Generally Intelligent #15: Martin Arjovsky, INRIA, on benchmarks for robustness and geometric information theory
Martín Arjovsky (Google Scholar) did his Ph.D. at NYU with Leon Bottou. Some of his well-known works include the Wasserstein GAN and a paradigm called Invariant Risk Minimization. In this episode, we...
Generally Intelligent #14: Yash Sharma, MPI-IS, on generalizability, causality, and disentanglement
Yash Sharma (Google Scholar) (Website) is a Ph.D. student at the International Max Planck Research School for Intelligent Systems. He previously studied electrical engineering at Cooper Union and has...
Generally Intelligent #13: Jonathan Frankle, MIT, on the lottery ticket hypothesis and the science of deep learning
Jonathan Frankle (Google Scholar) (Website) is finishing his PhD at MIT, advised by Michael Carbin. His main research interest is using experimental methods to understand the behavior of neural networks....
Generally Intelligent #12: Jacob Steinhardt, UC Berkeley, on machine learning safety, alignment and measurement
Jacob Steinhardt (Google Scholar) (Website) is an assistant professor at UC Berkeley. His main research interest is in designing machine learning systems that are reliable and aligned with human values....
Generally Intelligent #11: Vincent Sitzmann, MIT, on neural scene representations for computer vision and more general AI
Vincent Sitzmann (Google Scholar) (Website) is a postdoc at MIT. His work is on neural scene representations in computer vision. Ultimately, he wants to make representations that AI agents can use...
Generally Intelligent #10: Dylan Hadfield-Menell, UC Berkeley/MIT, on the value alignment problem in AI
Dylan Hadfield-Menell (Google Scholar) (Website) recently finished his PhD at UC Berkeley and is starting as an assistant professor at MIT. He works on the problem of designing AI algorithms that pursue...
Generally Intelligent #9: Drew Linsley, Brown, on inductive biases for vision and generalization
Drew Linsley (Google Scholar) (Website) is a Paul J. Salem senior research associate at Brown, advised by Thomas Serre. He is working on building computational models of the visual system that serve...
Generally Intelligent #8: Giancarlo Kerg, Mila, on approaching deep learning from mathematical foundations
Giancarlo Kerg (Google Scholar) is a PhD student at Mila, supervised by Yoshua Bengio and Guillaume Lajoie. He is working on out-of-distribution generalization and modularity in memory-augmented neural...
Generally Intelligent #7: Yujia Huang, Caltech, on neuro-inspired generative models
Yujia Huang (@YujiaHuangC) is a PhD student at Caltech, working at the intersection of deep learning and neuroscience. She worked on optics and biophotonics before venturing into machine learning. Now,...
Generally Intelligent #6: Julian Chibane, MPI-INF, on 3D reconstruction using implicit functions
Our next guest, Julian Chibane, is a PhD student at the Real Virtual Humans group at the Max Planck Institute for Informatics in Germany. His recent work centers around implicit functions for 3D reconstruction,...
Generally Intelligent #5: Katja Schwarz, MPI-IS, on GANs, implicit functions, and 3D scene understanding
Katja Schwartz (Google Scholar) came to machine learning from physics, and is now working on 3D geometric scene understanding at the Max Planck Institute for Intelligent Systems. Her most recent work,...
Generally Intelligent #4: Joel Lehman, OpenAI, on evolving intelligence, open-endedness, and reinforcement learning
Our fourth episode features Joel Lehman (Google Scholar), previously a founding member at Uber AI Labs and assistant professor at the IT University of Copenhagen. He's now a research scientist at OpenAI,...
Generally Intelligent #3: Cinjon Resnick, NYU, on activity and scene understanding
On this episode of Generally Intelligent, we interview Cinjon Resnick (Google Scholar), formerly from Google Brain and now doing his PhD at NYU, about why he believes scene understanding is critical...
Generally Intelligent #2: Sarah Jane Hong, Latent Space, on neural rendering & research process
Excited to release our second episode of Generally Intelligent! This time we’re featuring Sarah Jane Hong, co-founder of Latent Space, a startup building the first fully AI-rendered 3D engine in order...
Generally Intelligent #1: Kelvin Guu, Google AI, on language models & overlooked problems
Our first guest is Kelvin Guu, a senior research scientist at Google AI, where he develops new methods for machine learning and language understanding. Kelvin is the co-author of REALM: Retrieval-Augmented...
Generally Intelligent #0: A podcast for deep learning researchers
Over the past few years, we’ve been a part of countless conversations with various deep learning researchers about the hunches and processes that inform their work. These conversations happen as part of...