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).
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.
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 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.
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.
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”.
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.
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.
So they slapped some reinforcement learning on top of their LLM and are claiming that gives it “reasoning capabilities”? Or am I missing something?
No the article is badly worded. Earlier models already have reasoning skills with some rudimentary CoT, but they leaned more heavily into it for this model.
My guess is they didn’t train it on the 10 trillion words corpus (which is expensive and has diminishing returns) but rather a heavily curated RLHF dataset.
“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?
