- cross-posted to:
- technology@lemmy.zip
- cross-posted to:
- technology@lemmy.zip
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.”
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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).
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.
It is literally artificial intelligence though. Just because chatGPT doesn’t perform as a layperson imagined it would, it doesn’t mean it’s not AI. They just have an unrealistic expectation of what counts as AI along with the common misconception of AI and AGI being the same thing.
A chess playing robot uses artificial intelligence as well. It’s a narrow AI, meaning it can do one thing really well but that doesn’t translate to other things. AGI on the other hand stands for Artificial General Intelligence. Humans are an example of general intelligence meaning that we have the cognitive ability to perform well on several unrelated tasks.
Their work is making progress. What is irresponsible or dangerous? Im not understanding what you mean.
It’s irresponsible because making it sound like it’s true AI when it’s not is going to make it difficult to pull the plug when things go wrong and you’ll have the debate of whether it’s sentient or not and if it’s humane to kill it like a pet or a criminal. It’s more akin to using rainbow tables to help crack passwords and claiming your software is super powerful when in reality it’s nothing without the tables. (Very very rudimentary example that’s not supposed to be taken at face value).
It’s dangerous because talking about AI like it’s a reasoning/thinking thing is just not true, and we’re already seeing the big AI overlords try to justify how they created it with copyrighted material, which means the arguments over copyrighted material are being made and we’ll soon see those companies claim that it’s no different than a child looking up something on Google. It’s irresponsible because it screws over creative people and copyright holders that genuinely made a product or piece of art or book or something in their own free time and now it’s been ripped away to be used to create something else that will eventually push those copyright holders out.
The AI market is moving faster than the world is capable of keeping up with it, and that is a dangerous precedent to set for the future of this market. And for the record I don’t think we’re dealing with early generations of skynet or anything like that, we’re dealing with tools that have the capability to create economical collapse on a scale we’ve never seen, and if we don’t lay the ground rules now, then we will be in trouble.
Edit: A great example of this is https://v0.dev/chat it has the potential to put front end developers out of work and jobless. It’s simple now but give it time and it has the potential to create a frontend that rivals the best UX designs if the prompt is right.
I appreciate the effortful response but i dont think regulators would get caught up on colloquial names when weighing benefit versus harm and deciding to do something like ban a model.
We just arent close enough to the same perspective to discuss it further. Thanks again for the good faith clarification.
I think over-selling the “AI” with “reasoning/thinking” language becomes fraudulent and encourages inappropriate/dangerous applications.
Why does ai that has a “reasoning” step become dangerous?
assuming that “AI” has “reasoning” and using it in applications that require that is dangerous
It will be used to take control over peoples lives.
In any simple way it may be - denying job/insurance/care/etc, it will be hailed as using ‘reason’, while it just repeats patterns from the training sets.
It does not ‘reason’, because it can’t. Trying to sell it as such is very dangerous as it will be used against people, and it’s dishonest for the investors as well, as they will jump on it even though it’s not ‘true’ and it never will be for this model.
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.
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.
Thanks for illustrating my point.
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.
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.
Being better at prediction requires reasoning
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…
I’m the same with any programming question as long as the answer is Hello World
That’s a flat out lie, I use it for code all the time and it’s fantastic at writing useful functions if you tell it what you want. It’s also fantastic if you ask it to explain code or options for problem solving.
😋
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.
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!
So they slapped some reinforcement learning on top of their LLM and are claiming that gives it “reasoning capabilities”? Or am I missing something?
It’s like 3 lms on top of eachother in a trenchcoat, and appau a calculator so it gets math right
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 does not fail the 9.11 > 9.8 thing.
They hide the thought steps to mask this fact and to prevent others from benefiting from all of the finetuning data they paid for.
Well possibly but they also hide the chain of thought steps because as they point out in their article it needs to be able to think about things outside of what it’s normally allowed allowed to say which obviously means you can’t show the content. If you’re trying to come up with worst case scenarios for a situation you actually have to be able to think about those worst case scenarios
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.
Big leap for OpenAI, as in a kind of ML model they haven’t explored yet. Not that big for AI in general as others have done the same with similar result. Until they can make graphs where they look exceptionally better compared to other models than their own, it’s not that much of a breakthrough.
“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?
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.
What we want doesn’t have any impact on what our corporate overlords decide to inflict on us.
