It’s so funny how all this is only a problem within a capitalist frame of reference.
The hype should go the other way. Instead of bigger and bigger models that do more and more - have smaller models that are just as effective. Get them onto personal computers; get them onto phones; get them onto Arduino minis that cost $20 - and then have those models be as good as the big LLMs and Image gen programs.
That would be innovation, which I’m convinced no company can do anymore.
It feels like I learn that one of our modern innovations was already thought up and written down into a book in the 1950s, and just wasn’t possible at that time due to some limitation in memory, precision, or some other metric. All we did was do 5 decades of marginal improvement to get to it, while not innovating much at all.
This is why you’re seeing news articles from Sam Altman saying that AGI will blow past us without any societal impact. He’s trying to lessen the blow of the bubble bursting for AI/ML.
It’s been 5 minutes since the new thing did a new thing. Is it the end?
As I use copilot to write software, I have a hard time seeing how it’ll get better than it already is. The fundamental problem of all machine learning is that the training data has to be good enough to solve the problem. So the problems I run into make sense, like:
- Copilot can’t read my mind and figure out what I’m trying to do.
- I’m working on an uncommon problem where the typical solutions don’t work
- Copilot is unable to tell when it doesn’t “know” the answer, because of course it’s just simulating communication and doesn’t really know anything.
2 and 3 could be alleviated, but probably not solved completely with more and better data or engineering changes - but obviously AI developers started by training the models on the most useful data and strategies that they think work best. 1 seems fundamentally unsolvable.
I think there could be some more advances in finding more and better use cases, but I’m a pessimist when it comes to any serious advances in the underlying technology.
Not copilot, but I run into a fourth problem:
4. The LLM gets hung up on insisting that a newer feature of the language I’m using is wrong and keeps focusing on “fixing” it, even though it has access to the newest correct specifications where the feature is explicitly defined and explained.
Huh?
The smartphone improvements hit a rubber wall a few years ago (disregarding folding screens, that compose a small market share, improvement rate slowed down drastically), and the industry is doing fine. It’s not growing like it use to, but that just means people are keeping their smartphones for longer periods of time, not that people stopped using them.
Even if AI were to completely freeze right now, people will continue using it.
Why are people reacting like AI is going to get dropped?
People are dumping billions of dollars into it, mostly power, but it cannot turn profit.
So the companies who, for example, revived a nuclear power facility in order to feed their machine with ever diminishing returns of quality output are going to shut everything down at massive losses and countless hours of human work and lifespan thrown down the drain.
This will have an economic impact quite large as many newly created jobs go up in smoke and businesses who structured around the assumption of continued availability of high end AI need to reorganize or go out of business.
Search up the Dot Com Bubble.
Because in some eyes, infinite rapid growth is the only measure of success.
People pay real money for smartphones.
People pay real Money for AIaaS as well…
Hope?
Because novelty is all it has. As soon as it stops improving in a way that makes people say “oh that’s neat”, it has to stand on the practical merits of its capabilities, which is, well, not much.
I’m so baffled by this take. “Create a terraform module that implements two S3 buckets with cross-region bidirectional replication. Include standard module files like linting rules and enable precommit.” Could I write that? Yes. But does this provide an outstanding stub to start from? Also yes.
And beyond programming, it is otherwise having positive impact on science and medicine too. I mean, anybody who doesn’t see any merit has their head in the sand. That of course must be balanced with not falling for the hype, but the merits are very real.
I am so tired of the ai hype and hate. Please give me my gen art interest back please just make it obscure again to program art I beg of you
It’s still quite obscure to actually mess with AI art instead of just throwing prompts at it, resulting in slop of varying quality levels. And I don’t mean controlnet, but github repos with comfyui plugins with little explanation but a link to a paper, or “this is absolutely mathematically unsound but fun to mess with”. Messing with stuff other than conditioning or mere model selection.
I know, it’s actually still a beautiful community but much harder to talk to outsiders about
Theres no bracing for this, OpenAI CEO said the same thing like a year ago and people are still shovelling money at this dumpster fire today.
It’s had all the signs of a bubble for the last few years.
Good. I look forward to all these idiots finally accepting that they drastically misunderstood what LLMs actually are and are not. I know their idiotic brains are only able to understand simple concepts like “line must go up” and follow them like religious tenants though so I’m sure they’ll waste everyone’s time and increase enshitification with some other new bullshit once they quietly remove their broken (and unprofitable) AI from stuff.
Sigh I hope LLMs get dropped from the AI bandwagon because I do think they have some really cool use cases and love just running my little local models. Cut government spending like a madman, write the next great American novel, or eliminate actual jobs are not those use cases.
so long, see you all in the next hype. Any guesses?
