Talking Machines – Robohub https://robohub.org Connecting the robotics community to the world Mon, 05 Oct 2026 01:12:21 +0000 en-US hourly 1 https://wordpress.org/?v=7.1.3 Talking Machines: The pace of change and the public view of machine learning, with Peter Donnelly https://robohub.org/89432-2/ Thu, 19 Oct 2017 14:18:33 +0000 http://robohub.org/89432-2/
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.

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]]> Talking Machines: The long view and learning in person, with John Quinn https://robohub.org/talking-machines-the-long-view-and-learning-in-person-with-john-quinn/ Sat, 23 Sep 2017 17:34:00 +0000 http://robohub.org/talking-machines-the-long-view-and-learning-in-person-with-john-quinn/
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.

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]]> Talking Machines: Machine Learning in the Field and Bayesian Baked Goods, with Ernest Mwebaze https://robohub.org/talking-machines-machine-learning-in-the-field-and-bayesian-baked-goods-with-ernest-mwebaze/ Fri, 08 Sep 2017 15:37:14 +0000 http://robohub.org/talking-machines-machine-learning-in-the-field-and-bayesian-baked-goods-with-ernest-mwebaze/

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.

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]]> Talking Machines: Data Science Africa, with Dina Machuve https://robohub.org/talking-machines-data-science-africa-with-dina-machuve/ Fri, 25 Aug 2017 06:25:45 +0000 http://robohub.org/talking-machines-data-science-africa-with-dina-machuve/

In episode seven of season three we take a minute to break way from our regular format and feature a conversation with Dina Machuve of the Nelson Mandela African Institute of Science and Technology. We cover everything from her work to how cell phone access has changed data patterns. We got to talk with her at the Data Science Africa conference and workshop.

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]]> Talking Machines: The church of Bayes and collecting data, with Katherine Heller https://robohub.org/talking-machines-the-church-of-bayes-and-collecting-data-with-katherine-heller/ Fri, 11 Aug 2017 20:42:50 +0000 http://robohub.org/talking-machines-the-church-of-bayes-and-collecting-data-with-katherine-heller/

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.


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]]> Talking Machines: Getting a start in ML and applied AI at Facebook, with Joaquin Quiñonero Candela https://robohub.org/talking-machines-getting-a-start-in-ml-and-applied-ai-at-facebook-with-joaquin-quinonero-candela/ Tue, 01 Aug 2017 20:56:00 +0000 http://robohub.org/talking-machines-getting-a-start-in-ml-and-applied-ai-at-facebook-with-joaquin-quinonero-candela/ 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.

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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.



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]]> Talking Machines: Bias variance dilemma for humans and the arm farm, with Jeff Dean https://robohub.org/talking-machines-bias-variance-dilemma-for-humans-and-the-arm-farm-with-jeff-dean/ Mon, 03 Jul 2017 15:38:00 +0000 http://robohub.org/talking-machines-bias-variance-dilemma-for-humans-and-the-arm-farm-with-jeff-dean/ 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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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 https://robohub.org/talking-machines-overfitting-and-asking-ecological-questions-with-professor-emeritus-tom-dietterich/ Fri, 16 Jun 2017 11:00:14 +0000 http://robohub.org/?guid=014018bc660d40ee33ce4bb8d60a7a81

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” https://robohub.org/talking-machines-graphons-and-inferencing/ Tue, 30 May 2017 22:00:02 +0000 http://robohub.org/talking-machines-graphons-and-inferencing/

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 https://robohub.org/hosts-of-talking-machines-neil-lawrence-and-ryan-adams/ Fri, 28 Apr 2017 14:51:00 +0000 http://robohub.org/hosts-of-talking-machines-neil-lawrence-and-ryan-adams/

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: ANGLICAN and Probabilistic Programming https://robohub.org/talking-machines-anglican-and-probabilistic-programming/ Fri, 04 Nov 2016 10:30:19 +0000 http://robohub.org/talking-machines-anglican-and-probabilistic-programming/ talking-machines

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.

