Practical AI: Machine Learning & Data Science

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Description

Making artificial intelligence practical, productive, and accessible to everyone. Practical AI is a show in which technology professionals, business people, students, enthusiasts, and expert guests engage in lively discussions about Artificial Intelligence and related topics (Machine Learning, Deep Learning, Neural Networks, etc). The focus is on productive implementations and real-world scenarios that are accessible to everyone. If you want to keep up with the latest advances in AI, while keeping one foot in the real world, then this is the show for you!

Episode Date
A casual conversation concerning causal inference
51:27
Lucy D’Agostino McGowan, cohost of the Casual Inference Podcast and a professor at Wake Forest University, joins Daniel and Chris for a deep dive into causal inference. Referring to current events (e.g. misreporting of COVID-19 data in Georgia) as examples, they explore how we interact with, analyze, trust, and interpret data - addressing underlying assumptions, counterfactual frameworks, and unmeasured confounders (Chris’s next Halloween costume).
Nov 24, 2020
Building a deep learning workstation
49:27
What’s it like to try and build your own deep learning workstation? Is it worth it in terms of money, effort, and maintenance? Then once built, what’s the best way to utilize it? Chris and Daniel dig into questions today as they talk about Daniel’s recent workstation build. He built a workstation for his NLP and Speech work with two GPUs, and it has been serving him well (minus a few things he would change if he did it again).
Nov 17, 2020
Killer developer tools for machine learning
50:40
Weights & Biases is coming up with some awesome developer tools for AI practitioners! In this episode, Lukas Biewald describes how these tools were a direct result of pain points that he uncovered while working as an AI intern at OpenAI. He also shares his vision for the future of machine learning tooling and where he would like to see people level up tool-wise.
Nov 09, 2020
Reinforcement Learning for search
47:03
Hamish from Sajari blows our mind with a great discussion about AI in search. In particular, he talks about Sajari’s quest for performant AI implementations and extensive use of Reinforcement Learning (RL). We’ve been wanting to make this one happen for a while, and it was well worth the wait.
Oct 26, 2020
When data leakage turns into a flood of trouble
48:27
Rajiv Shah teaches Daniel and Chris about data leakage, and its major impact upon machine learning models. It’s the kind of topic that we don’t often think about, but which can ruin our results. Raj discusses how to use activation maps and image embedding to find leakage, so that leaking information in our test set does not find its way into our training set.
Oct 20, 2020
Productionizing AI at LinkedIn
55:00
Suju Rajan from LinkedIn joined us to talk about how they are operationalizing state-of-the-art AI at LinkedIn. She sheds light on how AI can and is being used in recruiting, and she weaves in some great explanations of how graph-structured data, personalization, and representation learning can be applied to LinkedIn’s candidate search problem. Suju is passionate about helping people deal with machine learning technical debt, and that gives this episode a good dose of practicality.
Oct 13, 2020
R, Data Science, & Computational Biology
54:08
We’re partnering with the upcoming R Conference, because the R Conference is well… amazing! Tons of great AI content, and they were nice enough to connect us to Daniel Chen for this episode. He discusses data science in Computational Biology and his perspective on data science project organization.
Oct 06, 2020
Learning about (Deep) Learning
53:17
In anticipation of the upcoming NVIDIA GPU Technology Conference (GTC), Will Ramey joins Daniel and Chris to talk about education for artificial intelligence practitioners, and specifically the role that the NVIDIA Deep Learning Institute plays in the industry. Will’s insights from long experience are shaping how we all stay on top of AI, so don’t miss this ‘must learn’ episode.
Sep 21, 2020
When AI goes wrong
58:48
So, you trained a great AI model and deployed it in your app? It’s smooth sailing from there right? Well, not in most people’s experience. Sometimes things goes wrong, and you need to know how to respond to a real life AI incident. In this episode, Andrew and Patrick from BNH.ai join us to discuss an AI incident response plan along with some general discussion of debugging models, discrimination, privacy, and security.
Sep 14, 2020
Speech tech and Common Voice at Mozilla
58:30
Many people are excited about creating usable speech technology. However, most of the audio data used by large companies isn’t available to the majority of people, and that data is often biased in terms of language, accent, and gender. Jenny, Josh, and Remy from Mozilla join us to discuss how Mozilla is building an open-source voice database that anyone can use to make innovative apps for devices and the web (Common Voice). They also discuss efforts through Mozilla fellowship program to develop speech tech for African languages and understand bias in data sets.
Sep 09, 2020
Getting Waymo into autonomous driving
1:00:35
Waymo’s mission is to make it safe and easy for people and things to get where they’re going. After describing the state of the industry, Drago Anguelov - Principal Scientist and Head of Research at Waymo - takes us on a deep dive into the world of AI-powered autonomous driving. Starting with Waymo’s approach to autonomous driving, Drago then delights Daniel and Chris with a tour of the algorithmic tools in the autonomy toolbox.
Sep 01, 2020
Hidden Door and so much more
56:03
Hilary Mason is building a new way for kids and families to create stories with AI. It’s called Hidden Door, and in her first interview since founding it, Hilary reveals to Chris and Daniel what the experience will be like for kids. It’s the first Practical AI episode in which some of the questions came from Chris’s 8yo daughter Athena. Hilary also shares her insights into various topics, like how to build data science communities during the COVID-19 Pandemic, reasons why data science goes wrong, and how to build great data-based products. Don’t miss this episode packed with hard-won wisdom!
