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Talking Machines

Updated 2 months ago

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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. 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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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.

iTunes Ratings

132 Ratings
Average Ratings
105
12
6
5
4

Frequent Deep Dives

By Toby Patterson - Aug 25 2016
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This is the most in-depth podcast on machine learning to date.

Fantastic show

By 01001011 01100110 - Jun 11 2016
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Gives insight not only to the new tech but the ideas behind machine learning and how it works.

iTunes Ratings

132 Ratings
Average Ratings
105
12
6
5
4

Frequent Deep Dives

By Toby Patterson - Aug 25 2016
Read more
This is the most in-depth podcast on machine learning to date.

Fantastic show

By 01001011 01100110 - Jun 11 2016
Read more
Gives insight not only to the new tech but the ideas behind machine learning and how it works.
Cover image of Talking Machines

Talking Machines

Latest release on Jun 13, 2020

The Best Episodes Ranked Using User Listens

Updated by OwlTail 2 months ago

Rank #1: Debating Project Debater and Hello NeurIPS

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In our last episode for season five Katherine and Neil debate his debating project debater and talk about whats coming up at NeurIPS. Hope to see you there!

Nov 21 2019

41mins

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Rank #2: Common Sense Problems and Learning about Machine Learning

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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 ToolboxPyMCMalletStanWekaTheanoCaffeSpearmint

Jan 29 2015

40mins

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Rank #3: Gaussian Processes, Grad School, and Richard Zemel

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Aug 23 2018

43mins

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Rank #4: The Deep End of Deep Learning

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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.

Apr 25 2019

19mins

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Rank #5: The Pace of Change and The Public View of ML

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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.

Oct 05 2017

40mins

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Rank #6: Eric Lander and Restricted Boltzmann Machines

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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.

Aug 18 2016

53mins

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Rank #7: Using Models in the Wild and Women in Machine Learning

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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).

Feb 12 2015

45mins

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Rank #8: Explainability and the Inexplicable

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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.

Apr 19 2018

43mins

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Rank #9: The Bezos Paradox and Machine Learning Languages

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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.

Feb 01 2019

41mins

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Rank #10: De-Enchanting AI with the Law

Nov 07 2019

20mins

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Rank #11: Getting a Start in ML and Applied AI at Facebook

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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.

Jul 13 2017

57mins

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Rank #12: Machine Learning and Society

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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.

Apr 08 2016

48mins

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Rank #13: Jupyter Notebooks and Modern Model Distribution

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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 Instiute

Feb 28 2019

36mins

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Rank #14: OpenAI and Gaussian Processes

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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.)

Jan 28 2016

35mins

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Rank #15: ANGLICAN and Probabilistic Programming

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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.

Sep 01 2016

44mins

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Rank #16: Not What But Why

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In this episode of Talking Machines we take a listen to Professor Engelhardt's TedX Boston talk, Not What But Why: Machine Learning for Understanding Genomics

Aug 15 2019

19mins

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Rank #17: Good Data Practice Rules

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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.

Apr 05 2018

51mins

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Rank #18: The Long View and Learning in Person

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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.

Sep 21 2017

1hr 5mins

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Rank #19: Spark and ICML

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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.

Jun 02 2016

39mins

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Rank #20: The Automatic Statistician and Electrified Meat

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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.

Mar 26 2015

45mins

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