For OpenAI, o1 represents a step toward its broader goal of human-like artificial intelligence. More practically, it does a better job at writing code and solving multistep problems than previous models. But it’s also more expensive and slower to use than GPT-4o. OpenAI is calling this release of o1 a “preview” to emphasize how nascent it is.
The training behind o1 is fundamentally different from its predecessors, OpenAI’s research lead, Jerry Tworek, tells me, though the company is being vague about the exact details. He says o1 “has been trained using a completely new optimization algorithm and a new training dataset specifically tailored for it.”
OpenAI taught previous GPT models to mimic patterns from its training data. With o1, it trained the model to solve problems on its own using a technique known as reinforcement learning, which teaches the system through rewards and penalties. It then uses a “chain of thought” to process queries, similarly to how humans process problems by going through them step-by-step.
At the same time, o1 is not as capable as GPT-4o in a lot of areas. It doesn’t do as well on factual knowledge about the world. It also doesn’t have the ability to browse the web or process files and images. Still, the company believes it represents a brand-new class of capabilities. It was named o1 to indicate “resetting the counter back to 1.”
I think this is the most important part (emphasis mine):
As a result of this new training methodology, OpenAI says the model should be more accurate. “We have noticed that this model hallucinates less,” Tworek says. But the problem still persists. “We can’t say we solved hallucinations.”
It’s a better prediction model. There’s no reasoning because it’s not understanding anything you’re typing. We’re not closer to general ai.
This article from last year compares LLMs to techniques used by “psychics” (cold reading, etc).
https://softwarecrisis.dev/letters/llmentalist/
I think it’s a great analogy (and an interesting article).
It may not be capable of truly understanding anything, but it sure seems to do a better job of it than the vast majority of people I talk to online. I might spend 45 minutes carefully typing out a message explaining my view, only for the other person to completely miss every point I made. With ChatGPT, though, I can speak in broken English, and it’ll repeat back the point I was trying to make much more clearly than I could ever have done myself.
I heard parrots are the pinnacle of conversation
I hate to say it bud, but the fact that you feel like you have more productive conversations with highly advanced autocomplete than you do with actual humans probably says more about you than it does about the current state of generative AI.
That’s not what I said, though.
You should have asked chatgpt to explain the comment to you cause that’s not what they say
LoL. You’re proving his point for him. He did not say that at all. Or maybe that’s the joke… I dunno.
It’s a (large) language model. It’s good at language tasks. Helps to have hundreds of Gigs of written “knowledge” in ram. Differing success rates on how that knowledge is connected.
It’s autocorrect so turbocharged, it can write math, and a full essay without constantly clicking the buttons on top of the iphone keyboard.
You want to keep a pizza together? Ah yes my amazing concepts of sticking stuff together tells me you should add 1/2 spoons of glue (preferably something strong like gorilla glue).
How to find enjoyment with rock? Ah, you can try making it as a pet, and having a pet rock. Having a pet brings many enjoyments such as walking it.
You want to keep a pizza together? Ah yes my amazing concepts of sticking stuff together tells me you should add 1/2 spoons of glue
That would be a good test to ask it that question and see if it comes up with a more coherent answer.
Thanks for illustrating my point.
I wish more people would realize this! We’re years away from a truly reasoning computer.
Right now it’s all mimicry. Mimicry that hallucinates no less…
I think most people do understand this and the naysayers get too caught up on the words being used, like how you still get people frothing over the mouth over the use of the word “intelligence” years after this has entered mainstream conversation. Most people using that word don’t literally think ChatGPT is a new form of intelligent life.
I don’t think anyone is actually claiming this is AGI though. Basically people are going around going “it’s not AGI you idiot”, when no one’s actually saying it is.
You’re arguing against a point no one’s making.
Except that we had to come up with the term “AGI” because idiots kept running around screaming “intelligence” stole the term “AI”.
No we didn’t, Artificial General Intelligence has been determined since the '90s.
We’ve always differentiated Artificial Intelligence and Artificial General Intelligence.
What we have now is AI, I don’t know anyone who’s claiming that it’s AGI though.
People keep saying people are saying that this is AGI, but I’ve not seen anyone say that, not in this thread or anywhere else. What I have seen said is people saying this is a step on the road to AGI which is debatable but it isn’t the same as saying this thing here is AGI.
Edit to add proof:
From Wikipedia although I’m sure you can find other sources if you don’t believe me.
The term “artificial general intelligence” was used as early as 1997, by Mark Gubrud in a discussion of the implications of fully automated military production and operations. A mathematical formalism of AGI was proposed by Marcus Hutter in 2000.
So all of this happened long before the rise of large language models so no the term has not been co-opted.