Aug 24, 2020
Building the world's most popular data science platform
59:12
Everyone working in data science and AI knows about Anaconda and has probably “conda” installed something. But how did Anaconda get started and what are they working on now? Peter Wang, CEO of Anaconda and creator of PyData and popular packages like Bokeh and DataShader, joins us to discuss that and much more. Peter gives some great insights on the Python AI ecosystem and very practical advice for scaling up your data science operation.
Aug 17, 2020
Practical AI turns 100!!! 🎉
1:09:53
We made it to 100 episodes of Practical AI! It has been a privilege to have had so many great guests and discussions about everything from AGI to GPUs to AI for good. In this episode, we circle back to the beginning when Jerod and Adam from The Changelog helped us kick off the podcast. We discuss how our perspectives have changed over time, what it has been like to host an AI podcast, and what the future of AI might look like. (GIVEAWAY!)
Aug 11, 2020
Attack of the C̶l̶o̶n̶e̶s̶ Text!
48:00
Come hang with the bad boys of natural language processing (NLP)! Jack Morris joins Daniel and Chris to talk about TextAttack, a Python framework for adversarial attacks, data augmentation, and model training in NLP. TextAttack will improve your understanding of your NLP models, so come prepared to rumble with your own adversarial attacks!
Aug 03, 2020
🤗 All things transformers with Hugging Face
46:43
Sash Rush, of Cornell Tech and Hugging Face, catches us up on all the things happening with Hugging Face and transformers. Last time we had Clem from Hugging Face on the show (episode 35), their transformers library wasn’t even a thing yet. Oh how things have changed! This time Sasha tells us all about Hugging Face’s open source NLP work, gives us an intro to the key components of transformers, and shares his perspective on the future of AI research conferences.
Jul 27, 2020
MLOps and tracking experiments with Allegro AI
51:08
DevOps for deep learning is well… different. You need to track both data and code, and you need to run multiple different versions of your code for long periods of time on accelerated hardware. Allegro AI is helping data scientists manage these workflows with their open source MLOps solution called Trains. Nir Bar-Lev, Allegro’s CEO, joins us to discuss their approach to MLOps and how to make deep learning development more robust.
Jul 20, 2020
Practical AI Ethics
52:30
The multidisciplinary field of AI Ethics is brand new, and is currently being pioneered by a relatively small number of leading AI organizations and academic institutions around the world. AI Ethics focuses on ensuring that unexpected outcomes from AI technology implementations occur as rarely as possible. Daniel and Chris discuss strategies for how to arrive at AI ethical principles suitable for your own organization, and what is involved in implementing those strategies in the real world. Tune in for a practical AI primer on AI Ethics!
Jul 14, 2020
The ins and outs of open source for AI
47:17
Daniel and Chris get you Fully-Connected with open source software for artificial intelligence. In addition to defining what open source is, they discuss where to find open source tools and data, and how you can contribute back to the open source AI community.
Jul 07, 2020
Operationalizing ML/AI with MemSQL
54:04
A lot of effort is put into the training of AI models, but, for those of us that actually want to run AI models in production, performance and scaling quickly become blockers. Nikita from MemSQL joins us to talk about how people are integrating ML/AI inference at scale into existing SQL-based workflows. He also touches on how model features and raw files can be managed and integrated with distributed databases.
Jun 29, 2020
Roles to play in the AI dev workflow
50:25
This full connected has it all: news, updates on AI/ML tooling, discussions about AI workflow, and learning resources. Chris and Daniel breakdown the various roles to be played in AI development including scoping out a solution, finding AI value, experimentation, and more technical engineering tasks. They also point out some good resources for exploring bias in your data/model and monitoring for fairness.
Jun 22, 2020
The long road to AGI
50:15
Daniel and Chris go beyond the current state of the art in deep learning to explore the next evolutions in artificial intelligence. From Yoshua Bengio’s NeurIPS keynote, which urges us forward towards System 2 deep learning, to DARPA’s vision of a 3rd Wave of AI, Chris and Daniel investigate the incremental steps between today’s AI and possible future manifestations of artificial general intelligence (AGI).
Jun 15, 2020
Explaining AI explainability
46:40
The CEO of Darwin AI, Sheldon Fernandez, joins Daniel to discuss generative synthesis and its connection to explainability. You might have heard of AutoML and meta-learning. Well, generative synthesis tackles similar problems from a different angle and results in compact, explainable networks. This episode is fascinating and very timely.
Jun 08, 2020
Exploring NVIDIA's Ampere & the A100 GPU
53:19
On the heels of NVIDIA’s latest announcements, Daniel and Chris explore how the new NVIDIA Ampere architecture evolves the high-performance computing (HPC) landscape for artificial intelligence. After investigating the new specifications of the NVIDIA A100 Tensor Core GPU, Chris and Daniel turn their attention to the data center with the NVIDIA DGX A100, and then finish their journey at “the edge” with the NVIDIA EGX A100 and the NVIDIA Jetson Xavier NX.
May 26, 2020
AI for Good: clean water access in Africa
42:30
Chandler McCann tells Daniel and Chris about how DataRobot engaged in a project to develop sustainable water solutions with the Global Water Challenge (GWC). They analyzed over 500,000 data points to predict future water point breaks. This enabled African governments to make data-driven decisions related to budgeting, preventative maintenance, and policy in order to promote and protect people’s access to safe water for drinking and washing. From this effort sprang DataRobot’s larger AI for Good initiative.