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]]> Talking Machines: Restricted Boltzmann Machines, with Eric Lander https://robohub.org/talking-machines-restricted-boltzmann-machines-with-eric-lander/ Fri, 09 Sep 2016 10:09:00 +0000 http://robohub.org/talking-machines-restricted-boltzmann-machines-with-eric-lander/ talking-machines

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 https://robohub.org/talking-machines-generative-art-and-hamiltonian-monte-carlo-with-doug-eck/ Wed, 17 Aug 2016 13:40:00 +0000 http://robohub.org/talking-machines-generative-art-and-hamiltonian-monte-carlo-with-doug-eck/ 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.

 

]]> Generative Art on PBS/YouTube

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 https://robohub.org/talking-machines-machine-learning-and-the-flint-water-crisis-with-jake-abernethy/ Wed, 27 Jul 2016 14:36:00 +0000 http://robohub.org/talking-machines-machine-learning-and-the-flint-water-crisis-with-jake-abernethy/ Testing lead in water during the Flint water crisis. Image credit: CC0 Public Domain

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 https://robohub.org/talking-machines-automatic-translation-and-t-sne-with-hal-daume/ Fri, 08 Jul 2016 21:24:00 +0000 http://robohub.org/talking-machines-automatic-translation-and-t-sne-with-hal-daume/ Reuters dataset (in 2D) landmark t-SNE using semantic hashing. Source: vdmaaten.github.io/tsne

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 https://robohub.org/talking-machines-fantasizing-cats-and-data-numbers-with-iain-murray/ Mon, 20 Jun 2016 21:43:00 +0000 http://robohub.org/talking-machines-fantasizing-cats-and-data-numbers-with-iain-murray/ Generating faces with Torch. Photo source: torch.ch

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 https://robohub.org/talking-machines-spark-and-icml-with-sinead-williamson/ Mon, 06 Jun 2016 18:16:00 +0000 http://robohub.org/talking-machines-spark-and-icml-with-sinead-williamson/ Source: Pexels/CC0

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 https://robohub.org/talking-machines-computational-learning-theory-and-machine-learning-for-understanding-cells-with-aviv-regev/ Tue, 24 May 2016 12:00:00 +0000 http://robohub.org/talking-machines-computational-learning-theory-and-machine-learning-for-understanding-cells-with-aviv-regev/ Stem cell. Source: CC0

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 https://robohub.org/talking-machines-sparse-coding-and-madbits-with-clement-farabet/ Mon, 09 May 2016 08:58:22 +0000 http://robohub.org/talking-machines-sparse-coding-and-madbits-with-clement-farabet/ 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.

]]>

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: Remembering David MacKay https://robohub.org/talking-machines-remembering-david-mackay/ Fri, 22 Apr 2016 17:05:00 +0000 http://robohub.org/talking-machines-remembering-david-mackay/ Recently Professor David MacKay passed away. We’ll spend this episode talking about his extensive body of work and its impacts. We’ll also talk with Philipp Hennig, a research group leader at the Max Planck Institute for Intelligent Systems, who trained in Professor MacKay’s group (with Ryan).

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Recently Professor David MacKay passed away. We’ll spend this episode talking about his extensive body of work and its impacts. We’ll also talk with Philipp Hennig, a research group leader at the Max Planck Institute for Intelligent Systems, who trained in Professor MacKay’s group (with Ryan).

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]]> Talking Machines: Machine learning and society, with Neil Lawrence https://robohub.org/talking-machines-machine-learning-and-society-with-neil-lawrence/ Fri, 08 Apr 2016 16:11:00 +0000 http://robohub.org/talking-machines-machine-learning-and-society-with-neil-lawrence/ talking-machinesEpisode 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 https://robohub.org/talking-machines-software-and-statistics-for-machine-learning-with-rob-tibshirani/ Sun, 27 Mar 2016 08:56:00 +0000 http://robohub.org/talking-machines-software-and-statistics-for-machine-learning-with-rob-tibshirani/ talking-machinesIn 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: Machine learning in healthcare and the AlphaGo matches https://robohub.org/talking-machines-machine-learning-in-healthcare-and-the-alphago-matches/ Mon, 14 Mar 2016 11:50:56 +0000 http://robohub.org/talking-machines-machine-learning-in-healthcare-and-the-alphago-matches/ Google DeepMind Challenge Match: Lee Sedol vs AlphaGo. Credit: Deepmind/YouTube

