Computational Learning Theory and Machine Learning for Understanding Cells

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.

Machine Learning and Society

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

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 Hopkins about applying machine learning to understanding health care data.

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 question about time series and we talk with Nick Patterson of the Broad Institute about everything from ancient DNA to Alan Turing. If you're as excited about AlphaGo playing Lee Sedol at Nick is, you can get details on the match on DeepMind's You Tube channel March 5th through the 15th.

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 event went. Lillian Lee (Cornell), Raia Hadsell (Google Deepmind), Been Kim (AI2/University of Washington), and Corinna Cortes (Google Research) gave invited talks at the 2015 event. WiML also released a directory of women in machine learning, if you’d like to listed, want to find a collaborator, or are looking for an expert to take part in an event, it’s an excellent resource. Plus, we talk with Jenn Wortman Vaughan, about the research she is doing at Microsoft Research which examines the assumptions we make about how humans actually act and using that to inform thinking about our interactions with computers.  

Want to learn more about the talks at WiML 2015? Here are the slides from each speaker. 

Lillian Lee

Corinna Cortes

Raia Hadsell

Been Kim

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 biggest thing in machine learning this year.


This is the last episode in season one. We want to thanks all our wonderful listeners for supporting the show, asking us questions, and making season two possible! We’ll be back in early January with the beginning of season two!

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 knowledge transfer between math and machine learning.

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 listener question about changing the number of features your data has.

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 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! 

We Need Your Help

Dear Listener, 

I hope you've been enjoying our show so far. Ryan and I have been having a fantastic time making it for you. But we've reached a point where if we're going to keep going, we need your help. 

Please donate to our Kickstarter

We take pride that our podcast is professional quality, but reaching that level of quality takes a lot of time, effort, and resources. We have to pay for studio time, audio engineering, and production time. 

But our greatest expense is probably the thing that you enjoy most about our show, our interviews. We're able to get interviews with the top experts in academia and industry because we're willing to go where they are. It may seems like a small thing, but it really makes a big difference. Unfortunately it's also an expensive difference, travel is not usually an expense that podcasts incur but we've found it's essential for making ours. 

We've got a few days left in our Kickstarter and we've raised a little more than half of the funds we need. We need your help now more than ever, so please lend a hand and let's continue Talking Machines! 

Best, 

Katherine Gorman

 

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. 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.

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

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 (great tutorial here) 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!