May 11, 2020
Ask us anything (about AI)
50:36
Daniel and Chris get you Fully-Connected with AI questions from listeners and online forums: What do you think is the next big thing? What are CNNs? How does one start developing an AI-enabled business solution? What tools do you use every day? What will AI replace? And more…
May 04, 2020
Reinforcement learning for chip design
44:34
Daniel and Chris have a fascinating discussion with Anna Goldie and Azalia Mirhoseini from Google Brain about the use of reinforcement learning for chip floor planning - or placement - in which many new designs are generated, and then evaluated, to find an optimal component layout. Anna and Azalia also describe the use of graph convolutional neural networks in their approach.
Apr 27, 2020
Exploring the COVID-19 Open Research Dataset
43:40
In the midst of the COVID-19 pandemic, Daniel and Chris have a timely conversation with Lucy Lu Wang of the Allen Institute for Artificial Intelligence about COVID-19 Open Research Dataset (CORD-19). She relates how CORD-19 was created and organized, and how researchers around the world are currently using the data to answer important COVID-19 questions that will help the world through this ongoing crisis.
Apr 20, 2020
Achieving provably beneficial, human-compatible AI
52:51
AI legend Stuart Russell, the Berkeley professor who leads the Center for Human-Compatible AI, joins Chris to share his insights into the future of artificial intelligence. Stuart is the author of Human Compatible, and the upcoming 4th edition of his perennial classic Artificial Intelligence: A Modern Approach, which is widely regarded as the standard text on AI. After exposing the shortcomings inherent in deep learning, Stuart goes on to propose a new practitioner approach to creating AI that avoids harmful unintended consequences, and offers a path forward towards a future in which humans can safely rely of provably beneficial AI.
Apr 13, 2020
COVID-19 Q&A and CORD-19
54:28
So many AI developers are coming up with creative, useful COVID-19 applications during this time of crisis. Among those are Timo from Deepset-AI and Tony from Intel. They are working on a question answering system for pandemic-related questions called COVID-QA. In this episode, they describe the system, related annotation of the CORD-19 data set, and ways that you can contribute!
Apr 06, 2020
Mapping the intersection of AI and GIS
49:38
Daniel Wilson and Rob Fletcher of ESRI hang with Chris and Daniel to chat about how AI powered modern geographic information systems (GIS) and location intelligence. They illuminate the various models used for GIS, spatial analysis, remote sensing, real-time visualization, and 3D analytics. You don’t want to miss the part about their work for the DoD’s Joint AI Center in humanitarian assistance / disaster relief.
Mar 30, 2020
Welcome to Practical AI
01:30
Practical AI is a weekly podcast that’s marking artificial intelligence practical, productive, and accessible to everyone. If world of AI affects your daily life, this show is for you. From the practitioner wanting to keep up with the latest tools & trends… (clip from episode #68) To the AI curious trying to understand the concepts at play and their implications on our lives… (clip from episode #39) Expert hosts Chris Benson and Daniel Whitenack are here to keep you fully-connected with the world of machine learning and data science. Please listen to a recent episode that interests you and subscribe today. We’d love to have you as a listener!
Mar 25, 2020
Speech recognition to say it just right
49:14
Catherine Breslin of Cobalt joins Daniel and Chris to do a deep dive on speech recognition. She also discusses how the technology is integrated into virtual assistants (like Alexa) and is used in other non-assistant contexts (like transcription and captioning). Along the way, she teaches us how to assemble a lexicon, acoustic model, and language model to bring speech recognition to life.
Mar 23, 2020
Building a career in Data Science
51:08
Emily Robinson, co-author of the book Build a Career in Data Science, gives us the inside scoop about optimizing the data science job search. From creating one’s resume, cover letter, and portfolio to knowing how to recognize the right job at a fair compensation rate. Emily’s expert guidance takes us from the beginning of the process to conclusion, including being successful during your early days in that fantastic new data science position.
Mar 16, 2020
What exactly is "data science" these days?
48:40
Matt Brems from General Assembly joins us to explain what “data science” actually means these days and how that has changed over time. He also gives us some insight into how people are going about data science education, how AI fits into the data science workflow, and how to differentiate yourself career-wise.
Mar 09, 2020
TensorFlow in the cloud
47:37
Craig Wiley, from Google Cloud, joins us to discuss various pieces of the TensorFlow ecosystem along with TensorFlow Enterprise. He sheds light on how enterprises are utilizing AI and supporting AI-driven applications in the Cloud. He also clarifies Google’s relationship to TensorFlow and explains how TensorFlow development is impacting Google Cloud Platform.
Mar 02, 2020
NLP for the world's 7000+ languages
54:50
Expanding AI technology to the local languages of emerging markets presents huge challenges. Good data is scarce or non-existent. Users often have bandwidth or connectivity issues. Existing platforms target only a small number of high-resource languages. Our own Daniel Whitenack (data scientist at SIL International) and Dan Jeffries (from Pachyderm) discuss how these and related problems will only be solved when AI technology and resources from industry are combined with linguistic expertise from those on the ground working with local language communities. They have illustrated this approach as they work on pushing voice technology into emerging markets.
Feb 24, 2020
Real-time conversational insights from phone call data
51:46
Daniel and Chris hang out with Mike McCourt from Invoca to learn about the natural language processing model architectures underlying Signal AI. Mike shares how they process conversational data, the challenges they have to overcome, and the types of insights that can be harvested.
Feb 17, 2020
AI-powered scientific exploration and discovery
42:33
Daniel and Chris explore Semantic Scholar with Doug Raymond of the Allen Institute for Artificial Intelligence. Semantic Scholar is an AI-backed search engine that uses machine learning, natural language processing, and machine vision to surface relevant information from scientific papers.
