[claytongulick]

My understanding (roughly) is that the way we got here was kind of by surprise. We've had a lot of the fundamental algorithms for a long time, but we ran into sort of a happy surprise when transformer models got scaled up - suddenly they started doing interesting things. Scaling them up even more made them start do do potentially useful things.

That happy discovery was never really a linear improvement path, though. We had an explosion of capability, but all along there have been active questions about how far the improvements would go with the current approach.

I think the point that a lot of researchers are making is that that we're starting to see those limits (with LLMs, at least).

There are also a lot of questions around business model and cost/value prop. Training and running these things at scale is enormously expensive. I'm seeing a lot of FOMO and gold rush mentality in the space, similar to the online streaming wars, and I'm not convinced of the long term viability of a lot of the companies. Especially once open models like llama are "good enough" and become commodities.

Of course, it's still early days and there's a ton of room for discovery, but it looks like we'll hit a limit with the current approach pretty soon.

Personally, I'd be OK with that. With the current state of things we have an interesting toy that can sometimes do useful work. It's an incremental quality of life improvement and another good tool in the chest, but it's not a civilization impacting technology.

That's probably for the best.

[mewpmewp2]

My question still stands. How could anyone besides OpenAI be confident of there being a limit if no one has managed to even build as strong model so far as OpenAI has? Only Claude Opus seems close, but still weaker at reasoning than GPT-4o. Better at creative writing though.

And only after 1.5 years? And especially of we just had an happy surprise like you mentioned. How does it make sense to already start claiming that we have hit the limits. How do we know there is no more scaling, optimisations and happy surprises?

[Izkata]

Quoting GP for context:

> That happy discovery was never really a linear improvement path, though. We had an explosion of capability, but all along there have been active questions about how far the improvements would go with the current approach.

> I think the point that a lot of researchers are making is that that we're starting to see those limits (with LLMs, at least).

The kinds of limitations we're "starting to see" are largely the same as they were a year ago. People were talking about it on here back then, but now it's becoming more apparent to more people as they get used to LLMs.

For those who saw it back then, this does look like we're hitting a limit. For others, not so much.

[mewpmewp2]

I'm not sure I understand this?

How do active questions about a technology imply we are approaching a brick wall?

How could researchers without having access to the latest state of the art - by OpenAI or any other unknown companies be able to even test that we could be approaching a brick wall? It seems to me that it would take trillions to find out what the exact limit is.

It's possible that we will get diminishing returns, but I don't see how we can confidently claim or know it?

> The kinds of limitations we're "starting to see" are largely the same as they were a year ago. People were talking about it on here back then, but now it's becoming more apparent to more people as they get used to LLMs.

I don't follow. GPT-3.5 was borderline useless at reasoning. But it still seemed amazing and what I wouldn't have thought to be possible in any near future.

And then GPT-4 was a crazy advancement over that to me. And I've been using it daily since it was available, for various use-cases. Are you saying we are seeing the limitations of GPT-4 specifically? Because, sure, GPT-4 is far from AGI, but I don't see how this implies that further scaling, optimisation, training data improvements, techniques like multi modality and other potential strategies that I might not be aware of couldn't bring another explosive step?

Also the fact that GPT-4 reasoning skill hasn't been reproduced by anyone else so far seems to leave me thinking that everyone except OpenAI are clueless. Claude Opus is close, like I've said before, but not quite GPT-4 levels in specific reasoning tasks that I'm using the API for.

If you can't reproduce GPT-4, how could we trust the assessment that we have hit a limit?