OpenAI, Google, Anthropic admit they can’t scale up their chatbots any further
OpenAI, Google, Anthropic admit they can’t scale up their chatbots any further
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.
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.
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.
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.
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.
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
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.
That's capitalism
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.
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.
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.
Claiming that David Gerrard an Amy Castor "don't understand the technology" is uh.... Hoo boy... Well it sure is a take.
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?
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.
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
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.
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.
Agreed. There's plenty of improvement to be had, but the gravy train of "more CPU or more data == better results" sounds like it's ending.
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.
Looks, like AI buble is slowly coming to end just like what happned to crypto and NFT buble.
When did the crypto bubble end? Bitcoin is at an all time high....
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.
Getting the GPU memory requirements down would be huge as well.
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."
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.
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.
Unlikely, but there's some percedent.
We've seen this pattern play out in video games a bunch of times.
Revolutionary new way to do things. It's cool, but not... You know...fun.
So we give up on it as a dead and and go back to the old ways for awhile.
Then somebody figures out how to (usually hard code) bumpers on the new revolutionary new way, such that it stays fun.
Now the revolutionary new way is the new gold stand and default approach.
For other industries, replace "fun" above with the correct goal for than industry. "Profitable" is one that the AI hucksters are being careful not to say...but "honest", "correct" and "safe" also come to mind.
We are right before the bit where we all decide it was a bad idea.
Which comes before we figure out hard-coding the bumpers can get us where we wanted, after a lot of work by really smart well paid humans.
I've seen industries skip the "all decide it was a bad idea" phase, and go straight to the "hard work by humans to make this fulfill the available promise" phase, but we don't actually look on track to, today.
Many current investors are convicned that their clever talking puppet is going to do the hard work of engineering the next generation of talking puppet.
I have some faith that we can reach that milestone. I'm familiar enough with the current generation of talking puppet to confidently declare that this won't be the time it happens.
My incentive in sharing all this is that I like over half of you reading there, and so figure I can give some of you a shot at not falling for this particular "investment phase" which is essentially, in practical terms, a con.
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.