Feb 10, 2020
Insights from the AI Index 2019 Annual Report
44:32
Daniel and Chris do a deep dive into The AI Index 2019 Annual Report, which provides unbiased rigorously-vetted data that one can use “to develop intuitions about the complex field of AI”. Analyzing everything from R&D and technical advancements to education, the economy, and societal considerations, Chris and Daniel lay out this comprehensive report’s key insights about artificial intelligence.
Feb 03, 2020
Testing ML systems
47:33
Production ML systems include more than just the model. In these complicated systems, how do you ensure quality over time, especially when you are constantly updating your infrastructure, data and models? Tania Allard joins us to discuss the ins and outs of testing ML systems. Among other things, she presents a simple formula that helps you score your progress towards a robust system and identify problem areas.
Jan 27, 2020
AI-driven automation in manufacturing
47:20
One of the things people most associate with AI is automation, but how is AI actually shaping automation in manufacturing? Costas Boulis from Bright Machines joins us to talk about how they are using AI in various manufacturing processes and in their “microfactories.” He also discusses the unique challenges of developing AI models based on manufacturing data.
Jan 20, 2020
How the U.S. military thinks about AI
48:52
Chris and Daniel talk with Greg Allen, Chief of Strategy and Communications at the U.S. Department of Defense (DoD) Joint Artificial Intelligence Center (JAIC). The mission of the JAIC is “to seize upon the transformative potential of artificial intelligence technology for the benefit of America’s national security… The JAIC is the official focal point of the DoD AI Strategy.” So if you want to understand how the U.S. military thinks about artificial intelligence, then this is the episode for you!
Jan 13, 2020
2019's AI top 5
58:05
Wow, 2019 was an amazing year for AI! In this fully connected episode, Chris and Daniel discuss their list of top 5 notable AI things from 2019. They also discuss the “state of AI” at the end of 2019, and they make some predictions for 2020.
Jan 06, 2020
AI for search at Etsy
46:14
We have all used web and product search technologies for quite some time, but how do they actually work and how is AI impacting search? Andrew Stanton from Etsy joins us to dive into AI-based search methods and to talk about neuroevolution. He also gives us an introduction to Rust for production ML/AI and explains how that community is developing.
Dec 23, 2019
Escaping the "dark ages" of AI infrastructure
50:00
Evan Sparks, from Determined AI, helps us understand why many are still stuck in the “dark ages” of AI infrastructure. He then discusses how we can build better systems by leveraging things like fault tolerant training and AutoML. Finally, Evan explains his optimistic outlook on AI’s economic and environmental health impact.
Dec 16, 2019
Modern NLP with spaCy
56:25
SpaCy is awesome for NLP! It’s easy to use, has widespread adoption, is open source, and integrates the latest language models. Ines Montani and Matthew Honnibal (core developers of spaCy and co-founders of Explosion) join us to discuss the history of the project, its capabilities, and the latest trends in NLP. We also dig into the practicalities of taking NLP workflows to production. You don’t want to miss this episode!
Dec 09, 2019
Making GANs practical
59:04
GANs are at the center of AI hype. However, they are also starting to be extremely practical and be used to develop solutions to real problems. Jakub Langr and Vladimir Bok join us for a deep dive into GANs and their application. We discuss the basics of GANs, their various flavors, and open research problems.
Dec 02, 2019
Build custom ML tools with Streamlit
44:15
Streamlit recently burst onto the scene with their intuitive, open source solution for building custom ML/AI tools. It allows data scientists and ML engineers to rapidly build internal or external UIs without spending time on frontend development. In this episode, Adrien Treuille joins us to discuss ML/AI app development in general and Streamlit. We talk about the practicalities of working with Streamlit along with its seemingly instant adoption by AI2, Stripe, Stitch Fix, Uber, and Twitter.
Nov 25, 2019
Intelligent systems and knowledge graphs
57:10
There’s a lot of hype about knowledge graphs and AI-methods for building or using them, but what exactly is a knowledge graph? How is it different from a database or other data store? How can I build my own knowledge graph? James Fletcher from Grakn Labs helps us understand knowledge graphs in general and some practical steps towards creating your own. He also discusses graph neural networks and the future of graph-augmented methods.
Nov 18, 2019
Robot hands solving Rubik's cubes
44:33
Everyone is talking about it. OpenAI trained a pair of neural nets that enable a robot hand to solve a Rubik’s cube. That is super dope! The results have also generated a lot of commentary and controversy, mainly related to the way in which the results were represented on OpenAI’s blog. We dig into all of this in on today’s Fully Connected episode, and we point you to a few places where you can learn more about reinforcement learning.
Nov 11, 2019
Open source data labeling tools
44:20
What’s the most practical of practical AI things? Data labeling of course! It’s also one of the most time consuming and error prone processes that we deal with in AI development. Michael Malyuk of Heartex and Label Studio joins us to discuss various data labeling challenges and open source tooling to help us overcome those challenges.
Nov 05, 2019
It's time to talk time series
42:45
Times series data is everywhere! I mean, seriously, try to think of some data that isn’t a time series. You have stock prices and weather data, which are the classics, but you also have a time series of images on your phone, time series log data coming off of your servers, and much more. In this episode, Anais from InfluxData helps us understand the range of methods and problems related to time series data. She also gives her perspective on when statistical methods might perform better than neural nets or at least be a more reasonable choice.
