#59 – Jeff Hawkins (Thousand Brains Theory)

The ultimate goal of neuroscience is to learn how the human brain gives rise to human intelligence and what it means to be intelligent. Understanding how the brain works is considered one of humanity’s greatest challenges.

Jeff Hawkins thinks that the reality we perceive is a kind of simulation, a hallucination, a confabulation. He thinks that our brains are a model reality based on thousands of information streams originating from the sensors in our body. Critically – Hawkins doesn’t think there is just one model but rather; thousands.

Jeff has just released his new book, A thousand brains: a new theory of intelligence. It’s an inspiring and well-written book and I hope after watching this show; you will be inspired to read it too.

Pod version: https://anchor.fm/machinelearningstreettalk/episodes/59—Jeff-Hawkins-Thousand-Brains-Theory-e16sb64

A Thousand Brains: A New Theory Of Intelligence by Jeff Hawkins

The Thousand Brains Theory of Intelligence


A Framework for Intelligence and Cortical Function Based on Grid Cells in the Neocortex

Your Brain Is Not an Onion With a Tiny Reptile Inside

Pruning Neural Networks at Initialization: Why are We Missing the Mark?

Dr. Keith Duggar https://twitter.com/DoctorDuggar
Connor Leahy https://twitter.com/npcollapse

Our thanks to:
Matthieu Thiboust (https://www.insightsfromthebrain.com/ + https://twitter.com/mthiboust)
Shwetha Bharadwaj (show research https://www.linkedin.com/in/shwetha-bharadwaj-2b926a1b2/)
Andreas Koepf (show research https://twitter.com/neurosp1ke?lang=en)

[00:00:00] Introduction
[00:03:03] The Neocortex
[00:09:58] Triune Brain
[00:12:24] Grid and place cells
[00:14:54] Reference frames
[00:21:03] Mountcastle
[00:25:46] Thousand brains theory of intelligence
[00:32:40] HTM
[00:41:12] Sparsity
[00:52:57] Main show kick off
[00:54:36] Tribalism in the ML Community
[00:57:14] Variation in approaches to the same goal
[00:59:43] Hawkins ideas validated, cortical uniformity
[01:02:25] Sparse distributed representations (SDRs)
[01:06:08] Reference frames as generalization
[01:10:29] Reference frame remapping
[01:14:14] Reference frames can generalize beyond three dimensions
[01:17:26] And generalize beyond spatial topology
[01:20:12] Intuitions behind why SDRs work well
[01:24:03] Are their capacity concerns with the SDR model
[01:27:11] At what level between GOFAI and Connectionism should focus our effort?
[01:31:33] The brain reasons by abstract movement through reference frames
[01:35:34] Human’s don’t know Universal Truth (if there is even such a thing)
[01:37:34] Learning elsewhere in the brain besides the neocortex
[01:40:44] Stochastic backpropagation in the human brain
[01:44:04] What’s missing from artificial neural networks? Numenta’s roadmap
[01:48:59] AGI Risk – the alignment problem
[01:54:07] AGI risk – the neocortex can thwart the old brain
[01:57:47] AGI risk – artificial evolution
[02:01:18] AGI risk – yes we need to think on and develop adequate control systems
[02:03:48] A balance of knowledge: innate, experiential, taught, or deduced
[02:16:09] post-show wrap-up
[02:16:59] Advancements in direction at Numenta
[02:19:50] AGI risk recap
[02:23:56] Ought did evolve from Is, humans are the proof
[02:26:29] When AGI overcomes our weaknesses
[02:28:54] Who doesn’t like forking?!
[02:30:29] Coherent synchronization as a measure of identity

#machinelearning #artificialintelligence

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