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A key problem is that the transformer operates over very short fragments of text. Long range correlations need to be maintained to ensure 100% accuracy in repro

A key problem is that the transformer operates over very short fragments of text. Long range correlations need to be maintained to ensure 100% accuracy in reproduction/inference of relationships. I realize this is a philosophical take. It also suggests a technical solution: model longer range correlations. GPTs seem so capable because previous models were not as good at this, but the context lengths modeled with transformers are still extremely short relative to those that we work with. In result, GPTs are very good at translation and reformatting over short ranges, but struggle to deal with long form texts---a typical technique makes summaries of summaries to build out a model of a longer text. That's hardly ideal, and it's not how our minds work.

sph 4y ago
That's a good observation, though I'm not sure it's just a matter of longer range correlations, i.e. fixable by merely adding more computational power. The truth problem is fundamentally different than what those neural networks are trying to solve. We got the learning part almost right, we have made no progress towards understanding. I might be completely off the mark, my knowledge of AI is very limited. But earlier today, while reading on Lisp's history and it's demise during the AI Winter, I very much enjoyed a Wikipedia article describing how symbolic AI, the main paradigm of AI research from the 60s to the 2000s, solves a completely different problem than the currently hyped neural networks—which are very good at learning, but not much more. We might need a complementary, unifying model of both approaches to reach AGI. https://en.m.wikipedia.org/wiki/Symbolic_artificial_intelligence#Controversies https://en.m.wikipedia.org/wiki/Symbolic_artificial_intellig...