Talking Machines – Robohub

Talking Machines: Bias variance dilemma for humans and the arm farm, with Jeff Dean

Talking Machines — Mon, 03 Jul 2017 15:38:00 +0000

In episode four of season three Neil introduces us to the ideas behind the bias variance dilemma (and how how we can think about it in our daily lives). Plus, we answer a listener question about how to make sure your neural networks don’t get fooled. Our guest for this episode is Jeff Dean, Google Senior Fellow in the Research Group, where he leads the Google Brain project. We talk about a closet full of robot arms (the arm farm!), image recognition for diabetic retinopathy, and equality in data and the community.

Fun Fact: Geoff Hinton’s distant relative invented the word tesseract. (How cool is that. Seriously.)

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Talking Machines: Overfitting and asking ecological questions, with Professor (Emeritus) Tom Dietterich

Talking Machines — Fri, 16 Jun 2017 11:00:14 +0000

Credit: Wikipedia Commons

In episode three, season three of Talking Machines, we dive into overfitting, take a listener question about unbalanced data and speak with Professor (Emeritus) Tom Dietterich from Oregon State University.

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Talking machines: Graphons and “inferencing”

Talking Machines — Tue, 30 May 2017 22:00:02 +0000

In episode two of season three Neil takes us through the basics on dropout, we chat about the definition of inference (It’s more about context than you think!) and hear an interview with Jennifer Chayes of Microsoft.

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Hosts of Talking Machines: Neil Lawrence and Ryan Adams

Talking Machines — Fri, 28 Apr 2017 14:51:00 +0000

Talking Machines is entering its third season and going through some changes. Our founding host Ryan is moving on and in his place, Neil Lawrence of Amazon is taking over as co-host. We say thank you and goodbye to Ryan with an interview about his work.

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Talking Machines: Restricted Boltzmann Machines, with Eric Lander

Talking Machines — Fri, 09 Sep 2016 10:09:00 +0000

In episode sixteen of season two, we get an introduction to Restricted Boltzmann Machines, take a listener question about tuning hyperparameters, plus, speak with Eric Lander of the Broad Institute.

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Talking Machines: Generative art and Hamiltonian Monte Carlo, with Doug Eck

Talking Machines — Wed, 17 Aug 2016 13:40:00 +0000

Generative Art on PBS/YouTube

In episode fifteen of season two, we talk about Hamiltonian Monte Carlo, take a listener question about unbalanced data, plus, speak with Doug Eck of Google’s Magenta project.

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Talking Machines: Machine learning and the Flint water crisis, with Jake Abernethy

Talking Machines — Wed, 27 Jul 2016 14:36:00 +0000

Testing lead in water during the Flint water crisis.

In episode fourteen of season two, we discuss Perturb-and-MAP and answer a listener question about classic artificial intelligence ideas being used in modern machine learning. Plus, we speak with Jake Abernethy from the University of Michigan about municipal data and his work on the Flint water crisis.

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Talking Machines: Automatic Translation and t-SNE, with Hal Daume

Talking Machines — Fri, 08 Jul 2016 21:24:00 +0000

Reuters dataset (in 2D) landmark t-SNE using semantic hashing. Source: vdmaaten.github.io/tsne

In episode thirteen of season two, we talk about t-Distributed Stochastic Neighbor Embedding (t-SNE), take a listener question about statistical physics, plus, speak with Hal Daume of the University of Maryland (who is great to follow on Twitter).

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Talking Machines: Fantasizing cats and data numbers, with Iain Murray

Talking Machines — Mon, 20 Jun 2016 21:43:00 +0000

Generating faces with Torch. Photo source: torch.ch

In episode twelve of season two, we discuss generative adversarial networks, take a listener question about using machine learning to improve or create products, and lastly, speak with Iain Murray from University of Edinburgh.

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Talking Machines: Spark and ICML, with Sinead Williamson

Talking Machines — Mon, 06 Jun 2016 18:16:00 +0000

Source: Pexels/CCO

In episode eleven of season two, we talk about the machine learning toolkit Spark and answer a listener question about the difference between Neural Information Processing Systems (NIPS) and International Conference on Machine Learning (ICML). Plus, we speak with Sinead Williamson from The University of Texas at Austin.

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Talking Machines: Computational learning theory and machine learning for understanding cells, with Aviv Regev

Talking Machines — Tue, 24 May 2016 12:00:00 +0000

Stem cell. Source: CC0

In episode ten of season two, we talk about Computational Learning Theory and Probably Approximately Correct Learning originated by Professor Leslie Valiant of SEAS at Harvard, we take a listener question about generative systems, plus we talk with Aviv Regev, Chair of the Faculty and Director of the Klarman Cell Observatory and the Cell Circuits Program at the Broad Institute.

