Generally Intelligent

Martín Arjovsky 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 discuss out-of-distribution generalization, geometric information theory, and the importance of good benchmarks.

Yash Sharma 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 spent time at Borealis AI and IBM Research. Yash’s early work was on adversarial examples and his current research interests span a variety of topics in representation disentanglement. In this episode, we discuss robustness to...

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. His current work focuses on finding sparse, trainable neural networks. **Highlights from our conversation:**  🕸  "Why is sparsity everywhere? This isn't an accident." 🤖  "If I gave you 500 GP...

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.  Some of his specific research directions include robustness, rewards specification and reward hacking, as well as scalable alignment. Highlights: 📜“Test accuracy is a very limited metric.”...

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 to solve the same visual tasks humans solve regularly, but that are currently impossible for AI. **Highlights from our conversation:** 👁 “Vision is about the question of building representations” 🧠...

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 the intended goal of their users, designers, and society in general.  This is known as the value alignment problem. Highlights from our conversation: 👨‍👩‍👧‍👦 How to align AI to human values...

Drew Linsley ( Google Scholar ) 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 the dual purpose of (1) explaining biological function and (2) extending artificial vision. Highlights from our conversation: 🧠 Building neural-inspired inductive biases into computer vision 🖼 A learning algo...

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 networks.  Highlights from our conversation: 🧮 Pure math foundations as an approach to progress and structural understanding in deep learning research 🧠 How a formal proof on the way self-atten...

Yujia Huang ( Website ) 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, she hopes to design “less artificial” artificial intelligence. Highlights from our conversation: 🏗 How recurrent generative feedback, a neuro-inspired design, improves adversarial robustness and and c...

Julian Chibane ( Google Scholar ) is a PhD student at the Real Virtual Humans group at the Max Planck Institute for Informatics in Germany.  His recent work centers around intrinsic functions for 3D reconstruction. Highlights from our conversation: 🖼 How, surprisingly, the IF-Net architecture learned reasonable representations of humans & objects without priors 🔢 A simple observation that led...

Katja Schwartz 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, “ Generative Radiance Fields for 3D-Aware Image Synthesis, ” revealed that radiance fields are a powerful representation for generative image synthesis, leading to 3D consistent models that render with high fidelity....

Joel Lehman was 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, where he focuses on open-endedness, reinforcement learning, and AI safety. Joel’s PhD dissertation introduced the novelty search algorithm. That work inspired him to write the popular science book, “ Why Greatness Cannot Be Planned ”, wit...

Cinjon Resnick was formerly from Google Brain and now is doing his PhD at NYU. We talk about why he believes scene understanding is critical to out of distribution generalization, and how his theses have evolved since he started his PhD. Some topics we over: How Cinjon started his research by trying to grow a baby through language and games, before running into a wall with this approach How spendi...

Sarah Jane Hong is the co-founder of Latent Space, a startup building the first fully AI-rendered 3D engine in order to democratize creativity. We touch on what it was like taking classes under Geoff Hinton in 2013, the trouble with using natural language prompts to render a scene, why a model’s ability to scale is more important than getting state-of-the-art results, and more.

We interview Kelvin Guu, a researcher at Google AI and the creator of REALM.  The conversation is a wide-ranging tour of language models, how computers interact with world knowledge, and much more.