Anti-AI Hype LLM Reading List

And I haven't seen any major change there over the last two months or so, what I have seen is that there are now better test suits and better tooling.

With that out of the way, I said it then and I'll say it now: a paper where Microsoft describes how well Microsoft products perform on benchmarks designed by Microsoft employees isn't science, and can't become science even if you write it in latex and put it on arxiv.

The biggest indicator that we have something here is that no one wants to argue results anymore because then the arguments fall apart.

Suddenly it's "4 isn't AGI because it isn't doing "real" reasoning" instead of "4 isn't AGI because it can't do this intelligent task all humans can"

Seeing how random gpt performs this task nowadays I became much more skeptical towards this (https://gpt-unicorn.adamkdean.co.uk/). Even if you argue the model is somehow crippled due to computation costs I'd still expect that gpt-4 could SOMETIMES draw something like a unicorn with vector shapes but perhaps not all the time. Certainly a one prompt one result conclusion seems to me really weird.

LLMs are somewhere between very useful to game-changing for me. But spammers will get far more utility and value out of LLMs than I can.

People who talk about usefulness or ethics or politics too often fail to specify key details, such as a time frame and/or discount rate. Not to mention risk preferences.

By the same logic one could argue that AI is the quintessential recurring hype cycle that always turns out to be a nothing-burger.

It's a particularly hard nut to crack because whether or not LLMs will turn out to be useful depends on future developments, particularly on whether there is a way to get them to behave consistently and predictably. Computers are useful because they are stupid pieces of junk that do always the same thing when given the same input.

If it turns out there isn't a viable solution to this, we are forever doomed to use LLMs pseudo-interactively, which is something, but a far cry from even the low end of what LLM proponents are promising. (e.g. all the current LLM "integrations" are very thin layers on top of the raw model).

It's fundamentally impossibile to tell, imho.

True. But I think I can make the case that dismissive skeptics have greater value than promoters: they can counter the worst of the promoters (e.g. the hucksters and idiots who only recently deleted their tweets promoting crypto and NFTs).

Despite what people think, there is rarely a particular downside to not being first to a technology: early adopters do not routinely make truly excess profits, they just get to take advantage of a wide-eyed, uncynical marketplace. Skeptics tend to moderate over time, and are a little better at spotting the sustainable opportunities than the boosters.

There is a particular downside to being an early victim of a scam or a lie in a new field. The scams are worse, more painful, more humiliating, and often happen before regulation creates meaningful penalties.

The tech world is negative about skeptics for no good reason, when you consider that robust technology itself is much like harnessed skepticism. Code that doesn't trust its inputs, test harnesses, margins of error, etc.

The cycle I think that is the most dangerous is the hype cycle that starts where developers don't talk sales and marketing people out of their worst claims (because it is in our short term interest not to have to get involved with all that stuff while we are working).