, that I’m sure you’ll enjoy! The notes for this show can be found at twimlai.com/talk/149.
Jun 11, 2018
AI for Materials Discovery with Greg Mulholland - TWiML Talk #148
42:22
In this episode I’m joined by Greg Mulholland, Founder and CEO of Citrine Informatics, which is applying AI to the discovery and development of new materials. Greg and I start out with an exploration of some of the challenges of the status quo in materials science, and what’s to be gained by introducing machine learning into this process. We discuss how limitations in materials manifest themselves, and Greg shares a few examples from the company’s work optimizing battery components and solar cells. We dig into the role and sources of data used in applying ML in materials, and some of the unique challenges to collecting it, and discuss the pipeline and algorithms Citrine uses to deliver its service. This was a fun conversation that spans physics, chemistry, and of course machine learning, and I hope you enjoy it. The notes for this show can be found at twimlai.com/talk/148.
Jun 07, 2018
Data Innovation & AI at Capital One with Adam Wenchel - TWiML Talk #147
46:20

In this episode I’m joined by Adam Wenchel, vice president of AI and Data Innovation at Capital One, to discuss how Machine Learning & AI are being integrated into their day-to-day practices, and how those advances benefit the customer. In our conversation, we look into a few of the many applications of AI at the bank, including fraud detection, money laundering, customer service, and automating back office processes. Adam describes some of the challenges of applying ML in financial services and how Capital One maintains consistent portfolio management practices across the organization. We also discuss how the bank has organized to scale their machine learning efforts, and the steps they’ve taken to overcome the talent shortage in the space. The notes for this show can be found at twimlai.com/talk/147.

Jun 04, 2018
Deep Gradient Compression for Distributed Training with Song Han - TWiML Talk #146
47:00
On today’s show I chat with Song Han, assistant professor in MIT’s EECS department, about his research on Deep Gradient Compression. In our conversation, we explore the challenge of distributed training for deep neural networks and the idea of compressing the gradient exchange to allow it to be done more efficiently. Song details the evolution of distributed training systems based on this idea, and provides a few examples of centralized and decentralized distributed training architectures such as Uber’s Horovod, as well as the approaches native to Pytorch and Tensorflow. Song also addresses potential issues that arise when considering distributed training, such as loss of accuracy and generalizability, and much more. The notes for this show can be found at twimlai.com/talk/146.
May 31, 2018
Masked Autoregressive Flow for Density Estimation with George Papamakarios - TWiML Talk #145
35:54
In this episode, University of Edinburgh Phd student George Papamakarios and I discuss his paper “Masked Autoregressive Flow for Density Estimation.” George walks us through the idea of Masked Autoregressive Flow, which uses neural networks to produce estimates of probability densities from a set of input examples. We discuss some of the related work that’s laid the groundwork for his research, including Inverse Autoregressive Flow, Real NVP and Masked Auto-encoders. We also look at the properties of probability density networks and discuss some of the challenges associated with this effort. The notes for this show can be found at twimlai.com/talk/145.
May 28, 2018
Training Data for Computer Vision at Figure Eight with Qazaleh Mirsharif - TWiML Talk #144
22:55
For today’s show, the last in our TrainAI series, I'm joined by Qazaleh Mirsharif, a machine learning scientist working on computer vision at Figure Eight. Qazaleh and I caught up at the TrainAI conference to discuss a couple of the projects she’s worked on in that field, namely her research into the classification of retinal images and her work on parking sign detection from Google Street View images. The former, which attempted to diagnose diseases like diabetic retinopathy using retinal scan images, is similar to the work I spoke with Ryan Poplin about on TWiML Talk #122. In my conversation with Qazaleh we focus on how she built her datasets for each of these projects and some of the key lessons she’s learned along the way. The notes for this show can be found at twimlai.com/talk/144. For series details, visit twimlai.com/trainai2018.
May 25, 2018
Agile Data Science with Sarah Aerni - TWiML Talk #143
39:33
Today we continue our TrainAI series with Sarah Aerni, Director of Data Science at Salesforce Einstein. Sarah and I sat down at the TrainAI conference to discuss her talk “Notes from the Field: The Platform, People, and Processes of Agile Data Science.” Sarah and I dig into the concept of agile data science, exploring what it means to her and how she’s seen it done at Salesforce and other places she’s worked. We also dig into the notion of machine learning platforms, which is also a keen area of interest for me. We discuss some of the common elements we’ve seen in ML platforms, and when it makes sense for an organization to start building one. The notes for this show can be found at twimlai.com/talk/143. For more details on the TrainAI series, visit twimlai.com/trainai2018
May 24, 2018
Tensor Operations for Machine Learning with Anima Anandkumar - TWiML Talk #142
36:00
In this episode of our TrainAI series, I sit down with Anima Anandkumar, Bren Professor at Caltech and Principal Scientist with Amazon Web Services. Anima joined me to discuss the research coming out of her “Tensorlab” at CalTech. In our conversation, we review the application of tensor operations to machine learning and discuss how an example problem–document categorization–might be approached using 3 dimensional tensors to discover topics and relationships between topics. We touch on multidimensionality, expectation maximization, and Amazon products Sagemaker and Comprehend. Anima also goes into how to tensorize neural networks and apply our understanding of tensor algebra to do perform better architecture searches. The notes for this show can be found at twimlai.com/talk/142. For series info, visit twimlai.com/trainai2018
May 23, 2018
Deep Learning for Live-Cell Imaging with David Van Valen - TWiML Talk #141
38:35
In today’s show, I sit down with David Van Valen, assistant professor of Bioengineering & Biology at Caltech. David joined me after his talk at the Figure Eight TrainAI conference to chat about his research using image recognition and segmentation techniques in biological settings. In particular, we discuss his use of deep learning to automate the analysis of individual cells in live-cell imaging experiments. We had a really interesting discussion around the various practicalities he’s learned about training deep neural networks for image analysis, and he shares some great insights into which of the techniques from the deep learning research have worked for him and which haven’t. If you’re a fan of our Nerd Alert shows, you’ll really like this one. Enjoy! The notes for this show can be found at twimlai.com/talk/141. For more information on this series, visit twimlai.com/trainai2018.