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Talking Machines: Sparse Coding and MADBITS, with Clement Farabet

Talking Machines — Mon, 09 May 2016 08:58:22 +0000

In episode nine of season two, we talk about sparse coding, take a listener question about the next big demonstration for AI after AlphaGo. Plus we talk with Clement Farabet about MADBITS and the work he’s doing at Twitter Cortex.

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Talking Machines: Machine learning and society, with Neil Lawrence

Talking Machines — Fri, 08 Apr 2016 16:11:00 +0000

Episode seven of season two is a little different than our usual episodes; Ryan and Katherine returned from a conference where they got to talk with Neil Lawrence from 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.

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Talking Machines: Software and statistics for machine learning, with Rob Tibshirani

Talking Machines — Sun, 27 Mar 2016 08:56:00 +0000

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.

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Talking Machines: Robotics and machine learning music videos, with Michael Littman

Talking Machines — Wed, 17 Feb 2016 14:47:30 +0000

Machine Learning A Cappella. Source: Udacity/YouTube

In episode three of season two, Ryan walks us through the Alpha Go results and answers a listeners’ question about using Gaussian processes for classifications. Plus, we talk with Michael Littman of Brown University about his work, robots, and making music videos.

Also not to be missed, Michael’s appearance in the recent Turbotax ad!

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Talking Machines: Gaussian processes and OpenAI, with IIya Sutskever

Talking Machines — Fri, 29 Jan 2016 22:25:19 +0000

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.)

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Talking Machines: Women in Machine Learning, with Hanna Wallach

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Talking Machines: Real human actions and Women in Machine Learning (WiML), with Jenn Wortman Vaughan

Talking Machines — Thu, 28 Jan 2016 20:20:58 +0000

In episode one of season two, we celebrate the 10th anniversary of Women in Machine Learning (WiML) with its co-founder — and our guest host for this episode — Hanna Wallach of Microsoft Research. We get a glimpse of how the 2015 event went, including how its evolved over ten years with Hanna and Jenn Wortman Vaughan, who also founded the event. Plus, we talk with Jenn about her work at Microsoft Research.

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Why we need journalism about machine learning

Talking Machines — Fri, 25 Sep 2015 15:14:00 +0000

Talking Machines is in the process of raising funds to defray the cost of producing our first season and to help us start production on our second season. On the show we’ve talked about how we’ll use the money (to pay for studio time, editing, and the cost of travel to get our great interviews). But we haven’t gotten to the heart of the question yet: Do we even need journalism about machine learning?

We need journalism about machine learning, artificial intelligence, and data science desperately. Not just to calm the public conversation, which always seems to be full of hype on these topics, but to make sure that work in our field is sustainable. And no one is going to make the case for our industry unless we do it ourselves.

I live in Cambridge, MA. A lot of the people here are scientists or are training to enter the field. From the vantage point of Cambridge, the answer seems to be a resounding yes, we do need journalism about these topics, and Talking Machines is a way for those in the field to access each other’s ideas, and for those in training to get exposure to work they might not have heard of.

But not all of our listeners live in Cambridge, or come from an academic background. We get letters from all over the world saying that Talking Machines has allowed them to better understand ideas they’d like to use in their business, helped them talk with their data teams, or helped them make the right hire.

Most importantly though, not all of our listeners think they live a life that has anything to do with machine learning, or computer science .. or science at all. We started Talking Machines because we wanted to open the world of machine learning up to a wider audience, to help them understand the reality of research in the field and the industry, and how that impacts their lives in a real way on a daily basis.

The public conversation around machine learning (and by extension artificial intelligence) is filled with extreme hype, both positive and negative. These extremes have lead to a crippling pattern of “winters” where interest, activity, and funding in the field dries up. If we present the reality of what is happening in the field in a way that invites the public to be part of the conversation, that arms them with the knowledge they need to participate, and we will create a more sustainable industry for ourselves. For our own benefit, and for the good of those who use the tools we make, it’s our responsibility to play a bigger role than we have before in the public conversation.

Talking Machines does just that. By introducing machine learning to a wide audience in a way that allows people in, we ensure realistic expectations of work coming out of both in the industry and the field. More than that, we allow people to understand the tools that they use every day and the impact that they have. It is our responsibility to make sure we are accurately represented, and only we can do that. Our project has been going on for a little under a year now, and we’ve made a difference in the accessibility of the field.

But if we’re going to keep going, we need your help to do so.

Support Talking Machines’ Kickstarter campaign to keep journalism on machine learning going strong!