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Oh, we know it's weighted connections. But there are many, many different ways to arrange those weighted connections. Human brains seem to have structures that resemble aspects of some, but not all, popular deep learning architectures. They also have many mechanisms that have yet to be replicated in artificial neural networks. For example, I continue to question two propositions that many others seem to take for granted when they try to predict what LLMs can and cannot do well: 1. LLMs can do generalized symbolic reasoning.
2. If a human does it symbolically, that's how it must be done.
Over the past couple years I've grown to be much more sympathetic to Searle's Chinese Room argument. LLMs are incredibly good at mimicking human behavior and performing tasks that were previously impossible for machines. But as you examine what they're doing more closely you start to see them failing in all sorts of interesting ways that remind you that they're still very much in an uncanny valley of sorts.Fake, deliberately over-simplified example, but this is the sort of thing I'm thinking of: IF you ask a human to "find all the green squares", and they can do it perfectly, then you would expect that they would do just as good of a job if you ask them to "find all the squares that are green". That sort of expectation does not work with GPT-4. Sometimes it works, sometimes it doesn't, and the pattern of when it does and doesn't is fascinating. I still don't know what to make of it, except to conclude that it's a very strong indication that assuming - explicitly or implicitly - that LLMs internally resemble human cognition is very much in keeping with the spirit (if not the actual letter) of Clarke's Third Law. |
Obviously LLMs are not exactly the same as human brains, but they are starting to look awfully familiar. And not all human brains are the same! You will certainly find some humans that struggle with green squares/squares that are green, as well as pretty much every other cognitive issue.