Google DeepMind Challenge Match: Lee Sedol vs AlphaGo. Credit: Deepmind/YouTube

In episode five of season two, Ryan walks us through variational inference. We put some listener questions about Go and how to play it to Andy Okun, president of the American Go Association (who is in Seoul, South Korea, watching the Lee Sedol/AlphaGo games). Plus, we hear from Suchi Saria of Johns Hopkins about applying machine learning to understanding health care data.

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]]> Talking Machines: AI safety and the legacy of Bletchley Park, with Nick Patterson https://robohub.org/talking-machines-ai-safety-and-the-legacy-of-bletchley-park-with-nick-patterson/ Mon, 29 Feb 2016 20:12:35 +0000 http://robohub.org/talking-machines-ai-safety-and-the-legacy-of-bletchley-park-with-nick-patterson/

In episode four of season two, we talk about some of the major issues in AI safety, (and how they’re not really that different from the questions we ask whenever we create a new tool.) We take a listener question about time series, and we talk with Nick Patterson of the Broad Institute about everything from ancient DNA to Alan Turing.


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]]> Talking Machines: Robotics and machine learning music videos, with Michael Littman https://robohub.org/talking-machines-robotics-and-machine-learning-music-videos-with-michael-littman/ Wed, 17 Feb 2016 14:47:30 +0000 http://robohub.org/talking-machines-robotics-and-machine-learning-music-videos-with-michael-littman/ Machine Learning A Cappella. Source: Udacity/YouTube

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 https://robohub.org/talking-machines-gaussian-processes-and-openai-with-iiya-sutskever/ Fri, 29 Jan 2016 22:25:19 +0000 http://robohub.org/talking-machines-gaussian-processes-and-openai-with-iiya-sutskever/

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: Real human actions and Women in Machine Learning (WiML), with Jenn Wortman Vaughan https://robohub.org/talking-machines-real-human-actions-and-women-in-machine-learning-wiml-with-jenn-wortman-vaughan/ Thu, 28 Jan 2016 20:20:58 +0000 http://robohub.org/talking-machines-real-human-actions-and-women-in-machine-learning-wiml-with-jenn-wortman-vaughan/

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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]]> Talking Machines: Probabilistic programming, with Ben Vigoda https://robohub.org/talking-machines-probabilistic-programming-with-ben-vigoda/ Thu, 26 Nov 2015 17:36:47 +0000 http://robohub.org/talking-machines-probabilistic-programming-with-ben-vigoda/ Image: Gamalon.com

Image: Gamalon.com

In episode twenty four we talk with Ben Vigoda about his work in probabilistic programming (everything from his thesis, to his new company). Ryan talks about Tensor Flow and Autograd for Torch, some open source tools that have been recently releases. Plus we talk a listener question about the biggestGamalon thing in machine learning this year.

This is the last episode in season one. We’ll be back in early January with season two!

We want to thank all our wonderful listeners for supporting the show, asking us questions, and making season two possible!

]]> Talking Machines: Digital humanities with David Mimno https://robohub.org/talking-machines-digital-humanities-with-david-mimno/ Fri, 06 Nov 2015 11:00:58 +0000 http://robohub.org/talking-machines-digital-humanities-with-david-mimno/ David Mimno, assistant professor of information science (ISP).

David Mimno, assistant professor of information science (ISP).

In episode 23 we talk with David Mimno of Cornell University about his work in the digital humanities, and explore what machine learning can tell us about lady zombie ghosts and huge bodies of literature. Ryan introduces us to probabilistic programming and we take a listener question about knowledge transfer between math and machine learning.

https://soundcloud.com/talkingmachines/probabilistic-programming-and-digital-humanities

]]> Crowdsourcing in machine learning, with Adam Kalai of Microsoft Research https://robohub.org/crowdsourcing-in-machine-learning-with-adam-kalai-of-microsoft-research/ Mon, 26 Oct 2015 18:02:08 +0000 http://robohub.org/crowdsourcing-in-machine-learning-with-adam-kalai-of-microsoft-research/ Microsoft_Research_Crowdsourcing_Machine_Learning_Adam_KalaiIn episode twenty two we talk with Adam Kalai of Microsoft Research New England about his work using crowdsourcing in Machine Learning, we take a look at the workshops being presented at NIPS this year, and we take a listener question about changing the number of features your data has.

