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.
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.
archomrade@midwest.social 1 year 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 1 year 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 1 year ago
ChatGPT doesn’t learn like that though, does it? I thought it was “static” with its training data.
grabyourmotherskeys@lemmy.world 1 year ago
I was speculating about how you can overcome hallucinations, etc., by supplying additional training data. Not specific to ChatGPT or even LLMs…
HiggsBroson@lemmy.world 1 year ago
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.
Toes@ani.social 1 year 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 1 year 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 1 year 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.