Talking Machines

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

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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, who was at Google.

As of this posing Carlin is under investigation and Scott has left Google after being suspended.

Today we pause in our regular format to talk about how we, as a community, can change.

Full disclosure: Neil and Katherine served as press chairs for NIPS 2017. They will hold the same post for ICML 2018 and NIPS 2018 and are working along with the other organizers of these events to effect change around these issues.

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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 inference by Hoffman, Wang, Blei and Paisley

http://arxiv.org/abs/1206.7051

A way of doing approximate inference for probabilistic models with potentially billions of data ... need I say more?

Austerity in MCMC Land: Cutting the Metropolis Hastings by Korattikara, Chen and Welling

http://arxiv.org/abs/1304.5299

Oh ... I do need to say more ... because these three are at it as well but from the sampling perspective. Probabilistic models for big data ... an idea so important it needed to be in the list twice.

Practical Bayesian Optimization of Machine Learning Algorithms by Snoek, Larochelle and Adams

http://arxiv.org/abs/1206.2944

This paper represents the rise in probabilistic numerics, I could also have chosen papers by Osborne, Hennig or others. There are too many papers out there already. Definitely an exciting area, be it optimisation, integration, differential equations. I chose this paper because it seems to have blown the field open to a wider audience, focussing as it did on deep learning as an application, so it let's me capture both an area of developing interest and an area that hits the national news.

Kernel Bayes Rule by Fukumizu, Song, Gretton

http://arxiv.org/abs/1009.5736

One of the great things about ML is how we have different (and competing) philosophies operating under the same roof. But because we still talk to each other (and sometimes even listen to each other) these ideas can merge to create new and interesting things. Kernel Bayes Rule makes the list.

http://www.cs.toronto.edu/~hinton/absps/imagenet.pdf

An obvious choice, but you don't leave the Beatles off lists of great bands just because they are an obvious choice.

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In episode seven of season six we talk with Michael Littman about his work in reinforcement learning, on scientific communication, and in the classroom.

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In episode eight of season six we talk with Alexander Rush and Shakir Mohamed about their work on ICLR this year which was first to take place in Ethiopia and then became totally virtual!

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