Yannic Kilcher explains the paper “Hopfield Networks is All You Need.”

Hopfield Networks are one of the classic models of biological memory networks. This paper generalizes modern Hopfield Networks to continuous states and shows that the corresponding update rule is equal to the attention mechanism used in modern Transformers. It further analyzes a pre-trained BERT model through the lens of Hopfield Networks and uses a Hopfield Attention Layer to perform Immune Repertoire Classification.

Content outline:

  • 0:00 – Intro & Overview
  • 1:35 – Binary Hopfield Networks
  • 5:55 – Continuous Hopfield Networks
  • 8:15 – Update Rules & Energy Functions
  • 13:30 – Connection to Transformers
  • 14:35 – Hopfield Attention Layers
  • 26:45 – Theoretical Analysis
  • 48:10 – Investigating BERT
  • 1:02:30 – Immune Repertoire Classification

Lex Fridman interviews Marcus Hutter ,a senior research scientist at DeepMind and professor at Australian National University.

Throughout his career of research, including with Jürgen Schmidhuber and Shane Legg, he has proposed a lot of interesting ideas in and around the field of artificial general intelligence, including the development of the AIXI model which is a mathematical approach to AGI that incorporates ideas of Kolmogorov complexity, Solomonoff induction, and reinforcement learning. This conversation is part of the Artificial Intelligence podcast.

OUTLINE:
0:00 – Introduction
3:32 – Universe as a computer
5:48 – Occam’s razor
9:26 – Solomonoff induction
15:05 – Kolmogorov complexity
20:06 – Cellular automata
26:03 – What is intelligence?
35:26 – AIXI – Universal Artificial Intelligence
1:05:24 – Where do rewards come from?
1:12:14 – Reward function for human existence
1:13:32 – Bounded rationality
1:16:07 – Approximation in AIXI
1:18:01 – Godel machines
1:21:51 – Consciousness
1:27:15 – AGI community
1:32:36 – Book recommendations
1:36:07 – Two moments to relive (past and future)