Oct 28, 2019
AI in the browser
49:40
We’ve mentioned ML/AI in the browser and in JS a bunch on this show, but we haven’t done a deep dive on the subject… until now! Victor Dibia helps us understand why people are interested in porting models to the browser and how people are using the functionality. We discuss TensorFlow.js and some applications built using TensorFlow.js
Oct 21, 2019
Blacklisted facial recognition and surveillance companies
49:25
The United States has blacklisted several Chinese AI companies working in facial recognition and surveillance. Why? What are these companies doing exactly, and how does this fit into the international politics of AI? We dig into these questions and attempt to do some live fact finding in this episode.
Oct 15, 2019
Flying high with AI drone racing at AlphaPilot
47:48
Chris and Daniel talk with Keith Lynn, AlphaPilot Program Manager at Lockheed Martin. AlphaPilot is an open innovation challenge, developing artificial intelligence for high-speed racing drones, created through a partnership between Lockheed Martin and The Drone Racing League (DRL). AlphaPilot challenged university teams from around the world to design AI capable of flying a drone without any human intervention or navigational pre-programming. Autonomous drones will race head-to-head through complex, three-dimensional tracks in DRL’s new Artificial Intelligence Robotic Racing (AIRR) Circuit. The winning team could win up to $2 million in prizes. Keith shares the incredible story of how AlphaPilot got started, just prior to its debut race in Orlando, which will be broadcast on NBC Sports.
Oct 07, 2019
AI in the majority world and model distillation
45:10
Chris and Daniel take some time to cover recent trends in AI and some noteworthy publications. In particular, they discuss the increasing AI momentum in the majority world (Africa, Asia, South and Central America and the Caribbean), and they dig into Hugging Face’s recent model distillation results.
Sep 30, 2019
The influence of open source on AI development
45:32
The All Things Open conference is happening soon, and we snagged one of their speakers to discuss open source and AI. Samuel Taylor talks about the essential role that open source is playing in AI development and research, and he gives us some tips on choosing AI-related side projects.
Sep 25, 2019
Worlds are colliding - AI and HPC
48:24
In this very special fully-connected episode of Practical AI, Daniel interviews Chris. They discuss High Performance Computing (HPC) and how it is colliding with the world of AI. Chris explains how HPC differs from cloud/on-prem infrastructure, and he highlights some of the challenges of an HPC-based AI strategy.
Sep 17, 2019
AutoML and AI at Google
58:38
We’re talking with Sherol Chen, a machine learning developer, about AI at Google and AutoML methods. Sherol explains how the various AI groups within Google work together and how AutoML fits into that puzzle. She also explains how to get started with AutoML step-by-step (this is “practical” AI after all).
Sep 09, 2019
On being humAIn
55:52
David Yakobovitch joins the show to talk about the evolution of data science tools and techniques, the work he’s doing to teach these things at Galvanize, what his HumAIn Podcast is all about, and more.
Aug 26, 2019
Serving deep learning models with RedisAI
46:17
Redis is a an open source, in-memory data structure store, widely used as a database, cache and message broker. It now also support tensor data types and deep learning models via the RedisAI module. Why did they build this module? Who is or should be using it? We discuss this and much more with Pieter Cailliau.
Aug 12, 2019
AI-driven studies of the ancient world and good GANs
54:51
Chris and Daniel take the opportunity to catch up on some recent AI news. Among other things, they discuss the increasing impact of AI on studies of the ancient world and “good” uses of GANs. They also provide some more learning resources to help you level up your AI and machine learning game.
Jul 30, 2019
AI code that facilitates good science
53:01
We’re talking with Joel Grus, author of Data Science from Scratch, 2nd Edition, senior research engineer at the Allen Institute for AI (AI2), and maintainer of AllenNLP. We discussed Joel’s book, which has become a personal favorite of the hosts, and why he decided to approach data science and AI “from scratch.” Joel also gives us a glimpse into AI2, an introduction to AllenNLP, and some tips for writing good research code. This episode is packed full of reproducible AI goodness!
Jul 19, 2019
Celebrating episode 50 and the neural net!
50:54
Woo hoo! As we celebrate reaching episode 50, we come full circle to discuss the basics of neural networks. If you are just jumping into AI, then this is a great primer discussion with which to take that leap. Our commitment to making artificial intelligence practical, productive, and accessible to everyone has never been stronger, so we invite you to join us for the next 50 episodes!
Jul 03, 2019
Exposing the deception of DeepFakes
55:15
This week we bend reality to expose the deceptions of deepfake videos. We talk about what they are, why they are so dangerous, and what you can do to detect and resist their insidious influence. In a political environment rife with distrust, disinformation, and conspiracy theories, deepfakes are being weaponized and proliferated as the latest form of state-sponsored information warfare. Join us for an episode scarier than your favorite horror movie, because this AI bogeyman is real!
Jun 25, 2019
Model inspection and interpretation at Seldon
43:44
Interpreting complicated models is a hot topic. How can we trust and manage AI models that we can’t explain? In this episode, Janis Klaise, a data scientist with Seldon, joins us to talk about model interpretation and Seldon’s new open source project called Alibi. Janis also gives some of his thoughts on production ML/AI and how Seldon addresses related problems.
Jun 17, 2019
GANs, RL, and transfer learning oh my!
51:32
Daniel and Chris explore three potentially confusing topics - generative adversarial networks (GANs), deep reinforcement learning (DRL), and transfer learning. Are these types of neural network architectures? Are they something different? How are they used? Well, If you have ever wondered how AI can be creative, wished you understood how robots get their smarts, or were impressed at how some AI practitioners conquer big challenges quickly, then this is your episode!
