Oh, I guess I read transistor. Not transformer. Thanks!
Yeah, a brain does exactly what it’s supposed to do. I can’t imagine how simulating something with alternative hardware could be any more efficient than the original, especially when that new hardware wasn’t designed to do the same thing.
That’s still talking about transistors, though. Even more directly, transformers are just translating text into high dimensional arrays where the semantic structure is captured (relative to all other possible embedding values). It’s an interesting approach to navigating semantic information, but there’s not any guarantee that our brain does the same thing. Either way, I’d bet our brain is doing its job without much accidental complexity, whereas modern transformers will always have the added complexity of encoding / decoding semantic information using hardware not designed for it.
Computer should maybe try staying in its lane. Leave the cognitive dissonance and delusional confidence to the experts.
Yeah, I said transformer because that seems to be the state of the art in AI architectures, but purpose built neural network hardware might not actually benefit from the same architecture.
A neural network made from analog hardware could theoretically replace a significant portion of an LLM’s processing and not be limited by things like floating point precision or clock speeds. Who needs floating point when you can literally just multiply voltages together with a couple transistor junctions at the speed of light?
Oh, I guess I read transistor. Not transformer. Thanks!
Yeah, a brain does exactly what it’s supposed to do. I can’t imagine how simulating something with alternative hardware could be any more efficient than the original, especially when that new hardware wasn’t designed to do the same thing.
That’s still talking about transistors, though. Even more directly, transformers are just translating text into high dimensional arrays where the semantic structure is captured (relative to all other possible embedding values). It’s an interesting approach to navigating semantic information, but there’s not any guarantee that our brain does the same thing. Either way, I’d bet our brain is doing its job without much accidental complexity, whereas modern transformers will always have the added complexity of encoding / decoding semantic information using hardware not designed for it.
Computer should maybe try staying in its lane. Leave the cognitive dissonance and delusional confidence to the experts.
Yeah, I said transformer because that seems to be the state of the art in AI architectures, but purpose built neural network hardware might not actually benefit from the same architecture.
A neural network made from analog hardware could theoretically replace a significant portion of an LLM’s processing and not be limited by things like floating point precision or clock speeds. Who needs floating point when you can literally just multiply voltages together with a couple transistor junctions at the speed of light?