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
Systems Design and Tools for Transparency 00:40:20 2018-10-05 02:37
How to Research in Hype and CIFAR's Strategy 00:37:07 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 00:43:43 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 00:45:02 2018-06-28 22:51
Explanations and Reviews 00:23:35 2018-06-14 19:18
Statements on Statements 00:26:47 2018-05-31 23:32
The Futility of Artificial Carpenters and Further Reading 00:37:18 2018-05-17 22:07
Economies, Work and AI 00:42:40 2018-05-03 21:35
Explainability and the Inexplicable 00:43:57 2018-04-19 21:49
Good Data Practice Rules 00:51:35 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 00:38:20 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 00:59:39 2017-09-08 03:40
Data Science Africa with Dina Machuve 00:48:13 2017-08-11 01:33
The Church of Bayes and Collecting Data 00:49:36 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" 00:41:41 2017-05-25 17:00
Hosts of Talking Machines: Neil Lawrence and Ryan Adams 00:33:36 2017-04-27 15:27
ANGLICAN and Probabilistic Programming 00:44:13 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 00:47:02 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 00:32:01 2016-07-07 18:07
Fantasizing Cats and Data Numbers 00:49:13 2016-06-16 18:50
Spark and ICML 00:39:01 2016-06-02 19:19
Computational Learning Theory and Machine Learning for Understanding Cells 00:40:47 2016-05-19 16:10
Sparse Coding and MADBITS 00:41:25 2016-05-05 19:08
Remembering David MacKay 00:53:15 2016-04-21 14:12
Machine Learning and Society 00:48:27 2016-04-08 05:13
Software and Statistics for Machine Learning 00:39:07 2016-03-24 13:15
Machine Learning in Healthcare and The AlphaGo Matches 00:48:31 2016-03-10 17:30
AI Safety and The Legacy of Bletchley Park 00:48:55 2016-02-25 16:24
Robotics and Machine Learning Music Videos 00:40:07 2016-02-11 17:00
OpenAI and Gaussian Processes 00:35:29 2016-01-28 19:20
Real Human Actions and Women in Machine Learning 00:59:31 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 00:26:44 2015-10-08 15:30
Data from Video Games and The Master Algorithm 00:46:17 2015-09-24 23:55
Strong AI and Autoencoders 00:36:03 2015-09-10 19:00
Active Learning and Machine Learning in Neuroscience 00:53:49 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 00:29:08 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 00:39:11 2015-06-18 18:35
The Economic Impact of Machine Learning and Using The Kernel Trick on Big Data 00:40:36 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 00:38:24 2015-04-23 16:31
Spinning Programming Plates and Creative Algorithms 00:35:18 2015-04-09 13:18
The Automatic Statistician and Electrified Meat 00:45:40 2015-03-26 15:15
The Future of Machine Learning from the Inside Out 00:28:14 2015-03-13 23:16
The History of Machine Learning from the Inside Out 00:32:36 2015-02-26 17:24
Using Models in the Wild and Women in Machine Learning 00:45:06 2015-02-12 16:40
Common Sense Problems and Learning about Machine Learning 00:40:55 2015-01-29 15:26
Machine Learning and Magical Thinking 00:35:10 2015-01-15 14:52
Hello World! 00:41:28 2015-01-01 19:09