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There are two possibilities. 1. Either you are correct and the neural networks humans have are exactly the same or very similar to the programs in the LLMs. Th
by Yizahi 2y ago
There are two possibilities.
1. Either you are correct and the neural networks humans have are exactly the same or very similar to the programs in the LLMs. Then it will be relatively easy to verify this - just scale one LLN to the human brain neuron count and supposedly it will acquire consciousness and start rapidly learning and creating on its own without prompts.
2. Or what we call neural networks in the computer programs is radically different and or insufficient to create AI.
I'm leaning to the second option, just from the very high level and rudimentary reading about current projects. Can be wrong of course. But I have yet to see any paper that refutes option 2, so it means that it is still possible.
- barrell 2y agoI agree with your stance - that being said there aren’t two options, one being identical or radically different. It’s not even a gradient between two choices, because there are several dimensions involved and nobody even knows what Superintelligence is anyways. If you wanted to reduce it down, I would say there are two possibilities: 1. Our understanding of Neurel Nets is currently sufficient to recreate intelligence, consciousness, or what have you 2. We’re lacking some understanding critical to intelligence/conciousness. Given that with a mediocre math education and a week you could pretty completely understand all of the math that goes into these neurel nets, I really hope there’s some understand we don’t yet have
- shwaj 2y agoThere are layers of abstraction on top of “the math”. The back propagation math for a transformer is no different than for a multi-layer perception, yet a transformer is vastly more capable than a MLP. More to the point, it took a series of non-trivial steps to arrive at the transformer architecture. In other words, understanding the lowest-level math is no guarantee that you understand the whole thing, otherwise the transformer architecture would have been obvious.
- theGnuMe 2y agoWe know architecture and training procedures matter in practice. MLPs and transformers are ultimately theoretically equivalent. That means there is an MLP that represent the any function a given transformer can. However, that MLP is hard to identify and train. Also the transformer contains MLPs as well...
- barrell 2y agoI don’t disagree that it’s non-trivial, but we’re comparing this to conciousness, intelligence, even life. Personally I think it’s apples and an orange grove, but I guess we’ll get our answer eventually. Pretty sure we’re on the path to take transformers to their limit, wherever that may be