AI is overhyped and unreliable -Goldman Sachs
https://www.404media.co/goldman-sachs-ai-is-overhyped-wildly-expensive-and-unreliable/
“Despite its expensive price tag, the technology is nowhere near where it needs to be in order to be useful for even such basic tasks”
If Goldman Sachs told me the sky was blue, I would go outside, check, and get an eye exam.
It’s weird to me that people on Lemmy are so anti ML. If you aren’t impressed, you haven’t used it enough. “Oh it’s not 100% perfect”
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.
@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
AI is dangerous…
The singularity is near…
- Define singularity in this context
- Define near in this context
I remember saying a year ago when everybody was talking about the AI revolution: The AI revolution already happened. We’ve seen what it can do, and it won’t expand much more.
Most people were shocked by that statement because it seemed like AI was just getting started. But here we are, a year later, and I still think it’s true.
AI development is indeed a series of S-curves and we’re currently nearing the peak of the curve. It’s going to be some time before the new S begins.
I think it all depends on how good our tools to detect AI generated content become. If it is not distinguishable, then the internet is probably about to be flooded by AI generated content which in turn means AI is going to be trained more and more with AI content, degrading the model in the process.
Those people were talking about the kind of AI we see in sci-fi, not the spellchecker-on-steroids we have today. There used to be a distinction, but marketing has muddied those waters. The sci-fi variety has been rebranded “AGI” so I guess the rest of that talk would go right along with it - the ‘AGI singularity’ and such.
All still theoretically possible, but I imagine climate will take us out or we’ll find some clever new way to make ourselves extinct before real AI …or AGI… becomes a thing.
Given AI’s energy needs, it’s already helping to take us out.
The AI revolution already happened. We’ve seen what it can do, and it won’t expand much more.
That’s like seeing a basic electronic calculator in the 60s and saying that computing won’t expand much more. Full-AI isn’t here yet, but it’s coming, and it will far exceed everything that we have right now.
Oh, I’m not saying that there won’t one day come a better technology that can do a lot more. What I’m saying is that the present technology will never do much more than it is already doing. This is not an issue of refining the technology for more applications. It’s a matter of completely developing a new type of technology.
In areas of generative text, summarizing articles and books, as well as writing short portions of code in order to assist humans, creating simple fan art, and meaningless images like avatars, and those stock photos at the top of articles, Perhaps creating short animations, Improving pattern recognition of things like speech and facial recognition… In all of these areas, AI was very rapidly revolutionary.
Generative AI will not become capable of doing things that it’s not already doing. Most of what it’s replacing are just worse computer programs. Some new technology will undoubtedly be revolutionary in the way that computers were a completely new revolution on top of basic function calculators. People are developing quantum computers, and mapping the precise functions of brain cells. If you want, you can download a completely mapped actual nematode brain right now. You can buy brain cells online, even human brain cells, and put them into computers. Maybe they can even run Doom. I have no idea what the next computing revolution will be capable of, but this one has mostly run its course. It has given us some very incredible tools in a very narrow scope, and those tools will continue to improve incrementally, but there will be no additional revolution.
That’s the thing though, that’s not comparable, and misses the point entirely. “AI” in this context and the conversations regarding it in the current day is specifically talking about LLMs. They will not improve to the point of general intelligence as that is not how they work. Hallucinations are inevitable with the current architectures and methods, and they lack a inherent understanding of concepts in general. It’s the same reason they can’t do math or logic problems that aren’t common in the training set. It’s not intelligence. Modern computers are built on the same principals and architectures as those calculators were, just iterated upon extensively. No such leap is possible using large language models. They are entirely reliant on a finite pool of data to try to mimic most effectively, they are not learning or understanding concepts the way “Full-AI” would need to to actually be reliable or able to generate new ideas.
it’s super weird that people think LLMs are so fundamentally different from neural networks, the underlying technology. neural network architectures are constantly improving, and LLMs are just a product of a ton of research and an emergence after the discovery of the transformer architecture. what LLMs have shown us is that we’re definitely on the right track using neural networks to solve a wide range of problems classified as “AI”
I think the main problem is applying LLM outside the domain of “complete this sentence”. It’s fine for what it is, and trained on huge datasets it obviously appears impressive, but it doesn’t know if it’s right or wrong, and evaluation metrics are different. In most traditional applications of neural networks, you have datasets with right and wrong answers, that’s not how these are trained, as there is no “right” answer to “tell me a joke.” So the training has to be based on what would likely fill in the blank. This could be an actual joke, a bad joke, a completely different topic, there’s no difference in the training data. The biases, incorrect answers, all the faults of this massive dataset are inherent in the model, and there’s no fixing that. They are fundamentally different in their application and evaluation (this extends to training) methods from other neural networks that are actually effective at what they do, like image processing and identification. The scope of what they’re trying to do with a finite dataset is not realistic and entirely unconstrained, as compared to more “traditional” neural networks, which are very narrow in scope exactly because of this issue.
