At a beach restaurant the other night I kept hearing a loud American voice cut across all conversation, going on and on about “AI” and how it would get into all human “workflows” (new buzzword?). His confidence and loudness was only matched by his obvious lack of understanding of how LLMs actually work.
I really like the idea of an LLM being narrowly configured to filter, summarize data which comes in at a irregular/organic form.
You would have to do it multiples in parallel with different models and slightly different configurations to reduce hallucinations (Similar to sensor redundancies in Industrial Safety Levels) but still, … that alone is a game changer in “parsing the real world” … that energy amount needed to do this “right >= 3x” is cut short by removing the safety and redundancy because the hallucinations only become apparent down the line somewhere and only sometimes.
They poison their own well because they jump directly to the enshittyfication stage.
So people talking about embedding it into workflow… hi… here I am! =D
So you’re saying we wont have any crowdsourced blockchain Web 2.0 AIs?
Please, stay with the time. We’re at Web 6.0 already.
The drones doing airstrikes might!
“Built to do my art and writing so I can do my laundry and dishes” – Embodied agents is where the real value is. The chatbots are just fancy tech demos that folks started selling because people were buying.
Eh, my best coworker is an LLM. Full of shit, like the rest of them, but always available and willing to help out.
Though the image generators are actually good. The visual arts will never be the same after this
Compare it to the microwave. Is it good at something, yes. But if you shoot your fucking turkey in it at Thanksgiving and expect good results, you’re ignorant of how it works. Most people are expecting language models to do shit that aren’t meant to. Most of it isn’t new technology but old tech that people slapped a label on as well. I wasn’t playing Soul Caliber on the Dreamcast against AI openents… Yet now they are called AI opponents with no requirements to be different. GoldenEye on N64 was man VS AI. Madden 1995… AI. “Where did this AI boom come from!”
Marketing and mislabeling. Online classes, call it AI. Photo editors, call it AI.
There is this seeming need to discredit AI from some people that goes overboard. Some friends and family who have never really used LLMs outside of Google search feel compelled to tell me how bad it is.
But generative AIs are really good at tasks I wouldn’t have imagined a computer doing just a few year ago. Even if they plateaued in place where they are right now it would lead to major shakeups in humanity’s current workflow. It’s not just hype.
The part that is over hyped is companies trying to jump the gun and wholesale replace workers with unproven AI substitutes. And of course the companies who try to shove AI where it doesn’t really fit, like AI enabled fridges and toasters.
Like what outcome?
I have seen gains on cell detection, but it’s “just” a bit better.
This is easy to say about the output of AIs… if you don’t check their work.
Alas, checking for accuracy these days seems to be considered old fogey stuff.
The part that is over hyped is companies trying to jump the gun and wholesale replace workers with unproven AI substitutes. And of course the companies who try to shove AI where it doesn’t really fit, like AI enabled fridges and toasters.
This is literally the hype. This is the hype that is dying and needs to die. Because generative AI is a tool with fairly specific uses. But it is being marketed by literally everyone who has it as General AI that can “DO ALL THE THINGS!” which it’s not and never will be.
The obsession with replacing workers with AI isn’t going to die. It’s too late. The large financial company that I work for has been obsessively tracking hours saved in developer time with GitHub Copilot. I’m an older developer and I was warned this week that my job will be eliminated soon.
The large financial company that I work for
So the company that is obsessed with money that you work for has discovered a way to (they think) make more money by getting rid of you and you’re surprised by this?
At least you’ve been forewarned. Take the opportunity to abandon ship. Don’t be the last one standing when the music stops.
I never said that I was surprised. I just wanted to point out that many companies like my own are already making significant changes to how they hire and fire. They need to justify their large investment in AI even though we know the tech isn’t there yet.
Computers have always been good at pattern recognition. This isn’t new. LLM are not a type of actual AI. They are programs capable of recognizing patterns and Loosely reproducing them in semi randomized ways. The reason these so-called generative AI Solutions have trouble generating the right number of fingers. Is not only because they have no idea how many fingers a person is supposed to have. They have no idea what a finger is.
The same goes for code completion. They will just generate something that fills the pattern they’re told to look for. It doesn’t matter if it’s right or wrong. Because they have no concept of what is right or wrong Beyond fitting the pattern. Not to mention that we’ve had code completion software for over a decade at this point. Llms do it less efficiently and less reliably. The only upside of them is that sometimes they can recognize and suggest a pattern that those programming the other coding helpers might have missed. Outside of that. Such as generating act like whole blocks of code or even entire programs. You can’t even get an llm to reliably spit out a hello world program.
“It’s part of the history of the field of artificial intelligence that every time somebody figured out how to make a computer do something—play good checkers, solve simple but relatively informal problems—there was a chorus of critics to say, ‘that’s not thinking’”
-Pamela McCorduck“AI is whatever hasn’t been done yet.”
- Larry TeslerThat’s the curse of the AI Effect.
Nothing will ever be “an actual AI” until we cross the barrier to an actual human-like general artificial intelligence like Cortana from Halo, and even then people will claim it isn’t actually intelligent.I mean, I think intelligence requires the ability to integrate new information into one’s knowledge base. LLMs can’t do that, they have to be trained on a fixed corpus.
Also, LLMs have a pretty shit-tastic track record of being able to differentiate correct data from bullshit, which is a pretty essential facet of intelligence IMO
LLMs have a perfect track record of doing exactly what they were designed to, take an input and create a plausible output that looks like it was written by a human. They just completely lack the part in the middle that properly understands what it gets as the input and makes sure the output is factually correct, because if it did have that then it wouldn’t be an LLM any more, it would be an AGI.
The “artificial” in AI does also stand for the meaning of “fake” - something that looks and feels like it is intelligent, but actually isn’t.
