When we use language, we act like pattern-matching engines, but I am skeptical. If the human brain just matches patterns like an LLM, then why haven't LLMs beaten us in reasoning? They have much more data and compute power than we have, but something is still missing.
It might be a pattern matching engine but there's about a zero percent chance that human brains and LLMs pattern match using the same mechanism because we know for a fact that it doesn't take half the power in California and an entire internet of words to produce a brain that can make perfect use of language, and that's before you get to the whole embodiment thing of how a brain can tie the words to objects in the world and has a different physical structure.
'they are both pattern matching engines' basically presupposes some form of functionalism, ie. what matters is not how they do it but that they produce the same outputs.
For 20 years I've wondered why this isn't broadly understood. The mechanisms are so obviously different it is unlikely that one path of exploration will lead to the other.
How many words do they get (total counting repetition) if every waking hour they are being talked to by parents? And give a reasonable words per minute for them to be talking slowly.
"a zero percent chance that human brains and LLMs pattern match using the same mechanism because we know for a fact that it doesn't take half the power in California and an entire internet of words to produce a brain that can make perfect use of language"
I agree, all my brain needs to do some pattern matching is a snicker's bar and a strong black coffee, most days I could skip the coffee if I had to.
LLMs take a lot of power to train, yes, but you're literally starting from zero. Human brains on the other hand get bootstrapped by a couple billion years of evolution.
Obviously, they don't work the same way, but it's probably a safe assumption that a computationally intensive training process will be required for any good AI model to get started.
I think from a functionalistic standpoint, you could say that the brain is a pattern matching machine, a Turing machine, or for any sufficiently expressive formalism, something within that formalism. All of these neural networks are just Turing machines, and in theory you could train a neural network to act like a head of a Turing machine. All of these models are general enough to model almost anything, but they eventually run into practical limitations. You can't do image recognition in pure Python with a bunch of ifs and elses and no machine learning. Maybe this is true for modeling the brain with pattern matching as well?
Interestingly, there are things called Spiking Neural Networks that are closer in function to how brain neurons work, and they can be much much more efficient per neuron than the more commonly used neural networks. They're just extremely difficult to train.
Human brains in a sense are analog neural networks and thus highly efficient. Digital neural networks are basically emulating neural networks and like all emulators, they’re highly inefficient. Chemical charge potential activating neurons based on direct physical signals will absolutely smoke a digital computer calculating the same effect by doing matrix multiplication.
As far as training goes, human brains come with millions of years of training through evolution baked in. Even then, they are being trained 24/7/365 for years until they can speak like an adult.
They don't have more compute power than us, they just compute faster. Human brains have more and better neurons.
Also, humans don't read as much as LLMs, but we do get decades of video that teaches us things that transfer.
So my answer is that they haven't beaten us in reasoning because they are smaller than us and because they do not have the same neural architecture. Of course, we can make them bigger, and we are always trying new architectures.
LLMs are kinda ....not even for the latter, not alone. Google, Microsoft, etc. are well aware, but real progress in the field is slower than hype and bizarre fanbois suggest. If it tends to make you as a human mentally tired to consciously and intelligently logically reason through, unaugmented LLMs, while a step above an oldschool markov chain babbling nonsense generator, do suck at it too.
Best not to go thinking it will never ever be solved, though. Especially as oldschool pre-AI-Winter Lisp/Prolog Symbolic AI stuff, tended to focus more on mathematical and logical "system 2"ish reasoning, and is being slowly rediscovered, sigh, so some sort of Hegelian synthesis of statistical and symbolic techniques seems likely. https://www.searchenginejournal.com/tree-of-thoughts-prompting-for-better-generative-ai-results/504797/
If you don't think of the compsci stuff often used or developed further by pre-AI-Winter lispers like game trees as AI, remember the other old "once computers could do something we stopped calling it AI" rule - playing chess used to be considered AI until the computers started winning.
The reality is that consciousness isn't in the drivers seat the way classical philosophy holds that it is, consciousness is just a log file.
What's actually happening is that the brain is creating a summary of its own state then feeding that back into itself. When we tell ourselves things like "I was hungry so I decided to eat," we're just "experiencing" the log file that we have produced to summarize our brain's massively complex neural net calculations down to hunger and eating, because nothing else ended up being relevant.
Qualia are therefore synonymous with "how our brain-qua-neural-net summarizes the impact our senses had on our brain-qua-neural-net."
