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Talking Machines

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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...Show More

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The Pace of Change and The Public View of ML

44:12 | Oct 5th, 2017

In episode ten of season three we talk about the rate of change (prompted by Tim Harford), take a listener question about the power of kernels, and talk with Peter Donnelly in his capacity with the Royal Society's Machine Learning Working Group about...Show More

The Sweetness of a Bitter Lesson and Bringing ML and Healthcare Closer

50:38 | Mar 28th

In episode six of season five we talk about Richard Sutton's A Bitter Lesson. Chat about IEEE's new Ethical Guidelines and talk with Andrew Beam Senior Fellownn at Flagship Pioneering, Head of Machine Learning for Flagship VL57 and Assistant Profess...Show More

Slowed Down Conferences and Even More Summer Schools

43:01 | Mar 14th

In episode five of season five we talk about the Stu Hunter conference, Summer schools options (DLRLSS!) and chat with Adrian Weller of the Alan Turing Institute

Jupyter Notebooks and Modern Model Distribution

36:57 | Feb 28th

In episode four of season five we talk about Jupyter Notebooks and Neil's dream of a world craft software and devices, we take a listener question about the conversation surrounding Open AI's GPT-2 its announcement and the coverage and we hear an int...Show More

Real World Real Time and Five Papers for Mike Tipping

1:01:32 | Feb 15th

In season five episode three we chat about take a listener question about Five Papers for Mike Tipping, take a listener question on AIAI and chat with Eoin O'Mahony of Uber Here are Neil's five papers. What are yours? Stochastic variational infe...Show More

The Bezos Paradox and Machine Learning Languages

41:01 | Feb 1st

In episode two of season five we unpack the Bezos Paradox (TM Neil Lawrence) take a listener question about best papers and chat with Dougal Maclaurin of Google Brain.

The Possibility Of Explanation and The End of Season Four

18:12 | Nov 29th, 2018

For the end of season four we take a break from our regular format and bring you a talk from Professor Finale Doshi Velez of Harvard University on the possibility of explanation Tune in next season!

Neural Information Processing Systems and Distributed Internal Intelligence Systems

36:36 | Nov 16th, 2018

In episode twenty one of season four we talk about distributed intelligence systems (mainly those internal to humans), talk about what were excited to see at the Conference on Neural Information Processing Systems and in advance of our trek to Canada...Show More

Data Driven Ideas and Actionable Privacy

45:19 | Nov 1st, 2018

In episode twenty of season four we talk about the importance of crediting your data, answer a listener question about internships vs salaried positions and talk with Matt Kusner of the Alan Turing institute the UK’s national institute for data scie...Show More

AI for Good and The Real World

32:34 | Oct 18th, 2018

In episode nineteen of season four we talk about causality in the real world, take a question about being surprised by the elephant in the room and talk with Kush Varshney of IBM.

Systems Design and Tools for Transparency

40:20 | Oct 5th, 2018

In episode 18 of season four we talk about systems design, (remember the 3 d's!), tools for transparency and fairness and we talk with Adria Gascon of The Alan Turing Institute, the UK’s national institute for data science and AI.

How to Research in Hype and CIFAR's Strategy

37:07 | Sep 20th, 2018

In episode 17 of season four we talk about how to research in a time of hype (and other lessons from Tom Griffiths book) Neil's love of variational methods, and with Chat with Elissa Strome director of the Pan-Canadian AI Strategy for CIFAR

Troubling Trends and Climbing Mountains

39:32 | Sep 7th, 2018

In this episode we talk about an article Troubling Trends in Machine learning Scholarship the difference between engineering and science (and the mountains you climb to span the distance) plus we talk with David Duvenaud of the University of Toronto

Simulated Learning and Real World Ethics

57:32 | Jul 27th, 2018

In episode thirteen of season four we chat about simulations, reinforcement learning, and Philippa Foot. We take a listener question about the update to the ACM code of ethics (first time since 1992!) and We talk with professor Mike Jordan.

ICML 2018 with Jennifer Dy

19:54 | Jul 12th, 2018

Season four episode twelve finds us at ICML! We bring you a special episode with Jennifer Dy, co-program chair of the conference.

Aspirational Asimov and How to Survive a Conference

45:02 | Jun 28th, 2018

In season four episode eleven we talk about the possibility of the NIPS conference changing its name, what to do at ICML, And we talk with Bernhard Schölkopf.

