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This is: My computational framework for the brain , published by Steven Byrnes on the AI Alignment Forum.
Crossposted from the AI Alignment Forum. May contain more technical jargon than usual.
By now I've written a bunch of blog posts on brain architecture and algorithms, not in any particular order and generally interspersed with long digressions into Artificial General Intelligence. Here I want to summarize my key ideas in one place, to create a slightly better entry point, and something I can refer back to in certain future posts that I'm planning. If you've read every single one of my previous posts (hi mom!), there's not much new here.
In this post, I'm trying to paint a picture. I'm not really trying to justify it, let alone prove it. The justification ultimately has to be: All the pieces are biologically, computationally, and evolutionarily plausible, and the pieces work together to explain absolutely everything known about human psychology and neuroscience. (I believe it! Try me!) Needless to say, I could be wrong in both the big picture and the details (or missing big things). If so, writing this out will hopefully make my wrongness easier to discover!
Pretty much everything I say here and its opposite can be found in the cognitive neuroscience literature. (It's a controversial field!) I make no pretense to originality (with one exception noted below), but can't be bothered to put in actual references. My previous posts have a bit more background, or just ask me if you're interested. :-P
So let's start in on the 7 guiding principles for how I think about the brain:
1. Two subsystems: "Neocortex" and "Subcortex"
This is the starting point. I think it's absolutely critical. The brain consists of two subsystems. The neocortex is the home of "human intelligence" as we would recognize it—our beliefs, goals, ability to plan and learn and understand, every aspect of our conscious awareness, etc. etc. (All mammals have a neocortex; birds and lizards have an homologous and functionally-equivalent structure called the "pallium".) Some other parts of the brain (hippocampus, parts of the thalamus & basal ganglia & cerebellum—see further discussion here) help the neocortex do its calculations, and I lump them into the "neocortex subsystem". I'll use the term subcortex for the rest of the brain (brainstem, hypothalamus, etc.).
Aside: Is this the triune brain theory? No. Triune brain theory is, from what I gather, a collection of ideas about brain evolution and function, most of which are wrong. One aspect of triune brain theory is putting a lot of emphasis on the distinction between neocortical calculations and subcortical calculations. I like that part. I'm keeping that part, and I'm improving it by expanding the neocortex club to also include the thalamus, hippocampus, lizard pallium, etc., and then I'm ignoring everything else about triune brain theory.
2. Cortical uniformity
I claim that the neocortex is, to a first approximation, architecturally uniform, i.e. all parts of it are running the same generic learning algorithm in a massively-parallelized way.
The two caveats to cortical uniformity (spelled out in more detail at that link) are:
There are sorta "hyperparameters" on the generic learning algorithm which are set differently in different parts of the neocortex—for example, different regions have different densities of each neuron type, different thresholds for making new connections (which also depend on age), etc. This is not at all surprising; all learning algorithms inevitably have tradeoffs whose optimal settings depend on the domain that they're learning (no free lunch).
As one of many examples of how even "generic" learning algorithms benefit from domain-specific hyperparameters, if you've seen a pattern "A then B then C" recur 10 times in a row, you will start un...
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