]]> Talking Machines: Treating cancer clusters, with Quaid Morris https://robohub.org/talking-machines-treating-cancer-clusters-with-quaid-morris/ Fri, 09 Oct 2015 20:18:00 +0000 http://robohub.org/talking-machines-treating-cancer-clusters-with-quaid-morris/ Via Wikimedia Commons

Via Wikimedia Commons

In episode twenty one  we talk with Quaid Morris of the University of Toronto, who is using machine learning to find a better way to treat cancers. Ryan introduces us to expectation maximization and we take a listener question about how to master machine learning.

]]> Talking Machines: The Master Algorithm, with UW’s Pedro Domingos https://robohub.org/talking-machines-the-master-algorithm-with-uws-pedro-domingos/ Fri, 25 Sep 2015 19:34:01 +0000 http://robohub.org/talking-machines-the-master-algorithm-with-uws-pedro-domingos/ The_Master_Algorithm_Pedros_DomingosIn episode 20 we chat with Pedro Domingos of the University of Washington, who has just published a book The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World.

We get some insight into Linear Dynamical Systems, which the Datta Lab at Harvard Medical School is doing some interesting work with. Plus, we take a listener question about using video games to generate labeled data (spoiler alert, it’s an awesome idea!)

We’re in the final hours of our Fundraising Campaign and we need your help! 

]]> Why we need journalism about machine learning https://robohub.org/why-we-need-journalism-about-machine-learning/ Fri, 25 Sep 2015 15:14:00 +0000 http://robohub.org/why-we-need-journalism-about-machine-learning/ Talking Machines

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! 

 

]]> Talking Machines: Strong AI and autoencoders, with Hugo Larochelle https://robohub.org/talking-machines-strong-ai-and-autoencoders-with-hugo-larochelle/ Mon, 14 Sep 2015 16:23:00 +0000 http://robohub.org/talking-machines-strong-ai-and-autoencoders-with-hugo-larochelle/ image: Engineering at Cambridge/flickr

image: Engineering at Cambridge/flickr

In episode nineteen we chat with Hugo Larochelle about his work on unsupervised learning, the International Conference on Learning Representations (ICLR), and his teaching style.

His Youtube courses are not to be missed and his twitter feed @Hugo_Larochelle is a great source for paper reviews.

Ryan introduces us to autoencoders (for more, turn to the work of Richard Zemel) plus we tackle the question of what is standing in the way of strong AI.

https://soundcloud.com/talkingmachines/strong-ai-and-autoencoders

Talking Machines is beginning development of season two! We need your help! Donate now on Kickstarter. 

]]> Talking Machines: Active learning and machine learning in neuroscience, with Microsoft’s Sham Kakade https://robohub.org/talking-machines-on-active-learning-and-machine-learning-in-neuroscience-with-microsofts-sham-kakade/ Thu, 27 Aug 2015 10:12:45 +0000 http://robohub.org/talking-machines-on-active-learning-and-machine-learning-in-neuroscience-with-microsofts-sham-kakade/ In episode eighteen we talk with Sham Kakade, of Microsoft Research New England, about his expansive work which touches on everything from neuroscience to theoretical machine learning. Ryan introduces us to active learning and we take a question on evolutionary algorithms.

Today we're announcing that season two of Talking Machines is moving into development, but we need your help! 

In order to raise funds, we've opened the show up to sponsorship and started a Kickstarter and we've got some great nerd cred prizes to thank you with. But more than just getting you a totally sweet mug your donation will fuel journalism about the reality of scientific research, something that is unfortunately hard to find. Lend a hand if you can! 

