I’m usually the one saying “AI is already as good as it’s gonna get, for a long while.”
This article, in contrast, is quotes from folks making the next AI generation - saying the same.
repeat after me: LLMs are not AI.
LLMs are one version of AI. It’s just one tiny part of AIs that are used every day, from chess bots to voice transcription, but they also are AI.
I would replace the word version with aspect. LLMs are merely one part of the puzzle that would be AI. Essentially what’s been constructed is the mouth and the part of the brain that can form words but without any of the reasoning or intelligence behind what the mouth says.
The same goes for the art AIs. They can paint pictures based on input but they can’t reason how those pictures should look. Which is why it requires so much tweaking to get them to output something that doesn’t look like it came out of a Lovecraft novel.
I don’t believe the “I” is an accurate term.
More like “Smart” Word generators.
Of course it changes meaning if you remove the qualifier.
Artificial
Adjective
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artificial (comparative more artificial, superlative most artificial)
Man-made; made by humans; of artifice.
The flowers were artificial, and he thought them rather tacky. -
Insincere; fake, forced or feigned.
Her manner was somewhat artificial.
In effect, man-made/fake intelligence.
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I think you are confusing AI with AGI.
https://en.m.wikipedia.org/wiki/Artificial_general_intelligence
Not at all. AI is something that uses rules, not statistical guesswork. A simple control loop is alreadu basic AI, but the core mechanism of LLMs is not (the parts before and after token association/prediction are). Don’t fall for marketing bullshit of some dumbass silicon valley snake oil vendors.
It’s absurd that some of the larger LLMs now use hundreds of billions of parameters (e.g. llama3.1 with 405B).
This doesn’t really seem like a smart usage of ressources if you need several of the largest GPUs available to even run one conversation.
I wonder how many GPUs my brain is
It’s a lot. Like a lot a lot. GPUs have about 150 billion transistors but those transistors only make 1 connection in what is essentially printed in a 2d space on silicon.
Each neuron makes dozens of connections, and there’s on the order of almost 100 billion neurons in a blobby lump of fat and neurons that takes up 3d space. And then combine the fact that multiple neurons in patterns firing is how everything actually functions and you have such absurdly high number of potential for how powerful human brains are.
At this point, I’m not sure there’s enough gpus in the world to mimic what a human brain can do.
That’s also just the electrical portion of our mind. There are whole levels of chemical, and chemical potentials at work. Neurones will fire differently depending on the chemical soup around them. Most of our moods are chemically based. E.g. adrenaline and testosterone making us more aggressive.
Our mind also extends out of our heads. Organ transplant recipricants have noted personality changes. Food preferences being the most prevailant.
The neurons only deal with ‘fast’ thinking. ‘slow’ thinking is far more complex and distributed.
I don’t think your brain can be reasonably compared with an LLM, just like it can’t be compared with a calculator.
LLMs are based on neural networks which are a massively simplified model of how our brain works. So you kind of can as long as you keep in mind they are orders of magnitude more simple.
At some point it becomes so “simplified” it’s arguably just not the same thing, even conceptually.
It is conceptually the same thing. A series of interconnected neurons with a firing threshold and weighted connections.
The simplification comes with how the information is transmitted and how our brain learns.
Many functions in the human body rely on quantum mechanical effects to function correctly. So to simulate it properly each connection really needs to be its own super computer.
But it has been shown to be able to encode information in a similar way. The learning the part is not even close.
It is conceptually the same thing. […] The learning the part is not even close.
Well… isn’t the “learning part” precisely the point? I don’t think anybody is excited about brains as “just” a computational device, rather the primary function of a brain is … learning.
No, we are nowhere close to learning as the human brain does. We don’t even really understand how it does at all.
The point is to encode solutions to problems that we can’t solve with standard programming techniques. Like vision, speech recognition and generation.
These problems are easy for humans and very difficult for computers. The same way maths is super easy for computers compared to humans.
By applying techniques our neurones use computer vision and speech have come on in leaps and bounds.
We are decades from getting anything close to a computer brain.
