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The Talking Machines

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Location:

Harvard University, Cambridge, MA

Twitter:

@TlkngMchns

Language:

English


Episodes

How to Research in Hype and CIFAR's Strategy

9/20/2018
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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

Duration:00:37:07

Troubling Trends and Climbing Mountains

9/7/2018
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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

Duration:00:41:21

Long Term Fairness

8/9/2018
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Duration:00:29:25

Simulated Learning and Real World Ethics

7/26/2018
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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.

Duration:00:59:36

ICML 2018 with Jennifer Dy

7/12/2018
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Season four episode twelve finds us at ICML! We bring you a special episode with Jennifer Dy, co-program chair of the conference.

Duration:00:20:36

Aspirational Asimov and How to Survive a Conference

6/28/2018
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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.

Duration:00:45:02

Explanations and Reviews

6/14/2018
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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 Ventures.

Duration:00:23:35

Statements on Statements

5/31/2018
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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 help researchers share their work. Having an extensive, and growing, open access portfolio is one important way we do this but it is important to remember that while open access has been around for 20 years now it still only accounts for a small...

Duration:00:26:47

The Futility of Artificial Carpenters and Further Reading

5/17/2018
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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 Gretton who was part of the team behind one of the Best Papers at NIPS 2017 For more reading we recommend Machine Learning Yearning, Talking Nets, The Mechanical Mind in History, and Colossus.

Duration:00:37:18

Economies, Work and AI

5/3/2018
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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.

Duration:00:42:40

Explainability and the Inexplicable

4/19/2018
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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.

Duration:00:43:57

Good Data Practice Rules

4/5/2018
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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.

Duration:00:53:28

Can an AI Practitioner Fix a Radio?

3/22/2018
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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 machine learning after having advanced training in a different program. Our guest on this episode is our second second time guest Peter Donnelly, Professor of Statistical Science at the University of Oxford, Director of the Wellcome Trust Center for...

Duration:00:45:30

Natural vs Artificial Intelligence and Doing Unexpected Work

3/8/2018
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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 2017 he gave a TedX talk about the topic. We also take a listener question about what maths you should take to get into building ML tools. Our guests this week are Moshe Vardi, Karen Ostrum George Distinguished Service Professor in Computational...

Duration:00:59:51

Scientific Rigor and Turning Information into Action

2/22/2018
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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 conversation with Ciira wa Maina, lecturer in the Department of Electrical and Electronic Engineering Dedan Kimathi University of Technology in Nyeri Kenya

Duration:00:39:06

Code Review for Community Change

2/8/2018
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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 after NIPS, researcher Kristian Lum outlined several instances of sexual harassment and abuse of power. In her post she mentioned Brad Carlin and a person who she referred to as S. We learned in reporting done by Bloomberg that S was Steven Scott,...

Duration:00:35:17

The Pace of Change and The Public View of ML

10/5/2017
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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.

Duration:00:40:49

The Long View and Learning in Person

9/21/2017
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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.

Duration:01:06:27

Machine Learning in the Field and Bayesian Baked Goods

9/7/2017
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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 tools) and we hear a conversation with Ernest Mwebaze of Makerere University.

Duration:00:59:24