Explanations and Reviews

23:35 | Jun 14th, 2018

In episode 10 of season 4 we chat about Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR, take a listener question about how reviews of papers work at NIPS and we hear from Sven Strohband, CTO of Khosla Vent...Show More

Statements on Statements

26:47 | May 31st, 2018

In episode 9 of season 4 we talk about the Statement on Nature Machine Intelligence. We reached out to Nature for a statement on the statement and received the following: “At Springer Nature we are very clear in our mission to advance discovery and ...Show More

The Futility of Artificial Carpenters and Further Reading

37:18 | May 17th, 2018

In episode eight of season four we review some recently published articles by Michael Jordan and Rodney Brooks (for more reading along these lines, Tom Dettriech is a great person to follow), we recommend some further reading, and talk with Arthur Gr...Show More

Economies, Work and AI

42:40 | May 3rd, 2018

In episode seven of season four we chat about Ellis and the UK AI Sector Deal , we take a listener question about the next AI winter and if/when it is coming, plus we hear from Christina Colclough Director of Platform and Agency Workers, Digitalizat...Show More

Explainability and the Inexplicable

43:57 | Apr 19th, 2018

In episode six of season four we chat about AI and religion, we take a listener question about personal bias checking and we hear from Been Kim of Google Brain.

Good Data Practice Rules

51:35 | Apr 5th, 2018

In episode five of season four we talk about the GDPR or as we like to think of it Good Data Practice Rules. (If you actually read it, you move to expert level!) We take a listener question about the power of approximate inference, and we hear from o...Show More

Can an AI Practitioner Fix a Radio?

44:17 | Mar 22nd, 2018

In episode four of season four we talk more about natural an artificial intelligences and thinking about diversity in systems. Reading Can a Biologist Fix a Radio is a great paper around these ideas. We take a listener question about moving into mach...Show More

Natural vs Artificial Intelligence and Doing Unexpected Work

58:28 | Mar 8th, 2018

In season four episode three of Talking Machines we chat about Neil’s recent thinking (definitely not work) on the core differences between natural intelligence and machine intelligence, he recently wrote blog post on the subject and in the fall of 2...Show More

Scientific Rigor and Turning Information into Action

42:20 | Feb 22nd, 2018

In episode two of season four we're proud to bring you the second annual "Hosts of Talking Machine's Episode"! Ryan and Neil chat about Ali Rahimi's speech at NIPS-17, Kate Crawford's talk The Trouble with Bias, and much more. We also get to hear a ...Show More

Code Review for Community Change

39:17 | Feb 8th, 2018

On this episode of Talking Machines we take a break from our regular format to talk about the “code review of community culture” that the AI, ML, Stats and Computer Science fields in general need to undergo.  In a blog post, that was put up shortly ...Show More

The Long View and Learning in Person

1:09:50 | Sep 21st, 2017

In episode nine of season three we chat about the difference between models and algorithms, take a listener question about summer schools and learning in person as opposed to learning digitally, and we chat with John Quinn of the United Nations Glob...Show More

Machine Learning in the Field and Bayesian Baked Goods

1:03:39 | Sep 8th, 2017

In episode eight of season three we return to the epic (or maybe not so epic) clash between frequentists and bayesians, take a listener question about the ethical questions generators of machine learning should be asking of themselves (not just their...Show More

Data Science Africa with Dina Machuve

52:13 | Aug 10th, 2017

In episode seven of season three we take a minute to break way from our regular format and feature a conversation with Dina Machuve of the Nelson Mandela African Institute of Science and Technology we cover everything from her work to how cell phone ...Show More

The Church of Bayes and Collecting Data

53:36 | Jul 28th, 2017

In episode six of season three we chat about the difference between frequentists and Bayesians, take a listener question about techniques for panel data, and have an interview with Katherine Heller of Duke

Getting a Start in ML and Applied AI at Facebook

1:01:47 | Jul 13th, 2017

In episode five of season three we compare and contrast AI and data science, take a listener question about getting started in machine learning, and listen to an interview with Joaquin Quiñonero Candela. For a great place to get started with foundati...Show More

Bias Variance Dilemma for Humans and the Arm Farm

54:10 | Jun 29th, 2017

In episode four of season three Neil introduces us to the ideas behind the bias variance dilemma (and how how we can think about it in our daily lives). Plus, we answer a listener question about how to make sure your neural networks don't get fooled....Show More

Overfitting and Asking Ecological Questions with ML

45:29 | Jun 15th, 2017

In this episode three of season three of Talking Machines we dive into overfitting, take a listener question about unbalanced data and talk with Professor (Emeritus) Tom Dietterich from Oregon State University.

