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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.

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Common Sense Problems and Learning about Machine Learning

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 (and its arch nemesis which we won’t link to), and how to learn about machine learning (Metacademy is a great place to start). We tackle a listener question about the dream of a one step solution to strong Artificial Intelligence and if Deep Neural Networks might be it. Plus, Ryan introduces us to a new way of thinking about questions in machine learning from Yoshua Bengio’s Lab at the University of Montreal out lined in their new paper, Identifying and attacking the saddle point problem in high-dimensional non-convex optimization, and Katherine brings up Facebook’s release of open source machine learning tools and we talk about what it might mean. If you want to explore some open source tools for machine learning we also recommend giving these a try:Super big list of ML Open Source Projects! Torch Gaussian Process Machine Learning ToolboxPyMCMalletStanWekaTheanoCaffeSpearmintSee omnystudio.com/listener for privacy information.


29 Jan 2015

Rank #1

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Gaussian Processes, Grad School, and Richard Zemel

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23 Aug 2018

Rank #2

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The Deep End of Deep Learning

In this episode as we prep for ICLR we take a break from our usual format to bring you a talk from Hugo LaRochelle at TedX Boston on Deep Learning.See omnystudio.com/listener for privacy information.


25 Apr 2019

Rank #3

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The Pace of Change and The Public View of ML

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 the work they've done on the public's views on AI and ML. See omnystudio.com/listener for privacy information.


5 Oct 2017

Rank #4

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Eric Lander and Restricted Boltzmann Machines

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.See omnystudio.com/listener for privacy information.


18 Aug 2016

Rank #5

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Using Models in the Wild and Women in Machine Learning

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 a listener question about scalability and the size of data sets. And Ryan takes us through topic modeling using Latent Dirichlet allocation (say that five times fast).See omnystudio.com/listener for privacy information.


12 Feb 2015

Rank #6

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Explainability and the Inexplicable

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. See omnystudio.com/listener for privacy information.


19 Apr 2018

Rank #7

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The Bezos Paradox and Machine Learning Languages

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.See omnystudio.com/listener for privacy information.


1 Feb 2019

Rank #8

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Getting a Start in ML and Applied AI at Facebook

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 foundational ideas in ML, take a look at Andrew Ng’s course on Coursera. Then check out Daphne Kohler’s course. Talking Machines is now working with Midroll to source and organize sponsors for our show. In order find sponsors who are a good fit for us, and of worth to you, we’re surveying our listeners. If you’d like to help us get a better idea of who makes up the Talking Machines community take the survey at http://podsurvey.com/MACHINES. See omnystudio.com/listener for privacy information.


13 Jul 2017

Rank #9

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Machine Learning and Society

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 learning and society. They discuss anthropomorphic intelligence, data ownership, and the ability to empathize. The entire episode is given over to this conversation in hopes that it will spur more discussion of these important issues as the field continues to grow.See omnystudio.com/listener for privacy information.


8 Apr 2016

Rank #10

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Jupyter Notebooks and Modern Model Distribution

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 interview with Brooks Paige of the Alan Turing InstiuteSee omnystudio.com/listener for privacy information.


28 Feb 2019

Rank #11

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OpenAI and Gaussian Processes

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 him.)See omnystudio.com/listener for privacy information.


28 Jan 2016

Rank #12

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ANGLICAN and Probabilistic Programming

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 using a neural network.See omnystudio.com/listener for privacy information.


1 Sep 2016

Rank #13

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Good Data Practice Rules

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 our guest Andrew Blake of The Alan Turing Institute.See omnystudio.com/listener for privacy information.


5 Apr 2018

Rank #14

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The Long View and Learning in Person

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 Global Pulse lab in Kampala, Uganda and Makerere University's Artificial Intelligence Research group.See omnystudio.com/listener for privacy information.

1hr 5mins

21 Sep 2017

Rank #15

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Spark and ICML

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.See omnystudio.com/listener for privacy information.


2 Jun 2016

Rank #16

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The Automatic Statistician and Electrified Meat

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 raw data and give you statistical reports and natural languages summaries of what trends that data shows. We get really hungry exploring Bayesian Non-parametrics through the stories of the Chinese Restaurant Process and the Indian Buffet Process (but remember, there’s no free lunch). Plus we take a listener question about how much we should rely on ourselves and our ideas about what intelligence in electrified meat looks like when we try to build machine intelligences.See omnystudio.com/listener for privacy information.


26 Mar 2015

Rank #17

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Automatic Translation and t-SNE

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.)See omnystudio.com/listener for privacy information.


7 Jul 2016

Rank #18

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Fantasizing Cats and Data Numbers

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.See omnystudio.com/listener for privacy information.


16 Jun 2016

Rank #19

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Starting Simple and Machine Learning in Meds

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 George!) We learn about how networks and graphs can help us understand latent properties of relationships, and we take a listener question about just how you find the right algorithm to solve a problem (Spoiler: start simple.)See omnystudio.com/listener for privacy information.


23 Apr 2015

Rank #20