Well at least until those who study intelligence and self-awareness actually come up with a comprehensive definition for it. Something we don’t even have currently. Which makes the situation even more silly. The people selling LLMs and AGNs as artificial intelligence are the PT Barnum of the modern era. This way to the egress folks come see the magnificent egress!
They already did. AGI - artificial general intelligence.
The thing is, AGI and AI are different things. Like your “LLMs aren’t real AI” thing , large language models are a type of machine learning model, and machine learning is a field of study in artificial intelligence.
LLMs are AI. Search engines are AI. Recommendation algorithms are AI. Siri, Alexa, self driving cars, Midjourney, Elevenlabs, every single video game with computer players, they are all AI. Because the term “Artificial Intelligence” by itself is extremely loose, and includes the types of narrow AI all of those are.
Which then get hit by the AI Effect, and become “just another thing computers can do now”, and therefore, “not AI”.That just Compares it to human level intelligence. Something which we cannot currently even quantify. Let alone understand. It’s ultimately a comparison, a simile not a scientific definition.
Search engines have always been databases. With interfaces programmed by humans. Not ai. They’ve never suddenly gained new functionality inexplicably. If there’s a new feature someone programmed it.
Search engines are however becoming llms and are getting worse for it. Unless you think eating rocks and glue is particularly intelligent. Because there is no comprehension there. It’s simply trying to make its output match patterns it recognizes. Which is a precursor step. But is not “intelligence”. Unless a program doing what it’s programed to do is artificial intelligence. Which is such a meaningless measure because that would mean notepad is artificial intelligence. Windows is artificial intelligence. Linux is artificial intelligence.
You can argue what you think the words should mean in your opinion in the field of artificial intelligence. I agree with some of them.
You can’t just throw out random Wikipedia links. For example, the Article on AGI explicitly says we don’t have a definition of what human level cognition actually is. Which is what the person you were replying to was saying. You’re doing a fallacious appeal to authority, except that the authority doesn’t agree with you.
That’s a disturbing handwave. “We don’t really know what intelligence is, so therefore, anything we call intelligence is fair game”
A thermometer tells me what temperature it is. It senses the ambient heat energy and responds with a numeric indicator. Is that intelligence?
My microwave stops when it notices steam from my popcorn bag. Is that intelligence?
If I open an encyclopedia book to a page about computers, it tells me a bunch of information about computers. Is that intelligence?
I never know what to think when I come across a comment like this one—which does describe, even if only at a surface level, how an LLM works—with 50% downvotes. Like, are people angry at reality, is that it?
With as much misinformation that’s being spread about regarding LLMs. It would only lose more people’s comprehension to go into anything more than a generalization.
The problem is people are being sold AGI. But chat GPT and all these other tools don’t even remotely qualify for that. They’re really nothing more than a glorified Alice chatbot system on steroids. The one neat new trick to all this is that they’ve automated the training a bit. But these llms have no more comprehension of their output or the input they were given than something like the old Alice chatbot.
These tools have been described as artificial intelligence to layman for decades at this point. It makes it really hard to change that calcified opinion. People would rather believe that it’s some magical thing not just probability and maths.
They are bullshit machines, trained to output something that users think is the right output.
Downvoting someone on the Internet is easier than tangentially modifying reality in a measurable way
Large context window LLMs are able to do quite a bit more than filling the gaps and completion. They can edit multiple files.
Yet, they’re unreliable, as they hallucinate all the time. Debugging LLM-generated code is a new skill, and it’s up to you to decide to learn it or not. I see quite an even split among devs. I think it’s worth it, though once it took me two hours to find a very obscure bug in LLM-generated code.
I have one of those at work now, but my experience with it is still quite limited. With Copilot it was quite useful for knocking up quick boutique solutions for particular problems (stitch together a load of PDFs sorted on a name heading), with the proviso that you might end up having to repair bleed between dependency versions and repair syntax. I couldn’t trust it with big refactors of existing systems.
If you consider debugging broken LLM-generated code to be a skill… sure, go for it. But, since generated code is able to use tons of unknown side effects and other seemingly (for humans) random stuff to achieve its goal, I’d rather take the other approach, where it takes a human half an hour to write the code that some LLM could generate in seconds, and not have to learn how to parse random mumbo jumbo from a machine, while getting a working result.
Writing code is far from being the longest part of the job; and you gingerly decided that making the tedious part even more tedious is a great idea to shorten the already short part of it…
What is your favorite flavor of kool aid?
Grape, my nigga.
Even if they plateaued in place where they are right now it would lead to major shakeups in humanity’s current workflow
Like which one? Because it’s now 2 years we have chatGPT and already quite a lot of (good?) models. Which shakeup do you think is happening or going to happen?
Computer programming has radically changed. Huge help having llm auto complete and chat built in. IDEs like Cursor and Windsurf.
I’ve been a developer for 35 years. This is shaking it up as much as the internet did.
I quit my previous job in part because I couldn’t deal with the influx of terrible, unreliable, dangerous, bloated, nonsensical, not even working code that was suddenly pushed into one of the projects I was working on. That project is now completely dead, they froze it on some arbitrary version.
When junior dev makes a mistake, you can explain it to them and they will not make it again. When they use llm to make a mistake, there is nothing to explain to anyone.
I compare this shake more to an earthquake than to anything positive you can associate with shaking.And so, the problem wasn’t the ai/llm, it was the person who said “looks good” without even looking at the generated code, and then the person who read that pull request and said, again without reading the code, “lgtm”.
If you have good policies then it doesn’t matter how many bad practice’s are used, it still won’t be merged.
The only overhead is that you have to read all the requests but if it’s an internal project then telling everyone to read and understand their code shouldn’t be the issue.
This is a problem with your team/project. It’s not a problem with the technology.
I hardly see it changed to be honest. I work in the field too and I can imagine LLMs being good at producing decent boilerplate straight out of documentation, but nothing more complex than that.
