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| Episode | Date |
|---|---|
|
Gods and Robots
|
Sep 09, 2021 |
|
Responsibility, Risk, and Publishing
|
Aug 19, 2021 |
|
ICML 2021: Test of Time(ly) Award
|
Jul 24, 2021 |
|
Learning with Less, Invisible Labor and Combating Anti-Blackness
|
Jul 09, 2021 |
|
Let's Reflect
|
Jun 13, 2020 |
|
Predicting Floods and Really Doing Good
|
May 29, 2020 |
|
ICLR: accessible, inclusive, virtual
|
May 14, 2020 |
|
Humans in the Loop and Outside of the Classroom
|
May 01, 2020 |
|
The Evolution of ML and Furry Little Animals
|
Apr 16, 2020 |
|
Talking Machines Live and Understanding Modeling Viruses
|
Apr 03, 2020 |
|
Prioritizing Problems and 100 episodes
|
Mar 20, 2020 |
|
The Great AI Fallacy
|
Mar 05, 2020 |
|
If a Machine Could Predict Your Death, Should it?
|
Feb 20, 2020 |
|
Predicting the Decade and Distributing Conferences
|
Feb 06, 2020 |
|
Debating Project Debater and Hello NeurIPS
|
Nov 21, 2019 |
|
De-Enchanting AI with the Law
|
Nov 07, 2019 |
|
How to Ask an Actionable Question
|
Oct 25, 2019 |
|
Children are the Future and Ada Lovelace Day
|
Oct 10, 2019 |
|
News from Neil and Updates from DALI
|
Sep 26, 2019 |
|
A Cooperative Path to Artificial Intelligence
|
Sep 13, 2019 |
|
What Does Red Sound Like
|
Aug 30, 2019 |
|
Not What But Why
|
Aug 15, 2019 |
|
Idea Pandemics and Workshop Walkthrough
|
Aug 01, 2019 |
|
PosterSession.ai and Deep Quaggles
|
Jul 18, 2019 |
|
The View from Addis Ababa
|
Jul 04, 2019 |
|
DSA Addis Ababa and ICML Los Angeles
|
Jun 21, 2019 |
|
Data Trusts and Citation Trends
|
Jun 06, 2019 |
|
Reproducibly and Revisiting History
|
May 23, 2019 |
|
Insights from AISTATS
|
May 10, 2019 |
|
The Deep End of Deep Learning
|
Apr 25, 2019 |
|
Exploring MARS and Getting back to Bayesics
|
Apr 11, 2019 |
|
The Sweetness of a Bitter Lesson and Bringing ML and Healthcare Closer
|
Mar 28, 2019 |
|
Slowed Down Conferences and Even More Summer Schools
|
Mar 14, 2019 |
|
Jupyter Notebooks and Modern Model Distribution
|
Feb 28, 2019 |
|
Real World Real Time and Five Papers for Mike Tipping
|
Feb 15, 2019 |
|
The Bezos Paradox and Machine Learning Languages
|
Feb 01, 2019 |
|
Being Global Bit by Bit
|
Jan 17, 2019 |
|
The Possibility Of Explanation and The End of Season Four
|
Nov 29, 2018 |
|
Neural Information Processing Systems and Distributed Internal Intelligence Systems
|
Nov 16, 2018 |
|
Data Driven Ideas and Actionable Privacy
|
Nov 01, 2018 |
|
AI for Good and The Real World
|
Oct 18, 2018 |
|
Systems Design and Tools for Transparency
|
Oct 05, 2018 |
|
How to Research in Hype and CIFAR's Strategy
|
Sep 20, 2018 |
|
Troubling Trends and Climbing Mountains
|
Sep 07, 2018 |
|
Gaussian Processes, Grad School, and Richard Zemel
|
Aug 23, 2018 |
|
Long Term Fairness
|
Aug 09, 2018 |
|
Simulated Learning and Real World Ethics
|
Jul 27, 2018 |
|
ICML 2018 with Jennifer Dy
|
Jul 12, 2018 |
|
Aspirational Asimov and How to Survive a Conference
|
Jun 28, 2018 |
|
Explanations and Reviews
|
Jun 14, 2018 |
|
Statements on Statements
|
May 31, 2018 |
|
The Futility of Artificial Carpenters and Further Reading
|
May 17, 2018 |
|
Economies, Work and AI
|
May 03, 2018 |
|
Explainability and the Inexplicable
|
Apr 19, 2018 |
|
Good Data Practice Rules
|
Apr 05, 2018 |
|
Can an AI Practitioner Fix a Radio?
