Generally Intelligent

A podcast by Kanjun Qiu

37 Episodio

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

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

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37 Episodio

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