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I thought about this too. People say that LLM models are only saying the most common tokens that come after the previous token. And that this makes them incompa
by volent 3y ago
I thought about this too. People say that LLM models are only saying the most common tokens that come after the previous token. And that this makes them incomparable to human intelligence.
But Humans are basically long running LLMs that are retrained in real-time. We are the product of our environment.
- 4death4 3y agoWhat makes you think that? What we can LLMs are a specific architecture that literally does predict the next token, one at a time. This isn’t the only way to generate text. Perhaps humans use a method that synthesizes all the text at once. Or some other, unimaginable way.
- FrustratedMonky 3y agoNot sure about everyone else, but I write one word at a time.
- the_other 3y agoYou may write one word at a time but the grammar of most languages forces their users to know what they’re going to write several words ahead of the current word.
- FrustratedMonky 3y agoOk. Why do you think GPT isn't doing that? or is different? It might be calculating the next word, because you can only write one word at a time, but you can't say that the current next word isn't influenced by a few words ahead. Don't think the current understanding of what LLM's do internally can rule that out.
- JKCalhoun 3y agoAnd often unsure about where I'm going until I get there, ha ha.
- simondotau 3y agoTo be fair, I rarely write more than a sentence or two in serial form, and I have often determined the point of a sentence before I write the first few words. Occasionally the act of writing out an idea in words clarifies or changes my mind about that idea, causing me to edit or rewrite what I’ve already written.
- deleted 3y ago[deleted]
- ofjcihen 3y agoBut are you writing to write or are you writing to convey a thought?
- dartos 3y agoYeah but do you write that one word based on the past 1000 or so words that come before it and how each of those words relate to each other with a perfect recollection of each word at the same time? And if you believe you do that subconsciously, then how can you be sure you don’t subconsciously plan a few words ahead?
- FrustratedMonky 3y agoI don't think I do. To a large degree, GPT does write better.
- dartos 3y agoWrite better as is has more correct and clear syntax, sure. But it doesn’t write better as in has new, challenging ideas. Or ways to move/relate to humans on a personal level. GPT writes clear, concise, authoritative, boring, and generic text.
- deleted 3y ago[deleted]
- Qwertious 3y agoWe ~~all~~ mostly do that partially, but some of us plan our comments ahead and go back and edit previous parts of it after review.
- taeric 3y agoAlmost certainly not true. Consider pronouns, as they reference other words, therefore cannot be written in isolation. Or simple rules about when to use "an" or "a". Shaping your words to sound correct is very common. Both in speech and in writing. Sometimes it is finding how to fit a word you want to use into a sentence. Sometimes it is building a rhyme. You may feel that you go a word at a time, but that really shows how embedded language is.
- FrustratedMonky 3y agoRight now we don't know if LLM's are also doing this, or not. In their calculating the 'next' word, as part of that 'weighting', are the 'simple rules' for future words, that you are saying humans do but LLM's can't.
- taeric 3y agoAh, fair. I was only commenting on the idea that you write one word at a time. Seems fairly unlikely to me. You expand out ideas.
- FrustratedMonky 3y agoLOL. Yes, started as a joke.
- 4death4 3y agoIt doesn't seem LLMs are capable of "understanding" that they don't "know" something, so there are certainly some observable differences. But the intent of my original comment was precisely to highlight that we don't know. It could be that next token predication is somehow mathematically equivalent to what we call consciousness. That would certainly be a revelation.
- ben_w 3y agoI seem to have two parts of my inner monologue, one comes up with complete concepts, the other puts them into words. When I started to notice this, I tried skipping the "make words" part to save time, as clearly I had not only had the thought but also was aware that I had already had the thought. This felt wrong in a way I have no words to describe as there's nothing else like that particular feeling of wrongness, though I can analogise it as being almost but not quite entirely unlike annoyance. Anyway, point is 80% of this comment was already in my head before I started typing; the other 20% was light editing and an H2G2 reference.
- FrustratedMonky 3y agoSounds kind of like some Zen concepts about moving beyond language. And that language is a faulty method for communicating.
- TerrifiedMouse 3y agoUsing that logic regular computers are AI too. Output is dependent on input - i.e. they are a product of their environment.
- xwowsersx 3y agoThe claim that "humans are basically long running LLMs" oversimplifies the complexity of human cognition and experience. Humans don't just process information; they experience emotions, desires, and subjective experiences that are deeply intertwined with their cognition. LLMs don't have feelings, motivations, or consciousness. Humans have inner subjective experience, self-awareness, and the ability to reflect on our own existence. LLMs don't. Humans can adapt to a wide range of environments and situations, drawing from a complex interplay of instincts, learned behaviors, emotions, and rational thought. LLMs are much more limited in their adaptability, since they focus primarily on the tasks they were designed for. Human cognition has evolved over millions of years and is rooted in a complex biological system, the brain. Yes, both LLMs and human brains process information, but the underlying mechanisms, structures, and functions are vastly different. I really wish people would stop this sort of cavalier reductionism of humans by saying we are basically LLMs. It isn't true.