Jun 11, 2019
Visualizing and understanding RNNs
46:18
Andreas Madsen, a freelance ML/AI engineer and Distill.pub author, joins us to discuss his work visualizing neural networks and recurrent neural units. Andreas discusses various neural unites, RNNs in general, and the “why” of neural network visualization. He also gives us his perspective on ML/AI freelancing and moving from web development to AI research.
Jun 04, 2019
How to get plugged into the AI community
1:02:21
Chris and Daniel take you on a tour of local and global AI events, and discuss how to get the most out of your experiences. From access to experts to developing new industry relationships, learn how to get your foot in the door and make connections that help you grow as an AI practitioner. Then drawing from their own wealth of experience as speakers, they dive into what it takes to give a memorable world-class talk that your audience will love. They break down how to select the topic, write the abstract, put the presentation together, and deliver the narrative with impact!
May 28, 2019
AI adoption in the enterprise
57:10
At the recent O’Reilly AI Conference in New York City, Chris met up with O’Reilly Chief Data Scientist Ben Lorica, the Program Chair for Strata Data, the AI Conference, and TensorFlow World. O’Reilly’s ‘AI Adoption in the Enterprise’ report had just been released, so naturally Ben and Chris wanted to do a deep dive into enterprise AI adoption to discuss strategy, execution, and implications.
May 21, 2019
When AI meets quantum mechanics
1:02:10
Can AI help quantum physicists? Can quantum physicists help the AI community? The answers are yes and yes! Dr. Shohini Ghose from Wilfrid Laurier University and Marcus Edwards from the University of Waterloo join us to discuss ML/AI’s impact on physics and quantum computing potential for ML/AI.
May 14, 2019
TensorFlow Dev Summit 2019
59:20
This week Daniel and Chris discuss the announcements made recently at TensorFlow Dev Summit 2019. They kick it off with the alpha release of TensorFlow 2.0, which features eager execution and an improved user experience through Keras, which has been integrated into TensorFlow itself. They round out the list with TensorFlow Datasets, TensorFlow Addons, TensorFlow Extended (TFX), and the upcoming inaugural O’Reilly TensorFlow World conference.
May 07, 2019
CTRL-labs lets you control machines with your mind
1:03:21
No, this isn’t science fiction! CTRL-labs is using neural signals and AI to build neural interfaces. Adam Berenzweig, from CTRL-labs R&D, joins us to explain how this works and how they have made it practical.
Apr 30, 2019
Deep Reinforcement Learning
45:35
While attending the NVIDIA GPU Technology Conference in Silicon Valley, Chris met up with Adam Stooke, a speaker and PhD student at UC Berkeley who is doing groundbreaking work in large-scale deep reinforcement learning and robotics. Adam took Chris on a tour of deep reinforcement learning - explaining what it is, how it works, and why it’s one of the hottest technologies in artificial intelligence!
Apr 23, 2019
Making the world a better place at the AI for Good Foundation
51:39
Longtime listeners know that we’re always advocating for ‘AI for good’, but this week we have taken it to a whole new level. We had the privilege of chatting with James Hodson, Director of the AI for Good Foundation, about ways they have used artificial intelligence to positively-impact the world - from food production to climate change. James inspired us to find our own ways to use AI for good, and we challenge our listeners to get out there and do some good!
Apr 15, 2019
GIPHY's celebrity detector
49:23
GIPHY’s head of R&D, Nick Hasty, joins us to discuss their recently released celebrity detector project. He gives us all of the details about that project, but he also tells us about GIPHY’s origins, AI in general at GIPHY, and more!
Apr 08, 2019
The landscape of AI infrastructure
51:33
Being that this is “practical” AI, we decided that it would be good to take time to discuss various aspects of AI infrastructure. In this full-connected episode, we discuss our personal/local infrastructure along with trends in AI, including infra for training, serving, and data management.
Apr 02, 2019
Growing up to become a world-class AI expert
1:05:37
While at the NVIDIA GPU Technology Conference 2019 in Silicon Valley, Chris enjoyed an inspiring conversation with Anima Anandkumar. Clearly a role model - not only for women - but for anyone in the world of AI, Anima relayed how her lifelong passion for mathematics and engineering started when she was only 3 years old in India, and ultimately led to her pioneering deep learning research at Amazon Web Services, CalTech, and NVIDIA.
Mar 25, 2019
Social AI with Hugging Face
39:06
Clément Delangue, the co-founder & CEO of Hugging Face, joined us to discuss fun, social, and conversational AI. Clem explained why social AI is important, what products they are building (social AIs who learn to chit-chat, talk sassy and trades selfies with you), and how this intersects with the latest research in AI for natural language. He also shared his vision for how AI for natural language with develop over the next few years.
Mar 18, 2019
The White House Executive Order on AI
40:35
The White House recently published an “Executive Order on Maintaining American Leadership in Artificial Intelligence.” In this fully connected episode, we discuss the executive order in general and criticism from the AI community. We also draw some comparisons between this US executive order and other national strategies for leadership in AI.
Mar 11, 2019
Staving off disaster through AI safety research
51:00
While covering Applied Machine Learning Days in Switzerland, Chris met El Mahdi El Mhamdi by chance, and was fascinated with his work doing AI safety research at EPFL. El Mahdi agreed to come on the show to share his research into the vulnerabilities in machine learning that bad actors can take advantage of. We cover everything from poisoned data sets and hacked machines to AI-generated propaganda and fake news, so grab your James Bond 007 kit from Q Branch, and join us for this important conversation on the dark side of artificial intelligence.