May 22, 2018
Checking in with the Master w/ Garry Kasparov - TWiML Talk #140
34:39
In this episode I’m joined by legendary chess champion, author, and fellow at the Oxford Martin School, Garry Kasparov. Garry and I sat down after his keynote at the Figure Eight Train AI conference in San Francisco last week. Garry and I discuss his bouts with the chess-playing computer Deep Blue–which became the first computer system to defeat a reigning world champion in their 1997 rematch–and how that experience has helped shaped his thinking on artificially intelligent systems. We explore his perspective on the evolution of AI, the ways in which chess and Deep Blue differ from Go and Alpha Go, and the significance of DeepMind’s Alpha Go Zero. We also talk through his views on the relationship between humans and machines, and how he expects it to change over time. The notes for this show can be found at twimlai.com/talk/140. For more information on this series, visit twimlai.com/trainai2018.
May 21, 2018
Exploring AI-Generated Music with Taryn Southern - TWiML Talk #139
34:08
In this episode I’m joined by Taryn Southern - a singer, digital storyteller and Youtuber, whose upcoming album I AM AI will be produced completely with AI based tools. Taryn and I explore all aspects of what it means to create music with modern AI-based tools, and the different processes she’s used to create her singles Break Free, Voices in My Head, and more. She also provides a rundown of the many tools she’s used in this space, including Google Magenta, Watson Beat, AMPer, Landr and more. This was a super fun interview that I think you’ll get a kick out of. The notes for this show can be found at twimlai.com/talk/139
May 17, 2018
Practical Deep Learning with Rachel Thomas - TWiML Talk #138
45:51
In this episode, i'm joined by Rachel Thomas, founder and researcher at Fast AI. If you’re not familiar with Fast AI, the company offers a series of courses including Practical Deep Learning for Coders, Cutting Edge Deep Learning for Coders and Rachel’s Computational Linear Algebra course. The courses are designed to make deep learning more accessible to those without the extensive math backgrounds some other courses assume. Rachel and I cover a lot of ground in this conversation, starting with the philosophy and goals behind the Fast AI courses. We also cover Fast AI’s recent decision to switch to their courses from Tensorflow to Pytorch, the reasons for this, and the lessons they’ve learned in the process. We discuss the role of the Fast AI deep learning library as well, and how it was recently used to held their team achieve top results on a popular industry benchmark of training time and training cost by a factor of more than ten. The notes for this show can be found at twimlai.com/talk/138
May 14, 2018
Kinds of Intelligence w/ Jose Hernandez-Orallo - TWiML Talk #137
45:26
In this episode, I'm joined by Jose Hernandez-Orallo, professor in the department of information systems and computing at Universitat Politècnica de València and fellow at the Leverhulme Centre for the Future of Intelligence, working on the Kinds of Intelligence Project. Jose and I caught up at NIPS last year after the Kinds of Intelligence Symposium that he helped organize there. In our conversation, we discuss the three main themes of the symposium: understanding and identifying the main types of intelligence, including non-human intelligence, developing better ways to test and measure these intelligences, and understanding how and where research efforts should focus to best benefit society. The notes for this show can be found at twimlai.com/talk/137.
May 10, 2018
Taming arXiv with Natural Language Processing w/ John Bohannon - TWiML Talk #136
55:38
In this episode i'm joined by John Bohannan, Director of Science at AI startup Primer. As you all may know, a few weeks ago we released my interview with Google legend Jeff Dean, which, by the way, you should definitely check if you haven’t already. Anyway, in that interview, Jeff mentions the recent explosion of machine learning papers on arXiv, which I responded to jokingly by asking whether Google had already developed the AI system to help them summarize and track all of them. While Jeff didn’t have anything specific to offer, a listener reached out and let me know that John was in fact already working on this problem. In our conversation, John and I discuss his work on Primer Science, a tool that harvests content uploaded to arxiv, sorts it into natural topics using unsupervised learning, then gives relevant summaries of the activity happening in different innovation areas. We spend a good amount of time on the inner workings of Primer Science, including their data pipeline and some of the tools they use, how they determine “ground truth” for training their models, and the use of heuristics to supplement NLP in their processing. The notes for this show can be found at twimlai.com/talk/136
May 07, 2018
Epsilon Software for Private Machine Learning with Chang Liu - TWiML Talk #135
47:37
In this episode, our final episode in the Differential Privacy series, I speak with Chang Liu, applied research scientist at Georgian Partners, a venture capital firm that invests in growth stage business software companies in the US and Canada. Chang joined me to discuss Georgian’s new offering, Epsilon, a software product that embodies the research, development and lessons learned helps in helping their portfolio companies deliver differentially private machine learning solutions to their customers. In our conversation, Chang discusses some of the projects that led to the creation of Epsilon, including differentially private machine learning projects at BlueCore, Work Fusion and Integrate.ai. We explore some of the unique challenges of productizing differentially private ML, including business, people and technology issues. Finally, Chang provides some great pointers for those who’d like to further explore this field. The notes for this show can be found at twimlai.com/talk/135
May 04, 2018
Scalable Differential Privacy for Deep Learning with Nicolas Papernot - TWiML Talk #134
01:00:47
In this episode of our Differential Privacy series, I'm joined by Nicolas Papernot, Google PhD Fellow in Security and graduate student in the department of computer science at Penn State University. Nicolas and I continue this week’s look into differential privacy with a discussion of his recent paper, Semi-supervised Knowledge Transfer for Deep Learning From Private Training Data. In our conversation, Nicolas describes the Private Aggregation of Teacher Ensembles model proposed in this paper, and how it ensures differential privacy in a scalable manner that can be applied to Deep Neural Networks. We also explore one of the interesting side effects of applying differential privacy to machine learning, namely that it inherently resists overfitting, leading to more generalized models. The notes for this show can be found at twimlai.com/talk/134.
