Talking Machines

Talking Machines

Talking Machines is your window into the world of machine learning. Your hosts, Katherine Gorman and Neil Lawrence, bring you clear conversations with experts in the field, insightful discussions of industry news, and useful answers to your questions. Machine learning is changing the questions we can ask of the world around us, here we explore how to ask the best questions and what to do with the answers.

Episodes

Title Duration Published Consumed
Gods and Robots 00:40:05 2021-09-09 19:15
Responsibility, Risk, and Publishing 00:25:40 2021-08-20 00:37
ICML 2021: Test of Time(ly) Award 00:19:18 2021-07-24 20:21
Learning with Less, Invisible Labor and Combating Anti-Blackness 00:36:33 2021-07-09 23:33
Let's Reflect 00:00:30 2020-06-13 19:57
Predicting Floods and Really Doing Good 00:39:12 2020-05-29 22:24
ICLR: accessible, inclusive, virtual 00:38:42 2020-05-14 19:15
Humans in the Loop and Outside of the Classroom 00:38:04 2020-05-01 04:10
The Evolution of ML and Furry Little Animals 00:47:59 2020-04-16 21:39
Talking Machines Live and Understanding Modeling Viruses 00:39:53 2020-04-03 02:15
Prioritizing Problems and 100 episodes 00:30:56 2020-03-20 05:00
The Great AI Fallacy 00:48:03 2020-03-05 22:47
If a Machine Could Predict Your Death, Should it? 00:18:07 2020-02-20 23:45
Predicting the Decade and Distributing Conferences 01:06:43 2020-02-06 21:13
Debating Project Debater and Hello NeurIPS 00:41:50 2019-11-21 22:51
De-Enchanting AI with the Law 00:20:10 2019-11-08 00:32
How to Ask an Actionable Question 00:38:58 2019-10-25 03:26
Children are the Future and Ada Lovelace Day 00:54:51 2019-10-10 22:09
News from Neil and Updates from DALI 01:08:32 2019-09-26 14:53
A Cooperative Path to Artificial Intelligence 00:17:50 2019-09-13 04:15
What Does Red Sound Like 00:49:58 2019-08-30 03:56
Not What But Why 00:19:58 2019-08-15 18:49
Idea Pandemics and Workshop Walkthrough 00:59:16 2019-08-01 23:36
PosterSession.ai and Deep Quaggles 00:45:17 2019-07-18 22:52
The View from Addis Ababa 00:22:43 2019-07-04 18:04
DSA Addis Ababa and ICML Los Angeles 00:55:45 2019-06-21 02:55
Data Trusts and Citation Trends 00:54:15 2019-06-07 01:52
Reproducibly and Revisiting History 00:46:10 2019-05-23 16:55
Insights from AISTATS 00:52:09 2019-05-10 04:12
The Deep End of Deep Learning 00:19:23 2019-04-26 00:01
Exploring MARS and Getting back to Bayesics 01:08:54 2019-04-11 14:34
The Sweetness of a Bitter Lesson and Bringing ML and Healthcare Closer 00:50:38 2019-03-28 22:38
Slowed Down Conferences and Even More Summer Schools 00:43:02 2019-03-14 23:11
Jupyter Notebooks and Modern Model Distribution 00:36:57 2019-02-28 20:34
Real World Real Time and Five Papers for Mike Tipping 01:01:33 2019-02-15 02:11
The Bezos Paradox and Machine Learning Languages 00:41:02 2019-02-01 04:12
Being Global Bit by Bit 00:48:58 2019-01-18 00:12
The Possibility Of Explanation and The End of Season Four 00:18:13 2018-11-29 16:03
Neural Information Processing Systems and Distributed Internal Intelligence Systems 00:36:36 2018-11-16 01:17
Data Driven Ideas and Actionable Privacy 00:45:20 2018-11-01 14:05
AI for Good and The Real World 00:32:35 2018-10-19 00:05
Systems Design and Tools for Transparency 00:40:21 2018-10-05 02:37
How to Research in Hype and CIFAR's Strategy 00:37:08 2018-09-20 13:27
Troubling Trends and Climbing Mountains 00:39:32 2018-09-07 06:40
Gaussian Processes, Grad School, and Richard Zemel 00:43:44 2018-08-23 15:28
Long Term Fairness 00:29:25 2018-08-10 00:17
Simulated Learning and Real World Ethics 00:57:32 2018-07-27 03:35
ICML 2018 with Jennifer Dy 00:19:54 2018-07-12 15:33
Aspirational Asimov and How to Survive a Conference 00:45:03 2018-06-28 22:51
Explanations and Reviews 00:23:35 2018-06-14 19:18
Statements on Statements 00:26:48 2018-05-31 23:32
The Futility of Artificial Carpenters and Further Reading 00:37:19 2018-05-17 22:07
Economies, Work and AI 00:42:41 2018-05-03 21:35
Explainability and the Inexplicable 00:43:58 2018-04-19 21:49