Mar 04, 2019
OpenAI's new "dangerous" GPT-2 language model
40:29
This week we discuss GPT-2, a new transformer-based language model from OpenAI that has everyone talking. It’s capable of generating incredibly realistic text, and the AI community has lots of concerns about potential malicious applications. We help you understand GPT-2 and we discuss ethical concerns, responsible release of AI research, and resources that we have found useful in learning about language models.
Feb 25, 2019
AI for social good at Intel
37:57
While at Applied Machine Learning Days in Lausanne, Switzerland, Chris had an inspiring conversation with Anna Bethke, Head of AI for Social Good at Intel. Anna reveals how she started the AI for Social Good program at Intel, and goes on to share the positive impact this program has had - from stopping animal poachers, to helping the National Center for Missing & Exploited Children. Through this AI for Social Good program, Intel clearly demonstrates how a for-profit business can effectively use AI to make the world a better place for us all.
Feb 20, 2019
GirlsCoding.org empowers young women to embrace computer science
40:37
Chris sat down with Marta Martinez-Cámara and Miranda Kreković to learn how GirlsCoding.org is inspiring 9–16-year-old girls to learn about computer science. The site is successfully empowering young women to recognize computer science as a valid career choice through hands-on workshops, role models, and by smashing prevalent gender stereotypes. This is an episode that you’ll want to listen to with your daughter!
Feb 13, 2019
How Microsoft is using AI to help the Earth
44:41
Chris caught up with Jennifer Marsman, Principal Engineer on the AI for Earth team at Microsoft, right before her speech at Applied Machine Learning Days 2019 in Lausanne, Switzerland. She relayed how the team came into being, what they do, and some of the good deeds they have done for Mother Earth. They are giving away $50 million (US) in grants over five years! It was another excellent example of AI for good!
Feb 04, 2019
New year’s resolution: dive into deep learning!
35:34
Fully Connected – a series where Chris and Daniel keep you up to date with everything that’s happening in the AI community. If you’re anything like us, your New Year’s resolutions probably included an AI section, so this week we explore some of the learning resources available for artificial intelligence and deep learning. Where you go with it depends upon what you want to achieve, so we discuss academic versus industry career paths, and try to set you on the Practical AI path that will help you level up.
Jan 28, 2019
IBM's AI for detecting neurological state
41:43
Ajay Royyuru and Guillermo Cecchi from IBM Healthcare join Chris and Daniel to discuss the emerging field of computational psychiatry. They talk about how researchers at IBM are applying AI to measure mental and neurological health based on speech, and they give us their perspectives on things like bias in healthcare data, AI augmentation for doctors, and encodings of language structure.
Jan 21, 2019
2018 in review and bold predictions for 2019
42:24
Fully Connected – a series where Chris and Daniel keep you up to date with everything that’s happening in the AI community. This week we look back at 2018 - from the GDPR and the Cambridge Analytica scandal, to advances in natural language processing and new open source tools. Then we offer our predications for what we expect in the year ahead, touching on just about everything in the world of AI.
Jan 14, 2019
Finding success with AI in the enterprise
40:41
Susan Etlinger, an Industry Analyst at Altimeter, a Prophet company, joins us to discuss The AI Maturity Playbook: Five Pillars of Enterprise Success. This playbook covers trends affecting AI, and offers a maturity model that practitioners can use within their own organizations - addressing everything from strategy and product development, to culture and ethics.
Dec 17, 2018
So you have an AI model, now what?
39:54
Fully Connected – a series where Chris and Daniel keep you up to date with everything that’s happening in the AI community. This week we discuss all things inference, which involves utilizing an already trained AI model and integrating it into the software stack. First, we focus on some new hardware from Amazon for inference and NVIDIA’s open sourcing of TensorRT for GPU-optimized inference. Then we talk about performing inference at the edge and in the browser with things like the recently announced ONNX JS.
Dec 10, 2018
Pachyderm's Kubernetes-based infrastructure for AI
41:40
Joe Doliner (JD) joined the show to talk about productionizing ML/AI with Pachyderm, an open source data science platform built on Kubernetes (k8s). We talked through the origins of Pachyderm, challenges associated with creating infrastructure for machine learning, and data and model versioning/provenance. He also walked us through a process for going from a Jupyter notebook to a production data pipeline.
Dec 03, 2018
BERT: one NLP model to rule them all
38:53
Fully Connected – a series where Chris and Daniel keep you up to date with everything that’s happening in the AI community. This week we discuss BERT, a new method of pre-training language representations from Google for natural language processing (NLP) tasks. Then we tackle Facebook’s Horizon, the first open source reinforcement learning platform for large-scale products and services. We also address synthetic data, and suggest a few learning resources.
Nov 27, 2018
UBER and Intel’s Machine Learning platforms
28:49
We recently met up with Cormac Brick (Intel) and Mike Del Balso (Uber) at O’Reilly AI in SF. As the director of machine intelligence in Intel’s Movidius group, Cormac is an expert in porting deep learning models to all sorts of embedded devices (cameras, robots, drones, etc.). He helped us understand some of the techniques for developing portable networks to maximize performance on different compute architectures. In our discussion with Mike, we talked about the ins and outs of Michelangelo, Uber’s machine learning platform, which he manages. He also described why it was necessary for Uber to build out a machine learning platform and some of the new features they are exploring.
Nov 19, 2018
Analyzing AI's impact on society through art and film
44:04
Brett Gaylor joins Chris and Daniel to chat about the recently announced winners of Mozilla’s creative media awards, which focuses on exposing the impact of AI on society. These winners include a film that responds to the audience (via AI recognized emotions) and an interesting chatbot called Wanda.
