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> I don't know how relevant this argument even is anymore considering I was wrong about the move rate. Is 1 in ~1650 really underperforming a 1800 Elo Human ? H

> I don't know how relevant this argument even is anymore considering I was wrong about the move rate. Is 1 in ~1650 really underperforming a 1800 Elo Human ? How much so ? Yes, I would still be surprised if an 1800 elo human makes 1/1650 illegal moves during formal matches. > If "first principles" were what we thought they were, we wouldn't be using GPT or artificial neural networks for nearly all computer intelligence tasks. We would be using symbolic systems. This would only be true if you believe in a singular definition of intelligence where you believe there is only one right way to get an answer. There are lots of reasons we don't train AI from first principles, one of the biggest being that neural networks are just flat-out easier to train. Training from first principles is annoying and time consuming and requires either an understanding of first principles or some simulation or methodology to consistently demonstrate those principles to the AI. That is hard to do. But first principles and predicate logic haven't failed as a way to accomplishing tasks or as a way to reason about problems. First principles are ill-suited for an extremely specific field. And now people are acting like however AIs work that has to necessarily match how humans learn? That's not the case, what I am getting at when I talk about diversity of intelligence is that you don't need to map to human reasoning in order to build tools that are useful and that generally solve certain categories of tasks, sometimes even better than humans do. It's incredibly limiting to define what kinds of intelligence do and don't exist in the world based purely on what is useful for training an AI. ---- Humans observably do not learn the same way as GPT does, we do not start out by learning language and then logic, we do the opposite. Any study of any infant will show you that humans develop logical capabilities before we develop language skills. However that process is happening, it factually, observably happens in the opposite order than what we see occurring in LLMs. We do not need to know exactly how that process works to know that it is different from how GPT learns. And we certainly don't need to look at a poorly understood process and assume that it necessarily must match whatever the current state-of-the-art methodology we have to simulate it. As you accurately say, we do not know how to define intelligence broadly -- so it's wildly reductive to say "but we do know how to build neural networks, and when we tried to replicate first principles or composite logic that didn't work well, so probably that doesn't exist" -- I just don't view that as a serious take. I still feel like you're falling into the trap of getting really invested into showing that GPT is meeting some human standard of reasoning, and I don't understand why it matters whether or not GPT is meeting that standard. Why is it a problem to say, "we don't understand how humans work, but we're seeing surprising differences between GPT's capabilities and the capabilities of comparable humans, and that suggests there is something very different going on under the hood."? Does that actually make GPT any less interesting or useful? I don't think so.

famouswaffles 3y ago
>This would only be true if you believe in a singular definition of intelligence where you believe there is only one right way to get an answer. The whole idea of the first principles movement was that it was the "right way" anchored by how humans supposedly approached problems and many of these brilliant minds thought intelligence could not exist without "symbolic manipulation". Well it wasn't "the right way" because it mostly failed and even the little niches it did work out produced systems that didn't behave anything like we did. Anything other than clear definitions and unambiguous axioms (aka the real world) and logic systems fall apart. I don't need to believe there is only one path to intelligence (I don't) to see the first principle way just doesn't cut it. General Intelligence will never happen this way. Narrow real world intelligence will never happen this way. Decades of the brightest minds toiling and some people still don't seem to get it. Logic scales poorly if your problem space isn't exactly defined. >There are lots of reasons we don't train AI from first principles, one of the biggest being that neural networks are just flat-out easier to train. The single biggest reason is that it doesn't work for most aspects of intelligence we observe in the real world. >Training from first principles is annoying and time consuming, but it hasn't failed as a way for accomplishing tasks Yes it has. >it was ill-suited for an extremely specific field. Ah yes, the very specific fields of language, vision and speech synthesis and understanding, reinforcement learning, OCR and many more. These were all things attempted with GOFAI systems prior to NNs. Not only that, these systems had decades of a headstart. Symbolic systems were not the underdog here. They were discarded because they struggled to succeed. Symbolic NLP had gone by the wayside long before even transformers and GPT arrived. That's how poor they were. Like Frederick Jelineck famously said, "Every time I fire a linguist, the performance of the system goes up." First principles are an idea that never actually left the realm of fiction. >Humans observably do not learn the same way as GPT does, we do not start out by learning language and then logic, we do the opposite. This doesn't actually matter. A language model is trying to predict the next token. It is trying to reverse engineer the computation that could have led to an utterance. It is trying to model the casual processes that led to the text. It's not trying to draw shadows. It's trying to build the object whose shadow is a result. I'm not saying this means GPT is a human or is doing everything the human way(whatever that may be) but gradient descent to predict a language corpus and evolution after millions of years(which is itself just another dumb optimizer not first principling anything) can converge on the same solution. Babies will communicate coherently much earlier in a non speaking language so vocal production is obviously a huge barrier that has nothing to do with language understanding itself. Humans not formally trained in logic are typically very bad at it. We're obviously not logic creatures. This is exactly my point. How humans think they work are how they actually work are very different things entirely.

famouswaffles 3y ago

>This would only be true if you believe in a singular definition of intelligence where you believe there is only one right way to get an answer. The whole idea of the first principles movement was that it was the "right way" anchored by how humans supposedly approached problems and many of these brilliant minds thought intelligence could not exist without "symbolic manipulation". Well it wasn't "the right way" because it mostly failed and even the little niches it did work out produced systems that didn't behave anything like we did. Anything other than clear definitions and unambiguous axioms (aka the real world) and logic systems fall apart. I don't need to believe there is only one path to intelligence (I don't) to see the first principle way just doesn't cut it. General Intelligence will never happen this way. Narrow real world intelligence will never happen this way. Decades of the brightest minds toiling and some people still don't seem to get it. Logic scales poorly if your problem space isn't exactly defined. >There are lots of reasons we don't train AI from first principles, one of the biggest being that neural networks are just flat-out easier to train. The single biggest reason is that it doesn't work for most aspects of intelligence we observe in the real world. >Training from first principles is annoying and time consuming, but it hasn't failed as a way for accomplishing tasks Yes it has. >it was ill-suited for an extremely specific field. Ah yes, the very specific fields of language, vision and speech synthesis and understanding, reinforcement learning, OCR and many more. These were all things attempted with GOFAI systems prior to NNs. Not only that, these systems had decades of a headstart. Symbolic systems were not the underdog here. They were discarded because they struggled to succeed. Symbolic NLP had gone by the wayside long before even transformers and GPT arrived. That's how poor they were. Like Frederick Jelineck famously said, "Every time I fire a linguist, the performance of the system goes up." First principles are an idea that never actually left the realm of fiction. >Humans observably do not learn the same way as GPT does, we do not start out by learning language and then logic, we do the opposite. This doesn't actually matter. A language model is trying to predict the next token. It is trying to reverse engineer the computation that could have led to an utterance. It is trying to model the casual processes that led to the text. It's not trying to draw shadows. It's trying to build the object whose shadow is a result. I'm not saying this means GPT is a human or is doing everything the human way(whatever that may be) but gradient descent to predict a language corpus and evolution after millions of years(which is itself just another dumb optimizer not first principling anything) can converge on the same solution. Babies will communicate coherently much earlier in a non speaking language so vocal production is obviously a huge barrier that has nothing to do with language understanding itself. Humans not formally trained in logic are typically very bad at it. We're obviously not logic creatures. This is exactly my point. How humans think they work are how they actually work are very different things entirely.