Scientific Rigor and Turning Information into Action
Code Review for Community Change
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 NIPS, researcher...
The Pace of Change and The Public View of ML
The Long View and Learning in Person
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.
Machine Learning in the Field and Bayesian Baked Goods
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...
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...
Getting a Start in ML and Applied AI at Facebook
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...
Bias Variance Dilemma for Humans and the Arm Farm
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...
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