I often use LLMs to work on my personal projects and - for example - often Claude or ChatGPT 4o spit out programs that don’t compile, use inexistent functions, are bloated etc. Possibly for languages with more training (like Python) they do better, but I can’t see it as a “radical change” and more like a well configured snippet plugin and auto complete feature.
LLMs can’t count, can’t analyze novel problems (by definition) and provide innovative solutions…why would they radically change programming?
You’re missing it. Use Cursor or Windsurf. The autocomplete will help in so many tedious situations. It’s game changing.
ChatGPT 4o isn’t even the most advanced model, yet I have seen it do things you say it can’t. Maybe work on your prompting.
That is my experience, it’s generally quite decent for small and simple stuff (as I said, distillation of documentation). I use it for rust, where I am sure the training material was much smaller than other languages. It’s not a matter a prompting though, it’s not my prompt that makes it hallucinate functions that don’t exist in libraries or make it write code that doesn’t compile, it’s a feature of the technology itself.
GPTs are statistical text generators after all, they don’t “understand” the problem.
It’s also pretty young, human toddlers hallucinate and make things up. Adults too. Even experts are known to fall prey to bias and misconception.
I don’t think we know nearly enough about the actual architecture of human intelligence to start asserting an understanding of “understanding”. I think it’s a bit foolish to claim with certainty that LLMs in a MoE framework with self-review fundamentally can’t get there. Unless you can show me, materially, how human “understanding” functions, we’re just speculating on an immature technology.
As much as I agree with you, humans can learn a bunch of stuff without first learning the content of the whole internet and without the computing power of a datacenter or consuming the energy of Belgium. Humans learn to count at an early age too, for example.
I would say that the burden of proof is therefore reversed. Unless you demonstrate that this technology doesn’t have the natural and inherent limits that statistical text generators (or pixel) have, we can assume that our mind works differently.
Also you say immature technology but this technology is not fundamentally (I.e. in terms of principle) different from what Weizenabum’s ELIZA in the '60s. We might have refined model and thrown a ton of data and computing power at it, but we are still talking of programs that use similar principles.
So yeah, we don’t understand human intelligence but we can appreciate certain features that absolutely lack on GPTs, like a concept of truth that for humans is natural.
Exactly this. Things have already changed and are changing as more and more people learn how and where to use these technologies. I have seen even teachers use this stuff who have limited grasp of technology in general.
Review of legal documents.
Oh boy…what can possibly go wrong for documents where small minutiae like wording can make a huge difference.
Creating legal documents, no. Reviewing legal documents for errors and inaccuracies totally.
No, not that either. Unless you consider “use LLM to summarize the changes/errors/inaccuracies, then have a human read the whole thing again” an improvement over “just have a human read the whole thing”.
Because LLM will do all these things:
- point you toward issues
- point you toward non-issues
- not point you toward issues
- change stuff even when “instructed” not to
If there is one thing you don’t want to throw an LLM at without full, unbiased review, it’s documents where the wording is legally binding. And if you have to do a full, unbiased review to begin with, where you can’t even trust your tool to have highlighted all the important parts, you may as well not bother with the tool.
I really can’t see this being done by any sane person. Why would you have a generator of text reviewing stuff (besides grammar)? Do you have any reference of some companies doing this, perhaps?
Its complex pattern matching and looking up existing case law online. This work has been outsourced to contracting companies for at least 7 years that I’m aware of. If it is something that can be documented in a run book for non professionals to do for twenty cents on the dollar then there is no reason it can’t be done by a script for .002.
Aside from a handful of business that tried to do that and failed miserably, some of them failing in actual court, you mean?
See now, I would prefer AI in my toaster. It should be able to learn to adjust the cook time to what I want no matter what type of bread I put in it. Though is that realky AI? It could be. Same with my fridge. Learn what gets used and what doesn’t. Then give my wife the numbers on that damn clear box of salad she buys at costco everytime, which take up a ton of space and always goes bad before she eats even 5% of it. These would be practical benefits to the crap that is day to day life. And far more impactful then search results I can’t trust.
See now, I would prefer AI in my toaster.
I was so hoping that was toasty the toaster! Waffles? How about a bagel?
There’s a good point here that like about 80% of what we’re calling AI right now… isn’t even AI or even LLM. It’s just… algorithm, code, plain old math. I’m pretty sure someone is going to refer to a calculator as AI soon. “Wow, it knows math! Just like a person! Amazing technology!”
(That’s putting aside the very question of whether LLMs should even qualify as AIs at all.)
In my professional experience, AI seems to be just a faster way to generate an algorithm that is really hard to debug. Though I am dev-ops/sre so I am not as deep in it as the devs.
You better believe that AI-powered toaster would only accept authorized bread from a bakery that paid top dollar to the company that makes them. To ensure the best quality possible and save you from inferior toast, of course.
or you go to make some toast and it spends 15 minutes downloading “updates” before you can use it
Lol, enshitification should at least take a few months… I hope.
oh wow who would have guessed that business consultancy companies are generally built on top of bullshitting about things which you dont really have a grasp on
I saved a lot of time due to ChatGPT. Need to sign up some of my pupils for a competition by uploading their data in a csv-File to some plattform? Just copy and paste their data into chsatgpt and prompt it to create the file. The boss (headmaster) wants some reasoning why I need some paid time for certain projects? Let ChatGPT do the reasoning. Need some exercises for one of my classes that doesn’t really come to grips with while-loops? let ChatGPT create those exercises (some smartasses will of course have ChatGPT then solve those exercises). The list goes on…
You are an asshole if you’re uploading student data to a mining operation.
Just copy and paste [student personal data] into [3rd parties database]
Yeah, that’s a problem, especially in Europe. Im unsure about US, but it’s definitely a breach of GDPR.
ChatGPT is basically like a really good intern, and I use it heavily that way. I run literally every email through it and say “respond to so and so, say xyz” and then maybe a little refining, copy paste, done.
