Comment by HarHarVeryFunny
1 year ago
We're learning valuable lessons from all modern large-scale (post-AlexNet) NN architectures, transformers included, and NNs (but maybe trained differently) seem a viable approach to implement AGI, so we're making progress ... but maybe LLMs will be more inspiration than part of the (a) final solution.
OTOH, maybe pre-trained LLMs could be used as a hardcoded "reptilian brain" that provides some future AGI with some base capabilities (vs being sold as newborn that needs 20 years of parenting to be useful) that the real learning architecture can then override.
I would think they'd be more likely to form the language centre of a composite AGI brain. If you read through the known functions of the various areas involved in language[0] they seem to map quite well to the capabilities of transformer based LLMs especially the multi-modal ones.
[0] https://en.wikipedia.org/wiki/Language_center
It's not obvious that an LLM - a pre-trained/frozen chunk of predictive statistics - would be amenable to being used as an integral part of an AGI that would necessarily be using a different incremental learning algorithm.
Would the transformer architecture be compatible with the needs of an incremental learning system? It's missing the top down feedback paths (finessed by SGD training) needed to implement prediction-failure driven learning that feature so heavily in our own brain.
This is why I could more see a potential role for a pre-trained LLM as a separate primitive subsystem to be overidden, or maybe (more likely) we'll just pre-expose an AGI brain to 20 years of sped-up life experience and not try to import an LLM to be any part of it!
Its entirely possible to have an AGI language model that is periodically retrained as slang, vernacular, and semantic embeddings shift in their meaning. I have little doubt that something very much like an LLM (a machine that turns high dimensional intent into words) will form an AGIs 'language center' at some point.
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