Good Data Practice Rules 00:51:36 2018-04-05 22:00
Can an AI Practitioner Fix a Radio? 00:44:17 2018-03-22 18:56
Natural vs Artificial Intelligence and Doing Unexpected Work 00:58:28 2018-03-08 23:07
Scientific Rigor and Turning Information into Action 00:38:21 2018-02-22 22:44
Code Review for Community Change 00:35:17 2018-02-08 13:09
The Pace of Change and The Public View of ML 00:40:12 2017-10-05 07:02
The Long View and Learning in Person 01:05:50 2017-09-21 18:52
Machine Learning in the Field and Bayesian Baked Goods 00:59:40 2017-09-08 03:40
Data Science Africa with Dina Machuve 00:48:14 2017-08-11 01:33
The Church of Bayes and Collecting Data 00:49:37 2017-07-28 02:05
Getting a Start in ML and Applied AI at Facebook 00:57:47 2017-07-14 01:14
Bias Variance Dilemma for Humans and the Arm Farm 00:50:10 2017-06-29 18:51
Overfitting and Asking Ecological Questions with ML 00:41:29 2017-06-15 21:28
Graphons and "Inferencing" 00:41:42 2017-05-25 17:00
Hosts of Talking Machines: Neil Lawrence and Ryan Adams 00:33:37 2017-04-27 15:27
ANGLICAN and Probabilistic Programming 00:44:14 2016-09-01 17:45
Eric Lander and Restricted Boltzmann Machines 00:53:57 2016-08-18 19:37
Generative Art and Hamiltonian Monte Carlo 00:47:03 2016-08-04 16:36
Perturb-and-MAP and Machine Learning in the Flint Water Crisis 00:38:26 2016-07-21 12:07
Automatic Translation and t-SNE 00:32:02 2016-07-07 18:07
Fantasizing Cats and Data Numbers 00:49:13 2016-06-16 18:50
Spark and ICML 00:39:02 2016-06-02 19:19
Computational Learning Theory and Machine Learning for Understanding Cells 00:40:48 2016-05-19 16:10
Sparse Coding and MADBITS 00:41:26 2016-05-05 19:08
Remembering David MacKay 00:53:16 2016-04-21 14:12
Machine Learning and Society 00:48:27 2016-04-08 05:13
Software and Statistics for Machine Learning 00:39:08 2016-03-24 13:15
Machine Learning in Healthcare and The AlphaGo Matches 00:48:32 2016-03-10 17:30
AI Safety and The Legacy of Bletchley Park 00:48:56 2016-02-25 16:24
Robotics and Machine Learning Music Videos 00:40:08 2016-02-11 17:00
OpenAI and Gaussian Processes 00:35:30 2016-01-28 19:20
Real Human Actions and Women in Machine Learning 00:59:32 2016-01-14 12:35
Open Source Releases and The End of Season One 00:40:40 2015-11-22 21:37
Probabilistic Programming and Digital Humanities 00:48:12 2015-11-05 22:45
Workshops at NIPS and Crowdsourcing in Machine Learning 00:47:45 2015-10-22 14:53
Machine Learning Mastery and Cancer Clusters 00:26:45 2015-10-08 15:30
Data from Video Games and The Master Algorithm 00:46:18 2015-09-24 23:55
Strong AI and Autoencoders 00:36:04 2015-09-10 19:00
Active Learning and Machine Learning in Neuroscience 00:53:50 2015-08-27 17:12
Machine Learning in Biology and Getting into Grad School 00:48:26 2015-08-13 19:07
Machine Learning for Sports and Real Time Predictions 00:29:09 2015-07-30 17:06
Really Really Big Data and Machine Learning in Business 00:23:46 2015-07-16 18:57
Solving Intelligence and Machine Learning Fundamentals 00:30:11 2015-07-02 23:31
Working With Data and Machine Learning in Advertising 00:39:12 2015-06-18 18:35
The Economic Impact of Machine Learning and Using The Kernel Trick on Big Data 00:40:37 2015-06-04 15:57
How We Think About Privacy and Finding Features in Black Boxes 00:33:43 2015-05-21 21:46
Interdisciplinary Data and Helping Humans Be Creative 00:34:17 2015-05-07 18:32
Starting Simple and Machine Learning in Meds 00:38:25 2015-04-23 16:31
Spinning Programming Plates and Creative Algorithms 00:35:19 2015-04-09 13:18
The Automatic Statistician and Electrified Meat 00:45:41 2015-03-26 15:15
The Future of Machine Learning from the Inside Out 00:28:15 2015-03-13 23:16
The History of Machine Learning from the Inside Out 00:32:37 2015-02-26 17:24
Using Models in the Wild and Women in Machine Learning 00:45:07 2015-02-12 16:40
Common Sense Problems and Learning about Machine Learning 00:40:56 2015-01-29 15:26
Machine Learning and Magical Thinking 00:35:11 2015-01-15 14:52
Hello World! 00:41:29 2015-01-01 19:09