May 03, 2018
Differential Privacy at Bluecore with Zahi Karam - TWiML Talk #133
39:13
In this episode of our Differential Privacy series, I'm joined by Zahi Karam, Director of Data Science at Bluecore, whose retail marketing platform specializes in personalized email marketing. I sat down with Zahi at the Georgian Partners portfolio conference last year, where he gave me my initial exposure to the field of differential privacy, ultimately leading to this series. Zahi shared his insights into how differential privacy can be deployed in the real world and some of the technical and cultural challenges to doing so. We discuss the Bluecore use case in depth, including why and for whom they build differentially private machine learning models. The notes for this show can be found at twimlai.com/talk/133
May 01, 2018
Differential Privacy Theory & Practice with Aaron Roth - TWiML Talk #132
44:10
In the first episode of our Differential Privacy series, I'm joined by Aaron Roth, associate professor of computer science and information science at the University of Pennsylvania. Aaron is first and foremost a theoretician, and our conversation starts with him helping us understand the context and theory behind differential privacy, a research area he was fortunate to begin pursuing at its inception. We explore the application of differential privacy to machine learning systems, including the costs and challenges of doing so. Aaron discusses as well quite a few examples of differential privacy in action, including work being done at Google, Apple and the US Census Bureau, along with some of the major research directions currently being explored in the field. The notes for this show can be found at twimlai.com/talk/132.
Apr 30, 2018
Optimal Transport and Machine Learning with Marco Cuturi - TWiML Talk #131
33:25
In this episode, i’m joined by Marco Cuturi, professor of statistics at Université Paris-Saclay. Marco and I spent some time discussing his work on Optimal Transport Theory at NIPS last year. In our discussion, Marco explains Optimal Transport, which provides a way for us to compare probability measures. We look at ways Optimal Transport can be used across machine learning applications, including graphical, NLP, and image examples. We also touch on GANs, or generative adversarial networks, and some of the challenges they present to the research community. The notes for this show can be found at twimlai.com/talk/131.
Apr 26, 2018
Collecting and Annotating Data for AI with Kiran Vajapey - TWiML Talk #130
41:20
In this episode, I’m joined by Kiran Vajapey, a human-computer interaction developer at Figure Eight. In this interview, Kiran shares some of what he’s has learned through his work developing applications for data collection and annotation at Figure Eight and earlier in his career. We explore techniques like data augmentation, domain adaptation, and active and transfer learning for enhancing and enriching training datasets. We also touch on the use of Imagenet and other public datasets for real-world AI applications. If you like what you hear in this interview, Kiran will be speaking at my AI Summit April 30th and May 1st in Las Vegas and I’ll be joining Kiran at the upcoming Figure Eight TrainAI conference, May 9th&10th in San Francisco. The notes for this show can be found at twimlai.com/talk/130
Apr 23, 2018
Autonomous Aerial Guidance, Navigation and Control Systems with Christopher Lum - TWiML Talk #129
54:04
Ok, In this episode, I'm joined by Christopher Lum, Research Assistant Professor in the University of Washington’s Department of Aeronautics and Astronautics. Chris also co-heads the University’s Autonomous Flight Systems Lab, where he and his students are working on the guidance, navigation, and control of unmanned systems. In our conversation, we discuss some of the technical and regulatory challenges of building and deploying Unmanned Autonomous Systems. We also talk about some interesting work he’s doing on evolutionary path planning systems as well as an Precision Agriculture use case. Finally, Chris shares some great starting places for those looking to begin a journey into autonomous systems research. The notes for this show can be found at twimlai.com/talk/129.
Apr 19, 2018
Infrastructure for Autonomous Vehicles with Missy Cummings - TWiML Talk #128
43:18
In this episode, I’m joined by Missy Cummings, head of Duke University’s Humans and Autonomy Lab and professor in the department of mechanical engineering. In addition to being an accomplished researcher, Missy also became one of the first female fighter pilots in the US Navy following the repeal of the Combat Exclusion Policy in 1993. We discuss Missy’s research into the infrastructural and operational challenges presented by autonomous vehicles, including cars, drones and unmanned aircraft. We also cover trust, explainability, and interactions between humans and AV systems. This was an awesome interview and i'm glad we’re able to bring it to you! The notes for this show can be found at twimlai.com/talk/128.
Apr 16, 2018
Hyper-Personalizing the Customer Experience w/ AI with Rob Walker - TWiML Talk #127
42:43
In this episode, we're joined by Rob Walker, Vice President of decision management and analytics at Pegasystems, a leading provider of software for customer engagement and operational excellence. Rob and I discuss what’s required for enterprises to fully realize the vision of providing a hyper-personalized customer experience, and how machine learning and AI can be used to determine the next best action an organization should take to optimize sales, service, retention, and risk at every step in the customer relationship. Along the way we dig into a couple of key areas, specifically some of the techniques his organization uses to allow customers to manage the tradeoff between model performance and transparency, particularly in light of new laws like GDPR, and how all this ties to an enterprise’s ability to manage bias and ethical issues when deploying ML. We cover a lot of ground in this one and I think you’ll find Rob’s perspective really interesting. The notes for this show can be found at twimlai.com/talk/127.
Apr 12, 2018
Information Extraction from Natural Document Formats with David Rosenberg - TWiML Talk #126
46:48
In this episode, I’m joined by David Rosenberg, data scientist in the office of the CTO at financial publisher Bloomberg, to discuss his work on “Extracting Data from Tables and Charts in Natural Document Formats.” Bloomberg is dealing with tons of financial and company data in pdfs and other unstructured document formats on a daily basis. To make meaning from this information more efficiently, David and his team have implemented a deep learning pipeline for extracting data from the documents. In our conversation, we dig into the information extraction process, including how it was built, how they sourced their training data, why they used LaTeX as an intermediate representation and how and why they optimize on pixel-perfect accuracy. There’s a lot of interesting info in this show and I think you’re going to enjoy it. The notes for this show can be found at twimlai.com/talk/126.
