Talking Machines

Talking Machines

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Talking Machines is your window into the world of machine learning. Your hosts, Katherine Gorman and Neil Lawrence, 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.


If a Machine Could Predict Your Death, Should it?
Feb 20 • 18 min
in episode two of season six we hear Ziad Obermeyer’s talk from TedX Boston entitled If a Machine Could Predict Your Death, Should it?
Predicting the Decade and Distributing Conferences
Feb 6 • 66 min
In episode one of season six we make some predictions about what will happen in the field in the next decade and talk with Margot Gerritsen about her work and WiDS
Debating Project Debater and Hello NeurIPS
Nov 21, 2019 • 41 min
In our last episode for season five Katherine and Neil debate his debating project debater and talk about whats coming up at NeurIPS. Hope to see you there!
De-Enchanting AI with the Law
Nov 7, 2019 • 20 min
in episode twenty two of season five we hear a talk from Kenneth Anderson on how the field of AI and the law can work together to form regulation from TedX Boston
How to Ask an Actionable Question
Oct 24, 2019 • 38 min
In Episode 21 of Season five we sit down with Marzyeh Ghassemi to talk about her work and how she’s refined her focus.
Children are the Future and Ada Lovelace Day
Oct 10, 2019 • 54 min
In episode twenty of season five we talk with Neil about a discussion he had about the impact of ML tools on children talk about the new Diversity Dashboard from the Turing Institute in response to a question about cool things for Ada Lovelace day plus we…
News from Neil and Updates from DALI
Sep 26, 2019 • 68 min
In episode eighteen of season five we talk about DALI, get some big news about the next thing for Neil and talk with Benjamin Akera.
A Cooperative Path to Artificial Intelligence
Sep 12, 2019 • 17 min
In episode eighteen of season five we hear Michael Littman’s talk A Cooperative Path to Artificial Intelligence
What Does Red Sound Like
Aug 29, 2019 • 49 min
In episode seventeen of season five we talk about Why Red Doesn’t Sound Like a Bell, take a listener question about our Turing brackets (and Invent the Very Good Sort Awards) and listen to a chat with Tewodros Abebe
Not What But Why
Aug 15, 2019 • 19 min
In this episode of Talking Machines we take a listen to Professor Engelhardt’s TedX Boston talk, Not What But Why: Machine Learning for Understanding Genomics
Idea Pandemics and Workshop Walkthrough
Aug 1, 2019 • 59 min
in episode 15 of season five of Talking Machines we’ chat about the recently announced workshops at NeurIPS 2019, find ourselves in the middle of an I Love Lucy Episode about technical term usage and talk with Randy Goebel of the Alberta Machine…
PosterSession.ai and Deep Quaggles
Jul 18, 2019 • 45 min
In episode 14 of season five we talk about On the marginal likelihood and cross-validation, Katherine is STILL excited about PosterSession.ai, we invent Deep Quaggles and listen to a conversation with professor Elaine Nsoesie of BU
The View from Addis Ababa
Jul 4, 2019 • 22 min
In episode thirteen of season five we bring you a the rest of our conversation with Michael Melese from Addis Ababa University and Charles Saidu of Baze University Abuja
DSA Addis Ababa and ICML Los Angeles
Jun 20, 2019 • 55 min
In episode twelve of season five we bring you a rundown of Data Science Africa’s latest workshop answer a listener question about what got us excited at ICML and hear the first part of our conversation with Michael Melese from Addis Ababa University and…
Data Trusts and Citation Trends
Jun 6, 2019 • 54 min
In episode eleven of season five, we dig in to just what a data trust actually is, take a look at citation trends and other places (PMLR) you can dig up data to understand the field and talk with Raia Hadsell of DeepMind.
Reproducibly and Revisiting History
May 23, 2019 • 46 min
In episode ten of season five we talk about reproducibility, take a listener question on re understanding the history of the field given where we are now and how other fields are reviewing their own history and listen to a conversation with Graham Taylor…
Insights from AISTATS
May 9, 2019 • 52 min
In episode nine of season five we talk about some interesting work from AISTATS, dive into unbiased implicit variational inference, and chat with Jon McAuliffe CIO of Voleon
The Deep End of Deep Learning
Apr 25, 2019 • 19 min
In this episode as we prep for ICLR we take a break from our usual format to bring you a talk from Hugo LaRochelle at TedX Boston on Deep Learning.