OpenAI doesn’t want you to know that though, they want their work to show progress so they get more investor money. It’s pretty fucking disgusting and dangerous to call this tech any form of artificial intelligence. The homogeneous naming conventions to make this tech sound human is also dangerous and irresponsible.
trained to answer more complex questions, faster than a human can.
I can answer math questions really really fast. Not correct though, but like REALLY fast!
It scores 83% on a qualifying exam for the international mathematics olympiad compared to the previous model’s 13% so…
When you say previous model, you mean gemini with alpha geometry (an actual RL method)? Which scored a silver?
I mean not only google did it before, they also released their details unlike openai’s “just trust me bro, its RL”.
Openai also said that we should reserve 25k tokens for this “reasoning” and they will be charged the same as output tokens which is exorbitantly high (60$ for 1m tokens).
And the cherry on top is that they won’t even give us these “reasoning” tokens. How the hell am I supposed to improve my prompts if I can’t even see it? How would I reduce the hallucinations without it?
My personal experience is that, it does have an extra reasoning thing going for itself but in no way does it make openai’s tactics tolerable. The quality does not increase enough to justify its cost per token, let alone their “reasoning tokens” BS.
I’m the same with any programming question as long as the answer is Hello World
So for those not familar with machine learning, which was the practical business use case for “AI” before LLMs took the world by storm, that is what they are describing as reinforcement learning. Both are valid terms for it.
It’s how you can make an AI that plays Mario Kart. You establish goals that grant points, stuff to avoid that loses points, and what actions it can take each “step”. Then you give it the first frame of a Mario Kart race, have it try literally every input it can put in that frame, then evaluate the change in points that results. You branch out from that collection of “frame 2s” and do the same thing again and again, checking more and more possible future states.
At some point you use certain rules to eliminate certain branches on this tree of potential future states, like discarding branches where it’s driving backwards. That way you can start opptimizing towards the options at any given time that get the most points im the end. Keep the amount of options being evaluated to an amount you can push through your hardware.
Eventually you try enough things enough times that you can pretty consistently use the data you gathered to make the best choice on any given frame.
The jank comes from how the points are configured. Like AI for a delivery robot could prioritize jumping off balconies if it prioritizes speed over self preservation.
Some of these pitfalls are easy to create rules around for training. Others are far more subtle and difficult to work around.
Some people in the video game TAS community (custom building a frame by frame list of the inputs needed to beat a game as fast as possible, human limits be damned) are already using this in limited capacities to automate testing approaches to particularly challenging sections of gameplay.
So it ends up coming down to complexity. Making an AI to play Pacman is relatively simple. There are only 4 options every step, the direction the joystick is held. So you have 4n states to keep track of, where n is the number of steps forward you want to look.
Trying to do that with language, and arguing that you can get reliable results with any kind of consistency, is blowing smoke. They can’t even clearly state what outcomes they are optimizing for with their “reward” function. God only knows what edge cases they’ve overlooked.
My complete out of my ass guess is that they did some analysis on response to previous gpt output, tried to distinguish between positive and negative responses (or at least distinguish against responses indicating that it was incorrect). They then used that as some sort of positive/negative points heuristic.
People have been speculating for a while that you could do that, crank up the “randomness”, have it generate multiple responses behind the scenes and then pit those “pre-responses” against each other and use that criteria to choose the best option of the “pre-responses”. They could even A/B test the responses over multiple users, and use the user responses as further “positive/negative points” reinforcement to feed back into it in a giant loop.
Again, completely pulled from my ass. Take with a boulder of salt.
Again, completely pulled from my ass. Take with a boulder of salt.
You’re under arrest. That’s ass-salt.
Fuck you, that made me smile. And I haven’t even had my coffee yet.
Sorry for hitting you at a vulnerable time.
To be a little nitpicky most of the AI that can play Mario kart are trained not with a reinforcement learning algorithm, but woth a genetic algorithm, which is a sort of different thing.
Reinforcement learning is rather like how you teach a child. Show them a bunch of good stuff, and show them a bunch of bad stuff, and tell them which is the good stuff and which is the bad stuff.
Genetic algorithms are where you just leave it alone, simulate the evolutionary process on an accelerated time scale, and let normal evolutionary processes take over. Much easier, and less processor intensive, plus you don’t need huge corpuses of data. But it takes ages, and it also sometimes results in weird behaviors because evolution finds a solution you never thought of, or it finds a solution to a different problem to the one you were trying to get it to find a solution to.
… sometimes results in weird behaviors because evolution finds a solution you never thought of, or it finds a solution to a different problem to the one you were trying to get it to find a solution to.
Those outcomes seem especially beneficial.
