PODCAST · technology
Talking Machines
by Talking Machines
Talking Machines is your window into the world of machine learning. Your hosts, Katherine Gorman and Ryan Adams, 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.
Episodes
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Machine Learning in Healthcare and The AlphaGo Matches
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...
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AI Safety and The Legacy of Bletchley Park
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.) One place you can go for other opinions on AI safety is the Future of Life Institute. We take a listener…
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Robotics and Machine Learning Music Videos
In episode three of season two Ryan walks us through the Alpha Go results and takes a lister 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...
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OpenAI and Gaussian Processes
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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Real Human Actions and Women in Machine Learning
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. Hanna and Jenn Wortman Vaughan, who also helped to found the event, tell us how about how the 2015…
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Open Source Releases and The End of Season One
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...
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Probabilistic Programming and Digital Humanities
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...
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Workshops at NIPS and Crowdsourcing in Machine Learning
In episode twenty two we talk with Adam Kalai of Microsoft Research New England about his work using crowdsourcing in Machine Learning, the language made of shapes of words, and New England Machine Learning Day. We take a look at the workshops being presented at NIPS this year, and we take a...
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Machine Learning Mastery and Cancer Clusters
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.
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Data from Video Games and The Master Algorithm
In episode 20 we chat with Pedro Domingos of the University of Washington, he's 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…
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Strong AI and Autoencoders
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....
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Active Learning and Machine Learning in Neuroscience
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...
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Machine Learning in Biology and Getting into Grad School
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...
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Machine Learning for Sports and Real Time Predictions
In 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…
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Really Really Big Data and Machine Learning in Business
In 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…
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Solving Intelligence and Machine Learning Fundamentals
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…
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Working With Data and Machine Learning in Advertising
In 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…
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The Economic Impact of Machine Learning and Using The Kernel Trick on Big Data
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...
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How We Think About Privacy and Finding Features in Black Boxes
In 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, the responsibilities that come with using ML tools and making data more open. We learn about the Markov decision process (and what happens when you...
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Interdisciplinary Data and Helping Humans Be 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…
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Starting Simple and Machine Learning in Meds
In 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…
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Spinning Programming Plates and Creative Algorithms
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...
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The Automatic Statistician and Electrified Meat
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...
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The Future of Machine Learning from the Inside Out
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…
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The History of Machine Learning from the Inside Out
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...
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Using Models in the Wild and Women in Machine Learning
In 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...
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Common Sense Problems and Learning about Machine Learning
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)…
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Machine Learning and Magical Thinking
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....
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Hello World!
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...
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ABOUT THIS SHOW
Talking Machines is your window into the world of machine learning. Your hosts, Katherine Gorman and Ryan Adams, 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
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Frequently Asked Questions
How many episodes does Talking Machines have?
Talking Machines currently has 110 episodes available on PodParley. New episodes are automatically indexed when they're published to the podcast feed.
What is Talking Machines about?
Talking Machines is your window into the world of machine learning. Your hosts, Katherine Gorman and Ryan Adams, 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...
How often does Talking Machines release new episodes?
Talking Machines publishes regularly with 110 episodes available. Check the episode list for the typical release schedule.
Where can I listen to Talking Machines?
You can listen to Talking Machines on PodParley by clicking any episode. We provide an embedded audio player for direct listening, and you can also subscribe via your preferred podcast app using the RSS feed.
Who hosts Talking Machines?
Talking Machines is created and hosted by Talking Machines.
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