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But any specific training and inference scripts and the definition of the neural network architecture are, likewise, a negligibly small part of the complexity of implementable AGI – from the hardware level with optimizations for specific instructions, to the structure contained in the training data. What you and @meh commit is a fallacy, judging human complexity going by the full stack of human production but limit our consideration of AI to the high-level software slice.
Human-specific DNA is what makes us humans, it's the chief differentiator in the space of nontrivial possible outcomes; it is, in principle, possible to grow a human embryo (maybe a shitty one) in a pig's uterus, in an artificial womb or even using a nonhuman oocyte, but no combination of genuine non-genomic human factors would suffice without human DNA.
The most interesting part is that we know that beings very similar to us in all genomic and non-genomic ways and even in the architecture of their brains lack general intelligence and can't do anything much more impressive than current gen models. So general intelligence also can't be all that complex. We haven't had the population to evolve a significant breakthrough – our brain is a scaled-up primate brain which in turn is a generic mammalian brain with some quantitative polish, and its coolest features reemerge in drastically different lineages at similar neural scales.
Carmack's analogy is not perfectly spoken, but on point.
This, basically. GPT-3 started as a few thousand lines of code that instantiated a transformer model several hundred gigabytes in size and then populated this model with useful weights by training it, at the cost of a few million dollars worth of computing resources, on 45 TB of tokenized natural language text — all of Wikipedia, thousands of books, archives of text crawled from the web.
Run in "inference" mode, the model takes a stream of tokens and predicts the next one, based on relationships between tokens that it inferred during the training process. Coerce a model like this a bit with RLHF, give it an initial prompt telling it to be a helpful chatbot, and you get ChatGPT, with all of the capabilities it demonstrates.
So by way of analogy the few thousand lines of code are brain-specific genes, the training/inference processes occupying hundreds of gigabytes of VRAM across multiple A100 GPUs are the brain, and the training data is "experience" fed into the brain.
Preexisting compilers, libraries, etc. are analogous to the rest of the biological environment — genes that code for things that aren't brain-specific but some of which are nonetheless useful in building brains, cellular machinery that translates genes into proteins, etc.
The analogy isn't perfect, but it's surprisingly good considering it relies on biology and computing being comprehensible through at least vaguely corresponding abstractions, and it's not obvious a priori that they would be.
Anyway, Carmack and many others now believe this basic approach — with larger models, more data, different types of data, and perhaps a few more architectural innovations — might solve the hard parts of intelligence. Given the capability breakthroughs the approach has already delivered as it has been scaled and refined, this seems fairly plausible.
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