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.
Absolutely
“breaktrough” here is that they got an LLM - which is a very specific kind of NN, for language - to do it)
To some degree this is how humans are able to go about creating abstractions. Intelligence isn’t 1:1 with language but it’s part of the puzzle. Communication of your mathematical concepts and abstractions in a way that can be replicated and confirmed using a rigorous proofing/scientific method requires the use of communication through language.
Speech and writing are touch at a distance. Speech moves the air to eventually touch nerve endings in ear and brain. Similarly, yet very differen, writing stores ideas (symbols, emotions, images, words, etc) as an abstraction on/in some type of storage media (ink on paper, stone etching stone, laser cutting words into metal, a stick in the mud…) to reflect just the right wavelengths of light into sensors in your retina focused by your lenses “touching” you from a distance as well.
Having two+ “language” models be capable of using an abstraction to solve mathematical ideas is absolutely the big deal…
Hmmm?