That’s like seeing a basic electronic calculator in the 60s and saying that computing won’t expand much more.
“Who would ever need more than 640K of RAM?” -Bill Gates
Sure.
GPT4 is not that. Neither will GPT5 be that. They are language models that marketing is calling AI. They have a very specific use case, and it’s not something that can replace any work/workers that requires any level of traceability or accountability. It’s just “the thing the machine said”.
Marketing latched onto “AI” because blockchain and cloud and algorithmic had gotten stale and media and CEOs went nuts. Samsung is now producing an “AI” vacuum that adjusts suction between hardwood and carpet. That’s not new technology. That’s not even a new way of doing that technology. It’s just jumping on the bandwagon.
Marketing latched onto “AI” because blockchain and cloud and algorithmic had gotten stale and media and CEOs went nuts.
Notably, this also coincided with the first higher interest rate environment in the broader economy in over a decade.
Full-AI isn’t here yet, but it’s coming, and it will far exceed everything that we have right now.
go back to school, hopefully your next statement won’t sound as dumb.
It’ll expand but it will take 5-10 years. Just like Web 1.0 and 2.0.
Not with the current tech. It can go faster, have more detailed output, maybe consume less too, but there seems to be a ceiling on what LLM and their derivative can do. There has been no improvement in that regard, and people that look into it are pretty confident that it won’t happen at this point.
Wow, I hate Goldman Sachs, but I think they’re on to something here…
OK boomer
Oh yeah? It’s great for porn, Goldman Sachs, you bunch of suit wearing degenerates. I bet a lot of people would argue that’s pretty freaking useful.
In 2 interviews, the interviewees claim that the investors may lose faith in return of investment if “the killer application of AI” is not available in 18 months. In other words, if the AI is a bubble, it will burst in only 18 months.
Financially? Yeah, AI is a bubble for sure. Gobs of money are being poured in with few results to show for it. That bubble will burst. But just like the dotcom bubble, that doesn’t mean the technology is useless or won’t change the world, just not instantly over night with a single investment, which is what the investment groups expect.
This technology requires finance. You can’t train a model without millions of dollars.
If the money goes the technology is dead until the cost of the training machines comes down a few orders of magnitude.
At least in the US, the research is fairly isolated from capital markets. The military pours huge amounts of money into research on new tech like this, often over ambitiously and with no real expectation of short term returns. Even if there is a financial bubble burst that shuts down a lot of the commercial operations, universities and military contractors will continue working and publishing papers improving the state of the art until industry decides it’s time to try commercializing it again. It’s the basic pattern that has brought us most of the major tech innovations in the US.
That seems like a fair assumption. I would argue we are at the peak of the bubble and only recently we’ve seen the suits (Goldman Sachs and more broadly analysts at banks) start asking questions about ROI and real use cases.
NO KIDDING YOU WORLD-DESTROYING MONEYHUNGRY COCKROACHES
I mean they aren’t wrong. From an efficiency standpoint, current AI is like using a 350hp car engine to turn a childs rock tumbler, or spin art thingy. Sure, it produces some interesting outputs, at the cost of way too much energy for what is being done. That is the current scenario of using generalized compute or even high end GPUs for AI.
Best I can tell is, the “way forward” is further development of ASICs that are specific to the model being run. This should increase efficiency, decrease the ecological impact (less electricity usage) and free up silicon and components, possibly decreasing price and increasing availablity of things like consumer graphics cards again (but I won’t hold my breath for that part).
Goldman Sachs is overhyped and unreliable.
I do find the similarities between the function of AI and the function of a corporation to be quite interesting…
It’s all the bullshitting going on in both.
Heartbreaking: The Worst Person You Know Just Made A Great Point
Yeah but it’s Goldman Sachs saying it. Presumably because they haven’t invested in AI.
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?
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?
The problem is experts in AI are biased towards AI (it pays their salaries).
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.
I’d say they know a thing or two about finance… so maybe they didn’t invest because they see it as overhype?
I don’t care what they think. What have they ever done for me?
Yeah… It’s machine learning with a hype team.
There are some great applications, but they are very narrow
We taught linear algebra to talk real pretty.
Oh your a dirty eigenvector arnt you! I’m going to transpose you so hard they won’t know you from a probability matrix!
