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Aschenbrenner is a smart charlatan, he's probably going to do very well in the politics of AI.
My opinion is that the way he has everyone fooled and the way he has zeroed in on the superpower competition aspect makes it clear what he is after. Power. Has he gotten as US citizenship yet? He'll need that.
There's going to be an enormous growth in computing power, possible hardware improvements (e.g. the Beff Jezos guy has some miniaturised parallel analog computer that's supposedly going to be great for AI stuff.. ). But iirc, the models can't really improve easily because there's not the best data to pretrain them, so now everyone is trying to figure out how to automatically generate good synthetic data and use that to train better models, combine different modalities (text/ images etc). All stuff that's hardly comprehensible to outsiders, so people like Leopold can go around and say stuff with confidence.
Likely, yes, but how computationally and energy expensive it's going to be matters a whole lot. Like e.g. aren't they basically near hitting physical limits pretty soon? That'd cap lowering power costs, right?
And scaling up chip production to 1000x isn't as easy as it sounds either. Especially if Chinese get scared and start engaging in sabotage.
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.
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