AI vagina Fleshlight beds. You just find your sleep inside one and it will do you all night long! Telling you stories of any topic. Massaging you in every possible way. Playing your favorite music. It’s like a living room! Oh I’m sleeping in the living room again. Yeah I’m in the dog house. But that’s why you need an AI vagina Fleshlight bed!
Get a few more hours of sleep
I woke up at 4 this morning. The fridge made a big ice maker noise that sounded like a door getting slammed. Anyway here I am shit posting and reading shit posts.
Until Open AI announces a new 5t model or something and then the hype refreshes
Nice, looking forward to it! So much money and time wasted on pipe dreams and hype. We need to get back to some actually useful innovation.
AI was 99% a fad. Besides OpenAI and Nvidia, none of the other corporations bullshitting about AI have made anything remotely useful using it.
Absolutely not true. Disclaimer, I do work for NVIDIA as a forward deployed AI Engineer/Solutions Architect—meaning I don’t build AI software internally for NVIDIA but I embed with their customers’ engineering teams to help them build their AI software and deploy and run their models on NVIDIA hardware and software.
To state this as simply as possible: I wouldn’t have a job if our customers weren’t seeing tremendous benefit from AI technology. The companies I work with typically are very sensitive to CapX and OpX costs of AI—they self-serve in private clouds. If it doesn’t help them make money (revenue growth) or save money (efficiency), then it’s gone—and so am I. I’ve seen it happen; entire engineering teams laid off because a technology just couldn’t be implemented in a cost-effective way.
LLMs are a small subset of AI and Accelerated-Compute workflows in general.
To state this as simply as possible: I wouldn’t have a job if our customers weren’t seeing tremendous benefit from AI technology.
Right because corporate management doesn’t ever blindly and stupidly overinvest in fads that blow up in their faces…
I work with typically are very sensitive to CapX and OpX costs of AI—they self-serve in private clouds. If it doesn’t help them make money (revenue growth) or save money (efficiency), then it’s gone—and so am I.
You clearly have no clue what you’re on about. As someone with a degrees and experience in both CS and Finance all I have to say is that’s not at all how these things work. Plenty of companies lose money on these things in the hopes that their FP&A projection fever dreams will come true. And they’re wrong much more often than you seem to think. FP&A is more art than science and you can get financial models to support any argument you want to make to convince management to keep investing in what you think they should. And plenty of CEOs and boards are stupid enough to buy it. A lot of the AI hype has been bought and sold that way in the hopes that it would be worthwhile eventually or that other alternatives can’t be just as good or better.
I’ve seen it happen; entire engineering teams laid off because a technology just couldn’t be implemented in a cost-effective way.
This is usually what happens once they finally realize spending money on hype doesn’t pay off and go back to more established business analytics, operations research, and conventional software which never makes mistakes if it’s programmed correctly.
LLMs are a small subset of AI and Accelerated-Compute workflows in general.
No one ever said otherwise. And we’re talking about AI only, no moving the goalposts to accelerated computing, which is a mechanism through which to implement a wide range of solutions and not a specific one in and of itself.
That’s fair. I see what I see at an engineering and architecture level. You see what you see at the business level.
That said. I stand by my statement because I and most of my colleagues in similar roles get continued, repeated and expanded-scope engagements. Definitely in LLMs and genAI in general especially over the last 3-5 years or so, but definitely not just in LLMs.
“AI” is an incredibly wide and deep field; much more so than the common perception of what it is and does.
Perhaps I’m just not as jaded in my tech career.
operations research, and conventional software which never makes mistakes if it’s programmed correctly.
Now this is where I push back. I spent the first decade of my tech career doing ops research/industrial engineering (in parallel with process engineering). You’d shit a brick if you knew how much “fudge-factoring” and “completely disconnected from reality—aka we have no fucking clue” assumptions go into the “conventional” models that inform supply-chain analytics, business process engineering, etc. To state that they “never make mistakes” is laughable.
I would say LLMs specifically are in that ball park. Things like machine vision have been boringly productive and relatively un hyped.
There’s certainly some utility to LLMs, but it’s hard to see through all the crazy over estimations and being shoved everywhere by grifters.
Nvidia made money, but I’ve not seen OpenAI do anything useful, and they are not even profitable.
ChatGPT is basically the best LLM of its kind. As for Nvidia I’m not talking about hardware I’m talking about all of the models it’s trained to do everything from DLSS and ACE to creating virtual characters that can converse and respond naturally to a human being.
