Look, if you know a way to convert a PDF to text with less than 500GB of VRAM and 2000W of power used for twenty seconds, I’m all ears.
I’m…but…no…wait….
We’re not going to make it.
No, we aren’t.
.djvu
Is that a new fringe edge compute mixture of experts from Antro-GPTx? Sounds gemini-flipiti-rad!
We are repeating an old pattern in computing: throw more hardware at the problem until efficiency becomes impossible to ignore. Bigger models have delivered remarkable gains, but they’re increasingly expensive. The next breakthroughs may come less from adding parameters and more from smarter architectures, better algorithms and more efficient inference.
DeepSeek has really led the way here, especially as they are a bit more hardware constrained. Plus they openly publish their findings and release open source models, so high hopes there.
It’s probably China’s play to pop the AI bubble, but I’m all for it (:
I wonder what all is in the deepseek code that is malicious. I’d like to try it but don’t want a million Mb/s of tracker shit across my network and can’t run it myself.
AFAIK, their open models are distributed as weights, not executables and are therefore not able to start network connections / run code. There is of course tool-calling functionality but that just works by having the model output a special pattern and having something external run predetermined commands based on that.
They are open source models, nothing malicious about them. I’d be much more careful about where you run your agents on. The wrong prompt can even make a non-malicious model misbehave.
The beauty of it is that it doesn’t need tracker shit. It works to destroy the US AI bubble even without anything malicious within.
And considering how hard the US researchers have been trying to control the output of their general LLMs, with little success, why would the Chinese have found a way before everyone else even thinks it’s possible?
Okay. I’ll give it a whirl then.
Except there likely won’t be a lot of further breakthroughs if we burn down our planet faster than we already do.
This is all an expenditure of vast amounts of energy for literally no gain for anybody except a handful of billionaires and their corporations.
That’s literally exactly what Chinese researchers are doing at DeepSeek and they’ve built frontier models with that philosophy
It isn’t about content generation at all. It’s about pattern recognition and prediction, which, in the hands of those with the most power to change the world, offers insights into our collective behavior that rulers from every age would have committed genocide to get. AI will tell them how to better build the prison the poor are being impoverished into.
Stop spreading criti-hype! Zuck didn’t invent a mind-control ray with targeted advertising, and Sam Altman doesn’t run a terminator factory with GenAI.
Wtf are you talking about? Do you even know the nature of data usage today? You can’t identify reality from science fiction?
AI enables them to better do what they have already been doing with analytics and user data. They are already doing it and have been for decades.
I’ve seen it first hand on smaller software products during analytics reviews and telemetry design discussions during preproduction through product launch and post launch. I know the questions that gets asked, the purpose of a telemetry hook for a user action, heard what they wished they could track and why. I know how they can cohort a user base, how they extrapolate and predict user behavior and user characteristics from that data to target content. There’s laws already written to prevent some data collection because of what is known can be done. That’s a small software product with a few millions users, not Amazon or Google who have billions of users, many of whom give them access to their entire phone telemetry at all times, cross device access and service wide account tracking, across decades of their lives. Location, region, timezone, battery usage, app usage, age, phone numbers, address, gender, mac ids, wifi connections and data usage etc etc etc.
With just my gender and age, you can make predictions, of some accuracy, using existing research data, about my life, who I am, what I think and how I behave. Every single piece of data more allows further clarity and breadth. That knowledge is what gives them more and more accurate predictions about me, you and all of us. Now they want cameras everywhere, microphones everywhere, OS real ID and VPNs to be banned so there is no anonymity. They want as much data as possible because they now have a data pattern recognition system(LLMs) that can effectively make use of that amount of data.
FFS, this isn’t science fiction anymore, it’s here, now. And those companies have never had your, or my, interests or well-being in mind. They will use it for power, as they always have.
It has the same flaw as every other overreaching evil. We outnumber them. A significant number on our side is willing to kill the other side.
Ima die in the crossfire for sure. But the evildoers always assume they are going to win and they literally never do. They always lose. Expensively.
It’s a dying echo. Nothing more.
