@coffee_with_cream@sh.itjust.works @technology@LemmyWorld@mastodon.world I don’t hate it but do think it’s overhyped by wallstreet’s usual infinite growth assumption

I was fully on board until, like, a year ago. But the more I used it, the more obviously it came undone.

I initially felt like it could really help with programming. And it looked like it, too - when you fed it toy problems where you don’t really care about how the solution looks, as long as it’s somewhat OK. But once you start giving it constraints that stem from a real project, it just stops being useful. It ignores constraints (use this library, do not make additional queries, …), and when you point out its mistake and ask it to to better it goes “oh, sorry! Here, let me do the same thing again, with the same error!”.

If you’re working in a less common language, it even dreams up non-existing syntax.

Even the one thing it should be good at - plain old language - it sucks ass at. It’s become so easy to spot LLM garbage, just due to its style.

Worse, asking it to proofread a text for spelling and grammar mistakes, but to explicitly do not change the wording or style, there’s about a 50/50 chance it will either

• change your wording or style, or
• point out errors that are not even in the original text in the first place!

I could honestly go on and on, but what it boils down to is: it is able to string together words that make it sound like it knows what it is doing, but it is just that, a facade. And it looks like for more and more people, the spell is finally breaking.

In terms of practical commercial uses, these highly human in the loop systems are about where it is and there are practical applications and products build off of it. I think what was sold though is a much more of either a replacement of people or a significant jump in functionality.

For example, there are products that will give you an AI summary of a structured or fairly uniform document like a generic press release, but there’s not really a good replacement for something to read backgrounds on 50 different companies and figure out which one you should invest in without a human basically doing all of that work themselves anyway just to check the work of the AI. The latter is what is being sold to make the enormous cost of hosting and training AI worth it.

1. Define singularity in this context
2. Define near in this context

The problem is experts in AI are biased towards AI (it pays their salaries).

I’d say they know a thing or two about finance… so maybe they didn’t invest because they see it as overhype?

Presumably because they haven’t invested in AI.

Presumably is carrying all the weight of your whole post here

Perhaps we could get a non-biased opinion and also from an actual expert rather than some finance ghoul who really doesn’t know anything?

I also hate banks, but usually those guys can sniff out market failures way ahead of the rest of us. All their bacon rides on that, after all

You dare to challenge Goldman Sacks?

It’s noteworthy because it’s Goldman Sachs. Lots of money people are dumping it into AI. When a major outlet for money people starts to show skepticism, that could mean the bubble is about to pop.