Nov 12, 2018
Getting into data science and AI
30:12
Himani Agrawal joins Daniel and Chris to talk about how she got into data science and artificial intelligence, and offers advice to others getting into these fields. She goes on to describe the role of artificial intelligence and machine learning within AT&T and telecom in general.
Nov 05, 2018
AIs that look human and create portraits of humans
34:53
In this new and updates show, Daniel and Chris discuss, among other things, efforts to use AI in art and efforts to make AI interfaces look human. They also discuss some learning resources related to neural nets, AI fairness, and reinforcement learning.
Oct 31, 2018
Fighting bias in AI (and in hiring)
41:04
Lindsey Zuloaga joins us to discuss bias in hiring, bias in AI, and how we can fight bias in hiring with AI. Lindsey tells us about her experiences fighting bias at HireVue, where she is director of data science, and she gives some practical advice to AI practitioners about fairness in models and data.
Oct 22, 2018
PyTorch 1.0 vs TensorFlow 2.0
44:20
Chris and Daniel are back together in another news/updates show. They discuss PyTorch v1.0, some disturbing uses of AI for tracking social credit, and learning resources to get you started with machine learning.
Oct 15, 2018
Artificial intelligence at NVIDIA
44:45
NVIDIA Chief Scientist Bill Dally joins Daniel Whitenack and Chris Benson for an in-depth conversation about ‘everything AI’ at NVIDIA. As the leader of NVIDIA Research, Bill schools us on GPUs, and then goes on to address everything from AI-enabled robots and self-driving vehicles, to new AI research innovations in algorithm development and model architectures. This episode is so packed with information, you may want to listen to it multiple times.
Oct 08, 2018
OpenAI, reinforcement learning, robots, safety
33:08
We met up with Wojciech Zaremba at the O’Reilly AI conference in SF. He took some time to talk to us about some of his recent research related to reinforcement learning and robots. We also discussed AI safety and the hype around OpenAI.
Oct 01, 2018
Answering recent AI questions from Quora
48:53
An amazing panel of AI innovators joined us at the O’Reilly AI conference to answer the most pressing AI questions from Quora. We also discussed trends in the industry and some exciting new advances in FPGA hardware.
Sep 18, 2018
AI in healthcare, synthesizing dance moves, hardware acceleration
20:53
Chris and Daniel discuss new advances in AI research (including a creepy dancing video), how AI is creating opportunity for new chip startups, and uses of deep learning in healthcare. They also share some great learning resources, including one of Chris’s favorite online courses.
Sep 03, 2018
Robot Perception and Mask R-CNN
46:43
Chris DeBellis, a lead AI data scientist at Honeywell, helps us understand what Mask R-CNN is and why it’s useful for robot perception. We also explore how this method compares with other convolutional neural network approaches and how you can get started with Mask R-CNN.
Aug 27, 2018
Open source tools, AI for Dota, and enterprise ML adoption
31:51
This week, Daniel and Chris talk about playing Dota at OpenAI, O’Reilly’s machine learning survey, AI-oriented open source (Julia, AutoKeras, Netron, PyTorch), robotics, and even the impact AI strategy has on corporate and national interests. Don’t miss it!
Aug 21, 2018
Behavioral economics and AI-driven decision making
50:26
Mike Bugembe teaches us how to build a culture of data-driven decision making within a company, leverage behavioral economics, and identify high value use cases for AI.
Aug 13, 2018
Eye tracking, Henry Kissinger on AI, Vim
28:59
Chris and Daniel help us wade through the week’s AI news, including open ML challenges from Intel and National Geographic, Henry Kissinger’s views on AI, and a model that can detect personality based on eye movements. They also point out some useful resources to learn more about pandas, the vim editor, and AI algorithms.
Aug 06, 2018
Understanding the landscape of AI techniques
44:46
Jared Lander, the organizer of NYHackR and general data science guru, joined us to talk about the landscape of AI techniques, how deep learning fits into that landscape, and why you might consider using R for ML/AI.
Jul 30, 2018
Government use of facial recognition and AI at Google
18:17
In this episode, Chris and Daniel discuss the latest news, including an article about Google’s AI principles, and they highlight some useful resources to help you level up.
Jul 23, 2018
Detecting planets with deep learning
45:16
Andrew Vanderburg of UT Austin and Christ Shallue of Google Brain join us to talk about their deep learning collaboration, which involved searching through a crazy amount of space imagery to find new planets.
Jul 16, 2018
Data management, regulation, the future of AI
48:25
Matthew Carroll and Andrew Burt of Immuta talked with Daniel and Chris about data management for AI, how data regulation will impact AI, and schooled them on the finer points of the General Data Protection Regulation (GDPR).
Jul 09, 2018
Helping African farmers with TensorFlow
42:40
Amanda Ramcharan, Latifa Mrisho, and Peter McCloskey joined Daniel and Chris to talk about how Penn State University are collaborating to help African farmers increase their yields via a TensorFlow powered mobile app.
Jul 02, 2018
Putting AI in a box at MachineBox
45:04
Mat Ryer and David Hernandez joined Daniel and Chris to talk about MachineBox, building a company around AI, and democratizing AI.
Jul 02, 2018
Meet your Practical AI hosts
35:28
In this inaugural episode of Practical AI — Adam Stacoviak and Jerod Santo sit down with Daniel Whitenack and Chris Benson to discuss their experiences in Artificial Intelligence, Machine Learning, and Data Science and what they hope to accomplish as hosts of this podcast.
Jul 02, 2018