Yes but we are effectively disconnected from our mutual self interest. We should already have risen up together and our complacency is a testament to their existing ability to pacify us.
The losing that you’re talking about happens on the scale of centuries, not years. History shows that empires built on malice can last for centuries. Look at the Romans, for example. Even the Soviet Union lasted 70 years.
If this echo dies out in a hundred years, what good is that going to do my son and myself?
“Generative” “AI” is about generation yes
“Generative” is a misnomer. It will never generate anything new, it can only regurgitate existing ideas based on patterns that already exist. It’s very good at pattern recognition and summarizing, but lacks the ability to form a distinct new idea.
It will never generate something novel. Whether it will generate something “new” depends on your definition of “new,” which is a little more ambiguous than “novel.”
Sorry if I’m being too pedantic.
Nope, pedant away. That’s a better way to convey what I was trying to say. Thanks.
Sure, but then neither will most people.
It’s only good at summarizing things which have coherence to its training set. Any ability to summarize input outside of its training is accidental.
lacks the ability to form a distinct new idea.
Yeah, but it’s got that in common with a frighteningly large number of people…
See management, marketing, streaming, social media, etc…
It’s generating a prediction of our behavior for them to use to better control us.
<takes another hit from the bong>
In 1897, they built the first music synthesizer. It worked, but it took up the basement of an entire city-block sized building, so it was essentially useless. After a few decades of development, it could fit in a suitcase, and be carried around.
Data Centers are like that 1897 synthesizer. Sure, it works, but at what cost? It clearly isn’t ready for prime time. Go back to the drawing board, tweak the problems, including regulations, and maybe in a couple of decades, we take another run at the new and improved version.
Data Centers are like that 1897 synthesizer. Sure, it works, but at what cost? It clearly isn’t ready for prime time. Go back to the drawing board, tweak the problems, including regulations, and maybe in a couple of decades, we take another run at the new and improved version.
The issue with AI is not a technical or development problem. It’s not even a regulation problem. It’s a capitalism problem. Infinite growth will still be as unscalable in a hundred years as it is now no matter how good and mature the tech is.
There are also hard mathematical limits stalling AI growth. Frontier models haven’t improved in like a year despite being fed money by basically the entire global economy. Diminishing returns on steroids basically. They’re already at the limit of what they can make, and going further gives a much smaller improvement in the model, and now I hear there might not be enough human written material on the internet to train them.
It also looks like hallucinations are inherent to LLMs and you can’t get rid of them. It’s a side effect of the model. What commercial applications are there then, if you can’t guarantee the output? It’s worse than a human for most things since it doesn’t know truth from lie and will confidently say both as if they’re fact. It also looks like prompt injection isn’t something you can fully guard against either.
What’s the value proposition when you can’t trust the output and the model might give a massive refund or discount to a customer and the courts rule the AI speaks on behalf of your company?
Diminishing returns on steroids? No, clearly we just need to pump EVEN MORE MONEY AND DATA into this
If we just vaporize the future of everyone under 60 we can make our auto correct engine 3% less likely to lie out of its ass 🤡
It’s worse than a human for most things since it doesn’t know truth from lie and will confidently say both as if they’re fact
It works for most executives and sales folks.
Baseless confidence is the recipe for business success, which is why they love these AI chatbots.
Bigger problem for the business leaders is how sycophantic they want to be to the user. If an insurance company used it for claims, it might actually approve a claim, and that would be unforgivable for them.
Haven’t been improved in a year? By what metric are you basing that assertion?
IP theft is probably the main one I’ll concede the point on - that damage was done long ago so they haven’t “improved” on it.
But whether it’s reasoning or generation or building things… that’s a crazy take. Unless you consider them like a chatbot companion? I wouldn’t really know much on that front, I’ll concede.
It’s been marginal improvements for like 18 months now. I don’t know if you remember what they promised that long ago but current frontier models just ain’t it.
If you don’t believe me, then why? Put your argument in numbers.
Anthropic and openAI have both spent nation-state levels of money training these models and they only seem marginally better compared to the last ones? Maybe larger context and better reasoning but they still hallucinate, they still make the same mistakes and pitfalls.
