Generally Intelligent

36 Episoder

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

   Publisert: 21.12.2021
2. Episode 15: Martín Arjovsky, INRIA, on benchmarks for robustness and geometric information theory

   Publisert: 15.10.2021
3. Episode 14: Yash Sharma, MPI-IS, on generalizability, causality, and disentanglement

   Publisert: 24.9.2021
4. Episode 13: Jonathan Frankle, MIT, on the lottery ticket hypothesis and the science of deep learning

   Publisert: 10.9.2021
5. Episode 12: Jacob Steinhardt, UC Berkeley, on machine learning safety, alignment and measurement

   Publisert: 18.6.2021
6. Episode 11: Vincent Sitzmann, MIT, on neural scene representations for computer vision and more general AI

   Publisert: 20.5.2021
7. Episode 10: Dylan Hadfield-Menell, UC Berkeley/MIT, on the value alignment problem in AI

   Publisert: 12.5.2021
8. Episode 09: Drew Linsley, Brown, on inductive biases for vision and generalization

   Publisert: 2.4.2021
9. Episode 08: Giancarlo Kerg, Mila, on approaching deep learning from mathematical foundations

   Publisert: 27.3.2021
10. Episode 07: Yujia Huang, Caltech, on neuro-inspired generative models

    Publisert: 18.3.2021
11. Episode 06: Julian Chibane, MPI-INF, on 3D reconstruction using implicit functions

    Publisert: 5.3.2021
12. Episode 05: Katja Schwarz, MPI-IS, on GANs, implicit functions, and 3D scene understanding

    Publisert: 24.2.2021
13. Episode 04: Joel Lehman, OpenAI, on evolution, open-endedness, and reinforcement learning

    Publisert: 17.2.2021
14. Episode 03: Cinjon Resnick, NYU, on activity and scene understanding

    Publisert: 1.2.2021
15. Episode 02: Sarah Jane Hong, Latent Space, on neural rendering & research process

    Publisert: 7.1.2021
16. Episode 01: Kelvin Guu, Google AI, on language models & overlooked research problems

    Publisert: 15.12.2020