Bill Gates feels Generative AI has plateaued, says GPT-5 will not be any better::The billionaire philanthropist in an interview with German newspaper Handelsblatt, shared his thoughts on Artificial general intelligence, climate change, and the scope of AI in the future.
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.
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 and most of us get through most days on little more than this.
It’s true that we can do more. Some of us, anyway. How many people actually exercise math and logic though? Sometimes it seems like… not a lot. And 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.
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.
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.
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.
ChatGPT doesn’t learn like that though, does it? I thought it was “static” with its training data.
I was speculating about how you can overcome hallucinations, etc., by supplying additional training data. Not specific to ChatGPT or even LLMs…
You can finetune LLMs using smaller datasets, or with RLHF (reinforcement learning from human feedback) wherein people can give ratings to responses and the model can be either “rewarded” or “penalized” based off of the ratings for a given output. This retrains the LLM to produce outputs that people prefer.
Active Learning Models. Though public exposure can eaily fuck it up, without adult supervision. With proper supervision though, there’s promise.
So it will always have the biases of the supervisors
Bias is inevitable. Whether it is AI or any other knowledge based system. We just have to be cognizant of it and try to remedy it.
Toronto is Canadian New York. It wants to be the capital and probably should be but it doesn’t speak enough French.
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.
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.
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.