Even with tokens getting dramatically cheaper inference on mythos or other frontier models is so expensive they need to start replacing skilled professionals and right now they just can’t. Productivity doesn’t seem to go up from AI use, if anything net productivity for an org goes down from people outsourcing human cognition onto their colleagues.
“How about instead of me summarizing this report i use Claude and then my colleague spends the cognitive effort deciding if Claude lied or not”
The guy using AI for everything looks super productive and the people stuck dealing with the work he’s “getting the AI to do for him” look like under performers when they’re actually load bearing in this new setup.
Tokens aren’t going to get cheaper. Tokens must get more expensive, and soon. AI companies are making big losses even if you ignore the stratospheric debt for the immemse quantity of hardware.
Yeah you’re right they just appear cheaper due to circular financing and some memory improvements. I was trying to steel man my argument into the strongest possible point that still fails to make sense.
It’s worse than a human for most things since it doesn’t know truth from lie and will confidently say both as if they’re fact.
I think that’s where you’re wrong. It’s really not worse than a human. It’s smarter than like 90% of the population already. No it’s not perfect but it doesn’t have to be. Humans literally hallucinate and lie, all the time. AI hallucinations is an athropomorphism, AI metaphorically hallucinates, humans actually literally do.
The 10+% of the time it’s wrong you can’t blame it, sue it, imprison it, or apply leins against it if it causes real world damages to people or the company that operates it
“For entertainment purposes only” is still the level of liability AI companies operate at. Air Canada just had a ruling that anything their bot says is the speech of Air Canada and they’re bound by it. So far there’s always ways to prompt inject, and you sometimes don’t even need to do that. Just guiding the conversation in ways that are difficult to pin malice on to is enough.
Air Canada just had a ruling that anything their bot says is the speech of Air Canada and they’re bound by it.
That’s great, i agree that makes sense!
But must make number go up by any means
Chanting:
Number go up! Number go up! #️⃣💨⬆️❗
In 1897, they built the first music synthesizer. It worked, but it took up the basement of an entire city-block sized building, so it was essentially useless. After a few decades of development, it could fit in a suitcase, and be carried around.
As per the 1890s, it probably also had to be lubricated with orphaned child blood.
Don’t give the AI MAGAs any ideas.
And back then nobody asked why do we need a mountain sized synthesizer if the children itself make already all the funny noises.
It’s not even that it isn’t ‘ready for prime time’, largely to the extent it works, it works with not so crazy requirements.
The problem is that they don’t settle for what it is, they try to overextend it. In software development for example, in the cases where it works at all, you are 95% of the possible successes within 3 or 4 iterations. Problem is they demand that extra 5% which takes an order of magnitude more. Note that ‘100%’ here is the max success possible with AI, not 100% success in general, that number varies greatly with context. So it might be in a certain scenario more like going from 19% AI curated to 20% AI curated, at huge incremental expense.
AI is the idea of putting a million monkeys in a room with a typewriter and waiting for Shakespeare.
The smart people already knew the monkeys would just starve to death. The business majors are just now figuring that out.
It was the best of times, it was the blurst of times.
The ten thousandth monkey typed out the word “the” , fund them everything they’ll type out Shakespeare!
Dude, the math says it would be cheaper to fund research into necromancy to revive the actual Shakespeare than this.
Is there a petition?
Maybe a kickstarter or something?
I’ll buy that for a dollar!
To be or not to be, that is…
Yeah no shit Sherlock. AI chat bots are just a way to market and sell a dystopian surveillance apparatus to the masses wrapped in the guise of it will right your bullshit corporate emails and messages for you all the while the government and the corporations are fucking you in the ass.
It was a barely functional technology that provides convenience and laziness well hiding it’s true purpose, Machine learning algorithms for facial recognition license prints tracking making it efficient and relatively economical to spy on and control an entire population.
what makes you think advances in LLMs have anything to do with ML for Computer vision? If you wanted the latter, you would’ve gotten that way cheaper than by training on reddit text.
