> somebody figured out how to make a computer do something
Well, I would argue that in most deterministic AI systems the thinking was all done by the AI researchers and then encoded for the computer. That’s why historically it’s been easy to say, “No, the machine isn’t doing any thinking, but only applying thinking that’s embedded within.” I think that line of argument becomes less obvious when you have learning systems where the behavior is training dependent. It’s still fairly safe to argue that the best LLMs today are not yet thinking, at least not in a way a human does. But in another generation or two? It will become much harder to deny.
In many ways LLMs are a regression compared to what was before. They solve a huge class of problems quickly and cheaply, but they also have severe limitations that older methods didn't have.
So no, it's not a linear progress story like in a sci-fi story.
> It’s still fairly safe to argue that the best LLMs today are not ... thinking
I agree completely.
> But in another generation or two? It will become much harder to deny.
Unless there is something ... categorically different about what an LLM does and in a generation or two we can articulate what that is (30 years of looking at something makes it easier to understand ... sometimes).
> It’s still fairly safe to argue that the best LLMs today are not yet thinking, at least not in a way a human does. But in another generation or two? It will become much harder to deny.
Current LLMs have a hard division between training and inference time; human brains don’t-we train as we infer (although we probably do a mix of online/offline training: you build new connections while awake, but then pruning and consolidation happens while you sleep). I think softening the training-vs-inference division is a necessary (but possibly not sufficient) condition for closing the artificial-vs-human intelligence gap. But that softening is going to require completely different architectures from current LLMs, and I don’t think anyone has much of an idea what those new architectures will look like, or how long it will take for them to arrive
Well, I would argue that in most deterministic AI systems the thinking was all done by the AI researchers and then encoded for the computer. That’s why historically it’s been easy to say, “No, the machine isn’t doing any thinking, but only applying thinking that’s embedded within.” I think that line of argument becomes less obvious when you have learning systems where the behavior is training dependent. It’s still fairly safe to argue that the best LLMs today are not yet thinking, at least not in a way a human does. But in another generation or two? It will become much harder to deny.