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It'd make me feel better if someone could muster a rebuttal that explained with specificity why further improvements aren't going to be sufficient to breach the "smarter than human" barrier.
There's an existence proof in the sense that human intelligence exists and if they can figure out how to combine hardware improvements, algorithm improvements, and possibly better data to get to human level, even if the power demands are absurd, that's a real turning point.
A lot of smart people and smart orgs are throwing mountains of money at the tech. In what ways are they wrong?
To sum it up, to train superhuman performance you need superhumanly good data. Now, I'd be all okay for the patient, obvious approach there - eugenics, creating better future generations.
I'll quote twitter again
I'd not say they're wrong. Even present day polished applications with a lot of new compute could do a lot of stuff. They're betting they'll be able to make use of that compute even if AGI is late.
And remember, the money is essentially free for them. Those power stations will be profitable even if datacenters aren't, the datacenters will generate money even if taking over the world isn't a ready option. & There's no punishing interest rates for the big boys. That's for chumps with credit cards.
It isn't clear we need superhumanly good data. Humans can make novel discoveries if they have a sufficiently good understanding of existing data and sufficiently good mental horsepower to use that data, i.e. extrapolate from their set of 'training data' and accurately test those extrapolations to discover new, useful data.
It seems like we just need to get an AI to approximately Von Neumann level and if it starts making good contributions to various fields at that point we can have it solve problems that hold up AI development. We're seeing hints of this now with Alphafold 3 and AlphaProteo.
Right now, the one thing that appears to be a hard hurdle for AIs are navigating real world environments, where there is far more chaos and variables that don't interact with each other linearly.
It can be difficult to see a new true innovation coming when every single company starts slapping "AI Powered!" as a feature on their products, but I think the case that AI will make surprising leaps in the next few years is stronger than it will inexplicably stagnate.
It is.
LLMs and similar systems aren't human, not in the slightest.
They're nowhere near that. People are happy they can count letters correctly.
As stated, be really nice if there was a sound case for why this won't change in the near future.
The jump to where we are was sudden and surprising, the next one could be as well.
If a cat surprises you with a sudden jump to a wardrobe top just under a ceiling, how likely is it to then surprise you with jumping through the ceiling ?
There's no superhuman data, this isn't as easy. What happened until know was just adding more scale and it led to increasing returns until it got to the point there was no new data to add. And this is still a system that's basically a glorified retrieval tool because it has trouble with basic logic unless it uses 'chain of thought' which is pretty expensive and not much better.
So do I, but they gave me the PhD anyway. Applied math, so there were a lot of computers rather than just the traditional coffee maker and a chalkboard, but you'd be surprised at how heavily even the more-respectable pure mathematicians rely on that chalkboard (or whiteboard, these days) to keep track of a chain of thought.
And on that subject, math is one field where you can add new perfect-quality synthetic data with no obvious bound. Generating a proof may be NP-complete, but verifying a rigorous proof can be done by non-AI computer programs. Both OpenAI and Meta have started using Lean to train models to generate new proofs. It's not quite as good a target as Go self-play, since "play against yourself" is a naturally good difficulty-level for self-improvement, whereas some theorems are deceptively complex to state and easy to prove or vice-versa, but it's not a slow-growing data set in the same way as "mimic our recorded human language" is, and yet it's natural to translate back and forth between the formal proof language and the basic-logic-plus-much-more subset of human languages.
I'd still say it's possible that there's some other "spark" of out-of-sample creativity that humans have and models trained under current techniques just can't acquire, but if that's what you're shooting for, at least lead your target. Even if current progress does stall out, we may end up going from "has trouble with basic logic" to "(dis?)proved the Riemann hypothesis" before things plateau again.
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The entire course of technological progress was us surprising ourselves by jumping through what we thought for millenia was the ceiling. Either we've been achieving superhuman data every time, or there is much more human data than we think.
It's ridiculous to conflate human cognition and culture with LLMs. Quality of thought depends on data to a much more limited degree, people can also discern truth from nonsense with some reflection.
LLMs can't.
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