Notice though, that all these improvements have been with pretty basic transformer models that output all their tokens-- no internal thoughts, no search, no architecture improvements and things are only fed through them once.
But we could add internal thoughts-- we could make the model generate tokens that aren't part of its output but are there for it to better figure out its next token. This was tried QuietSTAR.
Hochreiter is also active with alternative models, and there's all the microchip design companies, Groq, Etched, etc. trying to speed up models and reduce model running cost.
Therefore, I think there's room for very great improvements. They may not come right away, but there are so many obvious paths to improve things that I think it's unreasonable to think progress has stalled. Also, presumably GPT-5 isn't far away.
It feels like there’s an assumption in the community that this will be almost trivial.
I suspect it will be one of the hardest tasks humanity has ever endeavoured. I’m guessing it has already been tried many times in internal development.
I suspect if you start creating a feedback loop with these models they will tend to become very unstable very fast. We already see with these more linear LLMs that they can be extremely sensitive to the values of parameters like the temperature settings, and can go “crazy” fairly easily.
With feedback loops it could become much harder to prevent these AIs from spinning out of control. And no I don’t mean in the “become an evil paperclip maximiser” kind of way. Just plain unproductive insanity.
I think I can summarise my vision of the future in one sentence: AI psychologists will become a huge profession, and it will be just as difficult and nebulous as being a human psychologist.
I personally think it's not going to be incredibly difficult. Obviously, the way it was done with QuietSTaR is somewhat expensive, but I see many reasonable approaches here that could be considered.
High temperature will obviously lead to randomness, that's what it, evening out the probabilities of the possibilities for the next token. So obviously a high temperature will make them 'crazy' and low temperature will lead to deterministic output. People have come up with lots of ideas about sampling, but this isn't really an instability of transformer models.
It's a problem with any model outputing probabilities for different alternative tokens.
>I suspect if you start creating a feedback loop with these models they will tend to become very unstable very fast. We already see with these more linear LLMs that they can be extremely sensitive to the values of parameters like the temperature settings, and can go “crazy” fairly easily.
I'm in the process of spinning out one of these tools into a product: they do not. They become smarter at the price of burning GPU cycles like there's no tomorrow.
I'd go as far as saying we've solved AGI, it's just that the energy budget is larger than the energy budget of the planet currently.
But we could add internal thoughts-- we could make the model generate tokens that aren't part of its output but are there for it to better figure out its next token. This was tried QuietSTAR.
Hochreiter is also active with alternative models, and there's all the microchip design companies, Groq, Etched, etc. trying to speed up models and reduce model running cost.
Therefore, I think there's room for very great improvements. They may not come right away, but there are so many obvious paths to improve things that I think it's unreasonable to think progress has stalled. Also, presumably GPT-5 isn't far away.