 

]]> image: Thomas Hawk/flickr

image: Thomas Hawk/flickr

In episode eighteen we talk with Sham Kakade, of Microsoft Research New England, about his expansive work that touches on everything from neuroscience to theoretical machine learning.

Ryan introduces us to active learning and we take a question on evolutionary algorithms.

https://soundcloud.com/talkingmachines/active-learning-and-machine-learning-in-neuroscience

Today we’re announcing that season two of Talking Machines is moving into development, but we need your help! 

In order to raise funds, we’ve opened the show up to sponsorship and started a Kickstarter and we’ve got some great nerd cred prizes to thank you with. But more than just getting you a totally sweet mug, your donation will fuel journalism about the reality of scientific research, something that is unfortunately hard to find. Lend a hand if you can!

 

]]> Talking Machines: Machine learning in biology, with Microsoft’s Jennifer Listgarten https://robohub.org/talking-machines-on-machine-learning-in-biology-with-microsofts-jennifer-listgarten/ Fri, 14 Aug 2015 04:10:47 +0000 http://robohub.org/talking-machines-on-machine-learning-in-biology-with-microsofts-jennifer-listgarten/ In episode seventeen we talk with Jennifer Listgarten of  Microsoft Research New England about her work using machine learning to answer questions in biology. Recently, she’s used to machine learning to make CRISPR more efficient and sort populations in GWAS studies. We take a question from a listener about the development of computational biology and Ryan gives us some great advice on how to get into grad school (Spoiler alert: apply to the lab, not the program.)

]]> image: Jer Thorp/flickr

image: Jer Thorp/flickr

In episode seventeen we talk with Jennifer Listgarten of  Microsoft Research New England about her work using machine learning to answer questions in biology.

Recently, she’s used machine learning to make CRISPR more efficient and sort populations in GWAS studies. We take a question from a listener about the development of computational biology and Ryan gives us some great advice on how to get into grad school (Spoiler alert: apply to the lab, not the program.)

https://soundcloud.com/talkingmachines/machine-learning-in-biology-and-getting-into-grad-school

]]> Machine learning for sports & realtime predictions, w Microsoft Research’s Danny Tarlow https://robohub.org/machine-learning-for-sports-realtime-predictions-w-microsoft-researchs-danny-tarlow/ Fri, 31 Jul 2015 14:05:54 +0000 http://robohub.org/machine-learning-for-sports-realtime-predictions-w-microsoft-researchs-danny-tarlow/ aerial_view_football_field_stadiumIn episode sixteen we chat with Danny Tarlow of Microsoft Research Cambridge (in the UK not MA). Danny (along with Chris Maddison and Tom Minka) won best paper at NIPS 2014 for his paper A* Sampling. We talk with him about his work in applying machine learning to sports and politics.

Plus we take a listener question on making real time predictions using machine learning, and we demystify backpropagation. You can use Torch, Theano or Autograd to explore backprop more.

]]> Really really big data and machine learning in business, with Max Welling https://robohub.org/really-really-big-data-and-machine-learning-in-business-with-max-welling/ Fri, 17 Jul 2015 15:02:42 +0000 http://robohub.org/really-really-big-data-and-machine-learning-in-business-with-max-welling/ AI_evolution_dataIn episode fifteen we talk with Max Welling, of the University of Amsterdam and University of California Irvine. We talk with him about his work with extremely large data and big business and machine learning. Max was program co-chair for NIPS in 2013 when Mark Zuckerberg visited the conference, an event which Max wrote very thoughtfully about. We also take a listener question about the relationship between machine learning and artificial intelligence. Plus, we get an introduction to change point detection. For more on change point detection check out the work of Paul Fearnhead of Lancaster University. Ryan also has a paper on the topic from way back when.

]]> Talking Machines: On ‘solving intelligence’, with DeepMind’s Nando de Freitas https://robohub.org/talking-machines-solving-intelligence-with-deepminds-nando-de-freitas/ Fri, 03 Jul 2015 14:56:12 +0000 http://robohub.org/talking-machines-solving-intelligence-with-deepminds-nando-de-freitas/ In episode fourteen we talk with Nando de Freitas. He’s a professor of Computer Science at the University of Oxford and a senior staff research scientist Google DeepMind. Right now he’s focusing on solving intelligence. (No biggie) Ryan introduces us to anchor words and how they can help us expand our ability to explore topic models. Plus, we take a question about the fundamentals of tackling a problem with machine learning.