42
The Answer to the Ultimate Question of Life, The Universe, and Everything
Larger models train faster (need less compute), for reasons not fully understood. These large models can then be used as teachers to train smaller models more efficiently. I’ve used Qwen 14B (14 billion parameters, quantized to 6-bit integers), and it’s not too much worse than these very large models.
Lately, I’ve been thinking of LLMs as lossy text/idea compression with content-addressable memory. And 10.5GB is pretty good compression for all the “knowledge” they seem to retain.
I don’t think Qwen was trained with distillation, was it?
It would be awesome if it was.
Also you should try Supernova Medius, which is Qwen 14B with some “distillation” from some other models.
Hmm. I just assumed 14B was distilled from 72B, because that’s what I thought llama was doing, and that would just make sense. On further research it’s not clear if llama did the traditional teacher method or just trained the smaller models on synthetic data generated from a large model. I suppose training smaller models on a larger amount of data generated by larger models is similar though. It does seem like Qwen was also trained on synthetic data, because it sometimes thinks it’s Claude, lol.
Thanks for the tip on Medius. Just tried it out, and it does seem better than Qwen 14B.
Llama 3.1 is not even a “true” distillation either, but its kinda complicated, like you said.
Yeah Qwen undoubtedly has synthetic data lol. It’s even in the base model, which isn’t really their “fault” as its presumably part of the web scrape.
That’s capitalism
Seeing as how the full unquantized FP16 for Llama 3.1 405B requires around a terabyte of VRAM (16 bits per parameter + context), I’d say way more than several.
It’s pretty obvious that they will hit a ceiling.
Quick buck is over. And now it’s time again for base research to create better approach.
I really wish we had a really advanced AI with reasonable resource consumption within my lifetime. I don’t think it’s unreasonable as we have got really far in the last 30 years of computational technology.
I really wish we had a really advanced AI with reasonable resource consumption within my lifetime.
You only wish that for as long as it doesn’t happen. Have you looked at the world we live in? Such tools would be controlled by the same billionaire dipshits for their personal gain as all social media is being used already.
It open the gates for open source development, as the social media we are right now.
I to wish for a better world to live in. But this is not they world.
That made less than zero sense…
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We’ve come a long way in computing, but the computational power difference between a human brain and a computer is significant. LLMs were just a smart way to have computers learn pattern recognition. While important, it isn’t anything close to artificial general intelligence (AGI), which is what the term AI usually means.
Yeah. AI may grind for a while but hardly anyone has put the current stuff to work, yet. We will be feeling the benefits of what is released right now for a decade to come. I am working on a very rudimentary application that will use ML at work and it won’t come out for 12 more months, and it hardly does anything but make the most obvious decisions 10m times faster than I can. But it’s going to fundamentally change our labor model.
There are regular folks applying amazing technologies that go way beyond content generation.
The tech may grind but the application of that tech is barely getting its feet and should run hard for a decade.
You’re not using a typewriter.
How to tell someone uses Hacker News without asking:
The problem isn’t with the AI. It’s with how it’s being treated. It’s currently being sold as if it were general intelligence. Which it’s not. It should instead be treated like it’s a mindless tool. Something that is inert on its own. Useful for some things but only in a limited sense. Unfortunately the companies, who have spent millions of dollars developing these things, are trying to sell it as the “do-all” artificial intelligence that people have grown up seeing in sci-fi media. Which it 100% is not.
Every company have always oversell their own products. This is not new.
Coca Cola is also just a carbonated sweet drink and it’s being sold as happiness, socialization and the meaning of Christmas in a bottle.
Companies oversell, it’s called marketing. It’s shit practice but it’s not nothing new.
That does not make the technology worse (or better). Current AI technology has its uses. With a big problem in how resource hungry it is. But it’s fairly useful.
Your point is valid. Companies do use marketing to sell their products by using lots of outrageous claims. And my problem isn’t really what the companies it’s mostly with the people who are buying that bullshit.
P.S. your Coca-Cola example would have been better if you had reached back to their origins when they were sold as a tonic that cures just about everything. What they’re being sold as right now is just a soda and all of the current marketing around it is just nostalgia bait which everybody uses for everything especially around Christmas time.
I understand folks don’t like AI but this “article” is like a reddit post with lots of links to subjects which are vague and need the link text to tell us what is important, instead of relying on the actual article.