Graphons and "Inferencing"

41:41 | May 25th, 2017

In episode two of season three Neil takes us through the basics on dropout, we chat about the definition of inference (It's more about context than you think!) and hear an interview with Jennifer Chayes of Microsoft.

Hosts of Talking Machines: Neil Lawrence and Ryan Adams

33:36 | Apr 27th, 2017

Talking Machines is entering its third season and going through some changes. Our founding host Ryan is moving on and in his place Neil Lawrence of Amazon is taking over as co host. We say thank you and good bye to Ryan with an interview about his wo...Show More

ANGLICAN and Probabilistic Programming

44:13 | Sep 1st, 2016

In episode seventeen of season two we get an introduction to Min Hashing, talk with Frank Wood the creator of ANGLICAN, about probabilistic programming and his new company, INVREA, and take a listener question about how to choose an architecture when...Show More

Eric Lander and Restricted Boltzmann Machines

53:57 | Aug 18th, 2016

In episode sixteen of season two, we get an introduction to Restricted Boltzmann Machines, we take a listener question about tuning hyperparameters,  plus we talk with Eric Lander of the Broad Institute.

Generative Art and Hamiltonian Monte Carlo

47:02 | Aug 4th, 2016

In episode fifteen of season two, we talk about Hamiltonian Monte Carlo, we take a listener question about unbalanced data, plus we talk with Doug Eck of Google’s Magenta project.

Perturb-and-MAP and Machine Learning in the Flint Water Crisis

38:26 | Jul 21st, 2016

In episode fourteen of season two, we talk about Perturb-and-MAP, we take a listener question about classic artificial intelligence ideas being used in modern machine learning, plus we talk with Jake Abernethy of the University of Michigan about muni...Show More

Automatic Translation and t-SNE

32:01 | Jul 7th, 2016

In episode thirteen of season two, we talk about t-Distributed Stochastic Neighbor Embedding (t-SNE) we take a listener question about statistical physics, plus we talk with Hal Daume of the University of Maryland. (who is a great follow on Twitter.)

Fantasizing Cats and Data Numbers

49:13 | Jun 16th, 2016

In episode twelve of season two, we talk about generative adversarial networks, we take a listener question about using machine learning to improve or create products, plus we talk with Iain Murray of the University of Edinburgh.

Spark and ICML

39:01 | Jun 2nd, 2016

In episode eleven of season two, we talk about the machine learning toolkit  Spark, we take a listener question about the differences between NIPS and ICML conferences, plus we talk with Sinead Williamson of The University of Texas at Austin.

Computational Learning Theory and Machine Learning for Understanding Cells

40:47 | May 19th, 2016

In episode ten of season two, we talk about Computational Learning Theory and Probably Approximately Correct Learning originated by Professor Leslie Valiant of SEAS at Harvard, we take a listener question about generative systems, plus we talk with A...Show More

Sparse Coding and MADBITS

41:25 | May 5th, 2016

In episode nine of season two, we talk about sparse coding, take a listener question about the next big demonstration for AI after AlphaGo. Plus we talk with Clement Farabet about MADBITS and the work he’s doing at Twitter Cortex.

Remembering David MacKay

53:15 | Apr 21st, 2016

Recently Professor David MacKay passed away. We’ll spend this episode talking about his extensive body of work and its impacts. We’ll also talk with Philipp Hennig, a research group leader at the Max Planck Institute for Intelligent Systems, who trai...Show More

Machine Learning and Society

48:27 | Apr 8th, 2016

Episode seven of season two is a little different than our usual episodes, Ryan and Katherine just returned from a conference where they got to talk with Neil Lawrence of the University of Sheffield about some of the larger issues surrounding machine...Show More

Software and Statistics for Machine Learning

39:07 | Mar 24th, 2016

In episode six of season two, we talk about how to build software for machine learning (and what the roadblocks are), we take a listener question about how to start exploring a new dataset, plus, we talk with Rob Tibshirani of Stanford University.

