Yeah and I think he may be scaling to like true AGI. Very possible LLMs just don’t become AGI, you need some extra juice we haven’t come up with yet, in addition to computational power no one can afford yet.
Comment on Bill Gates feels Generative AI has plateaued, says GPT-5 will not be any better
astronaut_sloth@mander.xyz 11 months ago
Cool, Bill Gates has opinions. I think he’s being hasty and speaking out of turn and only partially correct. From my understanding, the “big innovation” of GPT-4 was adding more parameters and scaling up compute. The core algorithms are generally agreed to be mostly the same from earlier versions (not that we know for sure since OpenAI has only released a technical report). Based on that, the real limit on this technology is compute and number of parameters (as boring as that is), and so he’s right that the algorithm design may have plateaued. However, we really don’t know what will happen if truly monster rigs with tens-of-trillions of parameters are used when trained on the entirety of human written knowledge (morality of that notwithstanding), and that’s where he’s wrong.
OldWoodFrame@lemm.ee 11 months ago
astronaut_sloth@mander.xyz 11 months ago
Except that scaling alone won’t lead to AGI. It may generate better, more convincing text, but the core algorithm is the same. That “special juice” is almost certainly going to come from algorithmic development rather than just throwing more compute at the problem.
0ops@lemm.ee 11 months ago
See my reply to the person you replied to. I think you’re right that there will need to be more algorithmic development (like some awareness of it’s confidence so that the network can say IDK instead of hallucinating its best guess). Fundamentally though, llm’s don’t have the same dimensions of awareness that a person does, and I think that that’s the main bottleneck of human-like understanding.
0ops@lemm.ee 11 months ago
My hypothesis is that that “extra juice” is going to be some kind of body. More senses than text-input, and more ways to manipulate itself and the environment than text-output. Basically, right now llm’s can kind of understand things in terms of text descriptions, but will never be able to understand it the way a human can until it has all of the senses (and arguably physical capabilities) that a human does. Thought experiment: can you describe your dog without sensory details, directly or indirectly? Behavior had to be observed somehow. Time is a sense too.
LinuxSBC@lemm.ee 11 months ago
First, they do have senses. For example, many LLMs can “see” images. Second, they’re actually pretty good at describing things. What they’re really bad at is analysis and logic, which is not related to senses at all.
0ops@lemm.ee 11 months ago
I’m not so convinced that logic is completely unrelated to the senses. How did you learn to count, add, and subtract mentally? You used your fingers. I don’t know about you, but even though I don’t count my fingers anymore I still tend to “visualize” math operations. Would I be capable of that if I were born blind? Maybe I’d figure out how to do the same thing in a different dimension of awareness, but I have no doubt that being able to conceptualize visually helps my own logic. As for more complicated math, I can’t do that mentally either, I need a calculator and/or scratch paper. Maybe analogues to those can be implemented into the model? Maybe someone should just train a model on khan academy videos, and it’ll pick this stuff up emergency? I’m not saying that the ability to visualize is the only roadblock though, I’m sure that improvements could be made to the models themselves, but I bet that it’ll be key to human-like reasoning
lorkano@lemmy.world 11 months ago
The problem is that between gpt 3 and 4 there is massive increase in number of parameters, but not massive increase in its abilities
scarabic@lemmy.world 11 months ago
I’ll listen to his opinions more than some, but unfortunately this article doesn’t say anything interesting about why he has this opinion. I guess the author supposes we will simply regard him as an oracle on name recognition alone.
Vlyn@lemmy.zip 11 months ago
You got it the wrong way around. We already have a ton of compute and what this kind of AI can do is pretty cool.
But adding more compute power and parameters won’t solve the inherent problems.
No matter what you do, it’s still just a text generator guessing the next best word. It doesn’t do real math or logic, it gets basic things wrong and hallucinates new fake facts.
Sure, it will get slightly better still, but not much. You can throw a million times the power at it and it will still fuck up in just the same ways.
archomrade@midwest.social 11 months ago
This is short-sighted.
The jump to GPT 3.5 was preceded by the same general misunderstanding (we’ve reached the limit of what generative pre-trained transformers can do, we’ve reached diminishing returns, ECT.) and then a relatively small change (AFAIK it was a couple additional layers of transforms and a refinement of the training protocol) and suddenly it was displaying behaviors none of the experts expected.
Small changes will compound when factored over billions of nodes, that’s just how it goes. It’s just that nobody knows which changes will have that scale of impact, and what emergent qualities happen as a result.
