The next Moravec's paradox

Submitted by CharlisonX t3_10rpxze in singularity

back when computers first started, researchers were quick to assign them the novel problems of calculus, navier stokes and other hard physics problems that computers solved flawlessly, but simple perception was a deadly bane to the machines.

With the GPT family acing every single test on STEM fields lately, I can't help but think of the same thing happening now. with AI taking over the services branches of legal, software, art, and the research/medical areas. All while being useless on blue collar/menial jobs like welding, driving or moving/hauling.

Thoughts?

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CertainMiddle2382 t1_j6wup8c wrote

Context.

Hard physical problems happen in a very controlled context, that context is often a “fiction” of reality deemed close enough but simple enough to be useful.

Even all “common” mathematics had to be declared to happen inside a red taped safe space named ZFC, otherwise the unrelenting waves of complexity outside of it would have torn down everything we could be trying to build.

Everything is about context.

“Perception”, “real life” happens in a much more complicated context. That context is not sandboxed and contains all the all little sandboxes we built to make our thinking work.

To model those simple concepts , you practically need to have a internalized model of the whole world…

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Iffykindofguy t1_j6x09sl wrote

Its far from useless on blue-collar jobs. It will start out as an aid on those jobs like any other. Already see it in a lot of city infrastructure jobs. My town has an app that you (a city worker, not a random) can take and place your phone on a bridge and based off the vibrations (and a few other variables taken from video footage you get also with the phone after) it can tell you where the bridge is most likely to need repairs. They can unload trucks with robots now, something that a few years ago I watched a ted talk on how that would be impossible unless everything in the truck was uniform or prepackaged a certain way that made the loading so inefficient the unloading benefits were all lost. Theyre coming for all jobs.

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RobLocksta t1_j6x71ox wrote

"A deadly bane" seems a bit dramatic

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Iffykindofguy t1_j6xqkgq wrote

Replying to CertainMiddle2382 (#1,704,652)

Humans are machines. AI's physical body will be a machine. Youre getting lost in the details. My point was simply that a lot of blue collar replacements you seem to think would need entire world simulations for don't require that.

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Iffykindofguy t1_j6xxswa wrote

Replying to Economy_Variation365 (#1,705,434)

I work in production so we were filming a demo but the only thing that was set up was the processor, the trucks we worked on were actually just normal trucks that the facility got every day. Didnt work perfectly but that was mostly in the install process, once it was up it was churning.

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Surur t1_j6ydcw0 wrote

The lesson you should take away is, like perception, operating in the real world will also fall to computers in the end.

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No_Ninja3309_NoNoYes t1_j6ykf7b wrote

Altman is not a superhero. He can't take on the whole world. GPT currently are too inefficient to be the road to AGI. Maybe neuromorphic hardware and spiking neural networks can do better. AI can't really deal with all use cases right now because it needs a lot of data and the world is moving too fast. Look at ChatGPT. It is lagging the world. It is not as efficient as a search engine.

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ImoJenny t1_j6zutx7 wrote

There are limits to ML, but it's not that blue collar work will be immune from it. In fact we are already seeing increasing use of robotics in areas that were previously thought to be too variable and unpredictable for them like construction sites.

Nor have computers replaced theoretical physicists. They just made it necessary for most physicists to also have an above average grasp of programing.

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Ivanthedog2013 t1_j702ai6 wrote

this was the only logical progression, better for it to master abstract concepts and progress into more concrete jobs where the systems are predicated off of the abstractions.

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Lawjarp2 t1_j71qg5d wrote

It will actually be pretty amazing at blue collar jobs. It's infact bad at white collar jobs. The reason you see so much more automation in white collar jobs right now is

(1) Easy to automate blue collar jobs are already automated.

(2) Data is not easily available or cost of training on data is very high.

(3) Cheaper to hire humans then to make expensive robot bodies.

Most blue collar jobs rely on experience which is what Neural networks should be good at. It's just that training to live in the real world is slow and hard.

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