What the fuck you aren’t kidding. I have comment replies to trolls that are longer than that article. The over the top citations also makes me think this was entirely written by an actual AI bot that was lrompted to supply x amoint of sources in their article. Lol
I see a lot of links here and there to this domain but I haven’t really read anything from there. I’m literally just scrolling through these comments to see if anyone has a comment like yours.
My impression was that it’s just a blog but you calling it “a reddit post” is also interesting. What’s with this site? It looks like a decent amount of people think these takes are interesting. I have to deal with a lot of management people who love AI buzzwords, so a whole blog just ripping into it really speaks to me.
So long and thanks for all the fish habitat?
A 4 paragraph “article” lol
Are you suggesting “pivot-to-ai.com” isn’t the pinnacle of journalism?
Lol, I didn’t even notice the name
It’s a known problem - though of course, because these companies are trying to push AI into everything and oversell it to build hype and please investors, they usually try to avoid recognizing its limitations.
Frankly I think that now they should focus on making these models smaller and more efficient instead of just throwing more compute at the wall, and actually train them to completion so they’ll generalize properly and be more useful.
They might be right but I read some of the linked articles on this blog (?), the authors just come off as not really knowing much about current AI technologies, and at the same time very very arrogant.
The article talks about LLM developers / operators. Not sure how you got from that to “current AI technologies” - a completely unrelated topic.
Though, I don’t think that means they won’t get any better. It just means they don’t scale by feeding in more training data. But that’s why OpenAI changed their approach and added some reasoning abilities. And we’re developing/researching things like multimodality etc… There’s still quite some room for improvements.
Looks, like AI buble is slowly coming to end just like what happned to crypto and NFT buble.
Sure, except for the thousands of products working pretty well with current gen. And it’s not like it’s over, now we’ve hit the limit of “just throw more data at the thing”.
Now there aren’t gonna be as many breakthroughs that make it better every few months, instead there’s gonna be thousand small improvements that make it more capable slowly and steadily. AI is here to stay.
The bubble popping doesn’t have to do with its staying power, just that the days of, “Hey, I invented this brand new AI
that’s totally not just a wrapper for ChatGPT. Want to invest a billion dollars‽” are over. AGI is not “just out of reach.”Getting the GPU memory requirements down would be huge as well.
When did the crypto bubble end? Bitcoin is at an all time high…
The bubble was when we were being sold block chain as the solution to every problem. I feel like that bubble ended in 2019 or 2020.
Things that actually benefitted from block chain are still around, of course.
Unrelated side rant: I’m pissed about pogs going away, though. Pogs were fun. I should still be able to buy pogs.
I smell a sentient AI trying to throw us off it’s plans for world domination…
Everyone ignore this comment please. I’m quite human. I have the normal 7 fingers (edit: on each of my three hands!) and everything.
Cylons. I knew it.
Can’t be, I haven’t fucked one yet, and everyone knows Cylonism is an STD.
Unless I’m an Eskimo brother and don’t know it…
OpenAI, Google, Anthropic admit they can’t scale up their chatbots any further
Lol, no they didn’t. The quotes this articles are using are talking about LLMs not chatbots. This is yet another stupid article from someone who doesn’t understand the technology. There is a lot of legitimate criticism for the way this technology is being implemented but FFS get the basics right at least.
Are you asserting that chatbots are so fundamentally different from LLMs that “oh shit we can’t just throw more CPU and data at this anymore” doesn’t apply to roughly the same degree?
I feel like people are using those terms pretty well interchangeably lately anyway
People that don’t understand those terms are using them interchangeably
LLM is the technology, Chatbot is an implementation of it. So yes a Chatbot as it’s talked about here is an LLM. Although obviously chatbots don’t have to be LLM, those that are not are irrelevant.
No, a chat bot as it’s talked about here is not an LLM. This article is discussing limitations of LLM training data and inferring that chat bots can not scale as a result. There are many techniques that can be used to continue to improve chat bots.
The chatbot is a front end to an LLM, you are being needlessly pedantic. What the chatbot serves you, is the result of LLM queries.
