If you’re talking some nonsense, silly singularity… than whatever, don’t care.

But if you’re asking when a model has a sustainable general intelligence, for me, it’s pretty easy…

When it makes financial sense to run it 24 hours a day.

There can be no such test because “AGI” is (or has become) a pseudo-philosophical/socio-political concept rather than a scientific one.

“I declare bankruptcy!” - Michael Scott

I DECLARE AGI!

"Homer, you can't just declare Artifical General Intelligence; you need to like, make something or something...mmmmrrrhh"

did they?

Artificial. General. Intelligence. The ability to solve (even partially or even badly solve) problems drawn from arbitrary problem domains without pretraining on the specific problem class. You can pose any problem of any type using natural language to an LLM and it will attempt a solution. That's literally all the term means.

You (and the rest of the media and many industry figures) are conflating artificial super-intelligence (reference point: humans) with artificial general intelligence (reference point: specialized/narrow GOFAI).

Seriously, if they’d done a huge splashy launch, we’d be reading one hackneyed comment after another about their fake hype or whatever.

If a video announcement and a press release would change a person's mind on whether this is AGI, I don't put a huge amount of weight on that person's conception of what AGI is.

Yes, the very explicit plan of both OpenAI and Anthropic is to use the not particularly efficient LLMs to automate their own AI engineering. That seems to be going well - on coding front and model tuning front so far. They have more planned.

And then use those to find fundamentally better new architectures for AI - that perhaps are as efficient as the human brain.

It might not work, but I didn't think it'd solve maths problems... So it might work. And if it happens, they'd use the data centres to run millions of instances of it.

It's scary, TBH.

I mean, the plan is to use these models to find and solve those gaps. That's kind of the whole pitch of these companies: they spend a TON of money upfront setting up this infrastructure, but each iteration yields a system capable of making the next iteration even better.

>The kickers is that if they do achieve (and solve) AGI in this way all the giant data centers would be mostly useless.

Perhaps. But only at that point, not leading up to that point.

It's kind of like setting up scaffolding to build something. You spend all of that time and money to build something just to tear it down in the end. But the point is that it's simply a cost to be able to build the actual thing you're building.

If these companies are able to achieve the results they're looking for, none of the investors involved are going to care that the datacenters and infrastructure they spent so much money.

If we manage to get to AGI and it looks, works and behaves like a human brain... I mean, cool, but that's a very useless AGI compared to the incredible stuff we have access to today.

The HN crowd I'm sure will still be unhappy calling it AGI because "it's not AGI unless its speech comes from the cerebral cortex region of the brain, otherwise it's just sparkling emoji" or something.

We have not "hit a wall" by any stretch yet. I don't understand how someone can even hold this viewpoint? It's mind boggling.

Harnesses magnify and make the intelligence actionable, but we have not reached limits on raw intelligence yet, not even close.

I think models using these harnesses were also RLHF'd hard on responding to looping instructions and following through on goals. Older models were tuned for basic chat responses.