The other day, my boss sent me an excel file with a shitload of data in it that he wanted me to analyze some such way. I just copy pasted it into gpt and asked it, and it spit out the correct response. Then my boss asked me to do something else that required a bit of excel finagling that I didn’t really know how to do, so i asked gpt, and it told me the formula, which worked immediately first try.
So basically it helps me accomplish tasks in seconds that previously would’ve taken hours. If anything, I think markets are currently undervalued, because remarkably, fucking NONE of my colleagues or friends are using it at all yet. Once there’s widespread adoption, which will pretty much have to happen if anyone wants to stay competitive once it gains more traction, look out…
Yeah, and Wikipedia is one of the most useful sites on the net, but it didn’t exactly result in the entire web becoming crowdsourced.
Those pupils will really thank you when they grow up and there isn’t enough fresh water because all the data centres are using it up far faster than it can be replenished.
https://utulsa.edu/news/data-centers-draining-resources-in-water-stressed-communities/
The poem about AI that often gets posted says “What are you trying to avoid? The living [of a life]?”
And yeah, that’s what it’s for, dodging shit you don’t want to do. I gotta produce some useless bullshit that no one’s going to read or care about: AI.
I don’t even mind AI art for things like LinkedIn posts, blogs like “What is warehouse management?” or “Top 10 finance trends in 2025” - SEO spam that no human will read. No one wants to write it, read it, or care about it- its just a x kb file to tell Google to look here.
One time, I needed to convince my boss’s boss that we needed to do something, and he wanted it in writing. Guess who wrote the proposal? And far more eloquently than I could have alone, in the time allowed. It required some good prompts, attentive proofreading, and a few drafts. But in the end, it was quite effective.
A big issue that a lot of these tech companies seem to have is that they don’t understand what people want; they come up with an idea and then shove it into everything. There are services that I have actively stopped using because they started cramming AI into things; for example I stopped dual-booting with Windows and became Linux-only.
AI is legitimately interesting technology which definitely has specialized use-cases, e.g. sorting large amounts of data, or optimizing strategies within highly restrained circumstances (like chess or go). However, 99% of what people are pushing with AI these days as a member of the general public just seems like garbage; bad art and bad translations and incorrect answers to questions.
I do not understand all the hype around AI. I can understand the danger; people who don’t see that it’s bad are using it in place of people who know how to do things. But in my teaching for example I’ve never had any issues with students cheating using ChatGPT; I semi-regularly run the problems I assign through ChatGPT and it gets enough of them wrong that I can’t imagine any student would be inclined to use ChatGPT to cheat multiple times after their grade the first time comes in. (In this sense, it’s actually impressive technology - we’ve had computers that can do advanced math highly accurately for a while, but we’ve finally developed one that’s worse at math than the average undergrad in a gen-ed class!)
The answer is that it’s all about “growth”. The fetishization of shareholders has reached its logical conclusion, and now the only value companies have is in growth. Not profit, not stability, not a reliable customer base or a product people will want. The only thing that matters is if you can make your share price increase faster than the interest on a bond (which is pretty high right now).
To make share price go up like that, you have to do one of two things; show that you’re bringing in new customers, or show that you can make your existing customers pay more.
For the big tech companies, there are no new customers left. The whole planet is online. Everyone who wants to use their services is using their services. So they have to find new things to sell instead.
And that’s what “AI” looked like it was going to be. LLMs burst onto the scene promising to replace entire industries, entire workforces. Huge new opportunities for growth. Lacking anything else, big tech went in HARD on this, throwing untold billions at partnerships, acquisitions, and infrastructure.
And now they have to show investors that it was worth it. Which means they have to produce metrics that show people are paying for, or might pay for, AI flavoured products. That’s why they’re shoving it into everything they can. If they put AI in notepad then they can claim that every time you open notepad you’re “engaging” with one of their AI products. If they put Recall on your PC, every Windows user becomes an AI user. Google can now claim that every search is an AI interaction because of the bad summary that no one reads. The point is to show “engagement”, “interest”, which they can then use to promise that down the line huge piles of money will fall out of this pinata.
The hype is all artificial. They need to hype these products so that people will pay attention to them, because they need to keep pretending that their massive investments got them in on the ground floor of a trillion dollar industry, and weren’t just them setting huge piles of money on fire.
I know I’m an enthusiast, but can I just say I’m excited about NotebookLLM? I think it will be great for documenting application development. Having a shared notebook that knows the environment and configuration and architecture and standards for an application and can answer specific questions about it could be really useful.
“AI Notepad” is really underselling it. I’m trying to load up massive Markdown documents to feed into NotebookLLM to try it out. I don’t know if it’ll work as well as I’m hoping because it takes time to put together enough information to be worthwhile in a format the AI can easily digest. But I’m hopeful.
That’s not to take away from your point: the average person probably has little use for this, and wouldn’t want to put in the effort to make it worthwhile. But spending way too much time obsessing about nerd things is my calling.
Being able to summarize and answer questions about a specific corpus of text was a use case I was excited for even knowing that LLMs can’t really answer general questions or logically reason.
But if Google search summaries are any indication they can’t even do that. And I’m not just talking about the screenshots people post, this is my own experience with it.
Maybe if you could run the LLM in an entirely different way such that you could enter a question and then it tells you which part of the source text statistically correlates the most with the words you typed; instead of trying to generate new text. That way in a worse case scenario it just points you to a part of the source text that’s irrelevant instead of giving you answers that are subtly wrong or misleading.
Even then I’m not sure the huge computational requirements make it worth it over ctrl-f or a slightly more sophisticated search algorithm.
Multiple times now, I’ve seen people post AI summaries of articles on Lemmy which miss out really, really important points.
Well an example of something I think it could solve would be: “I’m trying to set this application up to run locally. I’m getting this error message. Here’s my configuration files. What is not set up correctly, or if that’s not clear, what steps can I take to provide more helpful information?”