|
Mar 22, 2018 |
|
Natural vs Artificial Intelligence and Doing Unexpected Work
|
Mar 08, 2018 |
|
Scientific Rigor and Turning Information into Action
|
Feb 22, 2018 |
|
Code Review for Community Change
|
Feb 08, 2018 |
|
The Pace of Change and The Public View of ML
|
Oct 05, 2017 |
|
The Long View and Learning in Person
|
Sep 21, 2017 |
|
Machine Learning in the Field and Bayesian Baked Goods
|
Sep 08, 2017 |
|
Data Science Africa with Dina Machuve
|
Aug 10, 2017 |
|
The Church of Bayes and Collecting Data
|
Jul 28, 2017 |
|
Getting a Start in ML and Applied AI at Facebook
|
Jul 13, 2017 |
|
Bias Variance Dilemma for Humans and the Arm Farm
|
Jun 29, 2017 |
|
Overfitting and Asking Ecological Questions with ML
|
Jun 15, 2017 |
|
Graphons and "Inferencing"
|
May 25, 2017 |
|
Hosts of Talking Machines: Neil Lawrence and Ryan Adams
|
Apr 27, 2017 |
|
ANGLICAN and Probabilistic Programming
|
Sep 01, 2016 |
|
Eric Lander and Restricted Boltzmann Machines
|
Aug 18, 2016 |
|
Generative Art and Hamiltonian Monte Carlo
|
Aug 04, 2016 |
|
Perturb-and-MAP and Machine Learning in the Flint Water Crisis
|
Jul 21, 2016 |
|
Automatic Translation and t-SNE
|
Jul 07, 2016 |
|
Fantasizing Cats and Data Numbers
|
Jun 16, 2016 |
|
Spark and ICML
|
Jun 02, 2016 |
|
Computational Learning Theory and Machine Learning for Understanding Cells
|
May 19, 2016 |
|
Sparse Coding and MADBITS
|
May 05, 2016 |
|
Remembering David MacKay
|
Apr 21, 2016 |
|
Machine Learning and Society
|
Apr 08, 2016 |
|
Software and Statistics for Machine Learning
|
Mar 24, 2016 |
|
Machine Learning in Healthcare and The AlphaGo Matches
|
Mar 10, 2016 |
|
AI Safety and The Legacy of Bletchley Park
|
Feb 25, 2016 |
|
Robotics and Machine Learning Music Videos
|
Feb 11, 2016 |
|
OpenAI and Gaussian Processes
|
Jan 28, 2016 |
|
Real Human Actions and Women in Machine Learning
|
Jan 14, 2016 |
|
Open Source Releases and The End of Season One
|
Nov 22, 2015 |
|
Probabilistic Programming and Digital Humanities
|
Nov 05, 2015 |
|
Workshops at NIPS and Crowdsourcing in Machine Learning
|
Oct 22, 2015 |
|
Machine Learning Mastery and Cancer Clusters
|
Oct 08, 2015 |
|
Data from Video Games and The Master Algorithm
|
Sep 24, 2015 |
|
Strong AI and Autoencoders
|
Sep 10, 2015 |
|
Active Learning and Machine Learning in Neuroscience
|
Aug 27, 2015 |
|
Machine Learning in Biology and Getting into Grad School
|
Aug 13, 2015 |
|
Machine Learning for Sports and Real Time Predictions
|
Jul 30, 2015 |
|
Really Really Big Data and Machine Learning in Business
|
Jul 16, 2015 |
|
Solving Intelligence and Machine Learning Fundamentals
|
Jul 02, 2015 |
|
Working With Data and Machine Learning in Advertising
|
Jun 18, 2015 |
|
The Economic Impact of Machine Learning and Using The Kernel Trick on Big Data
|
Jun 04, 2015 |
|
How We Think About Privacy and Finding Features in Black Boxes
|
May 21, 2015 |
|
Interdisciplinary Data and Helping Humans Be Creative
|
May 07, 2015 |
|
Starting Simple and Machine Learning in Meds
|
Apr 23, 2015 |
|
Spinning Programming Plates and Creative Algorithms
|
Apr 09, 2015 |
|
The Automatic Statistician and Electrified Meat
|
Mar 26, 2015 |
|
The Future of Machine Learning from the Inside Out
|
Mar 13, 2015 |
|
The History of Machine Learning from the Inside Out
|
Feb 26, 2015 |
|
Using Models in the Wild and Women in Machine Learning
|
Feb 12, 2015 |
|
Common Sense Problems and Learning about Machine Learning
|
Jan 29, 2015 |
|
Machine Learning and Magical Thinking
|
Jan 15, 2015 |
|
Hello World!
|
Jan 01, 2015 |