All posts by Talking Machines

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.

If you enjoyed this episode, you may also want to listen to:

• Talking Machines: The long view and learning in person, with John Quinn
• Talking Machines: Machine Learning in the Field and Bayesian Baked Goods, with Ernest Mwebaze
• Talking Machines: Data Science Africa, with Dina Machuve
• Talking Machines: The church of Bayes and collecting data, with Katherine Heller
• Talking Machines: Getting a start in ML and applied AI at Facebook, with Joaquin Quiñonero Candela
• Talking Machines: Bias variance dilemma for humans and the arm farm, with Jeff Dean
• Talking Machines: Overfitting and asking ecological questions, with Tom Dietterich
• Talking Machines: Restricted Boltzmann Machines, with Eric Lander
• Talking Machines: Automatic Translation and t-SNE, with Hal Daume
• Talking Machines: Generative art and Hamiltonian Monte Carlo, with Doug Eck
• Talking Machines: Machine learning and the Flint water crisis, with Jake Abernethy
• Talking Machines: Gaussian processes and OpenAI, with IIya Sutskever

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

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.

If you enjoyed this episode, you may also want to listen to:

• Talking Machines: Machine Learning in the Field and Bayesian Baked Goods, with Ernest Mwebaze
• Talking Machines: Data Science Africa, with Dina Machuve
• Talking Machines: The church of Bayes and collecting data, with Katherine Heller
• Talking Machines: Getting a start in ML and applied AI at Facebook, with Joaquin Quiñonero Candela
• Talking Machines: Bias variance dilemma for humans and the arm farm, with Jeff Dean
• Talking Machines: Overfitting and asking ecological questions, with Tom Dietterich
• Talking Machines: Restricted Boltzmann Machines, with Eric Lander
• Talking Machines: Automatic Translation and t-SNE, with Hal Daume
• Talking Machines: Generative art and Hamiltonian Monte Carlo, with Doug Eck
• Talking Machines: Machine learning and the Flint water crisis, with Jake Abernethy
• Talking Machines: Gaussian processes and OpenAI, with IIya Sutskever

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

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.

If you enjoyed this episode, you may also want to listen to:

• Talking Machines: Data Science Africa, with Dina Machuve
• Talking Machines: The church of Bayes and collecting data, with Katherine Heller
• Talking Machines: Getting a start in ML and applied AI at Facebook, with Joaquin Quiñonero Candela
• Talking Machines: Bias variance dilemma for humans and the arm farm, with Jeff Dean
• Talking Machines: Overfitting and asking ecological questions, with Tom Dietterich
• Talking Machines: Restricted Boltzmann Machines, with Eric Lander
• Talking Machines: Automatic Translation and t-SNE, with Hal Daume
• Talking Machines: Generative art and Hamiltonian Monte Carlo, with Doug Eck
• Talking Machines: Machine learning and the Flint water crisis, with Jake Abernethy
• Talking Machines: Gaussian processes and OpenAI, with IIya Sutskever

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

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.

If you enjoyed this episode, you may also want to listen to:

• Talking Machines: The church of Bayes and collecting data, with Katherine Heller
• Talking Machines: Getting a start in ML and applied AI at Facebook, with Joaquin Quiñonero Candela
• Talking Machines: Bias variance dilemma for humans and the arm farm, with Jeff Dean
• Talking Machines: Overfitting and asking ecological questions, with Tom Dietterich
• Talking Machines: Restricted Boltzmann Machines, with Eric Lander
• Talking Machines: Automatic Translation and t-SNE, with Hal Daume
• Talking Machines: Generative art and Hamiltonian Monte Carlo, with Doug Eck
• Talking Machines: Machine learning and the Flint water crisis, with Jake Abernethy
• Talking Machines: Gaussian processes and OpenAI, with IIya Sutskever

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

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.

If you enjoyed this episode, you may also want to listen to:

• Talking Machines: The church of Bayes and collecting data, with Katherine Heller
• Talking Machines: Getting a start in ML and applied AI at Facebook, with Joaquin Quiñonero Candela
• Talking Machines: Bias variance dilemma for humans and the arm farm, with Jeff Dean
• Talking Machines: Overfitting and asking ecological questions, with Tom Dietterich
• Talking Machines: Restricted Boltzmann Machines, with Eric Lander
• Talking Machines: Automatic Translation and t-SNE, with Hal Daume
• Talking Machines: Generative art and Hamiltonian Monte Carlo, with Doug Eck
• Talking Machines: Machine learning and the Flint water crisis, with Jake Abernethy
• Talking Machines: Gaussian processes and OpenAI, with IIya Sutskever

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

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.

If you enjoyed this episode, you may also want to listen to:

• Talking Machines: Getting a start in ML and applied AI at Facebook, with Joaquin Quiñonero Candela
• Talking Machines: Bias variance dilemma for humans and the arm farm, with Jeff Dean
• Talking Machines: Overfitting and asking ecological questions, with Tom Dietterich
• Talking Machines: Restricted Boltzmann Machines, with Eric Lander
• Talking Machines: Automatic Translation and t-SNE, with Hal Daume
• Talking Machines: Generative art and Hamiltonian Monte Carlo, with Doug Eck
• Talking Machines: Machine learning and the Flint water crisis, with Jake Abernethy
• Talking Machines: Gaussian processes and OpenAI, with IIya Sutskever

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

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.

• Talking Machines: Bias variance dilemma for humans and the arm farm, with Jeff Dean
• Talking Machines: Overfitting and asking ecological questions, with Tom Dietterich
• Talking Machines: Restricted Boltzmann Machines, with Eric Lander
• Talking Machines: Automatic Translation and t-SNE, with Hal Daume
• Talking Machines: Generative art and Hamiltonian Monte Carlo, with Doug Eck
• Talking Machines: Machine learning and the Flint water crisis, with Jake Abernethy
• Talking Machines: Gaussian processes and OpenAI, with IIya Sutskever

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

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.)

• Talking Machines: Overfitting and asking ecological questions, with Tom Dietterich
• Talking Machines: Restricted Boltzmann Machines, with Eric Lander
• Talking Machines: Automatic Translation and t-SNE, with Hal Daume
• Talking Machines: Generative art and Hamiltonian Monte Carlo, with Doug Eck
• Talking Machines: Machine learning and the Flint water crisis, with Jake Abernethy
• Talking Machines: Gaussian processes and OpenAI, with IIya Sutskever

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

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.

If you enjoyed this episode, you may also want to listen to:

• Talking Machines: Restricted Boltzmann Machines, with Eric Lander
• Talking Machines: Automatic Translation and t-SNE, with Hal Daume
• Talking Machines: Generative art and Hamiltonian Monte Carlo, with Doug Eck
• Talking Machines: Machine learning and the Flint water crisis, with Jake Abernethy
• Talking Machines: Gaussian processes and OpenAI, with IIya Sutskever

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

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.

If you liked this article, you may also want to read:

• Talking Machines: Restricted Boltzmann Machines, with Eric Lander
• Talking Machines: Automatic Translation and t-SNE, with Hal Daume
• Talking Machines: Generative art and Hamiltonian Monte Carlo, with Doug Eck
• Talking Machines: Machine learning and the Flint water crisis, with Jake Abernethy
• Talking Machines: Gaussian processes and OpenAI, with IIya Sutskever

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