ChatGPT is always okay at that as long as you have everything set up according to the most common scenarios, but it tells you a lot of things that don’t apply or are wrong in the specific case. I would like to get answers that are informed by our specific setup instructions, security policies, design standards, etc. I don’t want to have to repeat “this is a Java spring boot application running on GCP integrating with redis on docker… blah blah blah”.
I can’t say whether it’s worth it yet, but I’m hopeful. I might do the same with ChatGPT and custom GPTs, but since I use my personal account for that, it’s on very shaky ground to upload company files to something like that, and I couldn’t share with the team anyway. It’s great to ask questions that don’t require specific knowledge, but I think I’d be violating company policy to upload anything.
We are encouraged to use NotebookLLM, however.
From a nerdy perspective, LLMs are actually very cool. The problem is that they’re grotesquely inefficient. That means that, practically speaking, whatever cool use you come up with for them has to work in one of two ways; either a user runs it themselves, typically very slowly or on a pretty powerful computer, or it runs as a cloud service, in which case that cloud service has to figure out how to be profitable.
Right now we’re not being exposed to the true cost of these models. Everyone is in the “give it out cheap / free to get people hooked” stage. Once the bill comes due, very few of these projects will be cool enough to justify their costs.
Like, would you pay $50/month for NotebookLM? However good it is, I’m guessing it’s probably not that good. Maybe it is. Maybe that’s a reasonable price to you. It’s probably not a reasonable price to enough people to sustain serious development on it.
That’s the problem. LLMs are cool, but mostly in a “Hey this is kind of neat” way. They do things that are useful, but not essential, but they do so at an operating cost that only works for things that are essential. You can’t run them on fun money, but you can’t make a convincing case for selling them at serious money.
Totally agree. It comes down to how often is this thing efficient for me if I pay the true cost. At work, yes it would save over $50/mo if it works well. At home it would be difficult to justify that cost, but I’d also use it less so the cost could be lower. I currently pay $50/mo between ChatGPT and NovelAI (and the latter doen’t operate at a loss) so it’s worth a bit to me just to nerd out over it. It certainly doesn’t save me money except in the sense that it’s time and money I don’t spend on some other endeavor.
My old video card is painfully slow for local LLM, but I dream of spending for a big card that runs closer to cloud speeds even if the quality is lower, for easier tasks.
You’re using the wrong tool.
Hell, notepad is the wrong tool for every use case, it exists in case you’ve broken things so thoroughly on windows that you need to edit a file to fix it. It’s the text editor of last resort, a dumb simple file editor always there when you need it.
Adding any feature (except possibly a hex editor) makes it worse at its only job.
… I don’t use Notepad. For anything. Hell, I don’t even use Windows.
Not sure where the wires got crossed here.
Then either you replied with your first post to the wrong post or you misread “windows putting AI into notepad” as notebookLLM? Because if not there is nothing obvious connecting your post to the parent
I don’t think anyone is putting AI into Notepad. It reads to me like a response to NotebookLLM but maybe I was wrong.
I did at least explain what my vision is and why I wanted it which… doesn’t sound anything like Notepad, I think.
I don’t think […]
Well, you think wrong: https://blogs.windows.com/windows-insider/2024/11/06/new-ai-experiences-for-paint-and-notepad-begin-rolling-out-to-windows-insiders/
I did at least explain what my vision is and why I wanted it which… doesn’t sound anything like Notepad, I think.
Might be, but the person you responded to wrote about windows putting AI into notepad, so everyone assumed you were responding to that and not writing about something that was not even mentioned
I’ve ran some college hw through 4o just to see and it’s remarkably good at generating proofs for math and algorithms. Sometimes it’s not quite right but usually on the right track to get started.
In some of the busier classes I’m almost certain students do this because my hw grades would be lower than the mean and my exam grades would be well above the mean.
Page doesn’t render properly.
You can read it here:
Thanks – it has been clear enough that an another AI winter is coming. Likely latest when the Global Financial Crisis 2 is here.
But the line must go up!
The article does mention that when the AI bubble is going down, the big players will use the defunct AI infrastructure and add it to their cloud business to get more of the market that way and, in the end, make the line go up.
That’s not what the article says.
They’re arguing that AI hype is being used as a way of driving customers towards cloud infrastructure over on-prem. Once a company makes that choice, it’s very hard to get them to go back.
They’re not saying that AI infrastructure specifically can be repurposed, just that in general these companies will get some extra cloud business out of the situation.
AI infrastructure is highly specialized, and much like ASICs for the blockchain nonsense, will be somewhere between “very hard” and “impossible” to repurpose.
Assuming a large decline in demand for AI compute, what would be the use cases for renting out older AI compute hardware on the cloud? Where would the demand come from? Prices would also go down with a decrease in demand.
Relaunching Stadia?
Haha. I believe the AMD Instinct / Nvidia Datacentre GPUs aren’t that great for gaming.
Big tech is out of ideas and needs AI to work in order to drive growth.
To have a bubble you need companies with no clear path to monetization, being over-valued to an extreme degree. This leaves me wondering : what company specifically ? Are they talking about nVidia ? OpenAI ? MidJourney ? Or the slew of LLM-powered SaaS products that have started appearing ? How exactly are we defining “over-valuation” here ? Are we talking about the tech industry as a whole ?
We often invite the comparison to the DotCom bubble but that’s apples to oranges. You had companies making social networks for dogs or similar bullshit, valued in the billions and getting a ticker at the stock market before making a single dime. Or companies with outlandish promises such as delivering to any home in the US, in <1 hour, for a low price, and building warehouses by the hundreds before having a storefront. What would be the 2024 equivalent ? If a bubble is about to deflate then there should be dozens of comparable examples.
Exactly. There’s a very clear path to monetisation for the bigger tech companies (ofc, not the random startup that screams “AI quantum computing blockchain reeeee”).
