Oh, I’m not saying that there won’t one day come a better technology that can do a lot more. What I’m saying is that the present technology will never do much more than it is already doing. This is not an issue of refining the technology for more applications. It’s a matter of completely developing a new type of technology.
In areas of generative text, summarizing articles and books, as well as writing short portions of code in order to assist humans, creating simple fan art, and meaningless images like avatars, and those stock photos at the top of articles, Perhaps creating short animations, Improving pattern recognition of things like speech and facial recognition… In all of these areas, AI was very rapidly revolutionary.
Generative AI will not become capable of doing things that it’s not already doing. Most of what it’s replacing are just worse computer programs. Some new technology will undoubtedly be revolutionary in the way that computers were a completely new revolution on top of basic function calculators. People are developing quantum computers, and mapping the precise functions of brain cells. If you want, you can download a completely mapped actual nematode brain right now. You can buy brain cells online, even human brain cells, and put them into computers. Maybe they can even run Doom. I have no idea what the next computing revolution will be capable of, but this one has mostly run its course. It has given us some very incredible tools in a very narrow scope, and those tools will continue to improve incrementally, but there will be no additional revolution.
HackyHorse3000@lemmy.world 2 months ago
That’s the thing though, that’s not comparable, and misses the point entirely. “AI” in this context and the conversations regarding it in the current day is specifically talking about LLMs. They will not improve to the point of general intelligence as that is not how they work. Hallucinations are inevitable with the current architectures and methods, and they lack a inherent understanding of concepts in general. It’s the same reason they can’t do math or logic problems that aren’t common in the training set. It’s not intelligence. Modern computers are built on the same principals and architectures as those calculators were, just iterated upon extensively. No such leap is possible using large language models. They are entirely reliant on a finite pool of data to try to mimic most effectively, they are not learning or understanding concepts the way “Full-AI” would need to to actually be reliable or able to generate new ideas.
chrash0@lemmy.world 2 months ago
it’s super weird that people think LLMs are so fundamentally different from neural networks, the underlying technology. neural network architectures are constantly improving, and LLMs are just a product of a ton of research and an emergence after the discovery of the transformer architecture. what LLMs have shown us is that we’re definitely on the right track using neural networks to solve a wide range of problems classified as “AI”
HackyHorse3000@lemmy.world 2 months ago
I think the main problem is applying LLM outside the domain of “complete this sentence”. It’s fine for what it is, and trained on huge datasets it obviously appears impressive, but it doesn’t know if it’s right or wrong, and evaluation metrics are different. In most traditional applications of neural networks, you have datasets with right and wrong answers, that’s not how these are trained, as there is no “right” answer to “tell me a joke.” So the training has to be based on what would likely fill in the blank. This could be an actual joke, a bad joke, a completely different topic, there’s no difference in the training data. The biases, incorrect answers, all the faults of this massive dataset are inherent in the model, and there’s no fixing that. They are fundamentally different in their application and evaluation (this extends to training) methods from other neural networks that are actually effective at what they do, like image processing and identification. The scope of what they’re trying to do with a finite dataset is not realistic and entirely unconstrained, as compared to more “traditional” neural networks, which are very narrow in scope exactly because of this issue.