It’s ok to say “we don’t know why this works” and also “there’s no reason to expect anything more from this methodology”. But I wouldn’t dismiss further improvements as a forgone possibility.
grabyourmotherskeys@lemmy.world 11 months ago
Another way to think of this is feedback from humans will refine results. If enough people tell it that Toronto is not the capital of Canada it will start biasing toward Ottawa, for example. I have a feeling this is behind the search engine roll out.
raptir@lemdro.id 11 months ago
ChatGPT doesn’t learn like that though, does it? I thought it was “static” with its training data.
Toes@ani.social 11 months ago
Toronto is Canadian New York. It wants to be the capital and probably should be but it doesn’t speak enough French.
generalpotato@lemmy.world 11 months ago
This is exactly it. And it’s funny you’re getting downvoted.
We don’t truly know the depth of ML yet and how these general models could potential change when a few vectors in the equation change, and that’s the big unknown with it. I agree with you here that Gates’ opinion is just that and isn’t particularly well informed. Especially in comparison to what some of the industry and ML experts are saying about how far we can go with the models, how they will evolve as we change parameters/vectors/dependencies and the impact of that evolution on potential applications. It’s just too early.
archomrade@midwest.social 11 months ago
I kinda get why I’m getting downvoted, honestly. The ChatGPT fanboys definitely give off an “NFT-grindset” kind of vibe, and they can be loud and overzealous with their prognosticating. It feels cathartic to make fun of the thing they’ve adopted as a centerpiece of their personality
None of that changes what is objectively the very real and very unexpected improvement these models are displaying, and we’re still not sure what it is they’re doing behind the curtain. “Predicting the next most likely word” is simply not a sufficient explanation for how these models seem to correctly interpret intent and apply factual knowledge stored in its dataset in abstract ways.
People want to squabble over anthropomorphic word choices and debate ‘consiousness’, and fair enough, its an interesting question. But that doesn’t really come close to what’s really interesting about the models gaining functionality when by all accounts they should only be ‘guessing the next most likely word’.
I’m not really interested in debating people who are performatively unimpressed by these products, but it bothers me that those people continue rolling their eyes when significant advancements are made. Like sure, it’s not new that ML algorithms can decode keystrokes from an audio recording, but it’s a big deal when those models can be run on consumer grade hardware and not just a super computer run by a three letter agency.
scarabic@lemmy.world 11 months ago
If humans are any kind of yardstick here, I’d say all this is true of us too on many levels. The brain is a shortcut engine, not a brute force computer. It’s not solving equations to help you predict where that tennis ball will bounce next. It’s making guesses based on its corpus of past experience. Good enough guesses are frankly our brains’ bread and butter.
It’s true that we can also do more than this. Some of us, anyway. How many people actually exercise math and logic? How many people hallucinate fake facts? A lot.
It’s much like evaluating self-driving cars. We may be tempted to say they’re just bloody awful, but so are human drivers.
Vlyn@lemmy.zip 11 months ago
I’d say the majority of humans know what 2 + 2 is. Chat GPT doesn’t. As it found the answer in some texts it will tell you 4, but all it takes is you telling it that’s wrong and suddenly it’s 5. So even for the most simple math problem it’s extremely easy to throw the whole thing off. Which also means for any prompt you put in it can go in wildly wrong directions at times.
And this is all with good input data, there’s plenty of trolls online and the data will only get worse (it already did, the original data up to 2021 was okayish, in the last year tons of crap was put out on top, some of it by Chat GPT itself. So the new model might input the crap it produced before, getting worse over time). The problem on top of that is that you don’t know the sources it used. If you ask about a recent event you might receive an insane answer it picked up from a right wing conspiracy site, you simply don’t know. There is no fact checking in place.
It’s a stunningly good text generator, but that’s all it is and it ever will be, at least until they do much more than just add more compute power to it.
astronaut_sloth@mander.xyz 11 months ago
I mean, that’s more-or-less what I said. We don’t know the theoretical limits of how good that text generation is when throwing more compute at it and adding parameters for the context window. Can it generate a whole book that is fairly convincing, write legal briefs off of the sum of human legal knowledge, etc.? Ultimately, the algorithm is the same, so like you said, the same problems persist, and the definition of “better” is wishy-washy.
Vlyn@lemmy.zip 11 months ago
It will obviously get even better, but you’ll never be able to rely on it. Sure, 99.9% of that generated legal document will look perfect, till you overlook one sentence where the AI hallucinated. There is no fact checking in there, that’s the issue.