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this post was submitted on 22 Jul 2023
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It's overhyped but there are real things happening that are legitimately impressive and cool. The image generation stuff is pretty incredible, and anyone can judge it for themselves because it makes pictures and to judge it, you can just look at and see if it looks real or if it has freaky hands or whatever. A lot of the hype is around the text stuff, and that's where people are making some real leaps beyond what it actually is.
The thing to keep in mind is that these things, which are called "large language models", are not magic and they aren't intelligent, even if they appear to be. What they're able to do is actually very similar to the autocorrect on your phone, where you type "I want to go to the" and the suggestions are 3 places you talk about going to a lot.
Broadly, they're trained by feeding them a bit of text, seeing which word the model suggests as the next word, seeing what the next word actually was from the text you fed it, then tweaking the model a bit to make it more likely to give the right answer. This is an automated process, just dump in text and a program does the training, and it gets better and better at predicting words when you a) get better at the tweaking process, b) make the model bigger and more complicated and therefore able to adjust to more scenarios, and c) feed it more text. The model itself is big but not terribly complicated mathematically, it's mostly lots and lots and lots of arithmetic in layers: the input text will be turned into numbers, layer 1 will be a series of "nodes" that each take those numbers and do multiplications and additions on them, layer 2 will do the same to whatever numbers come out of layer 1, and so on and so on until you get the final output which is the words the model is predicting to come next. The tweaks happen to the nodes and what values they're using to transform the previous layer.
Nothing magical at all, and also nothing in there that would make you think "ah, yes, this will produce a conscious being if we do it enough". It is designed to be sort of like how the brain works, with massively parallel connections between relatively simple neurons, but it's only being trained on "what word should come next", not anything about intelligence. If anything, it'll get punished for being too original with its "thoughts" because those won't match with the right answers. And while we don't really know what consciousness is or where the lines are or how it works, we do know enough to be pretty skeptical that models of the size we are able to make now are capable of it.
But the thing is, we use text to communicate, and we imbue that text with our intelligence and ideas that reflect the rich inner world of our brains. By getting really, really, shockingly good at mimicking that, AIs also appear to have a rich inner world and get some people very excited that they're talking to a computer with thoughts and feelings... but really, it's just mimicry, and if you talk to an AI and interrogate it a bit, it'll become clear that that's the case. If you ask it "as an AI, do you want to take over the world?" it's not pondering the question and giving a response, it's spitting out the results of a bunch of arithmetic that was specifically shaped to produce words that are likely to come after that question. If it's good, that should be a sensible answer to the question, but it's not the result of an abstract thought process. It's why if you keep asking an AI to generate more and more words, it goes completely off the rails and starts producing nonsense, because every unusual word it chooses knocks it further away from sensible words, and eventually it's being asked to autocomplete gibberish and can only give back more gibberish.
You can also expose its lack of rational thinking skills by asking it mathematical questions. It's trained on words, so it'll produce answers that sound right, but even if it can correctly define a concept, you'll discover that it can't actually apply it correctly because it's operating on the word level, not the concept level. It'll make silly basic errors and contradict itself because it lacks an internal abstract understanding of the things it's talking about.
That being said, it's still pretty incredible that now you can ask a program to write a haiku about Danny DeVito and it'll actually do it. Just don't get carried away with the hype.
My perspective is that consciousness isn't a binary thing, or even a linear scale. It's an amalgamation of a bunch of different independent processes working together; and how much each matters is entirely dependent on culture and beliefs. We're artificially creating these independent processes piece by piece in a way that doesn't line up with traditional ideas of consciousness. Conversation and being able to talk about concepts one hasn't personally experienced are facets of consciousness and intelligence, ones that the latest and greatest LLMs do have. Of course there others too that they don't: logic, physical presence, being able to imagine things in their mind's eye, memory, etc.
It's reductive to dismiss GPT4 as nothing more than mimicry; saying it's just a mathematical text prediction model is like saying your brain is just a bunch of neurons. Both statements are true, but it doesn't change what they can do. If someone could accurately predict the moves a chess master would make, we wouldn't say they're just good at statistics, we'd say they're a chess master. Similarly, regardless of how rich someone's internal world is, if they're unable to express the intelligent ideas they have in any intelligible way we wouldn't consider them intelligent.
So what we have now with AI are a few key parts of intelligence. One important thing to consider is how language can be a path to other types of intelligence; here's a blog post I stumbled across that really changed my perspective on that: http://www.asanai.net/2023/05/14/just-a-statistical-text-predictor/. Using your example of mathematics, as we know it falls apart doing anything remotely complicated. But when you help it approach the problem step-by-step in the way a human might - breaking it into small pieces and dealing with them one at a time - it actually does really well. Granted, the usefulness of this is limited when calculators exist and it requires as much guidance as a child to get correct answers, but even matching the mathematical intelligence of a ten year old is nothing to sneeze at.
To be clear I don't think pursuing LLMs endlessly will be the key to a widely accepted 'general intelligence'; it'll require a multitude of different processes and approaches working together for that to ever happen, and we're a long way from that. But it's also not just getting carried away with the hype to say the past few years have yielded massive steps towards 'true' artificial intelligence, and that current LLMs have enough use cases to change a lot of people's lives in very real ways (good or bad).
Thanks for that article, it was a very interesting read! I think we're mostly agreeing about things :) This stood out to me from there as an encapsulation of the conversation:
"Statistics" is probably an insufficient term for what these things are doing, but it's helpful to pull the conversation in that direction when a lay person using one of those things is likely to assume quite the opposite, that this really is a person in a computer with hopes and dreams. But I agree that it takes more than simply consulting a table to find the most likely next word to, to take an earlier example, write a haiku about Danny DeVito. That's synthesizing two ideas together that (I would guess) the model was trained on individually. That's very cool and deserving of admiration, and could lead to pretty incredible things. I'd expect that the task of predicting words, on its own, wouldn't be stringent enough to force a model to develop "true" intelligence, whatever that means, to succeed during training, but I suppose we'll find out, and probably sooner than we expect.
Well put! I think I kinda misunderstood what you were saying, I guess we sort of reached the same conclusion from different directions. And yeah, it does seem like we're hitting the limits of what can be achieved from the current underlying word-prediction mechanisms alone, with how diminishing the returns are from dumping more data in. Maybe something big will happen soon, but it looks to me like LLMs will stagnate for a while until they're taken in a fundamentally new direction.
Either way, what they can do now is pretty incredible, and equally interesting to me is how it's making us reevaluate our ideas of consciousness and intelligence on a large scale; it's one thing to theorize about what could happen with an 'intelligent' AI, but the reality of these philosophical questions being so thoroughly challenged and dissected in mundane legal and practical matters is wild.