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.
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.
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.
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.
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.
I was speculating about how you can overcome hallucinations, etc., by supplying additional training data. Not specific to ChatGPT or even LLMs…
Toronto is Canadian New York. It wants to be the capital and probably should be but it doesn’t speak enough French.
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.
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.
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.
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 its own 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.
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: Presumably you “understand” your dog - can you describe your dog without sensory details, directly or indirectly? Behavior had to be observed somehow. Time is a sense too. EDIT: before someone says it, as for feelings I’m not really sure, I’m not a biology guy. But my guess is we sense our own hormones as well