Machine Learning in Healthcare and The AlphaGo Matches

48:31 | Mar 10th, 2016

In episode five of Season two Ryan walks us through variational inference, we put some listener questions about Go and how to play it to Andy Okun, president of the American Go Association (who is in Seoul South Korea watching the Lee Sedol/AlphaGo g...Show More

AI Safety and The Legacy of Bletchley Park

48:55 | Feb 25th, 2016

In episode four of season two, we talk about some of the major issues in AI safety, (and how they’re not really that different from the questions we ask whenever we create a new tool.) One place you can go for other opinions on AI safety is the Futur...Show More

Robotics and Machine Learning Music Videos

40:07 | Feb 11th, 2016

In episode three of season two Ryan walks us through the Alpha Go results and takes a lister question about using Gaussian processes for classifications. Plus we talk with Michael Littman of Brown University about his work, robots, and making music v...Show More

OpenAI and Gaussian Processes

35:29 | Jan 28th, 2016

In episode two of season two Ryan introduces us to Gaussian processes, we take a listener question on K-means. Plus, we talk with Ilya Sutskever the director of research for OpenAI. (For more from Ilya, you can listen to our season one interview with...Show More

Real Human Actions and Women in Machine Learning

59:31 | Jan 14th, 2016

In episode one of season two, we celebrate the 10th anniversary of Women in Machine Learning (WiML) with its co-founder (and our guest host for this episode) Hanna Wallach of Microsoft Research. Hanna and Jenn Wortman Vaughan, who also helped to foun...Show More

Open Source Releases and The End of Season One

40:40 | Nov 22nd, 2015

In episode twenty four we talk with Ben Vigoda about his work in probabilistic programming (everything from his thesis, to his new company) Ryan talks about Tensor Flow and Autograd for Torch, some open source tools that have been recently releases. ...Show More

Probabilistic Programming and Digital Humanities

48:12 | Nov 5th, 2015

In episode 23 we talk with David Mimno of Cornell University about his work in the digital humanities (and explore what machine learning can tell us about lady zombie ghosts and huge bodies of literature) Ryan introduces us to probabilistic programmi...Show More

Workshops at NIPS and Crowdsourcing in Machine Learning

47:45 | Oct 22nd, 2015

In episode twenty two we talk with Adam Kalai of Microsoft Research New England about his work using crowdsourcing in Machine Learning, the language made of shapes of words, and New England Machine Learning Day. We take a look at the workshops being ...Show More

Machine Learning Mastery and Cancer Clusters

26:44 | Oct 8th, 2015

In episode twenty one  we talk with Quaid Morris of the University of Toronto, who is using machine learning to find a better way to treat cancers. Ryan introduces us to expectation maximization and we take a listener question about how to master mac...Show More

Data from Video Games and The Master Algorithm

46:17 | Sep 24th, 2015

In episode 20 we chat with Pedro Domingos of the University of Washington, he's just published a book The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. We get some insight into Linear Dynamical Systems which...Show More

Strong AI and Autoencoders

36:03 | Sep 10th, 2015

In episode nineteen we chat with Hugo Larochelle about his work on unsupervised learning, the International Conference on Learning Representations (ICLR), and his teaching style. His Youtube courses are not to be missed, and his twitter feed @Hugo_La...Show More

Active Learning and Machine Learning in Neuroscience

53:49 | Aug 27th, 2015

In episode eighteen we talk with Sham Kakade, of Microsoft Research New England, about his expansive work which touches on everything from neuroscience to theoretical machine learning. Ryan introduces us to active learning (great tutorial here) and w...Show More

Machine Learning in Biology and Getting into Grad School

48:26 | Aug 13th, 2015

In episode seventeen we talk with Jennifer Listgarten of  Microsoft Research New England about her work using machine learning to answer questions in biology. Recently, With her collaborator Nicolo Fusi, she used machine learning to make CRISPR more ...Show More

Machine Learning for Sports and Real Time Predictions

29:08 | Jul 30th, 2015

In episode sixteen we chat with Danny Tarlow of Microsoft Research Cambridge (in the UK not MA). Danny (along with Chris Maddison and Tom Minka) won best paper at NIPS 2014 for his paper A* Sampling. We talk with him about his work in applying machin...Show More

Really Really Big Data and Machine Learning in Business

23:46 | Jul 16th, 2015

In episode fifteen we talk with Max Welling, of the University of Amsterdam and University of California Irvine. We talk with him about his work with extremely large data and big business and machine learning. Max was program co-chair for NIPS in 201...Show More