12 upvotes for this thin conspiracy theory, congrats Lemmy.
If you think those datacenters being built are for LLMs only, I have a bridge to sell you.
ML and LLMs both need massive compute to work with the data sets involved.
If only the mainstream media would have said this since 2023. We maybe even have dodged the bullet called the Trump second term, but now we’re heading towards a global financial collapse.
Well if you have a better way to generate Jurassic Park “but everyone’s fat” videos I’d love to hear it.
Hiring fat people?
ok tough guy so how do I get fat T-rexes hm?
Hire some already underpaid VFX artists
Jurassic Pork
paywall removed: https://archive.ph/J3VLi
Now I can read the Atlantic for free while also DDOSing people. The future is now.
I think ive missed a reference, how does that link contribute to ddosing?
Wild stuff, thanks
For sure, you’re welcome.
It is no more different with the gold rushes to California, Oregon, and Alaska, where the real winners are the merchants selling pickaxes and work overalls.
It’s different in so far as we didn’t invest a trillion dollars in giant strip mines that destroyed these states.
Pre roasted coffee took off at this time.
Efficiency deserves more attention than hype.
Here here! well said!
Many dystopian science fiction books have already become reality. Look at “Enemy of the State” for example.
Heck, back in like what 1971? Enders game predicted the whole internet Influencer concept.
fwiw Ender’s Game was published in 1985. (Based on a short story from 1977, but that story didn’t have that subplot with Peter and Valentine.)
Thanks for that. I’m not sure where I got the idea. It was from the early 70s.
I bet we could find some story in the New Wave of science fiction that presaged influencers. Bug Jack Barron comes close (though it’s about a talk show host).
I’m a little skeptical of the idea that science fiction predicts things, tho. If you have a million stories out there, one of them’s bound to resemble whatever you’re claiming it predicted.
Voice to text strikes again!
Ah, got it! I can remove the link or leave it up for others’ reference. Let me know if you have a preference.
If you want, it’s no skin off my back. This whisper voice to text that I have on my Android is working pretty well, but there will be similar errors in the future.
So far it’s the best one I’ve found. I don’t have to specify punctuation or anything. Basically what you’re reading is exactly what it came out with. It beats the keyboard, which for some reason keeps putting C’s and V’s where I put spaces, and I have to go back and modify everything.
In that case I’ll leave it because it may help someone else. Cool about the Android voice to text. Do you mean you can whisper and it works?
wisper is the name of the ai/tech that drives the translation. there are implementations for other platforms. i got one on Linux, but haven’t really used it yet. it does work when there are other noises in the room.
This is an example of me using it in a noisy bar. The first paragraph was typed because I wasn’t sure it would work. But it works fine.
Cool, thank you!
I feel like literally everyone knows this now but theres so much invested in it theres no backing out.
Too many billionaires have a need to invest and a need for future gains. It’s a mental compulsion.
“It is difficult to get a man to understand something, when his salary depends upon his not understanding it!” -Upton Sinclair
The author seems to be confusing user scalability with performance scaling:
The problem with generative AI, in the industry’s own jargon, is that it does not scale. The cost of growing from, say, a thousand users to a million is a key factor that venture capitalists examine when they evaluate start-ups.
This is a question of whether openai can handle 1 million users asking chatgpt to write a basic html website. That can be scaled horizontally and is just a matter of building more data centers.
The author then goes on to conflate this user scaling with performance scaling:
Yet the returns are diminishing. The bigger an AI model is, the less it improves with each added parameter, and so it must be made bigger at a faster rate just to sustain steady progress. I asked a few AI researchers whether they could name any other real-world software that scales so poorly. None of them could think of any. Even outside the world of software, it’s hard to find a comparable example, given that economy of scale is the principle that has made light bulbs, cars, and clothing so affordable. By economic and engineering measures, generative AI might be the worst technology ever deployed.
This is a question of whether chatgpt can generate a full complex web app. For this there may be a limit to this bigger model approach but this is common to most technologies, performance sometimes has hard limits. You aren’t going to get a car to go 300 mph by making the engine bigger and adding more cylinders, there’s diminishing returns, that doesn’t make cars the worst technology ever deployed… maybe they are but for other reasons.
