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.