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"We're nowhere near the point where an AI can do your job, but we're well past the point where your boss can be suckered into firing you and replacing you with a bot that fails at doing your job." (Pl (pluralistic.net)

submitted 9 months ago by [email protected] to c/[email protected]

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[–] [email protected] 4 points 9 months ago (2 children)

Anything a human can be trained to do, a neural network can be trained to do.

Come on. This is a gross exaggeration. Neural nets are incredibly limited. Try getting them to even open a door. If we someday come up with a true general AI that really can do what you say, it will be as similar to today's neural nets as a space shuttle is to a paper airoplane.

[–] [email protected] 2 points 9 months ago (1 children)

https://www.youtube.com/watch?v=wXxrmussq4E

Have you not been paying attention to robotics recently? Opening doors is a solved problem with consumer grade hardware and software at this point.

[–] [email protected] 1 points 9 months ago

I wouldn't say 74k is consumer grade but Spot is very cool. I doubt that it is purely a neural net though, there is probably a fair bit of actionismnat work.

[–] [email protected] 1 points 9 months ago* (last edited 9 months ago) (1 children)

Try getting them to even open a door

For now there is: AI vs. Stairs, you may need to wait for a future video for "AI vs. Doors" 🤷

BTW, that is a rudimentary neural network.

[–] [email protected] 2 points 9 months ago (1 children)

I've seen a million of such demos but simulations like these are nothing like the real world. Moravec's paradox will make neural nets look like toddlers for a long time to come yet.

[–] [email protected] 1 points 9 months ago

Well, that particular demo is more of a cockroach than a toddler, the neural network used seems to not have even a million weights.

Moravec's paradox holds true because of two fronts:

Computing resources required
Lack of formal description of a behavior

But keep in mind that was in 1988, about 20 years before the first 1024-core multi-TFLOP GPU was designed, and that by training a NN, we're brute-forcing away the lack of a formal description of the algorithm.

We're now looking towards neuromorphic hardware on the trillion-"core" scale, computing resources will soon become a non-issue, and the lack of formal description will only be as much of a problem as it is to a toddler... before you copy the first trained NN to an identical body and re-training costs drop to O(0)... which is much less than even training a million toddlers at once.