Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio.
This is: Book review: "A Thousand Brains" , published by Jeff Hawkins, Steve Byrnes on the AI Alignment Forum.
Jeff Hawkins gets full credit for getting me first interested in the idea that neuroscience might lead to artificial general intelligence—an idea which gradually turned into an all-consuming hobby, and more recently a new job. I'm not alone in finding him inspiring. Andrew Ng claimed here that Hawkins helped convince him, as a young professor, that a simple scaled-up learning algorithm could reach Artificial General Intelligence (AGI). (Ironically, Hawkins scoffs at the deep neural nets built by Ng and others—Hawkins would say: "Yes yes, a simple scaled-up learning algorithm can reach AGI, but not that learning algorithm!!")
Hawkins's last book was On Intelligence in 2004. What's he been up to since then? Well, if you don't want to spend the time reading his journal articles or watching his research meetings on YouTube, good news for you—his new book, A Thousand Brains, is out! There’s a lot of fascinating stuff here. I'm going to pick and choose a couple topics that I find especially interesting and important, but do read the book for much more that I'm not mentioning.
A grand vision of how the brain works
Many expert neuroscientists think that the brain is horrifically complicated, and we are centuries away from understanding it well enough to build AGI (i.e., computer systems that have the same kind of common-sense and flexible understanding of the world and ability to solve problems that humans do). Not Jeff Hawkins! He thinks we can understand the brain well enough to copy its principles into an AGI. And he doesn't think that goal is centuries away. He thinks we're most of the way there! In an interview last year he guessed that we’re within 20 years of finishing the job.
The people arguing that the brain is horrifically complicated seem at first glance to have a strong case. The brain has a whopping
10
11
neurons with
10
14
synapses, packed full of intricate structure. One study found 180 distinct areas within the cerebral cortex. Neuroscience students pour over huge stacks of flashcards with terms like “striatum”, “habenula”, “stria medullaris”, “fregula”, and "interpeduncular nucleus". (Quiz: Which of those are real brain regions, and which are types of pasta?) Every year we get another 50,000 or so new neuroscience papers dumped into our ever-deepening ocean of knowledge about the brain, with no end in sight.
So the brain is indeed horrifically complicated. Right? Well, Jeff Hawkins and like-minded thinkers have a rebuttal, and it comes in two parts:
1. The horrific complexity of the “old brain” doesn’t count, because we don’t need it for AGI
According to Hawkins, much of the brain—including a disproportionate share of the brain's horrific complexity, like the interpeduncular nucleus I mentioned—just doesn’t count. Yes it’s complicated. But we don’t care, because understanding it is not necessary for building AGI. In fact, understanding it is not even helpful for building AGI!
I’m talking here about the distinction between what Hawkins calls “old brain vs new brain”. The “new brain” is the mammalian neocortex, a wrinkly sheet on that is especially enlarged in humans, wrapping around the outside of the human brain, about 2.5 mm thick and the size of a large dinner napkin (if you unwrinkled it). The “old brain” is everything else in the brain, which (says Hawkins) is more similar between mammals, reptiles, and so on.
“The neocortex is the organ of intelligence,” writes Hawkins. “Almost all the capabilities we think of as intelligence—such as vision, language, music, math, science, and engineering—are created by the neocortex. When we think about something, it is mostly the neocortex doing the thinking.. If we want to understand intelligence, then we have to un...
view more