The future is bright for those in the AI/Deep Learning space.

Global Deep Learning System Software Market Report from AMA Research highlights deep analysis on market characteristics, sizing, estimates and growth by segmentation, regional breakdowns& country along with competitive landscape, players market shares, and strategies that are key in the market. The exploration provides a 360° view and insights, highlighting major outcomes of the industry. These insights help the business decision-makers to formulate better business plans and make informed decisions to improved profitability. In addition, the study helps venture or private players in understanding the companies in more detail to make better informed decisions.

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 Jitendra Malik, a professor at Berkeley and one of the seminal figures in the field of computer vision, the kind before the deep learning revolution, and the kind after.

He has been cited over 180,000 times and has mentored many world-class researchers in computer science. This conversation is part of the Artificial Intelligence podcast.

Content index:

  • 0:00 – Introduction
  • 3:17 – Computer vision is hard
  • 10:05 – Tesla Autopilot
  • 21:20 – Human brain vs computers
  • 23:14 – The general problem of computer vision
  • 29:09 – Images vs video in computer vision
  • 37:47 – Benchmarks in computer vision
  • 40:06 – Active learning
  • 45:34 – From pixels to semantics
  • 52:47 – Semantic segmentation
  • 57:05 – The three R’s of computer vision
  • 1:02:52 – End-to-end learning in computer vision
  • 1:04:24 – 6 lessons we can learn from children
  • 1:08:36 – Vision and language
  • 1:12:30 – Turing test
  • 1:16:17 – Open problems in computer vision
  • 1:24:49 – AGI
  • 1:35:47 – Pick the right problem