But it takes ages, …
Is this process something that distributed computing could be leveraged for, akin to SETI@home?
I work in computer science but not really anything to do with AI so I’m only adjacently knowledgeable about it. But my understanding is unfortunately, no not really. The problem would be that if you run a bunch of evolutions in parallel you just get a bunch of independent AIs, all with slightly different parameters but they’re incapable of working together because they weren’t evolved to work together, they were evolved independently.
In theory you could come up with some kind of file format that allowed for the transfer of AI between each cluster, but you’d probably spend as much time transferring AI as you saved by having multiple iterations run at the same time. It’s n^n problem, where n is the number of AIs you have.
Genetic algorithms is a sort of broad category and there’s certainly ways you could federate and parallelize. I think autoML basically applies this within the ML space (multiple trainings explore a solution topology and convergence progress is compared between epochs, with low performers dropping out). Keep in mind, you can also use a genetic algorithm to learn how to explore an old fashioned state tree.
How much more time until they use the word “sentient”?
Until the bubble bursts
Is that even the goal? Do we want an AI that’s self aware because I thought that basically the whole point was to have an intelligence without a mind.
We don’t really want sapient AI because if we do that then we have to feel bad about putting it in robots and making them do boring jobs. Don’t we basically want guildless servants, isn’t that the point?
Yeah I was thinking more about it as marketing, than a real thing
For the servants bots, yes no sentience. For my in house AI assistant robot buddy/butler/nanny/driver - also yes no sentience.
It seems utopia/dystopia, but some things get discovered/invented by accident. The more companies and organizations (and even individuals) fiddle with AI improvement, the more the “odds” of a sentient AI (AGI) being accidentally created increases. Let’s not forget that there are lots of companies, organizations and individuals (yeah, individuals, people outside organizations but with lots of computing power and knowledge) simultaneously developing and training AIs. Well, maybe I’m wrong and just very optimistic for such thing to appear out of nowhere.
What we want doesn’t have any impact on what our corporate overlords decide to inflict on us.
I’m more concerned about them using the word “sapient.” My dog is sentient; it’s not a high bar to clear.
The meaning is ok. But “sentient” is so hot right now
Not until it has senses, which it currently does not have.
Can’t wait to read about it telling someone to put glue on pizza.
This is smarter. Will tell you how to pump a Calzone full of glue.
And provide the logical reasoning behind it!
“We have noticed that this model hallucinates less,” Tworek says. But the problem still persists. “We can’t say we solved hallucinations.”
On one hand, yeah, AI hallucinations.
On the other hand, have you met people?
Reinforcement learning my beloved ❤️
Interesting, thanks for sharing.
Technophobes are trying to downplay this because “AI bad”, but this is actually a pretty significant leap from GPT and we should all be keeping an eye on this, especially those who are acting like this is just more auto-predict. This is a completely different generation process than GPT which is just glorified auto-predict. It’s the difference between learning a language by just reading a lot of books in that language, and learning a language by speaking with people in that language and adjusting based on their feedback until you are fluent.
If you thought AI comments flooding social media was already bad, it’s soon going to get a lot harder to discern who is real, especially once people get access to a web-connected version of this model.
All signs point to this being a finetune of gpt4o with additional chain of thought steps before the final answer. It has exactly the same pitfalls as the existing model (9.11>9.8 tokenization error, failing simple riddles, being unable to assert that the user is wrong, etc.). It’s still a transformer and it’s still next token prediction. They hide the thought steps to mask this fact and to prevent others from benefiting from all of the finetuning data they paid for.
It’s weird how so many of these “technophobes” are IT professionals. Crazy that people would line up to go into a profession they so obviously hate and fear.
I’ve worked in tech for 20 years. Luddites are quite common in this field.
Read some history mate. The luddites weren’t technophobes either. They hated the way that capitalism was reaping all the rewards of industrializion. They were all for technological advancement, they just wanted it to benefit everyone.
I’m using the current-day usage of the term, but I think you knew that.
Gave it a try just now. Pretty terrible result.
At the same time, o1 is not as capable as GPT-4o in a lot of areas. It doesn’t do as well on factual knowledge about the world. It also doesn’t have the ability to browse the web or process files and images. Still, the company believes it represents a brand-new class of capabilities. It was named o1 to indicate “resetting the counter back to 1.”
I think it’s more of a proof of concept then a fully functioning model at this point.
To be fair, I did ask it to fact check an article, so that was probably not the best first choice
Facts. A “reasoning AI” has problems with … lemme check this again … facts?
Find the comment about psychics, it’s exactly the situation we are currently in.
Reasoning has nothing to do with knowledge though.
Show it.
