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this post was submitted on 14 Apr 2024
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Added to this finding, there's a perhaps greater reason to think LLMs will never deliver AGI. They lack independent reasoning. Some supporters of LLMs said reasoning might arrive via "emergent behavior". It hasn't.
People are looking to get to AGI in other ways. A startup called Symbolica says a whole new approach to AI called Category Theory might be what leads to AGI. Another is “objective-driven AI”, which is built to fulfill specific goals set by humans in 3D space. By the time they are 4 years old, a child has processed 50 times more training data than the largest LLM by existing and learning in the 3D world.
They can quite possibly be a useful component. They're the language center of the brain.
People who ever thought they would actually resemble intelligence were woefully uninformed of how complex intelligence is.
How complex is intelligence, though? People who were sure they don't were drawing from information we don't actually have.
Yeah, so many people are confidently stating "LLMs can't think like humans do!" When we're actually still pretty unclear on how humans think.
Sure, an LLM on its own may not be an AGI. But they're remarkably closer than we would have predicted they could get just a few years ago, and it may well be that we just need to add a bit more "special sauce" (memory, prompting strategies, perhaps a couple of parallel LLMs that specialize in different types of reasoning) to get them over the hump. At this point a lot of the research isn't going into simply "make it bigger!", it's going into "use LLMs smarter."
Obscenely.
The brain is stacks on stacks of insanely complicated systems. The fact that we know a ridiculous amount about the brain and are barely scratching the surface is exactly the point.
By that measure, we know everything about GPT-2, but again are just scratching the surface of how it works. I don't think you can draw the conclusion that LLMs can never be intelligent just from that.
We "know everything about it" because it's not that complicated.
You don't need to process every individual step a search algorithm has to understand how it works. LLMs are the same thing. They're just a big box of weighted probabilities. Complexity is more than just having a really big model.
We have bits and pieces of a lot of parts, but are nowhere near a complete understanding of any of them. We kind of know how neurotransmitters work, we kind of know how hormones work and interact with those neurotransmitters, we mostly know how individual neurons fire, we kind of know what different parts of the brain do, we kind of know how the brain adapts to physical damage...
We don't know any of the algorithms it follows. What we do know that it's a hell of a lot of interconnected parts, and they're all following very different rules.
It's not a search algorithm. If it is, that's an overfitted model, and it's detected and rejected. What a good foundation model is doing is just about as mysterious as the brain.
It's fundamentally extremely comparable mathematically and algorithmically. That's the point. Simulated annealing doesn't need to understand the search space to find a pretty good answer to a problem. It just needs to know what a good answer approximately looks like and nudge potential answers closer that way.
What LLMs are doing is not mysterious at all. Why a specific point in a model is what it is is, but there's no mystery to the algorithm. We can't even guess at most of the algorithms that make up the brain.
Simulated annealing is a search algorithm which finds a solution.
Backpropagation is a search algorithm which finds a function, which in a big enough network could be literally any of them that are computable. Once the network is trained and rolls out for consumers, backpropagation isn't used at all.
Those are two fundamentally different things. GPT-2 is trained, and is no longer a search algorithm by any useful definition. There's examples of small neural nets we can understand, and they're not doing search algorithms; Quanta did a story about some just last week. If you can do simulated annealing you should probably just look into NN algorithms in detail yourself, because then you can know how that's wrong without the internet's help.
I'm not calling it a search algorithm. I'm saying they all do the same math, and doing the math with more parallelism and variables doesn't make what it is a mystery.
Search algorithms searching for functions isn't new. Not knowing what each parameter corresponds to because you made your model huge doesn't make LLMs a mystery. It's still functionally one part. The hormone system is as complex as LLMs. Regulation of neurotransmitters is as complex as LLMs. Ignoring those external factors that are critical to how it works, individual portions of the brain are more complex than LLMs, then are all interconnected on top of that.
I fully believe we'll get to AGI eventually (probably not before we understand the brain a lot better), but the idea that one pretty simple algorithm is going to get us there is crazy. Human intelligence is a system of disparate systems of disparate systems at minimum.
So does having more parts make something a mystery, like the second paragraph, or not a mystery like the first?
I was a skeptic back in the day too, but they've already far exceeded what an algorithm I could write from memory seems like it should be able to do.
A combination of unique, varied parts is a complex algorithm.
A bunch of the same part repeated is a complex model.
Model complexity is not in any way similar to algorithmic complexity. They're only described using the same word because language is abstract.
So I guess it comes down to a neurology question. How much algorithmic complexity have we found in the brain?
As far as I'm aware, we've found a few islands of neurons that work together in an obvious way, to track location on a grid for example, and hormone cycles that form a nice negative feedback loop, to keep you at an acceptable blood-sugar level for example. Most of it is still a mystery glob of neurons and other cells, albeit with a fixed pattern of layers and folds.
If we had measured massive algorithmic complexity in the brain, I'd agree with you. As it is, though, it seems unclear how much of the structure we see is conventional algorithm, and how much is the equivalent of an ANN architecture, that ultimately does the same job as no structure but learns more efficiently, or even is just a biological spandrel.
I wonder where the line is drawn between an emergent behavior and a hallucination.
If someone expects factual information and gets a hallucination, they will think the llm is dumb or not helpful.
But if someone is encouraging hallucinations and wants fiction, they might think it's an emergent behavior.
In humans, what is the difference between an original thought, and a hallucination?
Hallucinations are unlike Human creative output. For one, ai hallucinations are unintentional. There's plenty of reasons if you actually think about the question why they are not the same. They are at best dreamlike, but dreams are an intentional process.
Sure there is intentional creative thought. But there are also unintentional creative thoughts. Moments of clarity, eureka moments, and strokes of inspiration. How do we differentiate these?
If we were to say that it is because of our subconscious is intentionally promoting these thoughts. Then we would need a method to test that, because otherwise the difference is moot.
Similar to how one might define the
IinAGIit's hard to form a consensus on general and often vague definitions like these.You are assigning far more vague grandeur to ai hallucinations than what they are in practice.
Maybe it's this arbitrary word,
hallucination? Which was recently borrowed from the human experience to explain why something which normally is factual like a computer is not computing facts.But if one were to think about it, what is the difference between a series on non factual hallucinations in a model and a person's individual experience of the world?
Before, we called these bugs or even issues. But now that it's in this black box of sorts that we can't alter the decision making process of as directly as before. There is this more human sounding name all of a sudden.
To clarify, when an llm gets a fact wrong because it has limited context or because it's foundational model is flawed, is that the same result as the experience someone has after consuming psychedelic mushrooms? No, I wouldn't say so. Nor is it the same when a team of scientists try to make a model actively hallucinate so they can find new chemical compounds.
Defining words can sometimes be very tricky, especially when they are applying to multiple areas of study. The more you drill into a definition, the more it becomes a metaphysical debate. But it is important to have these discussions because even the definition of something like
AGIkeeps changing. And infact only exist because the goal posts for aAImoved so much. What will stop a company which is trying to attract investors from just slapping anAGIlabel on their next release? And how will we differentiate what the spirit of the word is trying to convey from the sales pitch?Hallucinations are not qualia.
Please go talk to an llm for hallucinations, you can use duck duck gos implementation of chatgpt, and see why it's being used to mean a fairly different thing from human hallucinations.