Generally Intelligent

Een podcast door Kanjun Qiu

Probeer Podimo de eerste 60! dagen gratis

Luister 30 dagen gratis naar exclusieve podcasts en duizenden luisterboeken

37 Afleveringen

Episode 17: Andrew Lampinen, DeepMind, on symbolic behavior, mental time travel, and insights from psychology
Gepubliceerd: 28-2-2022
Episode 16: Yilun Du, MIT, on energy-based models, implicit functions, and modularity
Gepubliceerd: 21-12-2021
Episode 15: Martín Arjovsky, INRIA, on benchmarks for robustness and geometric information theory
Gepubliceerd: 15-10-2021
Episode 14: Yash Sharma, MPI-IS, on generalizability, causality, and disentanglement
Gepubliceerd: 24-9-2021
Episode 13: Jonathan Frankle, MIT, on the lottery ticket hypothesis and the science of deep learning
Gepubliceerd: 10-9-2021
Episode 12: Jacob Steinhardt, UC Berkeley, on machine learning safety, alignment and measurement
Gepubliceerd: 18-6-2021
Episode 11: Vincent Sitzmann, MIT, on neural scene representations for computer vision and more general AI
Gepubliceerd: 20-5-2021
Episode 10: Dylan Hadfield-Menell, UC Berkeley/MIT, on the value alignment problem in AI
Gepubliceerd: 12-5-2021
Episode 09: Drew Linsley, Brown, on inductive biases for vision and generalization
Gepubliceerd: 2-4-2021
Episode 08: Giancarlo Kerg, Mila, on approaching deep learning from mathematical foundations
Gepubliceerd: 27-3-2021
Episode 07: Yujia Huang, Caltech, on neuro-inspired generative models
Gepubliceerd: 18-3-2021
Episode 06: Julian Chibane, MPI-INF, on 3D reconstruction using implicit functions
Gepubliceerd: 5-3-2021
Episode 05: Katja Schwarz, MPI-IS, on GANs, implicit functions, and 3D scene understanding
Gepubliceerd: 24-2-2021
Episode 04: Joel Lehman, OpenAI, on evolution, open-endedness, and reinforcement learning
Gepubliceerd: 17-2-2021
Episode 03: Cinjon Resnick, NYU, on activity and scene understanding
Gepubliceerd: 1-2-2021
Episode 02: Sarah Jane Hong, Latent Space, on neural rendering & research process
Gepubliceerd: 7-1-2021
Episode 01: Kelvin Guu, Google AI, on language models & overlooked research problems
Gepubliceerd: 15-12-2020

2 / 2

Technical discussions with deep learning researchers who study how to build intelligence. Made for researchers, by researchers.

Visit the podcast's native language site

37 Afleveringen

Episode 17: Andrew Lampinen, DeepMind, on symbolic behavior, mental time travel, and insights from psychology

Episode 16: Yilun Du, MIT, on energy-based models, implicit functions, and modularity

Episode 15: Martín Arjovsky, INRIA, on benchmarks for robustness and geometric information theory

Episode 14: Yash Sharma, MPI-IS, on generalizability, causality, and disentanglement

Episode 13: Jonathan Frankle, MIT, on the lottery ticket hypothesis and the science of deep learning

Episode 12: Jacob Steinhardt, UC Berkeley, on machine learning safety, alignment and measurement

Episode 11: Vincent Sitzmann, MIT, on neural scene representations for computer vision and more general AI

Episode 10: Dylan Hadfield-Menell, UC Berkeley/MIT, on the value alignment problem in AI

Episode 09: Drew Linsley, Brown, on inductive biases for vision and generalization

Episode 08: Giancarlo Kerg, Mila, on approaching deep learning from mathematical foundations

Episode 07: Yujia Huang, Caltech, on neuro-inspired generative models

Episode 06: Julian Chibane, MPI-INF, on 3D reconstruction using implicit functions

Episode 05: Katja Schwarz, MPI-IS, on GANs, implicit functions, and 3D scene understanding

Episode 04: Joel Lehman, OpenAI, on evolution, open-endedness, and reinforcement learning

Episode 03: Cinjon Resnick, NYU, on activity and scene understanding

Episode 02: Sarah Jane Hong, Latent Space, on neural rendering & research process

Episode 01: Kelvin Guu, Google AI, on language models & overlooked research problems