I’d recommend everyone saying “it can’t understand anything and can’t think” to look at this example:
https://x.com/flowersslop/status/1834349905692824017
Try to solve it after seeing only the first image before you open the second and see o1’s response.
Let me know if you got it before seeing the actual answer.
This example doesn’t prove what you think it does. It shows pattern detection - something computers are inherently very well suited for - but it doesn’t demonstrate “reasoning” in any meaningful way.
I think if you can actually define reasoning, your comments (and those like yours) would be much more convincing. I’m just calling yours out because I’ve seen you up and down in this thread repeating it, but it’s a general observed of the vocal critics of the technology overall. Neither intelligence nor reasons (likewise understanding and knowing, for that matter) are easily defined in a way that is more useful than invoking spirits and ghosts. In this case, detecting patterns certainly seems a critical component of what we would consider to be reasoning. I don’t think it’s sufficient, buy it is absolutely necessary.
You should really look at the full CoT traces on the demos.
I think you think you know more than you actually know.
Got a link to that?
Yep:
https://openai.com/index/learning-to-reason-with-llms/
First interactive section. Make sure to click “show chain of thought.”
The cipher one is particularly interesting, as it’s intentionally difficult for the model.
The tokenizer is famously bad at two letter counts, which is why previous models can’t count the number of rs in strawberry.
So the cipher depends on two letter pairs, and you can see how it screws up the tokenization around the xx at the end of the last word, and gradually corrects course.
Will help clarify how it’s going about solving something like the example I posted earlier behind the scenes.
I’m getting so tired of the pessimists who are against AI. Granted, I can reflect and see my own similar attitude towards Trump: no matter what, I would never vote for him considering his history and who he is as a person. But treating the next generation of technology feels different than that to me; AI is the future, it’s the next revolution. Sure, there are several real issues to criticize and question (copyright, compensation, hallucination come to mind) but instead shit here on Lemmy just gets downvoted to hell with no explanation. I know this comment will get downvoted, but I just wish we could have a discussion about the future without shutting down every practical comment wanting to talk about it.
I’m kinda in the same boat but on the other side. I always try to argue with people about this. It gets me a lot of flak on pro AI posts but that won’t stop me. I usually get very aggressive replies and sometimes some fucked up dm’s too.
I’m against it because we are already seeing the consequences of this technology and it’s only getting worse. By the time laws catch up it’s gonna be too late and the damage will be done. For some technologies that’s not always the worst. But we already saw how long it took for anyone to do anything about the Internet when it came out, and we are still trying to this day. This shit is growing so fast we will all feel the whiplash. Sites like Facebook are getting absolutely flooded with so much AI that they are becoming almost unusable. And that’s before we even get into the shady shit people use AI for like making porn of people they know with the click of a button. I recently read an article about how bad deepfake porn is in South Korea (found the article. https://www.nytimes.com/2024/09/12/world/asia/south-korea-deepfake-videos.html). And in places like the US, where a lot of these companies are based, they are so slow to do anything about a problem it’s going to be too late by the time they get to it.
But besides all the awful things happening because of AI, I do have one personal gripe with the whole ordeal. Why are we so quick to replace the things we enjoy with AI? When I get home from work I like to make music and practice pixel art (I’m not very good at either yet). I’d much rather have AI replace my job than my hobbies. I’m down for things that are useful, but too much of this just gives me a bad gut feeling. Like their trying to replace people and not their jobs.
This may be the future. But it sounds like a pretty dystopian future to me. You already can’t believe everything you see on the Internet and this will only make it worse.
More and more advanced tools for automation are an important part of creating a post-scarcity future. If we can combine that with tearing down our current economic system - which inherently requires and thus has to manufacture scarcity - we can uplift our species in ways we can currently only imagine.
But this ain’t it bud. If I ask you for water and you hand me a glass of warm piss, I’m not “against drinking water” for refusing to gulp it down.
This isn’t AI. It isn’t - meaningfully and usefully - any form of automation at all. A bunch of conmen slapped the letters “AI” on the side of their bottle of piss and you’re drinking it down like it’s grandma’s peach tea.
The people calling out the fundamental flaws with these products aren’t doing so because we hate the entire concept of automation, any more than someone exposing a snake-oil salesman hates medicine. What we hate is being lied to. The current state of this technology is bullshit and hype. It is not fit for human consumption (other than recreationally) and the money being pumped into it could be put to far better uses. OpenAI may have lofty goals, but they have utterly failed at achieving them, and right now any true desire to create AGI has been totally subsumed by the need to keep pumping out slightly better looking versions of the same polished turd in order to convince investors to keep paying for their staggeringly high hosting costs.