They don’t want sapient AI either, why would they?
No one is trying for a self-aware artificial intelligence.
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.
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
Lol Lemmy has the funniest ai haters they drown out any real criticism with stupid strawman nonsense
I just love how people seem to want to avoid using the word lie.
It’s either misinformation, or alternative facts, or hallucinations.
Granted, a lie does tend to have intent behind it, so with ChatGPT, it’s probably better to say falsehood, instead. But either way, it’s not fact, it’s not truth, and people, especially schools, should stop using it as a credible source.
Being wrong is not the same as lying. When LLMs start giving wrong answers on purpose to mislead people we would have a big problem.
The thought of a maliciously deceptive AGI is terrifying to me. Many, many people will trust it until it’s too late.
There was a recent paper that argues ‘bullshitting’ is the most apt analogy. I.e. telling something to satisfy the other person without caring about the truth content of what you say
What about thw term “incorrect facts”?
Dang, OpenAI just pulled an Apple. Do something other people have already done with the same results (but importantly before they made a big fuss about it), claim it’s their innovation, give it a bloated name so people imagine it’s more than it is and produce a graph comparing themselves to themselves, hoping nobody will look at the competition.
Just like Apple, they have their own selling point, but instead they seem to prefer making up stuff while forgetting why people use em.
On a side note they also pulled an Elon. Where’s my AI companion that can comment on video in realtime and sing to me??? Ya had it “working” “live” a couple months ago, WHERE IS IT?!?
Meanwhile a bald turtle and his AI anime daughter on twitch can do exactly this, and he’s building her at home on nvidia GPUs.
(Vedal987 and Neuro-sama, if you’re curious)
Pulled an Apple?
I know you hate apple because android is way better but people loved their ipods, iphones, airpods and apple watches. Sure those things were made before but Apple did make them better. So I don’t know what your point is.
Assuming I’m an android fan for pointing out that Apple does shady PR. I literally mention that Apple devices have their selling point. And it isn’t UNMATCHED PERFORMANCE or CUTTING EDGE TECHNOLOGY as their adds seems to suggest. It’s a polished experience and beautiful presentation; that is unmatched. Unlike the hot mess that is android. Android also has its selling points, but this reply is already getting long. Just wanted to point out your pettiness and unwillingness to read more than a sentence.
I think I’ve used it if this is the latest available, and it’s terrible. It keeps feeding me wrong information, and when you correct it, it says you’re right… But if you ask it again, it again feeds you the wrong information.
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.
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.
Actually, they are hiding the full CoT sequence outside of the demos.
What you are seeing there is a summary, but because the actual process is hidden it’s not possible to see what actually transpired.
People are very not happy about this aspect of the situation.
It also means that model context (which in research has been shown to be much more influential than previously thought) is now in part hidden with exclusive access and control by OAI.
There’s a lot of things to be focused on in that image, and “hur dur the stochastic model can’t count letters in this cherry picked example” is the least among them.
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.
While truly defining pretty much any aspect of human intelligence is functionally impossible with our current understanding of the mind, we can create some very usable “good enough” working definitions for these purposes.
At a basic level, “reasoning” would be the act of drawing logical conclusions from available data. And that’s not what these models do. They mimic reasoning, by mimicking human communication. Humans communicate (and developed a lot of specialized language with which to communicate) the process by which we reason, and so LLMs can basically replicate the appearance of reasoning by replicating the language around it.
The way you can tell that they’re not actually reasoning is simple; their conclusions often bear no actual connection to the facts. There’s an example I linked elsewhere where the new model is asked to list states with W in their name. It does a bunch of preamble where it spells out very clearly what the requirements and process are; assemble a list of all states, then check each name for the presence of the letter W.
And then it includes North Dakota, South Dakota, North Carolina and South Carolina in the list.
Any human being capable of reasoning would absolutely understand that that was wrong, if they were taking the time to carefully and systematically work through the problem in that way. The AI does not, because all this apparent “thinking” is a smoke show. They’re machines built to give the appearance of intelligence, nothing more.
When real AGI, or even something approaching it, actually becomes a thing, I will be extremely excited. But this is just snake oil being sold as medicine. You’re not required to buy into their bullshit just to prove you’re not a technophobe.
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.
Show it.
Interesting, thanks for sharing.