]]> nandoResearchIn episode fourteen we talk with Nando de Freitas. He’s a professor of Computer Science at the University of Oxford and a senior staff research scientist Google DeepMind. Right now he’s focusing on solving intelligence. (No biggie.)

Ryan introduces us to anchor words and how they can help us expand our ability to explore topic models. Plus, we take a question about the fundamentals of tackling a problem with machine learning.

]]> Talking Machines: Data and machine learning in advertising, with Claudia Perlich https://robohub.org/talking-machines-data-and-machine-learning-in-advertising-with-claudia-perlich/ Fri, 19 Jun 2015 14:13:42 +0000 http://robohub.org/talking-machines-data-and-machine-learning-in-advertising-with-claudia-perlich/ digital_media_advertising_data_AI_machine_socialIn episode thirteen we talk with Claudia Perlich, Chief Scientist at Dstillery. We talk about her work using machine learning in digital advertising and her approach to data in competitions. We take a look at information leakage in competitions after ImageNet Challenge this year. The New York Times covered the events, and Neil Lawrence has been writing thoughtfully about it and its impact. Plus, we take a listener question about trends in data size.

]]> Talking Machines: The economic impact of machine learning, with Baidu’s Andrew Ng https://robohub.org/talking-machines-the-economic-impact-of-machine-learning-with-baidus-andrew-ng/ Thu, 04 Jun 2015 17:53:10 +0000 http://robohub.org/talking-machines-the-economic-impact-of-machine-learning-with-baidus-andrew-ng/ In episode twelve we talk with Andrew Ng, Chief Scientist at Baidu, about how speech recognition is going to explode the way we use mobile devices and his approach to working on the problem. We also discuss why we need to prepare for the economic impacts of machine learning. We’re introduced to Random Features for Large-Scale Kernel Machines, and talk about how using this twist on the Kernel trick can help you dig into big data. Plus, we take a listener question about the size of computing power in machine learning.

]]> Source: http://cs.stanford.edu/people/ang/

Source: Stanford University  

In episode twelve we talk with Andrew Ng, Chief Scientist at Baidu, about how speech recognition is going to explode the way we use mobile devices and his approach to working on the problem.

We also discuss why we need to prepare for the economic impacts of machine learning. We’re introduced to Random Features for Large-Scale Kernel Machines, and talk about how using this twist on the Kernel trick can help you dig into big data. Plus, we take a listener question about the size of computing power in machine learning.

]]> Talking Machines: Neil Lawrence on privacy in the age of machine learning https://robohub.org/talking-machines-neil-lawrence-on-privacy-in-the-age-of-machine-learning/ Fri, 22 May 2015 15:20:15 +0000 http://robohub.org/talking-machines-neil-lawrence-on-privacy-in-the-age-of-machine-learning/ data_eye_privacyIn episode eleven we chat with Neil Lawrence from the University of Sheffield. We talk about the problems of privacy in the age of machine learning, and the responsibilities that come with using machine learning tools and making data more open. We learn about the Markov decision process (and what happens when you use it in the real world and it becomes a partially observable Markov decision process) and take a listener question about finding insights into features in the black boxes of deep learning.

]]> Talking Machines: Interdisciplinary data, with David Blei https://robohub.org/talking-machines-interdisciplinary-data-with-david-blei/ Thu, 07 May 2015 19:54:54 +0000 http://robohub.org/talking-machines-interdisciplinary-data-with-david-blei/ In Episode 10 we talk with David Blei of Columbia University. We talk about his work on latent dirichlet allocation, topic models, the PhD program in data that he’s helping to create at Columbia and why exploring data is inherently multidisciplinary. We learn about Markov Chain Monte Carlo and take a listener question about how machine learning can make humans more creative.