Exploring MARS and Getting back to Bayesics
Apr 11, 2019 • 68 min
In episode seven of season five of we chat about MARS and Re: MARSOpenAI’s status changes and We talk with Jasper Snoek of Google Brain
The Sweetness of a Bitter Lesson and Bringing ML and Healthcare Closer
Mar 28, 2019 • 50 min
In episode six of season five we talk about Richard Sutton’s A Bitter Lesson. Chat about IEEE’s new Ethical Guidelines and talk with Andrew Beam Senior Fellownn at Flagship Pioneering, Head of Machine Learning for Flagship VL57 and Assistant Professor,…
Slowed Down Conferences and Even More Summer Schools
Mar 14, 2019 • 43 min
In episode five of season five we talk about the Stu Hunter conference, Summer schools options (DLRLSS!) and chat with Adrian Weller of the Alan Turing Institute
Jupyter Notebooks and Modern Model Distribution
Feb 28, 2019 • 36 min
In episode four of season five we talk about Jupyter Notebooks and Neil’s dream of a world craft software and devices, we take a listener question about the conversation surrounding Open AI’s GPT-2 its announcement and the coverage and we hear an…
Real World Real Time and Five Papers for Mike Tipping
Feb 14, 2019 • 61 min
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…
The Bezos Paradox and Machine Learning Languages
Jan 31, 2019 • 41 min
In episode two of season five we unpack the Bezos Paradox (TM Neil Lawrence) take a listener question about best papers and chat with Dougal Maclaurin of Google Brain.
Being Global Bit by Bit
Jan 17, 2019 • 48 min
In episode one of season five we talk about Bit by Bit, take a listener question on machine learning gatherings on the African continent (Deep Learning INDABA!DSA!) and hear an interview with Daphne Koller recorded at ODSC West
The Possibility Of Explanation and The End of Season Four
Nov 29, 2018 • 18 min
For the end of season four we take a break from our regular format and bring you a talk from Professor Finale Doshi Velez of Harvard University on the possibility of explanation Tune in next season!
Neural Information Processing Systems and Distributed Internal Intelligence Systems
Nov 15, 2018 • 36 min
In episode twenty one of season four we talk about distributed intelligence systems (mainly those internal to humans), talk about what were excited to see at the Conference on Neural Information Processing Systems and in advance of our trek to Canada we…
Data Driven Ideas and Actionable Privacy
Nov 1, 2018 • 45 min
In episode twenty of season four we talk about the importance of crediting your data, answer a listener question about internships vs salaried positions and talk with Matt Kusner of the Alan Turing institute the UK’s national institute for data science…
AI for Good and The Real World
Oct 18, 2018 • 32 min
In episode nineteen of season four we talk about causality in the real world, take a question about being surprised by the elephant in the room and talk with Kush Varshney of IBM.
Systems Design and Tools for Transparency
Oct 4, 2018 • 40 min
In episode 18 of season four we talk about systems design, (remember the 3 d’s!), tools for transparency and fairness and we talk with Adria Gascon of The Alan Turing Institute, the UK’s national institute for data science and AI.
How to Research in Hype and CIFAR’s Strategy
Sep 20, 2018 • 37 min
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
Troubling Trends and Climbing Mountains
Sep 7, 2018 • 39 min
In this episode we talk about an article Troubling Trends in Machine learning Scholarship the difference between engineering and science (and the mountains you climb to span the distance) plus we talk with David Duvenaud of the University of Toronto
Gaussian Processes, Grad School, and Richard Zemel
Aug 23, 2018 • 43 min
Long Term Fairness
Aug 9, 2018 • 29 min
Simulated Learning and Real World Ethics
Jul 26, 2018 • 57 min
In episode thirteen of season four we chat about simulations, reinforcement learning, and Philippa Foot. We take a listener question about the update to the ACM code of ethics (first time since 1992!) and We talk with professor Mike Jordan.
ICML 2018 with Jennifer Dy
Jul 12, 2018 • 19 min
Season four episode twelve finds us at ICML! We bring you a special episode with Jennifer Dy, co-program chair of the conference.
Aspirational Asimov and How to Survive a Conference
Jun 28, 2018 • 45 min
In season four episode eleven we talk about the possibility of the NIPS conference changing its name, what to do at ICML, And we talk with Bernhard Schölkopf.
