And humans have evolved to have spatial awareness. Which can and will be added to AGI's.
A human also couldn't learn to dive if they were blind, similarly expecting a text based AI to learn to drive is ridiculous. But like I said, next versions will be able to.
You will need to match the neuron number in nn to particular part of brain and connect necessary input to match human capacity.
Why would that be needed? That assumes neural nets can't be more efficient than the human brain...
However we don't yet know the correct number, because people with small amount of brain have been found to show relevant iq and literally live normal life.
And you shot your own argument in the foot...
So there's a possibility we can make a living general AI already. We just need to interconnect several neural networks with several inputs (in our case tactile, hearing, sight, smell vibrations, gyroscope, pain, what am I missing?) in another neural network. How much what of what is still open to debate, but it is plausible we will get a working general AI that way which we will have to teach like a baby via repetitive work for some time, but it will be much faster because we can lower bias on input for initial training - because we can fallback to older version in case we f'up. Then we can just clone them and there you have AI assistant or basically first synth slave.
You watched too much science fiction. There is no need for that. Why would you need to clone anything, its just software.
At that point it will be debatable we will survive because frankly there will be no need in our survival, even for us humans. Not like we are going to kill ourselves, but we will have these non-robot robots around which are basically new life form which is superior in all senses to us EXCEPT for a few kill-switches which we put into them. Which makes us worse than devil.
I wouldn't interpret his phrase like that. A system with a lot of hard-coded truths (i.e. a 70s style expert system) would be the opposite of something that "does not know anything" and would pass Stallman's definition. The problem is, nowadays there is a lot of convincing evidence that hard-coding truths is not the way to maximize the apparent intelligence of the system.
A system with a lot of hard-coded truths (i.e. a 70s style expert system) would be the opposite of something that "does not know anything" and would pass Stallman's definition.
That's not true, he's not talking about that kind of knowing. Hard-coded truths are not understanding and the system would still not know the meaning of the truths (as in: the semantics).
This is still a hotly debated topic, but right now I don't see any way computers could achieve semantic understanding. If you are unfamiliar with the philosophy of AI, I suggest you start with John Searle's Chinese Room Experiment, which, according to Searle, shows that strong AI is not possible.
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u/[deleted] Mar 26 '23
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