Lemmy is just incredibly biased against AI, as it could replace a shit ton of jobs and lead to a crazy amount of wealth inequality. However, people need to remember that the problem isn’t the tech- it’s the system that the tech is being innovated in.
Denying AI is just going to make this issue a lot worse. We need to work to make AI be beneficial for all of us instead of the capitalists. But somehow leftist talk surrounding AI has just been about hating on it/ denying it, instead of preparing for a world in which it would be critical infrastructure very soon.
What job could possibly replace…? If you can replace a job with LLMs it means either that the job is not needed on the first place (bullshit job) or that you can replace it with a dice (e.g., decision-making processes), since LLMs-output will depend essentially just on what is in the training material -which we don’t know (I.e., the answer is essentially random).
I don’t think it’s just Lemmy, i had similar conversations on Reddit. People don’t realize that the companies they claim are over-valued actually have very strong business fundamentals. That’s why in articles like OP’s they will never mention any names or figures. I guess it’s very convincing for outsiders but it doesn’t stand any amount of scrutiny.
If you take OpenAI for example, they went from 0 to 3.6B$ annual revenue in just two fucking years. How is that not worth a boatload of money ? Even Uber didn’t have that kind of growth and they burned a LOT more cash than OpenAI is burning right now.
As for the “AI quantum computing blockchain reeeee” projects… well they have a very hard time raising money right now and when they do, it’s at pretty modest valuations. The market is not as dumb as it is portrayed.
How is that not worth a boatload of money?
Because they spend $2.35 billion in operating costs for every $1 billion in revenue (gross, not net).
OpenAI loses money at an absolutely staggering rate, and every indication, even their own openly stated predictions, are that those costs will only increase.
And keep in mind, right now OpenAI gets a lot of their investment in the form of compute credits from Microsoft, which they get to spend at a massively discounted rate. That means that if they were actually buying their Azure time at market value they’d be closer to spending something like $5bn to make $1bn.
Again, I really need to be clear here, I’m not saying “to make 1 billion in profit.” I’m saying “revenue”. They lose money every time someone uses their services. The more users they have, the more their losses grow. Even paid users cost them more money than they pay in most cases.
This is like a store that buys products at $10 and sells them at $4. It is the most insanely unprofitable business plan imaginable.
And it’s not getting better. Conversions to paid plans are at about 3%. Their enterprise sales are abysmal. Training costs are increasing exponentially with each new generation of models. Attempts to make their models more compute efficient have so far failed utterly.
OpenAI’s path to profitability is basically “Invent true AGI.” It’s a wild fantasy with zero basis in reality that investors are shovelling money into because investors will shovel money into anything that promises infinite growth.
Because they spend $2.35 billion in operating costs for every $1 billion in revenue
This is entirely true but also completely normal for a hyperscaler in its first years. At this stage demonstrating a huge demand and your capacity to capture a lot of it is much more important than profitability. You don’t exactly bootstrap a company at this level of CapEx.
Their revenue is still growing at a staggering rate, showing no signs of slowing down, and enterprise sales are pretty respectable. I don’t know that i’d call 3/4 of a billion in enterprise sales abysmal for a startup in its 2nd year. YMMV i guess.
Sure there is some uncertainty about their model but that’s what VC backing is for, right ? They’re not building tin can factories with known and predictable business trends, and being valued at 40x your yearly revenue (not profit !) is pretty banal for a successful early stage Deep Tech. We may personally think it’s bullshit and choose not to invest in it but it’s still far from outrageous and very far from the definition of a bubble.
For the level of investment in and hype around this company? Yes, those enterprise sales are abysmal. When there are major news articles about their product every single week, they should be doing a lot better than that.
They have demonstrated zero ability at actually “hyperscale”. They have no path to getting those costs down. Their conversion rate from free to paid users is atrocious, and they’re already raising prices on their plans which is only going to worsen those conversion rates. Their costs to build future models are ballooning exponentially, and theres a decent chance that at some point Microsoft will get sick of subsidizing their compute costs.
Is it possible that they could be successful? Yes. But a lottery ticket would probably be a sounder investment.
For the record, OpenAI themselves are telling those VCs that they should think of their investment more as a “donation” with no expectation of future profit. Absolutely oozing confidence there.
Hypergrowth cult is always “interesting”. Because it happened with X, apparently it will happen with Y. Then, unavoidably, it goes through the boom and bust cycle. I am sick of it.
For the level of investment in and hype around this company? Yes, those enterprise sales are abysmal
I don’t see the connection. How are enterprise sales specifically relevant here ? Are enterprise customers known for jumping on top of early stage products where you’re from ? Cause where i’m from they’re known for being the last ones to board.
They have demonstrated zero ability at actually “hyperscale”.
How would you define hyperscale ? They have one of the biggest GPU fleets around and are likely serving trillions of tokens monthly. That falls well within the range of my personal definition.
They have no path to getting those costs down. Their conversion rate from free to paid users is atrocious, and they’re already raising prices on their plans which is only going to worsen those conversion rates
That’s just stuff you say. Atrocious (in your opinion), no path to getting those costs down (in your opinion). Alright, we get it, that’s not a company you’d invest in, but then again your investment thesis seems pretty conservative. If a company has to make billions of enterprise sales in its 2nd year, and have double digits conversion early on, then there’s not that many successful companies you would have invested in. You certainly wouldn’t have put a dime in Uber at 48B valuation 7 years ago - well those who did made a nice return on their investment.
Is it possible that they could be successful? Yes. But a lottery ticket would probably be a sounder investment.
Isn’t that the definition of VC-backed startups ? The alternative would be to build a time machine, travel back to the 18th century, and invest in the British textile industry. Sadly they don’t make this kind of predictible, risk-free and quickly profitable enterprises nowadays.
Absolutely oozing confidence there
Oh i won’t be the one to contradict you here. Sama is one sleazy motherfucker, that’s just written on his face. Sadly it doesn’t preclude him from building a historical hyperscaler with OpenAI.
