Maybe some, but not that much given the disclaimers I cited above. There's value in a qualitative confidence level for a statement, and I'd argue that this is something that LLMs do not seem to produce in practice without someone explicitly asking for it. The human author's ability to anticipate potential mistakes in their logic and communicate those ahead of time is not equivalent to the type of fabrications that LLMs routinely make.

> If you’re a non-expert in a field, I don’t think it’s a good sign if you’re writing a 10 part article about that field’s impact on society and getting basic facts wrong. How can I trust that the conclusions will be any more credible?

I don't know why an expert in LLM implementation would be inherently more qualified to analyze the second-order effects of their product than anyone else. There's precedent for people who are "too close" to something having biases that make them less effective at recognizing how tools will get used by non-experts, and society as a whole is largely composed of people who are not experts in LLM implementations. If you wanted to understand what the net effect of everyone having access to LLMs, having an understanding of people is probably more important than knowing exactly what an LLM does under the hood.

My point is that at least from the standpoint of "Why does this process exist in the way it does?", OpenAI is not their "new name" in any logical sense. If you aren't happy with the process used a year from now, it would be reasonable in my opinion to criticize OpenAI for not making it different somehow. I'd argue that a parent company trying to make substantive process changes in such a short window would be strictly a bad thing though, because it would mean they didn't take the time to fully understand the context of what it's trying to solve and why it's the way it is currently.

I don't really disagree with anything else you're saying about OpenAI here, but I still think it's somewhat disingenuous to name-check them in this context.

> wasn't OpenAI the company that was formed as a nonprofit to limit the risks of LLMs?

>> the whole “rationalist” movement is full of those lying fks

>>> Is Sam even a rationalist, or describe his views as rationalist?

The relevancy is that a question of how and why the company was formed isn't fully answered by only talking about his motivations.

Didn't the acquisition only happen a few weeks ago? Wouldn't it be more alarming if OpenAI had gone in and forced them to change their build process? Unless you're claiming that the article is lying about this being a description of what they've already been doing for a while (which seems a bit outlandish without more evidence), it's not clear to me why you're attributing this process to the parent company.

Don't get me wrong; there's plenty you can criticize OpenAI over, and I'm not taking a stance on your technical claims, but it seems somewhat disingenuous to phrase it like this.

Not only that, but the exceedingly niche C++ language and the MIT license!

Nobody calls calculators "artificial mathemeticians", though; we refer to them by a unique word that defines what they can and can't do in a far less fanciful and ambiguous way.

I think it's even worse than that: people were familiar with the term already, but from science fiction, where it referred to actually human-level intelligence. It's similar to the "hoverboard" thing from a while back, except this time with profoundly higher stakes and requires for more technical knowledge to be able to see that it is in fact touching the ground.

> LLMs are clearly unable to propose new, creative solutions for problems it has never seen before.

How do you reconcile this with this article that the author linked? It's not a novel problem, and it's only text: https://medium.com/the-generator/one-word-answers-expose-ai-...

I guess it's a form of engagement to give a wildly wrong answer, but I'm not convinced that the extra nuance you've introduced is really all that nuanced either.

I don't think anything you're saying here is in disagreement with the points they're making.

> I am generally outside the ML field, but I do talk with people in the field. One of the things they tell me is that we don’t really know why transformer models have been so successful, or how to make them better. This is my summary of discussions-over-drinks; take it with many grains of salt. I am certain that People in The Comments will drop a gazillion papers to tell you why this is wrong.

As I understand it, this article is basically a conglomeration of several attempts at an article that the author has attempted to make over the past decade or so considering the impacts of AI on society. In their own words:

> Some of these ideas felt prescient in the 2010s and are now obvious. Others may be more novel, or not yet widely-heard. Some predictions will pan out, but others are wild speculation. I hope that regardless of your background or feelings on the current generation of ML systems, you find something interesting to think about.

As for the "Bitter Lesson" part, they pretty much directly said that it wasn't the Bitter Lesson exactly, saying it might be a variant of it. Honestly, it felt more like a way of throwing in a reference to something that also might provoke thought, which was done throughout the piece (which again, is the entire point).

It's totally valid to say "this article didn't provoke much thought for me". I'm a bit confused at why you think a lack of specific domain knowledge in a domain that they literally state they are not an expert in would be disqualifying for that purpose though.