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> Personally I think the limits of this technique are far better than I would have thought 10 years ago. Honestly I've always surprised at the scepticism AI re
by kypro 3y ago
> Personally I think the limits of this technique are far better than I would have thought 10 years ago.
Honestly I've always surprised at the scepticism AI researchers have had of about the limits of training large neural nets with gradient descent. Where I've had my doubts is in the architecture of existing models and still think this is their primary limiting factor (more so than compute and network size).
I think the question that remains now is whether existing models are actually capable of producing a general intelligence that's well rounded and reliable enough to be competitive with human general intelligence. Personally, I think LLMs like GPT-4 are generally intelligent in most ways and should be considered AGI already (at least in a weak sense of the word), but they clearly have notable gaps in their intelligence such as their ability to be consistent, long-term memory, and ability to discern reality from delusion.
I don't think scaling existing models could possibly address these limitations – they seem to be emergent properties of an imperfect architecture. So I suspect we're still a few breakthroughs away from a general intelligence as well rounded as human general intelligence. That said, I suspect existing models (perhaps with a few minor tweaks) are generally intelligent enough that larger models alone are likely still able to replace the majority of human intellectual labour.
I guess what I'm touching on here is the need to more nuanced about what we mean by "AGI" at this point. I think it's quite likely (probable even) that in a few years we'll have an AI that's generally intelligent and capable enough that it can replace a large percentage of existing knowledge work – and also generally intelligent enough to be dangerous. But I suspect despite this it will still have really clear limitations in its abilities when contrasted with human general intelligence.
- xiphias2 3y agoI agree with you totally. For me AGI is achieved when AutoGPT is at a point when it's able to improve its own algorithm (specifically improve on the GPT architecture). Flash attention came out a bit less than a year ago (27 May 2022), which was a great scaling improvement for getting rid of O(n^2) memory requirement in the length of attention. I guess the next one we need will be one of the solutions that change the FLOPS for computing attention from n^2 to n*log(n) (there was already a paper that achieved it using resizable gauss filters where the size and the base convolution filter is learned separately). A few of these kind of ,,breakthroughs''/algorithmic improvements are the only ones needed to get to AGI (self improving machine) in my opinion.