“Today’s hype will have lasting effects that constrain tomorrow’s possibilities.”
Nope. No it won’t. I’d love to have the patience to be more diplomatic but they’re just wrong… and dumb.
I’m getting so sick of these anti AI cultists who seem to be made up of grumpy tech nerds behaving like “I was using AI before it was cool” hipsters and panicking artists and writers. Everyone needs to calm their tits right down. AI isn’t going anywhere. It’s giving creative and executive options to millions of people that just weren’t there before.
We’re in an adjustment phase right now and boundaries are being re-drawn around what constitutes creativity. My leading theory at the moment is that we’ll all mostly eventually settle down to the idea that AI is just a tool. Once we’re used to it and less starry eyed about it’s output then individual creativity, possibly supported by AI tools, will flourish again. It’s going to come down to the question of whether you prefer reading something cogitated, written, drawn or motion rendered by AI or you enjoy the perspective of a human being more. Both will be true in different scenarios I expect.
Honestly, I’ve had to nope out of quite a few forums and servers permanently now because all they do in there is circlejerk about the death of AI. Like this one theory that keeps popping up that image generating AI specifically is inevitably going to collapse in on itself and stop producing quality images. The reverse is so obviously true but they just don’t want to see it. Otherwise smart people are just being so stubborn with this and it’s, quite frankly, depressing to see.
Also, the tech nerds arguing that AI is just a fancy word and pixel regurgitating engine and that we’ll never have an AGI are probably the same people that were really hoping Data would be classified as a sentient lifeform when Bruce Maddox wanted to dissassemble him in “The Measure of a Man”.
How’s that for whiplash?
Models are not improving, companies are still largely (massively) unprofitable, the tech has a very high environmental impact (and demand) and not a solid business case has been found so far (despite very large investments) after 2 years.
That AI isn’t going anywhere is possible, but LLM-based tools might also simply follow crypto, VR, metaverses and the other tech “revolutions” that were just hyped and that ended nowhere. I can’t say it will go one way or another, but I disagree with you about “adjustment period”. I think generative AI is cool and fun, but it’s a toy. If companies don’t make money with it, they will eventually stop investing into it.
Also
Today’s hype will have lasting effects that constrain tomorrow’s possibilities
Is absolutely true. Wasting capital (human and economic) on something means that it won’t be used for something else instead. This is especially true now that it’s so hard to get investments for any other business. If all the money right now goes into AI, and IF this turns out to be just hype, we just collectively lost 2, 4, 10 years of research and investments on other areas (for example, environment protection). I am really curious about what makes you think that that sentence is false and stupid.
Models are not improving? Since when? Last week? Newer models have been scoring higher and higher in both objective and subjective blind tests consistently. This sounds like the kind of delusional anti-AI shit that the OP was talking about. I mean, holy shit, to try to pass off “models aren’t improving” with a straight face.
There is a bunch of research showing that model improvement is marginal compared to energy demand and/or amount of training data. OpenAI itself ~1 month ago mentioned that they are seeing a smaller improvements in Orion (I believe) vs GPT4 than there was between GPT 4 and 3. We are also running out of quality data to use for training.
Essentially what I mean is that the big improvements we have seen in the past seem to be over, now improving a little cost a lot. Considering that the costs are exorbitant and the difference small enough, it’s not impossible to imagine that companies will eventually give up if they can’t monetize this stuff.
It’s fucking fantastic news, tbh.
Here’s my take, let them dismiss it.
Let em! Remember Bitcoin at $15k after 2019?
Let em! And it’s justified! If Ai isn’t important right now, then why should its price be inflated to oblivion? Let it fall. Good! Lower prices for those of us that do see the value down the road.
That’s how speculative investment works. In no way is this bad. Are sales bad? Sit back and enjoy the show.
Are sales bad?
Of AI products? By all available metrics, yes, sales for AI driven products are atrocious.
Even the biggest name in AI is desperately unprofitable. OpenAI has only succeeded in converting 3% of their free users to paid users. To put that on perspective, 40% of regular Spotify users are on premium plans.
And those paid plans don’t even cover what it costs to run the service for those users. Currently OpenAI are intending to double their subscription costs over the next five years, and that still won’t be enough to make their service profitable. And that’s assuming that they don’t lose subscribers over those increased costs. When their conversion rate at their current price is only 3%, there’s not exactly an obvious appetite to pay more for the same thing.
And that’s the headline name. The key driver of the industry. And the numbers are just as bad everywhere else you look, either terrible, or deliberately obfuscated (remember, these companies sank billions of capex into this; if sales were good they’d be talking very openly and clearly about just how good they are).
I have no idea how people can consider this to be a hype bubble especially after the o3 release. It smashed the ARC AGI benchmark on the performance front. It ranks as the 175th best competitive coder in the world on Codeforces’ leaderboard.
o3 proved that it is possible to have at least an expert AGI if not a Virtuoso AGI (according to Deep mind’s definition of AGI). Sure, it’s not economical yet. But it will get there very soon (just like how the earlier GPTs were a lot dumber and took a lot more energy than the newer, smaller parameter models).
Please remember - fight to seize the means of production. Do not fight the means of production themselves.
It’s a bubble because OpenAI spend $2.35 for every $1.00 they make. Yes, you’re mathing right, that is a net loss.
It’s a bubble because all of the big players in AI development agree that future models will cost exponentially more money to train, for incremental gains. That means there is no path forward that doesn’t intensely amplify the unprofitability of an already deeply unprofitable industry.
It’s a bubble because newer models with better capabilities only cost more and more to run.
It’s a bubble because as far as anyone knows there will never be a solution to the hallucination problem.
It’s a bubble because despite investments treating it as a trillion dollar industry, no one has yet figured out a trillion dollar problem that AI can solve.