Solving Intelligence and Machine Learning Fundamentals

30:11 | Jul 2nd, 2015

In episode fourteen we talk with Nando de Freitas. He’s a professor of Computer Science at the University of Oxford and a senior staff research scientist Google DeepMind. Right now he’s focusing on solving intelligence. (No biggie) Ryan introduces us...Show More

Working With Data and Machine Learning in Advertising

39:11 | Jun 18th, 2015

In episode thirteen we talk with Claudia Perlich, Chief Scientist at Dstillery. We talk about her work using machine learning in digital advertising and her approach to data in competitions. We take a look at information leakage in competitions after...Show More

The Economic Impact of Machine Learning and Using The Kernel Trick on Big Data

40:36 | Jun 4th, 2015

In episode twelve we talk with Andrew Ng, Chief Scientist at Baidu, about how speech recognition is going to explode the way we use mobile devices and his approach to working on the problem. We also discuss why we need to prepare for the economic imp...Show More

How We Think About Privacy and Finding Features in Black Boxes

33:43 | May 21st, 2015

In episode eleven we chat with Neil Lawrence from the University of Sheffield. We talk about the problems of privacy in the age of machine learning, the responsibilities that come with using ML tools and making data more open. We learn about the Mark...Show More

Interdisciplinary Data and Helping Humans Be Creative

34:17 | May 7th, 2015

In Episode 10 we talk with David Blei of Columbia University. We talk about his work on latent dirichlet allocation, topic models, the PhD program in data that he’s helping to create at Columbia and why exploring data is inherently multidisciplinary....Show More

Starting Simple and Machine Learning in Meds

38:24 | Apr 23rd, 2015

In episode nine we talk with George Dahl, of  the University of Toronto, about his work on the Merck molecular activity challenge on kaggle and speech recognition. George recently successfully defended his thesis at the end of March 2015. (Congrats G...Show More

Spinning Programming Plates and Creative Algorithms

35:18 | Apr 9th, 2015

On episode eight we talk with Charles Sutton, a professor in the School of Informatics University of Edinburgh about computer programming and using machine learning how to better understand how it’s done well. Ryan introduces us to collaborative filt...Show More

The Automatic Statistician and Electrified Meat

45:40 | Mar 26th, 2015

In episode seven of Talking Machines we talk with Zoubin Ghahramani, professor of Information Engineering in the Department of Engineering at the University of Cambridge. His project, The Automatic Statistician, aims to use machine learning to take r...Show More

The Future of Machine Learning from the Inside Out

28:14 | Mar 13th, 2015

We hear the second part of our conversation with with Geoffrey Hinton (Google and University of Toronto), Yoshua Bengio (University of Montreal) and Yann LeCun (Facebook and NYU). They talk with us about this history (and future) of research on neura...Show More

The History of Machine Learning from the Inside Out

32:36 | Feb 26th, 2015

In episode five of Talking Machines, we hear the first part of our conversation with Geoffrey Hinton (Google and University of Toronto), Yoshua Bengio (University of Montreal) and Yann LeCun (Facebook and NYU). Ryan introduces us to the ideas in tens...Show More

Using Models in the Wild and Women in Machine Learning

45:06 | Feb 12th, 2015

In episode four we talk with Hanna Wallach, of Microsoft Research. She's also a professor in the Department of Computer Science, University of Massachusetts Amherst and one of the founders of Women in Machine Learning (better known as WiML). We take ...Show More

Common Sense Problems and Learning about Machine Learning

40:55 | Jan 29th, 2015

On episode three of Talking Machines we sit down with Kevin Murphy who is currently a research scientist at Google. We talk with him about the work he’s doing there on the Knowledge Vault, his textbook, Machine Learning: A Probabilistic Perspective (...Show More

Machine Learning and Magical Thinking

35:10 | Jan 15th, 2015

Today on Talking Machines we hear from Google researcher Ilya Sutskever about his work, how he became interested in machine learning, and why it takes a little bit of magical thinking. We take your questions, and explore where the line between human ...Show More

Hello World!

41:28 | Jan 1st, 2015

In the first episode of Talking Machines we meet our hosts, Katherine Gorman (nerd, journalist) and Ryan Adams (nerd, Harvard computer science professor), and explore some of the interviews you'll be able to hear this season. Today we hear some short...Show More