]]>

In Episode 10 we talk with David Blei of Columbia University. We talk about his work on latent dirichlet allocation, topic models, the PhD program in data that he’s helping to create at Columbia and why exploring data is inherently multidisciplinary. We learn about Markov Chain Monte Carlo and take a listener question about how machine learning can make humans more creative.

]]> Talking Machines: Developing effective medicine by predicting molecular activity, with George Dahl https://robohub.org/talking-machines-developing-effective-medicine-by-predicting-molecular-activity-with-george-dahl/ Fri, 24 Apr 2015 17:35:58 +0000 http://robohub.org/talking-machines-developing-effective-medicine-by-predicting-molecular-activity-with-george-dahl/ Kaggle The Home of Data ScienceIn episode nine we talk with George Dahl, of  the University of Toronto, about his work on the Merck molecular activity challenge on kaggle and speech recognition. George recently successfully defended his thesis at the end of March 2015. (Congrats George!) We learn about how networks and graphs can help us understand latent properties of relationships, and we take a listener question about just how you find the right algorithm to solve a problem (Spoiler: start simple.)

]]> Talking Machines: Spinning programming plates and creative algorithms, with Charles Sutton https://robohub.org/talking-machines-spinning-programming-plates-and-creative-algorithms-with-charles-sutton/ Thu, 09 Apr 2015 19:51:47 +0000 http://robohub.org/talking-machines-spinning-programming-plates-and-creative-algorithms-with-charles-sutton/ On episode eight we talk with Charles Sutton, a professor in the School of Informatics University of Edinburgh about computer programming and using machine learning how to better understand how it’s done well.

Ryan introduces us to collaborative filtering, a process that helps to make predictions about taste. Netflix and Amazon use it to recommend movies and items. It's the process that the Netflix Prize competition further helped to hone.

Plus, we take a listener question on creativity in algorithms.

]]> Plates-Spinning-on-SticksIn episode eight we talk with Charles Sutton, a professor in the School of Informatics University of Edinburgh about computer programming and using machine learning to better understand how it’s done well.

Ryan introduces us to collaborative filtering, a process that helps to make predictions about taste. Netflix and Amazon use it to recommend movies and items. It’s the process that the Netflix Prize competition further helped to hone.

Plus, we take a listener question on creativity in algorithms.

]]> Talking Machines: The automatic statistician and electrified meat, with Zoubin Ghahramani https://robohub.org/talking-machines-the-automatic-statistician-and-electrified-meat-with-zoubin-ghahramani/ Thu, 26 Mar 2015 14:15:03 +0000 http://robohub.org/talking-machines-the-automatic-statistician-and-electrified-meat-with-zoubin-ghahramani/ 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.

]]> Source: Automatic Statistician.

Source: Automatic Statistician.

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.

]]> Future of machine learning, w. Geoffrey Hinton, Yoshua Bengio, Yann LeCun (Part 2) https://robohub.org/future-of-machine-learning-w-geoffrey-hinton-yoshua-bengio-yann-lecun-part-2/ Tue, 17 Mar 2015 14:48:51 +0000 http://robohub.org/future-of-machine-learning-w-geoffrey-hinton-yoshua-bengio-yann-lecun-part-2/ We hear the second part of our conversation with with Geoffrey Hinton (Google and University of Toronto), Yoshua Bengio (University of Montreal) and Yann LeCun (Facebook and NYU). They talk with us about this history (and future) of research on neural nets. We explore how to use Determinantal Point Processes. Alex Kulesza  and Ben Taskar (who passed away recently) have done some really exciting work in this area, for more on DPPs check out their paper on the topic. 
Also, we take a listener question about machine learning and function approximation (spoiler alert: it is, and then again, it isn’t).

]]> We hear the second part of our conversation with with Geoffrey Hinton (Google and University of Toronto), Yoshua Bengio (University of Montreal) and Yann LeCun (Facebook and NYU), who talk with us about the history (and future) of research on neural nets. 

We explore how to use Determinantal Point Processes. Alex Kulesza  and Ben Taskar (who passed away recently) have done some really exciting work in this area, for more on DPPs check out their paper on the topic.

Also, we take a listener question about machine learning and function approximation (spoiler alert: it is, and then again, it isn’t).