You’re trying on a new top of the line VR headset and saying “Wow, this is incredible, how can anyone say this is a bubble?” Its not about how cool the tech is in isolation, it’s about its potential to effect widespread change. Facebook went in hard on VR, imagining a future where everyone worked from home while wearing VR headsets. But what they got was an expensive toy that only had niche uses.
AI performs do well on certain coding tasks because a lot of the individual problems that make up a particular piece of software have already been solved. It’s standard practice to design programs as individual units, each of which performs the smallest task possible, and which can then be assembled to complete more complex tasks. This fits very well into the LLM model of assembling pieces into their most likely expected configurations. But it cannot create truly novel code, except by a kind of trial and error mutation process. It cannot problem solve. It cannot identify a users needs and come up with ideal solutions to them. It cannot innovate.
This means that, at best, genAI in the software world becomes a tool for producing individual code elements, guided and shepherded by experienced programmers. It does not replace the software industry, merely augments it, and it does so at a cost that many companies simply may not feel is worth paying.
And that’s its best case scenario. In every other industry AI has been a spectacular failure. But it’s being invested in as if it will be a technological reckoning for every form of intellectual labour on earth. That is the absolute definition of a bubble.
Where, in that position piece, do they mention o3? Who “proved” this?
Additionally, I’m pretty sure that this “ARC AGI” benchmark is not using the same definition of AGI that you linked to by DeepMind. Conflating them is misleading. There is already so much misinformation out there about “AI”, don’t add to it.
Lastly, I struggle to take at face value essays written by for-profit companies claiming they have AGI (that DeepMind paper links to OpenAI essays). They only stand to gain monetarily by claiming that their AI is an AGI (to be clear, this is an opinion; I do not have evidence to suggest that OpenAI is being disingenuous).
o3 made the high score on ARC through brute force, not by being good. To raise the score from 75% to 87% required 175 times more computing power, but exactly stunning returns.
Why does it matter?
If it can through brute force, it can do it. That’s the first step towards true agi, nobody said the first AGI would be economical, this feels like a major goalpost shift if you’re acknowledging it can do it at all, isn’t that insane?
A little bit ago, everyone would’ve been saying this will never happen, that there was a natural wall simply because all it does is predict the next token, it’s been like, a few years of llm’s and they’re already getting this insane. We’re going to have AGI soon, it might not be a transformer, but billions upon billions of dollars are being thrown at this problem, there are people smart enough in the world to make this work, and this is the earliest sign that it’s coming.
I’m not convinced that it’s anywhere near an AGI, I’m convinced after combing through papers and code, that it’s an amazing parlor trick.
I’d love to be proven wrong, but everything I’ve seen and everything I’ve used in my studies ( using DNN to simulate neurodivergence and spinal disgenesis, which is kinda AI adjacent) leads me to believe that the current part won’t lead to anything but convincing parlor tricks.
The argument could be made that if a trick is convincing enough, does it matter if it’s intelligent or not.
What would convince you?
I’m not entirely sure.
A non-probabilistic algorithm, probably. Something that didn’t rely on the liklihood of association, and instead was capable of context and rationality.
Something that wouldn’t have a system capable of saying “Put glue on your pizza” because it would know that’s a silly thing to say to a human. A system that, when asked "Whats a good caustic detergent " wouldn’t be able to respond "Any good caustic detergent is a good caustic detergent " because duh. Something that doesn’t require thousands of hours of training to update and instead is capable of ingesting and rationalize new information on the fly.You’re probablistic, you just have an internal verifier, you think things that are silly, and then decide not to say them all the time. A human being often thinks things that they realize are silly before they say them… that’s an entirely unfair goal in the first place from my perspective, why does it have to be non-probablistic?
Are you not a general intelligence because sometimes your brain thinks silly things?
o3 currently works precisely that way, by the way, it generates hundreds of possible things, and then uses something that checks if the steps actually work, before it outputs. In fact, they then reinforce it on these correct logical steps, so it becomes better at not outputting illogical answers like you said.
it’s interesting that you said “not on the probability of the next word, but on context and rationality”
context IS pricesely that, you know what’s likely to come next because of the context, that’s you understanding context. YOU as a human being don’t even always get this right, you must realize we are not perfect beings, we think of possibilities and choose the right one. I think we’re much better at this right now, but i don’t think that’s a fundamental difference between us and o3.
Rationality is the internal verifier.
Something that doesn’t require thousands of hours of training to update and instead is capable of ingesting and rationalize new information on the fly.
Being able to do this is… exactly what arc-agi was testing. Literally the entire point of the benchmark, it can do that.
I’ve done the test by the way, I solved it by brute forcing possible solutions in my head, then checking if they were true… did you just divine the answers instantly?
Unless we invent cold fusion between the next 5 years, they will never be economical. They are the most energy inefficient thing ever invented by humanity and all prediction models state that it will cost more energy, not less, to keep making them better. They will never be energy efficient nor economical in their current state, and most companies are out of ideas on how to shake it up. Even the people who created generative models agree that they have just been brute forcing by making the models larger with more energy consumption. When you try to make them smaller or more energy efficient, they fall off the performance cliff and only produce garbage. I’m sure there are researchers doing cool stuff, but it is neither economical nor efficient.
Untrue. There are small models that produce better output than the previous “flagships” like GPT-2. Also, you can achieve much more than we currently do with far less energy by working on novel, specialised hardware (neuromorphic computing).
Your example is strange because, as far as I know, GPTs aren’t economical either.
Why is it getting an AGI stamp now? I was under the impression humanity has not delivered a sentient AI? Which is what the AGI title was supposed to be used for…has that been pulled back again?
Agi has nothing to do with sentience, which cannot be measured, openai, I think validly, defines it as a system that can do all intellectual labor.
So it’s now, can it do anything a human can do?..sans emotional traits.
It was never about sentience, sentience is a meaningless, unmeasurable term.
It’s a question of if it can replace humans in the workforce.
artificial general intelligence means it’s able to generalize its intelligence, not sentience at all.