Explanations and Reviews
Jun 14, 2018 • 23 min
In episode 10 of season 4 we chat about Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR, take a listener question about how reviews of papers work at NIPS and we hear from Sven Strohband, CTO of Khosla Ventures.
Statements on Statements
May 31, 2018 • 26 min
In episode 9 of season 4 we talk about the Statement on Nature Machine Intelligence. We reached out to Nature for a statement on the statement and received the following: “At Springer Nature we are very clear in our mission to advance discovery and help…
The Futility of Artificial Carpenters and Further Reading
May 17, 2018 • 37 min
In episode eight of season four we review some recently published articles by Michael Jordan and Rodney Brooks (for more reading along these lines, Tom Dettriech is a great person to follow), we recommend some further reading, and talk with Arthur Gretton…
Economies, Work and AI
May 3, 2018 • 42 min
In episode seven of season four we chat about Ellis and the UK AI Sector Deal , we take a listener question about the next AI winter and if/when it is coming, plus we hear from Christina Colclough Director of Platform and Agency Workers, Digitalization…
Explainability and the Inexplicable
Apr 19, 2018 • 43 min
In episode six of season four we chat about AI and religion, we take a listener question about personal bias checking and we hear from Been Kim of Google Brain.
Good Data Practice Rules
Apr 5, 2018 • 51 min
In episode five of season four we talk about the GDPR or as we like to think of it Good Data Practice Rules. (If you actually read it, you move to expert level!) We take a listener question about the power of approximate inference, and we hear from our…
Can an AI Practitioner Fix a Radio?
Mar 22, 2018 • 44 min
In episode four of season four we talk more about natural an artificial intelligences and thinking about diversity in systems. Reading Can a Biologist Fix a Radio is a great paper around these ideas. We take a listener question about moving into machine…
Natural vs Artificial Intelligence and Doing Unexpected Work
Mar 8, 2018 • 58 min
In season four episode three of Talking Machines we chat about Neil’s recent thinking (definitely not work) on the core differences between natural intelligence and machine intelligence, he recently wrote blog post on the subject and in the fall of 2017…
Scientific Rigor and Turning Information into Action
Feb 22, 2018 • 38 min
In episode two of season four we’re proud to bring you the second annual “Hosts of Talking Machine’s Episode”! Ryan and Neil chat about Ali Rahimi’s speech at NIPS-17, Kate Crawford’s talk The Trouble with Bias, and much more. We also get to hear a…
Code Review for Community Change
Feb 8, 2018 • 35 min
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…
The Pace of Change and The Public View of ML
Oct 5, 2017 • 40 min
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…
The Long View and Learning in Person
Sep 21, 2017 • 65 min
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…
Machine Learning in the Field and Bayesian Baked Goods
Sep 7, 2017 • 59 min
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…
Data Science Africa with Dina Machuve
Aug 10, 2017 • 48 min
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…
The Church of Bayes and Collecting Data
Jul 27, 2017 • 49 min
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
Getting a Start in ML and Applied AI at Facebook
Jul 13, 2017 • 57 min
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…
Bias Variance Dilemma for Humans and the Arm Farm
Jun 29, 2017 • 50 min
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…
Overfitting and Asking Ecological Questions with ML
Jun 15, 2017 • 41 min
In this episode three of season three of Talking Machines we dive into overfitting, take a listener question about unbalanced data and talk with Professor (Emeritus) Tom Dietterich from Oregon State University.
Graphons and “Inferencing”
May 25, 2017 • 41 min
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.
Hosts of Talking Machines: Neil Lawrence and Ryan Adams
Apr 27, 2017 • 33 min
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 good bye to Ryan with an interview about his work.
ANGLICAN and Probabilistic Programming
Sep 1, 2016 • 44 min
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…
Eric Lander and Restricted Boltzmann Machines
Aug 18, 2016 • 53 min
In episode sixteen of season two, we get an introduction to Restricted Boltzmann Machines, we take a listener question about tuning hyperparameters, plus we talk with Eric Lander of the Broad Institute.
Generative Art and Hamiltonian Monte Carlo
Aug 4, 2016 • 47 min
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.