That may have been true for the early LLM chatbots but not anymore. ChatGPT for instance, now writes code to answer logical questions. The o1 models have background token usage because each response is actually the result of multiple background LLM responses.
Yes of course I’m asserting that. While the performance of LLMs may be plateauing, the cost, context window, and efficiency is still getting much better. When you chat with a modern chat bot it’s not just sending your input to an LLM like the first public version of ChatGPT. Nowadays a single chat bot response may require many LLM requests along with other techniques to mitigate the deficiencies of LLMs. Just ask the free version of ChatGPT a question that requires some calculation and you’ll have a better understanding of what’s going on and the direction of the industry.
I think you’re agreeing, just in a rude and condescending way.
There’s a lot of ways left to improve, but they’re not as simple as just throwing more data and CPU at the problem, anymore.
I’m sorry if I’m coming across as condescending, that’s not my intent. It’s never been “as simple as just throwing more data and CPU at the problem”. There were algorithmic challenges for every LLM evolution. There are still lots of potential improvements using the existing training data. But even if there wasn’t, we’ll still see loads of improvements in chat bots because of other techniques.
Edit: typo
Claiming that David Gerrard an Amy Castor “don’t understand the technology” is uh… Hoo boy… Well it sure is a take.
The title of the article is literally a lie which is easily fact checked. Follow the links to quotes in the article to see what the quoted individuals actually said about the topic.
Please learn the difference between “lying” and “presenting a conclusion.”
I know the difference. Neither OpenAI, Google, or Anthropic have admitted they can’t scale up their chat bots. That statement is not true.
So is your autism diagnosed or undiagnosed?
I ask this as an autistic person, because the only charitable way to read what’s happening here is that you’re clearly struggling with statements that aren’t intended to be read completely literally.
The only other way to read it is that you’re arguing in bad faith, but I’ll assume thats not the case.
Also an autistic person here.
How are people supposed to tell this is an opinion?
And please dont say “by reading the article, maybe some (like me) do so but its well known that most people stop at the title.
Grammatically speaking it remains a direct statement. They admit == appear to hint == pure opinion (Title: “Ai cant be scaled further”)
While i am not disagreeing with the premise perse i have to perceive this as anti-ai propaganda at best, a attempt at misinformation at worst.
On a different note, do you believe things can only be an issue if neurotypical struggle with it? There is no good argument to not communicate more clearly in the context of sharing opinions with the world.
David and Amy are - openly - skeptics in the subject matters they write about. But it’s important to understand that being a skeptic is not inherently the same thing as being unfairly biased against something.
They cite their sources. They backup what they have to say. But they refuse to be charitable about how they approach their subjects, because it is their position that those subjects have not acted in a way that is deserving of charity.
This is a problem with a lot of mainstream journalism. A grocery store CEO will say “It’s not our fault, we have to raise prices,” and mainstream news outlets will repeat this statement uncritically, with no interrogation, because they are so desperate to avoid any appearance of bias. Donald Trump will say “Immigrants are eating dogs” and news outlets will simply repeat this claim as something he said, with adding “This claim is obviously insane and only an idiot would have made it.” Sometimes being overly fair to your subject is being unfair to objective truth.
Of course OpenAI et al are never going to openly admit that they can’t substantially improve their models any further. They are professional bullshitters, they didn’t suddenly come down with a case of honesty now. But their recent statements, when read with both a critical eye, and an understanding of the limitations of the technology, amount to a tacit admission that all the significant gains have already been made with this particular approach. That’s the claim being made in this headline.
I believe that the current LLM paradigm is a technological dead end. We might see a few additional applications popping up, in the near future; but they’ll be only a tiny fraction of what was promised.
My bet is that they’ll get superseded by models with hard-coded logic. Just enough to be able to correctly output “if X and Y are true/false, then Z is false”, without fine-tuning or other band-aid solutions.
Seems unlikely as that’s essentially what we had before and they were not very good at all.
If you’re referring to symbolic AI, I don’t think that the AI scene will turn 180° and ditch NN-based approaches. Instead what I predict is that we’ll see hybrids - where a symbolic model works as the “core” of the AI, handling the logic, and a neural network handles the input/output.