Apr 09, 2018
Human-in-the-Loop AI for Emergency Response & More w/ Robert Munro - TWiML Talk #125
49:50
In this episode, I chat with Rob Munro, CTO of the newly branded Figure Eight, formerly known as CrowdFlower. Figure Eight’s Human-in-the-Loop AI platform supports data science & machine learning teams working on autonomous vehicles, consumer product identification, natural language processing, search relevance, intelligent chatbots, and more. Rob and I had a really interesting discussion covering some of the work he’s previously done applying machine learning to disaster response and epidemiology, including a use case involving text translation in the wake of the catastrophic 2010 Haiti earthquake. We also dig into some of the technical challenges that he’s encountered in trying to scale the human-in-the-loop side of machine learning since joining Figure Eight, including identifying more efficient approaches to image annotation as well as the use of zero shot machine learning to minimize training data requirements. Finally, we briefly discuss Figure Eight’s upcoming TrainAI conference, which takes place on May 9th & 10th in San Francisco. Train AI you can join me and Rob, along with a host of amazing speakers like Garry Kasparov, Andrej Karpathy, Marti Hearst and many more and receive hands-on AI, machine learning and deep learning training through real-world case studies on practical machine learning applications. For more information on TrainAI, head over to figure-eight.com/train-ai, and be sure to use code TWIMLAI for 30% off your registration! For those of you listening to this on or before April 6th, Figure Eight is offering an even better deal on event registration. Use the code figure-eight to register for only 88 dollars. The notes for this show can be found at twimlai.com/talk/125.
Apr 05, 2018
Systems and Software for Machine Learning at Scale with Jeff Dean - TWiML Talk #124
56:38
In this episode I’m joined by Jeff Dean, Google Senior Fellow and head of the company’s deep learning research team Google Brain, who I had a chance to sit down with last week at the Googleplex in Mountain View. As you’ll hear, I was very excited for this interview, because so many of Jeff’s contributions since he started at Google in ‘99 have touched my life and work. In our conversation, Jeff and I dig into a bunch of the core machine learning innovations we’ve seen from Google. Of course we discuss TensorFlow, and its origins and evolution at Google. We also explore AI acceleration hardware, including TPU v1, v2 and future directions from Google and the broader market in this area. We talk through the machine learning toolchain, including some things that Googlers might take for granted, and where the recently announced Cloud AutoML fits in. We also discuss Google’s process for mapping problems across a variety of domains to deep learning, and much, much more. This was definitely one of my favorite conversations, and I'm pumped to be able to share it with you. The notes for this show can be found at twimlai.com/talk/124.
Apr 02, 2018
Semantic Segmentation of 3D Point Clouds with Lyne Tchapmi - TWiML Talk #123
38:06
In this episode I’m joined by Lyne Tchapmi, PhD student in the Stanford Computational Vision and Geometry Lab, to discuss her paper, “SEGCloud: Semantic Segmentation of 3D Point Clouds.” SEGCloud is an end-to-end framework that performs 3D point-level segmentation combining the advantages of neural networks, trilinear interpolation and fully connected conditional random fields. In our conversation, Lyne and I cover the ins and outs of semantic segmentation, starting from the sensor data that we’re trying to segment, 2d vs 3d representations of that data, and how we go about automatically identifying classes. Along the way we dig into some of the details, including how she obtained a more fine grain labeling of points from sensor data and the transition from point clouds to voxels. The notes for this show can be found at twimlai.com/talk/123.
Mar 29, 2018
Predicting Cardiovascular Risk Factors from Eye Images with Ryan Poplin - TWiML Talk #122
43:21
In this episode, I'm joined by Google Research Scientist Ryan Poplin, who recently co-authored the paper “Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning.” In our conversation, Ryan details his work training a deep learning model to predict various patient risk factors for heart disease, including some surprising ones like age and gender. We also dive into some interesting findings he discovered with regards to multi-task learning, as well as his use of an attention mechanisms to provide explainability. This was a really interesting discussion that I think you’ll really enjoy! The notes for this show can be found at twimlai.com/talk/122.
Mar 26, 2018
Reproducibility and the Philosophy of Data with Clare Gollnick - TWiML Talk #121
39:25
In this episode, i'm joined by Clare Gollnick, CTO of Terbium Labs, to discuss her thoughts on the “reproducibility crisis” currently haunting the scientific landscape. For a little background, a “Nature” survey in 2016 showed that "more than 70% of researchers have tried and failed to reproduce another scientist's experiments, and more than half have failed to reproduce their own experiments." Clare gives us her take on the situation, and how it applies to data science, along with some great nuggets about the philosophy of data and a few interesting use cases as well. We also cover her thoughts on Bayesian vs Frequentist techniques and while we’re at it, the Vim vs Emacs debate. No, actually I’m just kidding on that last one. But this was indeed a very fun conversation that I think you’ll enjoy! For the complete show notes, visit twimlai.com/talk/121.
Mar 22, 2018
Surveying the Connected Car Landscape with GK Senthil - TWiML Talk #120
30:16
In this episode, I’m joined by GK Senthil, director & chief product owner for innovation at Toyota Connected. GK and I spoke about some of the potential opportunities and challenges for smart cars. We discussed Toyota’s recently announced partnership with Amazon to embed Alexa in vehicles, and more generally the approach they’re taking to get connected car technology up to par with smartphones and other intelligent devices we use on a daily basis. We cover in-car voice recognition and touch on the ways ML & AI need to be developed to be useful in vehicles, as well as the approaches to getting there. The notes for this show can be found at twimlai.com/talk/120
Mar 19, 2018
Adversarial Attacks Against Reinforcement Learning Agents with Ian Goodfellow & Sandy Huang
50:04
In this episode, I’m joined by Ian Goodfellow, Staff Research Scientist at Google Brain and Sandy Huang, Phd Student in the EECS department at UC Berkeley, to discuss their work on the paper Adversarial Attacks on Neural Network Policies. If you’re a regular listener here you’ve probably heard of adversarial attacks, and have seen examples of deep learning based object detectors that can be fooled into thinking that, for example, a giraffe is actually a school bus, by injecting some imperceptible noise into the image. Well, Sandy and Ian’s paper sits at the intersection of adversarial attacks and reinforcement learning, another area we’ve discussed quite a bit on the podcast. In their paper, they describe how adversarial attacks can also be effective at targeting neural network policies in reinforcement learning. Sandy gives us an overview of the paper, including how changing a single pixel value can throw off performance of a model trained to play Atari games. We also cover a lot of interesting topics relating to adversarial attacks and RL individually, and some related areas such as hierarchical reward functions and transfer learning. This was a great conversation that I’m really excited to bring to you! For complete show notes, head over to twimlai.com/talk/119
Mar 15, 2018
Towards Abstract Robotic Understanding with Raja Chatila - TWiML Talk #118
49:02
In this episode, we're joined by Raja Chatila, director of Intelligent Systems and Robotics at Pierre and Marie Curie University in Paris, and executive committee chair of the IEEE global initiative on ethics of intelligent and autonomous systems. Raja and I had a great chat about his research, which deals with robotic perception and discovery. We discuss the relationship between learning and discovery, particularly as it applies to robots and their environments, and the connection between robotic perception and action. We also dig into the concepts of affordances, abstract teachings, meta-reasoning and self-awareness as they apply to intelligent systems. Finally, we touch on the issue of values and ethics of these systems. The notes for this show can be found at twimlai.com/talk/118.