Perturb-and-MAP and Machine Learning in the Flint Water Crisis
Jul 21, 2016 • 38 min
In episode fourteen of season two, we talk about Perturb-and-MAP, we take a listener question about classic artificial intelligence ideas being used in modern machine learning, plus we talk with Jake Abernethy of the University of Michigan about municipal…
Automatic Translation and t-SNE
Jul 7, 2016 • 32 min
In episode thirteen of season two, we talk about t-Distributed Stochastic Neighbor Embedding (t-SNE) we take a listener question about statistical physics, plus we talk with Hal Daume of the University of Maryland. (who is a great follow on Twitter.)
Fantasizing Cats and Data Numbers
Jun 16, 2016 • 49 min
In episode twelve of season two, we talk about generative adversarial networks, we take a listener question about using machine learning to improve or create products, plus we talk with Iain Murray of the University of Edinburgh.
Spark and ICML
Jun 2, 2016 • 39 min
In episode eleven of season two, we talk about the machine learning toolkit Spark, we take a listener question about the differences between NIPS and ICML conferences, plus we talk with Sinead Williamson of The University of Texas at Austin.
Computational Learning Theory and Machine Learning for Understanding Cells
May 19, 2016 • 40 min
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…
Sparse Coding and MADBITS
May 5, 2016 • 41 min
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.
Remembering David MacKay
Apr 21, 2016 • 53 min
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…
Machine Learning and Society
Apr 7, 2016 • 48 min
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…
Software and Statistics for Machine Learning
Mar 24, 2016 • 39 min
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.
Machine Learning in Healthcare and The AlphaGo Matches
Mar 10, 2016 • 48 min
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…
AI Safety and The Legacy of Bletchley Park
Feb 25, 2016 • 48 min
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…
Robotics and Machine Learning Music Videos
Feb 11, 2016 • 40 min
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…
OpenAI and Gaussian Processes
Jan 28, 2016 • 35 min
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.)
Real Human Actions and Women in Machine Learning
Jan 14, 2016 • 59 min
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…
Open Source Releases and The End of Season One
Nov 22, 2015 • 40 min
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…
Probabilistic Programming and Digital Humanities
Nov 5, 2015 • 48 min
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…
Workshops at NIPS and Crowdsourcing in Machine Learning
Oct 22, 2015 • 47 min
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…
Machine Learning Mastery and Cancer Clusters
Oct 8, 2015 • 26 min
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…
Data from Video Games and The Master Algorithm
Sep 24, 2015 • 46 min
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…
Strong AI and Autoencoders
Sep 10, 2015 • 36 min
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…
Active Learning and Machine Learning in Neuroscience
Aug 27, 2015 • 53 min
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…
Machine Learning in Biology and Getting into Grad School
Aug 13, 2015 • 48 min
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, With her collaborator Nicolo Fusi, she used machine learning to make CRISPR more…
Machine Learning for Sports and Real Time Predictions
Jul 30, 2015 • 29 min
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…
Really Really Big Data and Machine Learning in Business
Jul 16, 2015 • 23 min
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…
Solving Intelligence and Machine Learning Fundamentals
Jul 2, 2015 • 30 min
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…
Working With Data and Machine Learning in Advertising
Jun 18, 2015 • 39 min
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…
The Economic Impact of Machine Learning and Using The Kernel Trick on Big Data
Jun 4, 2015 • 40 min
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…
How We Think About Privacy and Finding Features in Black Boxes
May 21, 2015 • 33 min
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…
Interdisciplinary Data and Helping Humans Be Creative
May 7, 2015 • 34 min
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…
Starting Simple and Machine Learning in Meds
Apr 23, 2015 • 38 min
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…
Spinning Programming Plates and Creative Algorithms
Apr 9, 2015 • 35 min
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…
The Automatic Statistician and Electrified Meat
Mar 26, 2015 • 45 min
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…
The Future of Machine Learning from the Inside Out
Mar 13, 2015 • 28 min
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…
The History of Machine Learning from the Inside Out
Feb 26, 2015 • 32 min
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…
Using Models in the Wild and Women in Machine Learning
Feb 12, 2015 • 45 min
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…
Common Sense Problems and Learning about Machine Learning
Jan 29, 2015 • 40 min
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…
Machine Learning and Magical Thinking
Jan 15, 2015 • 35 min
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…
Hello World!
Jan 1, 2015 • 41 min
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…