"It's not very smart yet" is evidence for "it won't be very smart"? Also, hallucinations are dangerous, at least as far as humans are concerned.

There is more evidence in favor of “more improvements to come” than “ai winter approaches”.

A lot smart people seem to be saying that the existing approaches have room to improve simply by training with more data.

Based on what I’ve read and roughly speculating, it looks there is easily enough existing data for gpt-5 and probably a few more versions after.

I’m not sure how easy it will be to legally acquire all of the available data though.

None of this data is from Reddit, Twitter, etc.

Not to mention that we haven't really seen what GPT-4 can do to the world yet. Most people are still treating it as a cool toy, when it can probably replace 20% of standard office workers across all professions with the capabilities it has today.

I mean, ChatGPT is out-diagnosing medical professionals and out-advising experienced lawyers in some cases. How do you think it would fare against the average (essentially untrained) customer support representative?

The major effects are yet to come, and there doesn't even have to be a GPT-5 for that to happen – the world just needs to fully realize what GPT-4 already is.

Every “AI Winter” has seen continuing improvements, its just that the pace and impact of the improvements fell short of the accelerating anticipation driven by the preceding AI hype cycle and the demands of those investing capital into AI.

If there is another AI winter, it will be the same.

“More improvements to come” is not the opposite of “AI winter approaches”; its not as if expert systems stopped advancing after the second AI winter (or neural nets after the first.)

True.

And its also true that even in that nuanced form, a slowdown not a stop, there is no evidence of an imminent AI winter.

Major progress in model behavior is happening on the scale of months. Happening often based on relatively obvious or routine improvements, such as more data, more kinds of data, more computation, expanding the size of prompt windows, tuning of model architectures, etc.

And not only businesses, but consumers, are getting access to these tools in ways that are increasing demand for more improvements. And for these tools to be more available to all kinds of areas of work, creativity, and leisure.

So the resources that are being brought to bear on routine and more fundamental improvements are growing at an exceptional pace.

No sign of a saturation of progress in sight. Quite the reverse.

There’re also many “smart people” saying that simply having more data doesn’t turn a machine that can’t reason into one that can.

But that's exactly what's been happening. LLM's are a triumph of "big data" used by dumb algorithms to create a reasoning machine.

I’m not an expert and haven’t seen architecture of ChatGPT. But it’s probably far from dumb. You can’t simply put more data into any algorithm and expect improvement. You need a model to be able to take advantage of the data and train in reasonable time. That’s exactly what’s been happening in ML field.

Not indefinitely, obviously. But we haven’t exhausted the available data sources yet, and the curves for prediction error vs. amount of training data strongly imply using this additional data would result in increased performance.

Do you have specific reasons for thinking that gpt-4 is near the end of this trend? I haven’t seen any compelling arguments for this.

I haven’t said that. I pointed that you can’t simply put more data into it and expect improvements. There’s definitely a lot of engineering done on the models to make them capable of using all the data.

I can give you an example. You can’t simply add more convolutional layers to a CNN and feed more images. The architectures of image models include many tricks, like inception layers.

I suspect there’re similar challenges in LLMs. And I’m not sure whether current ChatGPT can use more data to become better at reasoning, or at some point they’d need to come up with different architectures.

Mostly, you can simply stack more transformers and (as long as you have layer norm), it can train on more data (of fixed sequence length).

There are some tricks & tips, but the vast majority lie outside of the architecture improvements.

> Based on my understanding of the approach behind ChatGPT, it is probably very close to a local maximum in terms of intelligence

To me that is an open question. OpenAI has not revealed a lot of the relevant stats behind GPT-4. I also haven't seen anything about their future LLM research (though I haven't looked very hard), but I think the above assertion remains to be seen.

Some humans never learn this, and those that do could take years or even decades of training. AI's are learning some things much faster, though since their "brains" are not like those of humans there's no reason to expect them to learn in the same way.

No, but children spend years learning basic mechanics, how to navigate 3-dimensional space, what causality is, etc. Underneath these laws of nature you find… logic and mathematical relationships. So I don't think it's surprising that a model whose perception is restricted to text (and now single images) as the sole sensory input/output might need much more training and data to learn logical reasoning.

What we don't know is what qualitative capabilities are unlocked at different perplexity levels.

I think this is a lot less speculative than "things will not continue improving the way they are currently improving."