Mar 12, 2018
Discovering Exoplanets w/ Deep Learning with Chris Shallue - TWiML Talk #117
46:29
Earlier this week, I had a chance to speak with Chris Shallue, Senior Software Engineer on the Google Brain Team, about his project and paper on “Exploring Exoplanets with Deep Learning.” This is a great story. Chris, inspired by a book he was reading, reached out on a whim to a Harvard astrophysics researcher, kicking off a collaboration and side project eventually leading to the discovery of two new planets outside our solar system. In our conversation, we walk through the entire process Chris followed to find these two exoplanets, including how he researched the domain as an outsider, how he sourced and processed his dataset, and how he built and evolved his models. Finally, we discuss the results of his project and his plans for future work in this area. This podcast is being published in parallel with Google’s release of the source code and data that Chris developed and used, which we’ll link to below, so if what you hear inspires you to dig into this area, you’ve got a nice head start. This was a really interesting conversation, and I'm excited to share it with you! The notes for this show can be found at twimlai.com/talk/117 The corresponding blog post for this project can be found at https://research.googleblog.com/2018/03/open-sourcing-hunt-for-exoplanets.html
Mar 08, 2018
Learning Active Learning with Ksenia Konyushkova - TWiML Talk #116
33:01
In this episode, I speak with Ksenia Konyushkova, Ph.D. student in the CVLab at Ecole Polytechnique Federale de Lausanne in Switzerland. Ksenia and I connected at NIPS in December to discuss her interesting research into ways we might apply machine learning to ease the challenge of creating labeled datasets for machine learning. The first paper we discuss is “Learning Active Learning from Data,” which suggests a data-driven approach to active learning that trains a secondary model to identify the unlabeled data points which, when labeled, would likely have the greatest impact on our primary model’s performance. We also discuss her paper “Learning Intelligent Dialogs for Bounding Box Annotation,” in which she trains an agent to guide the actions of a human annotator to more quickly produce bounding boxes. TWiML Online Meetup Update Join us Tuesday, March 13th for the March edition of the Online Meetup! Sean Devlin will be doing an in-depth review of reinforcement learning and presenting the Google DeepMind paper, "Playing Atari with Deep Reinforcement Learning." Head over to twimlai.com/meetup to learn more or register. Conference Update Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML. Early price ends February 2! The notes for this show can be found at https://twimlai.com/talk/116.
Mar 05, 2018
Machine Learning Platforms at Uber with Mike Del Balso - TWiML Talk #115
50:08
In this episode, I speak with Mike Del Balso, Product Manager for Machine Learning Platforms at Uber. Mike and I sat down last fall at the Georgian Partners Portfolio conference to discuss his presentation “Finding success with machine learning in your company.” In our discussion, Mike shares some great advice for organizations looking to get value out of machine learning. He also details some of the pitfalls companies run into, such as not have proper infrastructure in place for maintenance and monitoring, not managing their expectations, and not putting the right tools in place for data science and development teams. On this last point, we touch on the Michelangelo platform, which Uber uses internally to build, deploy and maintain ML systems at scale, and the open source distributed TensorFlow system they’ve created, Horovod. This was a very insightful interview, so get your notepad ready! Vote on our #MyAI Contest! Over the past few weeks, you’ve heard us talk quite a bit about our #MyAI Contest, which explores the role we see for AI in our personal lives! We received some outstanding entries, and now it’s your turn to check them out and vote for a winner. Do this by visiting our contest page at https://twimlai.com/myai. Voting remains open until Sunday, March 4th at 11:59 PM Eastern time. Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML at twimlai.com/ainy2018. The notes for this show can be found at twimlai.com/talk/115.
Mar 01, 2018
Inverse Programming for Deeper AI with Zenna Tavares - TWiML Talk #114
29:39
For today’s show, the final episode of our Black in AI Series, I’m joined by Zenna Tavares, a PhD student in the both the department of Brain and Cognitive Sciences and the Computer Science and Artificial Intelligence Lab at MIT. I spent some time with Zenna after his talk at the Strange Loop conference titled “Running Programs in Reverse for Deeper AI.” Zenna shares some great insight into his work on program inversion, an idea which lies at the intersection of Bayesian modeling, deep-learning, and computational logic. We set the stage with a discussion of inverse graphics and the similarities between graphic inversion and vision inversion. We then discuss the application of these techniques to intelligent systems, including the idea of parametric inversion. Last but not least, zenna details how these techniques might be implemented, and discusses his work on ReverseFlow, a library to execute tensorflow programs backwards, and Sigma.jl a probabilistic programming environment implemented in the dynamic programming language Julia. This talk packs a punch, and I’m glad to share it with you. Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML at twimlai.com/ainy2018. The notes for this show can be found at twimlai.com/talk/114. For complete contest details, visit twimlai.com/myai. For complete series details, visit twimlai.com/blackinai2018
Feb 26, 2018
Statistical Relational Artificial Intelligence with Sriraam Natarajan - TWiML Talk #113
48:47
In this episode, I speak with Sriraam Natarajan, Associate Professor in the Department of Computer Science at UT Dallas. While at NIPS a few months back, Sriraam and I sat down to discuss his work on Statistical Relational Artificial Intelligence. StarAI is the combination of probabilistic & statistical machine learning techniques with relational databases. We cover systems learning on top of relational databases and making predictions with relational data, with quite a few examples from the healthcare field. Sriraam and his collaborators have also developed BoostSRL, a gradient-boosting based approach to learning different types of statistical relational models. We briefly touch on this, along with other implementation approaches. Join the #MyAI Discussion! As a TWiML listener, you probably have an opinion on the role AI will play in our lives, and we want to hear your take. Sharing your thoughts takes two minutes, can be done from anywhere, and qualifies you to win some great prizes. So hit pause, and jump on over twimlai.com/myai right now to share or learn more. Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML at twimlai.com/ainy2018. The notes for this show can be found at twimlai.com/talk/113. For complete contest details, visit twimlai.com/myai.