Economies of scale also isn’t about performance scaling, it’s about capacity scaling. Capacity scaling for AI does reflect economies of scale, that’s why you have these large AI companies building large data centers.
That can be scaled horizontally and is just a matter of building more data centers.
At one of my old jobs “just” was considered a bad word
Ok, remove the just then, the point still stands that it is a solvable problem. We know how to make data centers, it may not be easy or cheap but it’s possible just like we know how to build car factories.
Yeah and the point is that model improvements so far have meant making huge increases in size, which offsets the datacenters scale out.
The whole point is that this is futile because we will always be playing catch-up to model sizes, to our ultimate downfall. The tech needs to be smarter not larger. That’s why the whole cloud AI business is shit and not going to work. as anyone with a brain has been saying from the beginning.
Jesus Christ man, people’s homes are being sized with eminent domain for this shit. It ain’t worth it.
The performance per parameter has been improving steadily though. Gemma 4 is ~4o level at a fraction of the parameters.
which offsets the data center scale out
This is only true if everyone is always using the top line model, which most people don’t. Both because most people just use the default, which is a low or mid tier model, and because it’s expensive. The top line models are becoming increasingly niche.
The tech needs to be smarter not larger.
I agree, that’s why more focus is being put on the harness and agent orchestration these days. You can achieve better results by having a large model orchestrate a bunch of smaller model agents to do simpler tasks then trying to have the large model one shot it. This doesn’t mean the whole cloud AI business is bullshit, they’re still going to need to build out a lot of capacity for these smaller models and still going to need large models to handle the planning and orchestration, it just means the call count for these larger models are going to be lower.
So it’s probably not going to be 1 million calls to a small model turns into 1 million calls to a larger model and the capacity never catches up, it’s going to be 1 million calls to a small model and 1,000 to a large one which is more feasible to build out.
people’s homes are being sized with eminent domain for this shit. It ain’t worth it.
I don’t agree with how the data centers are being rolled out, they can and should be built out with renewable energy and consent from the community which isn’t happening. I disagree that data centers shouldn’t be built at all or that it will be an unachievable Sisyphusian task to build them out.
deleted by creator
I wouldn’t separate performance scalability and user scalability as they ultimately go hand in hand together.
Ok think of them as different scaling factors then, maybe n for number of requests and s for size of requests and c for complexity of requests. Scaling for n can be done horizontally by building more data centers which is possible. Scaling for s or c requires building bigger models which has diminishing returns.
Scaling for n is required to make the software business model work, like the article says. Scaling for s or c though isn’t required as long as your average user keeps those constant, which is possible.
LLMs are inefficient by design.
They are less efficient when compared to what traditional computing can already do, eg. Arithmetic, structured data analysis etc. There are things that traditional computing can’t do, eg. writing an essay, that can only be compared to the human brain which is hard to do. So you can say AI is inefficient at calculating 2 + 2 , but it’s a hard case to make that it’s inefficient at writing an essay.
That can be scaled horizontally and is just a matter of building more data centers.
That was a spectacular way of outlining the fact that you don’t at all understand the problems and limits with “AI” without having any awareness or understanding that you don’t at all understand the problems and limits with “AI.”
Ok, explain to me why you can’t scale out existing small and mid tier models horizontally? yes there are current resource limits on chips and energy but we know how to build those out and those types of limits are common to nearly every other industry, it’s just that no other industry has generated such rapid demand/investment for infrastructure.
There’s no O(n^2) problem on number of requests that would make handling large scale rollout impossible like the article is suggesting.
If “AI” data centers are the only goal, sure, we could definitely bottleneck all technological progress into forcing a fractional level of slightly higher functionality to things that have already hit their potential at the cost of hobbling every other technology. Just fuck the environment, every level of consumer technology, limit the marketplace of ideas and materials to tech oligarchs, and cripple progress in literally any other field of technology, and it all just makes sense, right? Easy peasy.
if only AI companies optimized their AI to run on less compute (in the data centers)
