The whole point is that LLMs, especially the attention mechanism in transformers, have already paved the road to AGI. The main gap is the training data and its quality. Humans have generations of distilled knowledge — books, language, culture passed down over centuries. And on top of that we have the physical world — we watched birds fly, saw apples drop, touched hot things. Maybe we should train the base model with physical world data first, and then fine tune with the distilled knowledge.
lanstin|17 hours ago
There's plenty of training data, for a human. The LLM architecture is not as efficient as the brain; perhaps we can overcome that with enough twitter posts from PhDs, and enough YouTubes of people answering "why" to their four year olds and college lectures, but that's kind of an experimental question.
Starting a network out in a contrained body and have it learn how to control that, with a social context of parents and siblings would be an interesting experiment, especially if you could give it an inherent temporality and a good similar-content-addressable persistent memory. Perhaps a bit terrifying experiment, but I guess the protocols for this would be air-gapped, not internet connected with a credit card.