Feb 23, 2018
Classical Machine Learning for Infant Medical Diagnosis with Charles Onu - TWiML Talk #112
48:45
In this episode, part 4 in our Black in AI series, i'm joined by Charles Onu, Phd Student at McGill University in Montreal & Founder of Ubenwa, a startup tackling the problem of infant mortality due to asphyxia. Using SVMs and other techniques from the field of automatic speech recognition, Charles and his team have built a model that detects asphyxia based on the audible noises the child makes upon birth. We go into the process he used to collect his training data, including the specific methods they used to record samples, and how their samples will be used to maximize accuracy in the field. We also take a deep dive into some of the challenges of building and deploying the platform and mobile application. This is a really interesting use case, which I think you’ll enjoy. Join the #MyAI Discussion! As a TWiML listener, you probably have an opinion on the role AI will play in our lives, and we want to hear your take. Sharing your thoughts takes two minutes, can be done from anywhere, and qualifies you to win some great prizes. So hit pause, and jump on over twimlai.com/myai right now to share or learn more. Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML at twimlai.com/ainy2018. The notes for this show can be found at twimlai.com/talk/112. For complete contest details, visit twimlai.com/myai. For complete series details, visit twimlai.com/blackinai2018.
Feb 20, 2018
Learning "Common Sense" and Physical Concepts with Roland Memisevic - TWiML Talk #111
33:43
In today’s episode, I’m joined by Roland Memisevic, co-founder, CEO, and chief scientist at Twenty Billion Neurons. Roland joined me at the RE•WORK Deep Learning Summit in Montreal to discuss the work his company is doing to train deep neural networks to understand physical actions. In our conversation, we dig into video analysis and understanding, including how data-rich video can help us develop what Roland calls comparative understanding, or AI “common sense”. We briefly touch on the implications of AI/ML systems having comparative understanding, and how Roland and his team are addressing problems like getting properly labeled training data. Enter Our #MyAI Contest! Are you looking forward to the role AI will play in your life, or in your children’s lives? Or, are you afraid of what’s to come, and the changes AI will bring? Or, maybe you’re skeptical, and don’t think we’ll ever really achieve enough with AI to make a difference? In any case, if you’re a TWiML listener, you probably have an opinion on the role AI will play in our lives, and we want to hear your take. Sharing your thoughts takes two minutes, can be done from anywhere, and qualifies you to win some great prizes. So hit pause, and jump on over twimlai.com/myai right now to share or learn more. The notes for this show can be found at twimlai.com/talk/111.
Feb 15, 2018
Trust in Human-Robot/AI Interactions with Ayanna Howard - TWiML Talk #110
47:31
In this episode, the third in our Black in AI series, I speak with Ayanna Howard, Chair of the Interactive School of Computing at Georgia Tech. Ayanna joined me for a lively discussion about her work in the field of human-robot interaction. We dig deep into a couple of major areas she’s active in that have significant implications for the way we design and use artificial intelligence, namly pediatric robotics and human-robot trust. That latter bit is particularly interesting, and Ayanna provides a really interesting overview of a few of her experiments, including a simulation of an emergency situation, where, well, I don’t want to spoil it, but let’s just say as the actual intelligent beings, we need to make some better decisions. Enjoy! Are you looking forward to the role AI will play in your life, or in your children’s lives? Or, are you afraid of what’s to come, and the changes AI will bring? Or, maybe you’re skeptical, and don’t think we’ll ever really achieve enough with AI to make a difference? As a TWiML listener, you probably have an opinion on the role AI will play in our lives, and we want to hear your take. Sharing your thoughts takes two minutes, can be done from anywhere, and qualifies you to win some great prizes. So hit pause, and jump on over twimlai.com/myai right now to share or learn more. Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML at twimlai.com/ainy2018. The notes for this show can be found at twimlai.com/talk/110. For complete contest details, visit twimlai.com/myai. For complete series details, visit twimlai.com/blackinai2018.
Feb 13, 2018
Data Science for Poaching Prevention and Disease Treatment with Nyalleng Moorosi - TWiML Talk #109
54:18
For today’s show, I'm joined by Nyalleng Moorosi, Senior Data Science Researcher at The Council for Scientific & Industrial Research or CSIR, in Pretoria, South Africa. In our discussion, we discuss two major projects that Nyalleng is apart of at the CSIR, one, a predictive policing use case, which focused on understanding and preventing rhino poaching in Kruger National Park, and the other, a healthcare use case which focuses on understanding the effects of a drug treatment that was causing pancreatic cancer in South Africans. Along the way we talk about the challenges of data collection, data pipelines and overcoming sparsity. This was a really interesting conversation that I’m sure you’ll enjoy. Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML at twimlai.com/ainy2018. The notes for this show can be found at twimlai.com/talk/109. For complete contest details, visit twimlai.com/myaicontest. For complete series details, visit twimlai.com/blackinai2018.
