Panel: Responsible Data Science in the Fight Against COVID-19 - #370
In this discussion, we explore how data scientists and ML/AI practitioners can responsibly contribute to the fight against coronavirus and COVID-19. Four experts: Rex Douglass, Rob Munro, Lea Shanley, and Gigi Yuen-Reed shared a ton of valuable insight on the best ways to get involved. We've gathered all the resources that our panelists discussed during the conversation, you can find those at twimlai.com/talk/370.
29 Apr 202058min
Adversarial Examples Are Not Bugs, They Are Features with Aleksander Madry - #369
Today we’re joined by Aleksander Madry, Faculty in the MIT EECS Department, to discuss his paper “Adversarial Examples Are Not Bugs, They Are Features.” In our conversation, we talk through what we expect these systems to do, vs what they’re actually doing, if we’re able to characterize these patterns, and what makes them compelling, and if the insights from the paper will help inform opinions on either side of the deep learning debate.
27 Apr 202041min
AI for Social Good: Why "Good" isn't Enough with Ben Green - #368
Today we’re joined by Ben Green, PhD Candidate at Harvard and Research Fellow at the AI Now Institute at NYU. Ben’s research is focused on the social and policy impacts of data science, with a focus on algorithmic fairness and the criminal justice system. We discuss his paper ‘Good' Isn't Good Enough,’ which explores the 2 things he feels are missing from data science and machine learning research; A grounded definition of what “good” actually means, and the absence of a “theory of change.
23 Apr 202041min
The Evolution of Evolutionary AI with Risto Miikkulainen - #367
Today we’re joined by Risto Miikkulainen, Associate VP of Evolutionary AI at Cognizant AI. Risto joined us back on episode #47 to discuss evolutionary algorithms, and today we get an update on the latest on the topic. In our conversation, we discuss use cases for evolutionary AI and the latest approaches to deploying evolutionary models. We also explore his paper “Better Future through AI: Avoiding Pitfalls and Guiding AI Towards its Full Potential,” which digs into the historical evolution of AI.
20 Apr 202037min
Neural Architecture Search and Google’s New AutoML Zero with Quoc Le - #366
Today we’re super excited to share our recent conversation with Quoc Le, a research scientist at Google. Quoc joins us to discuss his work on Google’s AutoML Zero, semi-supervised learning, and the development of Meena, the multi-turn conversational chatbot. This was a really fun conversation, so much so that we decided to release the video! April 16th at 12 pm PT, Quoc and Sam will premiere the video version of this interview on Youtube, and answer your questions in the chat. We’ll see you there!
16 Apr 202054min
Automating Electronic Circuit Design with Deep RL w/ Karim Beguir - #365
Today we’re joined by return guest Karim Beguir, Co-Founder and CEO of InstaDeep. In our conversation, we chat with Karim about InstaDeep’s new offering, DeepPCB, an end-to-end platform for automated circuit board design. We discuss challenges and problems with some of the original iterations of auto-routers, how Karim defines circuit board “complexity,” the differences between reinforcement learning being used for games and in this use case, and their spotlight paper from NeurIPS.
13 Apr 202035min
Neural Ordinary Differential Equations with David Duvenaud - #364
Today we’re joined by David Duvenaud, Assistant Professor at the University of Toronto, to discuss his research on Neural Ordinary Differential Equations, a type of continuous-depth neural network. In our conversation, we talk through a few of David’s papers on the subject. We discuss the problem that David is trying to solve with this research, the potential that ODEs have to replace “the backbone” of the neural networks that are used to train today, and David’s approach to engineering.
9 Apr 202049min
The Measure and Mismeasure of Fairness with Sharad Goel - #363
Today we’re joined by Sharad Goel, Assistant Professor at Stanford. Sharad, who also has appointments in the computer science, sociology, and law departments, has spent recent years focused on applying ML to understanding and improving public policy. In our conversation, we discuss Sharad’s extensive work on discriminatory policing, and The Stanford Open Policing Project. We also dig into Sharad’s paper “The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning.”
6 Apr 202048min
