The set of tasks like that is simply traditional computer science. AGI is defined as doing what the brain does very efficiently, not doing what computers are already good at.
Don’t dismiss these tasks just by saying they aren’t part of AGI by definition.
The human brain is reasonably good at some tasks and utterly hopeless at others. The tasks early crude computers got turned to were mostly the places where the early crude computers could compete with brains, ie the tasks brains were hopeless at. So the first computers did arithmetic because brains are really really bad at arithmetic so even vacuum tubes were an improvement.
The modern field of AI is what is left when all the tasks that it is easy to do perfectly are removed.
Suppose someone finds a really good algorithm for quickly finding physics equations from experimental data tomorrow. No the algorithm doesn’t contain anything resembling a neural network. Would you dismiss that as “Just traditional computer science”? Do you think this can’t happen?
Imagine a hypothetical world in which there was an algorithm that could do everything that the human brain does better, and with a millionth of the compute. If someone invented this algorithm last week and
AGI is defined as doing what the brain does very efficiently, not doing what computers are already good at.
Wouldn’t that mean no such thing as AGI was possible. There was literally nothing the brain did efficiently, it was all stuff computers were already good at. You just didn’t know the right algorithm to do it.
Imagine a hypothetical world in which there was an algorithm that could do everything that the human brain does better, and with a millionth of the compute.
Based on the evidence at hand (as summarized in this article) - we probably don’t live in that world. The burden of proof is on you to show otherwise.
But in those hypothetical worlds, AGI would come earlier, probably well before the end phase of Moore’s Law.
I was using that as a hypothetical example to show that your definitions were bad. (In particular, the attempt to define arithmetic as not AI because computers were so much better at it.)
I also don’t think that you have significant evidence that we don’t live in this world, beyond the observation that if such an algorithm exists, it is sufficiently non-obvious that neither evolution or humans have found it so far.
A lot of the article is claiming the brain is thermodynamically efficient at turning energy into compute. The rest is comparing the brain to existing deep learning techniques.
I admit that I have little evidence that such an algorithm does exist, so its largely down to priors.
also don’t think that you have significant evidence that we don’t live in this world, beyond the observation that if such an algorithm exists, it is sufficiently non-obvious that neither evolution or humans have found it so far.
FWIW, I totally think that mental savants like Ramanujan (or “ordinary” geniuses like von Neumann) make a super-strong case for the existence of “algorithms evolution knows not”.
(Yes, they were humans, and were therefore running on the same evolutionary hardware as everybody else. But I don’t think it makes sense to credit their remarkable achievements to the hardware evolution produced; indeed, it seems almost certain that they were using that same hardware to run a better algorithm, producing much better results with the same amount of compute—or possibly less, in Ramanujan’s case!)
The set of tasks like that is simply traditional computer science. AGI is defined as doing what the brain does very efficiently, not doing what computers are already good at.
Don’t dismiss these tasks just by saying they aren’t part of AGI by definition.
The human brain is reasonably good at some tasks and utterly hopeless at others. The tasks early crude computers got turned to were mostly the places where the early crude computers could compete with brains, ie the tasks brains were hopeless at. So the first computers did arithmetic because brains are really really bad at arithmetic so even vacuum tubes were an improvement.
The modern field of AI is what is left when all the tasks that it is easy to do perfectly are removed.
Suppose someone finds a really good algorithm for quickly finding physics equations from experimental data tomorrow. No the algorithm doesn’t contain anything resembling a neural network. Would you dismiss that as “Just traditional computer science”? Do you think this can’t happen?
Imagine a hypothetical world in which there was an algorithm that could do everything that the human brain does better, and with a millionth of the compute. If someone invented this algorithm last week and
Wouldn’t that mean no such thing as AGI was possible. There was literally nothing the brain did efficiently, it was all stuff computers were already good at. You just didn’t know the right algorithm to do it.
Based on the evidence at hand (as summarized in this article) - we probably don’t live in that world. The burden of proof is on you to show otherwise.
But in those hypothetical worlds, AGI would come earlier, probably well before the end phase of Moore’s Law.
I was using that as a hypothetical example to show that your definitions were bad. (In particular, the attempt to define arithmetic as not AI because computers were so much better at it.)
I also don’t think that you have significant evidence that we don’t live in this world, beyond the observation that if such an algorithm exists, it is sufficiently non-obvious that neither evolution or humans have found it so far.
A lot of the article is claiming the brain is thermodynamically efficient at turning energy into compute. The rest is comparing the brain to existing deep learning techniques.
I admit that I have little evidence that such an algorithm does exist, so its largely down to priors.
FWIW, I totally think that mental savants like Ramanujan (or “ordinary” geniuses like von Neumann) make a super-strong case for the existence of “algorithms evolution knows not”.
(Yes, they were humans, and were therefore running on the same evolutionary hardware as everybody else. But I don’t think it makes sense to credit their remarkable achievements to the hardware evolution produced; indeed, it seems almost certain that they were using that same hardware to run a better algorithm, producing much better results with the same amount of compute—or possibly less, in Ramanujan’s case!)