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Talking Machines

Updated 2 days ago

Rank #120 in Technology category

Technology
News
Tech News
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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

126 Ratings
Average Ratings
101
12
5
4
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

126 Ratings
Average Ratings
101
12
5
4
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.

Listen to:

Cover image of Talking Machines

Talking Machines

Updated 2 days ago

Read more

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.

Gaussian Processes, Grad School, and Richard Zemel

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Aug 23 2018

43mins

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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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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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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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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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Idea Pandemics and Workshop Walkthrough

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in episode 15 of season five of Talking Machines we' chat about the recently announced workshops at NeurIPS 2019, find ourselves in the middle of an I Love Lucy Episode about technical term usage and talk with Randy Goebel of the Alberta Machine Intelligence Institute

Aug 01 2019

59mins

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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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The History of Machine Learning from the Inside Out

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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 tensor factorization methods for learning latent variable models (which is both a tongue twister and and one of the new tools in ML). To find out more on the topic, the paper Tensor decompositions for learning latent variable models is a good place to start. You can also take a look at the work of Daniel Hsu, Animashree Anandkumar and Sham M. Kakade Plus we take a listener question about just where statistics stops and machine learning begins.

Feb 26 2015

32mins

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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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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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Software and Statistics for Machine Learning

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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.

Mar 24 2016

39mins

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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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Simulated Learning and Real World Ethics

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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.

Jul 27 2018

57mins

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Economies, Work and AI

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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, Digitalization and Trade UNI Global Union.

May 03 2018

42mins

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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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Troubling Trends and Climbing Mountains

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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

Sep 07 2018

39mins

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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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Generative Art and Hamiltonian Monte Carlo

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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.

Aug 04 2016

47mins

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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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The Church of Bayes and Collecting Data

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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

Jul 28 2017

49mins

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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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De-Enchanting AI with the Law

Nov 07 2019

19mins

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How to Ask an Actionable Question

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In Episode 21 of Season five we sit down with Marzyeh Ghassemi to talk about her work and how she's refined her focus.

Oct 25 2019

38mins

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Children are the Future and Ada Lovelace Day

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In episode twenty of season five we talk with Neil about a discussion he had about the impact of ML tools on children talk about the new Diversity Dashboard from the Turing Institute in response to a question about cool things for Ada Lovelace day plus we sit down with Corinna Cortes of Google AI

Oct 10 2019

54mins

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News from Neil and Updates from DALI

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In episode eighteen of season five we talk about DALI, get some big news about the next thing for Neil and talk with Benjamin Akera.

Sep 26 2019

1hr 8mins

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A Cooperative Path to Artificial Intelligence

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In episode eighteen of season five we hear Michael Littman's talk A Cooperative Path to Artificial Intelligence

Sep 13 2019

17mins

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What Does Red Sound Like

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In episode seventeen of season five we talk about Why Red Doesn't Sound Like a Bell, take a listener question about our Turing brackets (and Invent the Very Good Sort Awards) and listen to a chat with Tewodros Abebe

Aug 30 2019

49mins

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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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Idea Pandemics and Workshop Walkthrough

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in episode 15 of season five of Talking Machines we' chat about the recently announced workshops at NeurIPS 2019, find ourselves in the middle of an I Love Lucy Episode about technical term usage and talk with Randy Goebel of the Alberta Machine Intelligence Institute

Aug 01 2019

59mins

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PosterSession.ai and Deep Quaggles

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In episode 14 of season five we talk about On the marginal likelihood and cross-validation, Katherine is STILL excited about PosterSession.ai, we invent Deep Quaggles and listen to a conversation with professor Elaine Nsoesie of BU

Jul 18 2019

45mins

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The View from Addis Ababa

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In episode thirteen of season five we bring you a the rest of our conversation with Michael Melese from Addis Ababa University and Charles Saidu of Baze University Abuja

Jul 04 2019

22mins

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DSA Addis Ababa and ICML Los Angeles

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In episode twelve of season five we bring you a rundown of Data Science Africa's latest workshop answer a listener question about what got us excited at ICML and hear the first part of our conversation with Michael Melese from Addis Ababa University and Charles Saidu of Baze University Abuja

Jun 21 2019

55mins

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Data Trusts and Citation Trends

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In episode eleven of season five, we dig in to just what a data trust actually is, take a look at citation trends and other places (PMLR) you can dig up data to understand the field and talk with Raia Hadsell of DeepMind.

Jun 06 2019

54mins

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Reproducibly and Revisiting History

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In episode ten of season five we talk about reproducibility, take a listener question on re understanding the history of the field given where we are now and how other fields are reviewing their own history and listen to a conversation with Graham Taylor of the Vector Institute.

May 23 2019

46mins

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Insights from AISTATS

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In episode nine of season five we talk about some interesting work from AISTATS, dive into unbiased implicit variational inference, and chat with Jon McAuliffe CIO of Voleon

May 10 2019

52mins

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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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Exploring MARS and Getting back to Bayesics

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In episode seven of season five of we chat about MARS and Re: MARS OpenAI's status changes and We talk with Jasper Snoek of Google Brain

Apr 11 2019

1hr 8mins

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

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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 Professor, Department of Epidemiology, Harvard T.H. Chan School of Public Health.

Here are some of the papers we got to chat about! Also, VL57 is hiring!

Adversarial attacks on Medical ML Science paper

Finlayson, S.G., Bowers, J.D., Ito, J., Zittrain, J.L., Beam, A.L. and Kohane, I.S., 2019. Adversarial attacks on medical machine learning. Science363(6433), pp.1287-1289.

Link: https://cyber.harvard.edu/story/2019-03/adversarial-attacks-medical-ai-health-policy-challenge

JAMA Papers

Beam, A.L. and Kohane, I.S., 2016. Translating artificial intelligence into clinical care. Jama316(22), pp.2368-2369.

Link: https://www.dropbox.com/s/4o1va07tqwvrxsn/Beam_TranslatingAI_2016.pdf?dl=0

Beam, A.L. and Kohane, I.S., 2018. Big data and machine learning in health care. Jama319(13), pp.1317-1318.

Link: https://www.dropbox.com/s/q1cixzmsdugq3vy/Beam_BigData_ML.pdf?dl=0

Opportunities in machine learning for healthcare:

Ghassemi, M., Naumann, T., Schulam, P., Beam, A.L. and Ranganath, R., 2018. Opportunities in machine learning for healthcare. arXiv preprint arXiv:1806.00388.

Link: https://arxiv.org/abs/1806.00388

Mar 28 2019

50mins

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Slowed Down Conferences and Even More Summer Schools

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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

Mar 14 2019

43mins

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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

Play