Generally Intelligent

Podcast autorstwa Kanjun Qiu

37 Odcinki

Episode 17: Andrew Lampinen, DeepMind, on symbolic behavior, mental time travel, and insights from psychology
Opublikowany: 28.02.2022
Episode 16: Yilun Du, MIT, on energy-based models, implicit functions, and modularity
Opublikowany: 21.12.2021
Episode 15: Martín Arjovsky, INRIA, on benchmarks for robustness and geometric information theory
Opublikowany: 15.10.2021
Episode 14: Yash Sharma, MPI-IS, on generalizability, causality, and disentanglement
Opublikowany: 24.09.2021
Episode 13: Jonathan Frankle, MIT, on the lottery ticket hypothesis and the science of deep learning
Opublikowany: 10.09.2021
Episode 12: Jacob Steinhardt, UC Berkeley, on machine learning safety, alignment and measurement
Opublikowany: 18.06.2021
Episode 11: Vincent Sitzmann, MIT, on neural scene representations for computer vision and more general AI
Opublikowany: 20.05.2021
Episode 10: Dylan Hadfield-Menell, UC Berkeley/MIT, on the value alignment problem in AI
Opublikowany: 12.05.2021
Episode 09: Drew Linsley, Brown, on inductive biases for vision and generalization
Opublikowany: 2.04.2021
Episode 08: Giancarlo Kerg, Mila, on approaching deep learning from mathematical foundations
Opublikowany: 27.03.2021
Episode 07: Yujia Huang, Caltech, on neuro-inspired generative models
Opublikowany: 18.03.2021
Episode 06: Julian Chibane, MPI-INF, on 3D reconstruction using implicit functions
Opublikowany: 5.03.2021
Episode 05: Katja Schwarz, MPI-IS, on GANs, implicit functions, and 3D scene understanding
Opublikowany: 24.02.2021
Episode 04: Joel Lehman, OpenAI, on evolution, open-endedness, and reinforcement learning
Opublikowany: 17.02.2021
Episode 03: Cinjon Resnick, NYU, on activity and scene understanding
Opublikowany: 1.02.2021
Episode 02: Sarah Jane Hong, Latent Space, on neural rendering & research process
Opublikowany: 7.01.2021
Episode 01: Kelvin Guu, Google AI, on language models & overlooked research problems
Opublikowany: 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 Odcinki

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