What’s holding back AGI is a complete lack of progress toward anything like intelligence. What we have now isn’t intelligent, it’s multi-variable probability.
It’s not that it’s not intelligent, it’s that predictive language models are obviously just one piece of the puzzle, and we’re going to need all the pieces to get to AGI. It’s looking incredibly doable if we figured out how to make something that’s dumb but sounds smarter than most of us already. We just need to connect it to other models that handle other things better.
Really appreciate the write up! I didn’t know the computing power required!
Another stupid question (if you don’t mind) - adding superconductors to GPUs doesn’t really se like it would make a huge difference on the heat generation. Sure, some of the heat generated is through trace resistance, but the overwhelming majority is the switching losses of the transistors which will not be effected by superconductor technology. Are we assuming these superconductors will be able to replace semiconductors too? Where are these CPU/GPU efficiencies coming from?
Semiconductors are used for transistors because they give us the ability to electrically control whether they conduct or resist electrical current. I don’t know what mechanism you’d use to do that with superconductors. I agree you don’t ‘have’ to have resistance in order to achieve this functionality, but at this time semiconductors or mechanical relays are the only ways we have to do that. My focus is not in semiconductor / IC design either so I may by way off base, but I don’t know of a mechanism that would allow superconductors to function as transistors (or “electrically controlled electrical connections”), but I really hope I’m wrong!
Simply throwing computing power at the existing models won’t get us general AI. It will let us develop bigger and more complex models, but there’s no guarantee that’ll get us closer to the real thing.
Stupid question probably - is computing power what is holding back general AI? I’ve not heard that.
What’s holding back AGI is a complete lack of progress toward anything like intelligence. What we have now isn’t intelligent, it’s multi-variable probability.
It’s not that it’s not intelligent, it’s that predictive language models are obviously just one piece of the puzzle, and we’re going to need all the pieces to get to AGI. It’s looking incredibly doable if we figured out how to make something that’s dumb but sounds smarter than most of us already. We just need to connect it to other models that handle other things better.
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You don’t speak predictively. It’s not one of the pieces, it’s a parlor trick.
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Really appreciate the write up! I didn’t know the computing power required!
Another stupid question (if you don’t mind) - adding superconductors to GPUs doesn’t really se like it would make a huge difference on the heat generation. Sure, some of the heat generated is through trace resistance, but the overwhelming majority is the switching losses of the transistors which will not be effected by superconductor technology. Are we assuming these superconductors will be able to replace semiconductors too? Where are these CPU/GPU efficiencies coming from?
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Semiconductors are used for transistors because they give us the ability to electrically control whether they conduct or resist electrical current. I don’t know what mechanism you’d use to do that with superconductors. I agree you don’t ‘have’ to have resistance in order to achieve this functionality, but at this time semiconductors or mechanical relays are the only ways we have to do that. My focus is not in semiconductor / IC design either so I may by way off base, but I don’t know of a mechanism that would allow superconductors to function as transistors (or “electrically controlled electrical connections”), but I really hope I’m wrong!
Simply throwing computing power at the existing models won’t get us general AI. It will let us develop bigger and more complex models, but there’s no guarantee that’ll get us closer to the real thing.