> Even the openly licensed ones are still the world’s most convoluted black boxes. We continue to have very little idea what they can do, how exactly they work and how best to control them.
LLM's aren't black boxes, intelligence is. Not understanding anything about things which display emergent intelligence is not a new trend: first cells, then the human brain, and now LLM's. To explicate the magic of how an LLM is able to maintain knowledge when would be analogous to understanding how the human brain synthesizes output. Yes - it is still something we should strive to understand in an explicit, programmatic way. But to de-black box the LLM would be to crack intelligence itself.
I agree that the mathematics motivating neural networks are well understood. We understand the heuristics behind them; obviously, we created them and they work. I more just mean at the most fundamental level, do we understand intelligence; could we reconstruct it without just ‘optimizing’ towards it. If we understand intelligence then we should be able to deterministically explain all behaviour of neural networks/LLM’s, instead of just poking them from the outside and seeing how they react.
LLM's aren't black boxes, intelligence is. Not understanding anything about things which display emergent intelligence is not a new trend: first cells, then the human brain, and now LLM's. To explicate the magic of how an LLM is able to maintain knowledge when would be analogous to understanding how the human brain synthesizes output. Yes - it is still something we should strive to understand in an explicit, programmatic way. But to de-black box the LLM would be to crack intelligence itself.