Comment by skeledrew

> True, but we have RAG to offset that.

In practice that doesn’t always work… I’ve seen cases where (a) the answer is in the RAG but the model can’t find it because it didn’t use the right search terms-embeddings and vector search reduces the incidence of that but cannot eliminate it; (b) the model decided not to use the search tool because it thought the answer was so obvious that tool use was unnecessary; (c) model doubts, rejects, or forgets the tool call results because they contradict the weights; (d) contradictions between data in weights and data in RAG produce contradictory or ineloquent output; (e) the data in the RAG is overly diffuse and the tool fails to surface enough of it to produce the kind of synthesis of it all which you’d get if the same info was in the weights

This is especially the case when the facts have changed radically since the model was trained, e.g. “who is the Supreme Leader of Iran?”

> We'll get there eventually. Keep in mind that the brain is now about 300k years into fine-tuning itself as this species classified as homo sapiens. LLMs haven't even been around for 5 years yet.

We probably will eventually-but I doubt we’ll get there purely by scaling existing approaches-more likely, novel ideas nobody has even thought of yet will prove essential, and a human-level AI model will have radical architectural differences from the current generation