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General intelligence is an ability to cope, adapt and thrive in an ecology: to start from a limited set of capabilities, and via exploration, acquire a rich competence. To develop conceptualisations, techniques of coordination and control, to form novel goals and strategies to realise them, and so on. General intelligence is a strategy to defer the acquisition of abilities from the process of construction/blueprinting (ie., genes, evolution..) to the living environment of the animal. The most generally intelligent animals are those that have nearly all of their sensory motor skills acquired during their life -- we learn to walk and so can learn to play the piano, and to build a rocket. There is a serious discontinuity in strategy to achive this defferal: the kinds of processes which "blueprint" the intelligence of a bacterium are discontinuous with the processes which a living animal needs to dynamically conceptualise its environment under shifts to its structure. Of the latter animals need: living adaption of their sensory-motor systems, heirachical coordination of their bodies, robust causal modelling, and so on. General intelligence is primitively a kind of movement, which becomes abstract only with a few hundred thousand years of culture. The earliest humans, able to lingusitically express almost nothing, were nevertheless generally intelligent. Present computer-science-led investigations into "intelligence" assume you can operate syntactically across the most peripheral consequences of general intelligence given by linguistic representations. This is profoundly misguided: each todller necessarily must learn to walk. You cannot just project a slideshow of walking, and get anywhere. And if you remove this capability and install a "walking module", you've remved the very capabilities which allow that child then to do anything new at all. There is nothing in the linguistic syntactical shadow of human intelligence to be found in creating generally capable systems. It's just overfitting to our 2024 reflections. |
Having said that, one can also make the case that LLMs start from a limited set of capabilities and, via exploration, acquire a rich competence. Only these are linguistic abilities and the exploration is exploration of a linguistic environment. Maybe the real intelligence is the friends we made along the way i.e. the general class of algorithms roughly called "backpropagation and gradient descent on a very high-dimensional neural network".