Feb 08, 2018
Security and Safety in AI: Adversarial Examples, Bias and Trust w/ Moustapha Cissé - TWiML Talk #108
50:57
In this episode I’m joined by Moustapha Cissé, Research Scientist at Facebook AI Research Lab (or FAIR) Paris. Moustapha’s broad research interests include the security and safety of AI systems, and we spend some time discussing his work on adversarial examples and systems that are robust to adversarial attacks. More broadly, we discuss the role of bias in datasets, and explore his vision for models that can identify these biases and adjust the way they train themselves in order to avoid taking on those biases. Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML at twimlai.com/ainy2018. Early price ends February 2! The notes for this show can be found at twimlai.com/talk/108. For complete contest details, visit twimlai.com/myaicontest. For complete series details, visit twimlai.com/blackinai2018.
Feb 06, 2018
Peering into the Home w/ Aerial.ai's Wifi Motion Analytics - TWiML Talk #107
44:14
In this episode I’m joined by Michel Allegue and Negar Ghourchian of Aerial.ai. Aerial is doing some really interesting things in the home automation space, by using wifi signal statistics to identify and understand what’s happening in our homes and office environments. Michel, the CTO, describes some of the capabilities of their platform, including its ability to detect not only people and pets within the home, but surprising characteristics like breathing rates and patterns. He also gives us a look into the data collection process, including the types of data needed, how they obtain it, and how it is parsed. Negar, a senior data scientist with Aerial, describes the types of models used, including semi-supervised, unsupervised and signal processing based models, and how they’ve scaled their platform, and provides us with some real-world use cases. Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML at twimlai.com/ainy2018. Early price ends February 2! The notes for this show can be found at twimlai.com/talk/107. For complete contest details, visit twimlai.com/myaicontest. For complete series details, visit twimlai.com/aiathome.
Feb 02, 2018
Physiology-Based Models for Fitness and Training w/ Firstbeat with Ilkka Korhonen - TWiML Talk #106
38:53
In this episode i'm joined by Ilkka Korhonen, Vice President of Technology at Firstbeat, a company whose algorithms are embedded in fitness watches from companies like Garmin and Suunto and which use your heartbeat data to offer personalized insights into stress, fitness, recovery and sleep patterns. We cover a ton about Firstbeat in the conversation, including how they transform the sensor readings into more actionable data, their use of a digital physiological model of the human body, how they use sensor data to identify and predict physiological changes within the body, and some of the opportunities that Firstbeat has to further apply ML in the future. Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML at twimlai.com/ainy2018. Early price ends February 2! The notes for this show can be found at twimlai.com/talk/106. For complete contest details, visit twimlai.com/myaicontest. For complete series details, visit twimlai.com/aiathome.
Feb 02, 2018
Machine Learning for Signal Processing Applications w/ Stuart Feffer & Brady Tsai - TWiML Talk #105
39:45
In this episode, I'm joined by Stuart Feffer, co-founder and CEO of Reality AI, which provides tools and services for engineers working with sensors and signals, and Brady Tsai, Business Development Manager at Koito, which develops automotive lighting solutions for car manufacturers. Stuart and Brady joined me at CES a few weeks ago after they announced a partnership to bring Adaptive Driving Beam, or ADB, headlights to North America. Brady explains what exactly ADB technology is and how it works, while Stuart walks me through the technical aspects of not only this partnership, but of the reality AI platform as a whole. Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML at twimlai.com/ainy2018. Early price ends February 2! The notes for this show can be found at twimlai.com/talk/105. For complete contest details, visit twimlai.com/myaicontest. For complete series details, visit twimlai.com/aiathome.
Feb 01, 2018
Personalizing the Ferrari Challenge Experience w/ Intel AI - TWiML Talk #104
40:37
In this episode, I'm joined by Andy Keller and Emile Chin-Dickey to discuss Intel's partnership with the Ferrari Challenge North American Series. Andy is a Deep Learning Data Scientist at Intel and Emile is Senior Manager of Marketing Partnerships at the company. In this show, Emile gives us a high-level overview of the Ferrari Challenge partnership and the goals of the collaboration. Andy & I then dive into the AI aspects of the project, including how the training data was collected, the techniques they used to perform fine-grained object detection in the video streams, how they built the analytics platform, some of the remaining challenges with this project, and more! Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML at twimlai.com/ainy2018. Early price ends February 2! The notes for this show can be found at twimlai.com/talk/104. For complete contest details, visit twimlai.com/myaicontest. For complete series details, visit twimlai.com/aiathome.
Jan 31, 2018
Deep Learning for 3D Sensors and Cameras in Lighthouse with Alex Teichman - TWiML Talk #103
45:17
In this episode, I sit down with Alex Teichman, CEO and Co-Founder of Lighthouse, a company taking a new approach to the in-home smart camera. Alex and I dig into what exactly the Lighthouse product is, and all the interesting stuff inside, including its combination of 3D sensing, computer vision, and NLP. We also talk about Alex’s process for building the Lighthouse network architecture, they tech stack the product is based on, and some things that surprised him in their efforts to get AI into a consumer product. Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML at twimlai.com/ainy2018. Early price ends February 2! The notes for this show can be found at twimlai.com/talk/103. For complete contest details, visit twimlai.com/myaicontest. For complete series details, visit twimlai.com/aiathome.
Jan 30, 2018
Computer Vision for Cozmo, the Cutest Toy Robot Everrrrr! with Andrew Stein - TWiML Talk #102
47:11
In this episode, I'm joined by Andrew Stein, computer vision engineer at consumer robotics company Anki, and his partner in crime Cozmo, a toy robot with tons of personality. Andrew joined me during the hustle and bustle of CES a few weeks ago to give me some insight into how Cozmo works, plays, and learns, and how he’s different from other consumer robots you may know, such as the Roomba. We discuss the types of algorithms that help power Cozmo, such as facial detection and recognition, 3D pose recognition, reasoning, and even some simple emotional AI. We also cover Cozmo’s functionality and programmability, including a cool feature called Code Lab. This was a really fun interview, and you’ll be happy to know there’s a companion video starring Cozmo himself right here: https://youtu.be/jUkacU1I0QI. Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML at twimlai.com/ainy2018. Early price ends February 2! The notes for this show can be found at twimlai.com/talk/102. For complete contest details, visit twimlai.com/myaicontest. For complete series details, visit twimlai.com/aiathome.