- JKCalhoun 3y agoI believe though a significant part of our mind is very much like an LLM. Not all of it of course.
- geraldwhen 3y agoYes, the parts of the brain attempting to apply correct grammar to a given idea are likely quite similar. As in, the part with producing language. I.e large language model.
- specialist 3y ago> Humans have inner subjective experience, self-awareness, and the ability to reflect on our own existence. "In the end, we are self-perceiving, self-inventing, locked-in mirages that are little miracles of self-reference." — Douglas Hofstadter, I Am a Strange Loop, p. 363 https://en.wikipedia.org/wiki/I_Am_a_Strange_Loop https://en.wikipedia.org/wiki/I_Am_a_Strange_Loop (Knowing nothing about AI, I have no idea how Hofstadter's philosophies have held up since.)
- ben_w 3y ago
- dartos 3y agoYou’re vastly oversimplifying humans. That’s like saying a physics simulation is basically an entire sub universe on your computer. It sounds true, but it’s just not. It’s a gross oversimplification I think Gödel had a proof for how it’s impossible to fully describe a system from within that system. That’s the nail in the coffin for AGI. No matter how much data we give it, no matter how big it is, it’ll never be “human intelligent” since it’s impossible for us to describe a loss function for being human or describe being human in a dataset. We’ll never be able to evaluate it, since we can’t fully describe what it means to communicate because to do that we’d need to communicate it and that process can’t be fully self describing. Not to say AI isn’t useful or impressive, but it’ll never be comparable to humans, truly.
- andsoitis 3y ago> I think Gödel had a proof for how it’s impossible to fully describe a system from within that system. Gödel's incompleteness theorems https://en.wikipedia.org/wiki/G%C3%B6del%27s_incompleteness_theorems https://en.wikipedia.org/wiki/G%C3%B6del%27s_incompleteness_...
- nebulousthree 3y agoIt doesn't have to be perfect. It just has to be convincing. That takes a lot less data and is much more tolerant to simplifications. Just look at the claims that were thrown around GPT's abilities at first.
- deleted 3y ago[deleted]
- Timon3 3y ago> I think Gödel had a proof for how it’s impossible to fully describe a system from within that system. That’s the nail in the coffin for AGI. Gödels theorems are about formal axiomatic theories. To apply them to human intelligence, you'd have to prove that human intelligence springs from formal axiomatic theories. I don't think this is possible, which would mean that you can't apply the theorems. > No matter how much data we give it, no matter how big it is, it’ll never be “human intelligent” since it’s impossible for us to describe a loss function for being human or describe being human in a dataset. How do you know? If we were able to fully record whatever is going on in someones brain, we should be able to build a loss function for it. How do you know that this is fundamentally impossible? > We’ll never be able to evaluate it, since we can’t fully describe what it means to communicate because to do that we’d need to communicate it and that process can’t be fully self describing. Why not? Again, if you argue that this is due to Gödels theorems, you'd have to prove that our communication itself is based on formal axiomatic theories.
- deleted 3y ago[deleted]
- danShumway 3y agoSometimes people use LLMs very broadly to talk about neural networks overall. But to be clear, humans don't have emergent reasoning from language, we learn language as part of our overall reasoning. The short evidence for that being that children are capable of solving logic and spatial puzzles before they learn how to speak. Humans learn concepts like object permanence before we learn language complicated enough to describe that concept. And obviously people are capable of reasoning without learning how to write or interpret text tokens, there are plenty of illiterate people in the world who are nonetheless indisputably intelligent agents. So ignoring other differences about how prediction works, humans are not similar to LLMs in the sense that LLMs are language models that when large enough either develop (or appear to develop depending on who you ask) reasoning capabilities. And that's not how humans work; we don't learn text tokens before we learn how to reason. But very often when people make this claim they're trying to make a broader claim about neural networks or the role of prediction in learning in general. People might disagree or agree with the broader claim, I still think it oversimplifies how humans work, but the point is -- they're not actually saying something specific about LLMs, even though it sounds that way sometimes. It's just that the terminology gets conflated in people's heads. We can have a debate about the similarities and differences between humans and neural networks, but I don't think anyone would seriously claim that GPT-4 in specific works the same way as a human does. I think people are using LLMs to refer to a broader category of AI research.
- FrustratedMonky 3y agoDefinitely, LLM's have gotten so much press, that many people arguing about 'AI', are thinking about LLM. And, LLM's are not all that a human can do. Language is not everything about a human. But there is an argument that there is part of the brain that produces language, and it has some LLM characteristics. It's just that the brain is bigger and does more than an LLM. So the brain is not an LLM. The brain has many components. What happens when you take the problem solving of something like AlphaGo/AlphaStar, with the Vision processing in Cars or DaLLe, and the language processing in LLM. Add in hearing, touch. It starts to look like the components of a brain.
- danShumway 3y ago