this post was submitted on 23 Nov 2023
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Many of the building blocks of computing come from complex abstractions built on top of less complex abstractions built on top of even simpler concepts in algebra and arithmetic. If Q* can pass middle school math, then building more abstractions can be a big leap.
Huge computing resources only seem ridiculous, unsustainable, and abstract until they aren't anymore. Like typing messages a bending glass screens for other people to read...
The thing is, in general computing it was humans who figured out how to build the support for complex abstractions up from support for the simplest concepts, whilst this would have to not just support the simple concepts but actually figure out and build support for complex abstractions by itself to be GAI.
Training a neural network to do a simple task (such as addition) isn't all that hard (I get the impression that the "breaktrough" here is that they got an LLM - which is a very specific kind of NN, for language - to do it), getting it to by itself build support for complex abstractions from support for simpler concepts is something else altogether.
I know jack shit, but actual mastery of first principles would seem a massive leap in LLM development. A shift from talented bullshitter to deductive extrapolator does sound worthy of notice/concern.
With middle school math you can fairly straightforwardly do math all the way to linear algebra. Calculus requires a bit of a leap, but this still leaves a lot of the math world available.