If you liked this article, you may also be interested in:

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

]]> Talking Machines: History of machine learning, w. Geoffrey Hinton, Yoshua Bengio, Yann LeCun https://robohub.org/talking-machines-history-of-machine-learning-w-geoffrey-hinton-yoshua-bengio-yann-lecun/ Thu, 26 Feb 2015 20:40:21 +0000 http://robohub.org/talking-machines-history-of-machine-learning-w-geoffrey-hinton-yoshua-bengio-yann-lecun/ 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 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.

 

]]> mind-AI-intelligence-learning-data-brainIn 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 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.

Robohub is an online platform that brings together leading communicators in robotics research, start-ups, business, and education from around the world. Learn more about us here. If you liked this article, you may also be interested in:

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

]]> Talking Machines: Women in Machine Learning (WiML), with Hanna Wallach https://robohub.org/talking-machines-women-in-machine-learning-wiml-with-hanna-wallach/ Thu, 12 Feb 2015 20:34:05 +0000 http://robohub.org/talking-machines-women-in-machine-learning-wiml-with-hanna-wallach/ wimlIn 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). 

If you liked this podcast, you may also be interested in:

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

]]> Talking Machines: Common sense problems & learning about machine learning with Kevin Murphy https://robohub.org/talking-machines-common-sense-problems-learning-about-machine-learning-with-kevin-murphy/ Thu, 29 Jan 2015 21:07:11 +0000 http://robohub.org/talking-machines-common-sense-problems-learning-about-machine-learning-with-kevin-murphy/ 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:

]]> Machine_Learning_Probabilistic_Perspective_Kevin_MurphyOn 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, as outlined 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:

]]> Talking Machines: Machine learning and magical thinking, with Ilya Sutskever https://robohub.org/talking-machines-machine-learning-and-magical-thinking-with-ilya-sutskever/ Thu, 15 Jan 2015 22:05:38 +0000 http://robohub.org/talking-machines-machine-learning-and-magical-thinking-with-ilya-sutskever/ mind-AI-intelligence-learning-data-brainRobohub is excited to bring you our newest series – the Talking Machines podcast. Hosted by Katherine Gorman and Ryan Adams, Talking Machines is your window into the world of machine learning. You can catch new episodes every two weeks on Thursdays.

Today on Talking Machines we hear from Google researcher Ilya Sutskever about his work, how he became interested in machine learning, and why it takes a little bit of magical thinking. We take your questions, and explore where the line between human programming and computer learning actually is. And we sift through some news from the field, Ryan explains the concepts behind one of the best papers  at NIPS this year, A * Sampling and Katherine brings up an open letter about research priorities and ethical questions that was recently published.

If you liked this podcast, you may also be interested in:

]]> Introducing Talking Machines: Your window into the world of machine learning https://robohub.org/introducing-talking-machines-your-window-into-the-world-of-machine-learning/ Thu, 01 Jan 2015 18:09:14 +0000 http://robohub.org/introducing-talking-machines-your-window-into-the-world-of-machine-learning/ AI_evolution_dataRobohub is excited to bring you our newest series – the Talking Machines podcast. Hosted by Katherine Gorman and Ryan Adams, Talking Machines is your window into the world of machine learning. You can catch new episodes every two weeks on Thursdays.

In the first episode of Talking Machines we meet our hosts, Katherine Gorman (nerd, journalist) and Ryan Adams (nerd, Harvard computer science professor), and explore some of the interviews you’ll be able to hear this season. Today we hear some short clips on big issues, we’ll get technical, but today is all about introductions.

We start with Kevin Murphy of Google talking about his textbook that has become a standard in the field. Then we turn to Hanna Wallach of Microsoft Research NYC and UMass Amherst and hear about the founding of WiML (Women in Machine Learning). Next we discuss academia’s relationship with business with Max Welling from the University of Amsterdam, program co-chair of  the 2013 NIPS conference (Neural Information Processing Systems). Finally, we sit down with three pillars of the field Yann LeCun, Yoshua Bengio, and Geoff Hinton to hear about where the field has been and where it might be headed.

]]>