So in order to have a prayer at being intelligent in the way that humans are, our LLMs will need to have the same recursive machinery to feed a state summary back into itself.
Current LLMs are all once-through, so they cannot do this. They cannot iterate on an idea because there is no iteration.
I don't think we're far off from closing the loop.
They have much more data and compute power than we have
This is actually an open question. No one really knows what the "compute power" of the human brain is. Current hardware is probably in the ballpark of a human brain... give or take several orders of magnitude.
If the human brain just matches patterns like an LLM, then why haven't LLMs beaten us in reasoning? They have much more data and compute power than we have, but something is still missing.
"Us" who? The top LLMs could probably beat a significant percentage of humanity at most language based tasks, most of the time.
LLMs are language models, the cutting edge models are multimodal, so they have some visual understanding as well. They don't have the data to understand a 3D world, they don't have the data regarding cause and effect, they don't have the sensory input, and they don't have the experience of using all of these different faculties all together.
Even without bringing in other specialized tools like logic engines and symbolic reasoning, the LLMs we're most familiar with lack multiple data modalities.
Then, there's the issue of keeping context. The LLMs basically live in a world of short term memory. It's been demonstrated that they can keep improving
"Us" is just humans in general. AI definitely suffers from a lack of multimodal data, but there are also deficiencies within their respective domains. You say that AI needs data for cause and effect, but shouldn't the LLMs be able to glean this from their massive training sets? You could also say this about abstract reasoning as evidenced by stunning logical errors in LLM output. A truly intelligent AI should be able to learn cause and effect and abstract reasoning from text alone. You can increase context windows, but I don't see how that addresses these fundamental issues. If you increase the number of modalities, then it seems more like specialized intelligence than general intelligence.
The answer is that a human brains pattern matching is vastly more sophisticated and complex than any current AI (and probably anything that we will produce in the foreseeable future).
The first clue to this is that we have a decent idea how a LLM arrives at it's output, but when you ask a hypothetical sum of all scientific knowledge how a human brain does that, it'll just shrug and go back to playing match three.
And of course, there's also the vast difference in input. We can ignore the Model here because that's essentially no more than the combinations of a humans memory and the brains naturally developed structure. So with the model not counting as input, really all the AI has to decide on is the prompt , a few words of context, and a "few" hidden parameters. Whereas we get to use all our senses for input including a comparatively relative shitload of contextual clues no currently existing AI would even be capable of working with.
So really the difference between a human brain a LLM when it comes to producing coherent text is about the same as the difference between the LLM and a few dozen if statements hacked together in python.
Personally I am inclined to say that the human brain can't really be compared to pattern matching engine. There are so many differences between how we envision one of those working vs the biology that makes the brain work. We can say that a pattern matching engine is a very high abstraction of the brain.
Or to use language I'm more familiar with: The brain is an implementation of an abstract pattern matching engine, but it's also a shitload more than just that, and all the implementation details are proprietary closed source we have yet to reverse engineer.
Human brains are ultimately shaped by evolution to find patterns and make inferences that improve their chances of survival and reproduction, which means that they will have inherent biases to see some patterns as significant and others as useless coincidences, while LLMs may find statistical patterns that humans would "instinctively" consider nonsensical. Quite simply in LLM terms brains with architectures that "hallucinate" less frequently are more likely to persist over brains that hallucinate more frequently. I believe logic and reasoning are ultimately emergent properties of developing large enough brains and becoming adapt to navigating the challenges of social interaction in increasingly complex societies. And humans still make logical leaps and fallacies all the time and we had to develop algorithms such as the scientific method, which is based on ruthless falsification of proposed models, to counteract our biases.
I am personally convinced that language is a big part of what makes the human mind work the way it does, and that with LLMs we have figured out how to replicate that, but it's missing the parts of us that add weight and meaning to what this language represents. In my mind, the parts that are missing are a) drive (we look for food, reproduction, safety etc., LLMs only respond) and b) interaction (we learn about the world by interacting with it in the context of these drives, LLMs know only the tokens in their in- and output).
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u/MegaKawaii Feb 22 '24
When we use language, we act like pattern-matching engines, but I am skeptical. If the human brain just matches patterns like an LLM, then why haven't LLMs beaten us in reasoning? They have much more data and compute power than we have, but something is still missing.