Jan 30, 2018
Expectation Maximization, Gaussian Mixtures & Belief Propagation, OH MY! w/ Inmar Givoni - Talk #101
48:53
In this episode i'm joined by Inmar Givoni, Autonomy Engineering Manager at Uber ATG, to discuss her work on the paper Min-Max Propagation, which was presented at NIPS last month in Long Beach. Inmar and I get into a meaty discussion about graphical models, including what they are and how they’re used, some of the challenges they present for both training and inference, and how and where they can be best applied. Then we jump into an in-depth look at the key ideas behind the Min-Max Propagation paper itself, including the relationship to the broader domain of belief propagation and ideas like affinity propagation, and how all these can be applied to a use case example like the makespan problem. This was a really fun conversation! Enjoy! Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML. Visit twimlai.com/ainy2018 for registration details. Early price ends February 2!
Jan 26, 2018
A Linear-Time Kernel Goodness-of-Fit Test - NIPS Best Paper '17 - TWiML Talk #100
23:38
In this episode, I speak with Arthur Gretton, Wittawat Jitkrittum, Zoltan Szabo and Kenji Fukumizu, who, alongside Wenkai Xu authored the 2017 NIPS Best Paper Award winner “A Linear-Time Kernel Goodness-of-Fit Test.” In our discussion, we cover what exactly a “goodness of fit” test is, and how it can be used to determine how well a statistical model applies to a given real-world scenario. The group and I the discuss this particular test, the applications of this work, as well as how this work fits in with other research the group has recently published. Enjoy! In our discussion, we cover what exactly a “goodness of fit” test is, and how it can be used to determine how well a statistical model applies to a given real-world scenario. The group and I the discuss this particular test, the applications of this work, as well as how this work fits in with other research the group has recently published. Enjoy! This is your last chance to register for the RE•WORK Deep Learning and AI Assistant Summits in San Francisco, which are this Thursday and Friday, January 25th and 26th. These events feature leading researchers and technologists like the ones you heard in our Deep Learning Summit series last week. The San Francisco will event is headlined by Ian Goodfellow of Google Brain, Daphne Koller of Calico Labs, and more! Definitely check it out and use the code TWIMLAI for 20% off of registration. The notes for this show can be found at twimlai.com/talk/100.
Jan 24, 2018
Solving Imperfect-Information Games with Tuomas Sandholm - NIPS ’17 Best Paper - TWiML Talk #99
29:17
In this episode I speak with Tuomas Sandholm, Carnegie Mellon University Professor and Founder and CEO of startups Optimized Markets and Strategic Machine. Tuomas, along with his PhD student Noam Brown, won a 2017 NIPS Best Paper award for their paper “Safe and Nested Subgame Solving for Imperfect-Information Games.” Tuomas and I dig into the significance of the paper, including a breakdown of perfect vs imperfect information games, the role of abstractions in game solving, and how the concept of safety applies to gameplay. We discuss how all these elements and techniques are applied to poker, and how the algorithm described in this paper was used by Noam and Tuomas to create Libratus, the first AI to beat top human pros in No Limit Texas Hold’em, a particularly difficult game to beat due to its large state space. This was a fascinating interview that I'm really excited to share with you all. Enjoy! This is your last chance to register for the RE•WORK Deep Learning and AI Assistant Summits in San Francisco, which are this Thursday and Friday, January 25th and 26th. These events feature leading researchers and technologists like the ones you heard in our Deep Learning Summit series last week. The San Francisco will event is headlined by Ian Goodfellow of Google Brain, Daphne Koller of Calico Labs, and more! Definitely check it out and use the code TWIMLAI for 20% off of registration. The notes for this show can be found at twimlai.com/talk/99
Jan 22, 2018
Separating Vocals in Recorded Music at Spotify with Eric Humphrey - TWiML Talk #98
28:22
In today’s show, I sit down with Eric Humphrey, Research Scientist in the music understanding group at Spotify. Eric was at the Deep Learning Summit to give a talk on Advances in Deep Architectures and Methods for Separating Vocals in Recorded Music. We discuss his talk, including how Spotify's large music catalog enables such an experiment to even take place, the methods they use to train algorithms to isolate and remove vocals from music, and how architectures like U-Net and Pix2Pix come into play when building his algorithms. We also hit on the idea of “creative AI,” Spotify’s attempt at understanding music content at scale, optical music recognition, and more. This show is part of a series of shows recorded at the RE•WORK Deep Learning Summit in Montreal back in October. This was a great event and, in fact, their next event, the Deep Learning Summit San Francisco is right around the corner on January 25th and 26th, and will feature more leading researchers and technologists like the ones you’ll hear here on the show this week, including Ian Goodfellow of Google Brain, Daphne Koller of Calico Labs, and more! Definitely check it out and use the code TWIMLAI for 20% off of registration. The notes for this show can be found at twimlai.com/talk/98
Jan 19, 2018
Accelerating Deep Learning with Mixed Precision Arithmetic with Greg Diamos - TWiML Talk #97
40:34
In this show I speak with Greg Diamos, senior computer systems researcher at Baidu. Greg joined me before his talk at the Deep Learning Summit, where he spoke on “The Next Generation of AI Chips.” Greg’s talk focused on some work his team was involved in that accelerates deep learning training by using mixed 16-bit and 32-bit floating point arithmetic. We cover a ton of interesting ground in this conversation, and if you’re interested in systems level thinking around scaling and accelerating deep learning, you’re really going to like this one. And of course, if you like this one, you’re also going to like TWiML Talk #14 with Greg’s former colleague, Shubho Sengupta, which covers a bunch of related topics. This show is part of a series of shows recorded at the RE•WORK Deep Learning Summit in Montreal back in October. This was a great event and, in fact, their next event, the Deep Learning Summit San Francisco is right around the corner on January 25th and 26th, and will feature more leading researchers and technologists like the ones you’ll hear here on the show this week, including Ian Goodfellow of Google Brain, Daphne Koller of Calico Labs, and more! Definitely check it out and use the code TWIMLAI for 20% off of registration.
Jan 17, 2018