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The burden of proof is on the people warning us of the impending AI apocalypse. The fact is we are nowhere close to AI. We don't understand how the brain works.

The burden of proof is on the people warning us of the impending AI apocalypse. The fact is we are nowhere close to AI. We don't understand how the brain works. A review of the ML literature will also show we barely understand how neural nets work.

tlb 11y ago
I believe in the precautionary principle which says the exact opposite. "The precautionary principle ... states that if an action or policy has a suspected risk of causing harm to the public or to the environment, in the absence of scientific consensus that the action or policy is not harmful, the burden of proof that it is not harmful falls on those taking an action." [0] [0] https://en.wikipedia.org/wiki/Precautionary_principle https://en.wikipedia.org/wiki/Precautionary_principle
- mindcrime 11y ago
  If we adopted really strict adherence to that rule as the bar to research, there would be no scientific progress at all. I'm not convinced that would be a desirable thing.
  - mikepalmer 11y ago
    well, the clever solution here is not to demand a stop to all AI research, but rather to speed it up to reduce the chance that a single bad actor will get too far ahead... i.e., to get "post-singularity" ASAP, and safely. Definitely bold... might be just crazy enough to work! Would love to see the arguments laid out in a white paper. Reminds me of the question of how far ahead in cryptology is the NSA compared to the open research community.
- argonaut 11y ago
  You haven't shown the precautionary principle is the right principle to follow, you've only invoked it.
lightcatcher 11y ago
The fact that we don't understand how neural nets work despite the excellent results makes an AI apocalypse more likely, not less. This means that if we ever create strong AI, we will likely not understand it initially. Note: I'm personally not too worried about the AI apocalypse, but I think "we don't even understand neural nets" should cause more concern, not less.
- argonaut 11y ago
  True AI will require several breakthroughs. The fact that we don't understand neural nets means that much of the progress thus far has been hand-wavy and hacky iterative improvement (engineering, vs. theory). This means progress is very likely to plateau. When everything is hacky and you don't understand what's going on, those breakthroughs are not going to happen.

tlb 11y ago

I believe in the precautionary principle which says the exact opposite. "The precautionary principle ... states that if an action or policy has a suspected risk of causing harm to the public or to the environment, in the absence of scientific consensus that the action or policy is not harmful, the burden of proof that it is not harmful falls on those taking an action." [0] [0] https://en.wikipedia.org/wiki/Precautionary_principle https://en.wikipedia.org/wiki/Precautionary_principle

mindcrime 11y ago
If we adopted really strict adherence to that rule as the bar to research, there would be no scientific progress at all. I'm not convinced that would be a desirable thing.
- mikepalmer 11y ago
  well, the clever solution here is not to demand a stop to all AI research, but rather to speed it up to reduce the chance that a single bad actor will get too far ahead... i.e., to get "post-singularity" ASAP, and safely. Definitely bold... might be just crazy enough to work! Would love to see the arguments laid out in a white paper. Reminds me of the question of how far ahead in cryptology is the NSA compared to the open research community.
argonaut 11y ago
You haven't shown the precautionary principle is the right principle to follow, you've only invoked it.

lightcatcher 11y ago

The fact that we don't understand how neural nets work despite the excellent results makes an AI apocalypse more likely, not less. This means that if we ever create strong AI, we will likely not understand it initially. Note: I'm personally not too worried about the AI apocalypse, but I think "we don't even understand neural nets" should cause more concern, not less.

argonaut 11y ago
True AI will require several breakthroughs. The fact that we don't understand neural nets means that much of the progress thus far has been hand-wavy and hacky iterative improvement (engineering, vs. theory). This means progress is very likely to plateau. When everything is hacky and you don't understand what's going on, those breakthroughs are not going to happen.