5 ms·

Biology/biochemistry can squeeze a good amount of computation from just 20 watts. Even if we could get equal computation from silicon and have an algorithm to

Biology/biochemistry can squeeze a good amount of computation from just 20 watts. Even if we could get equal computation from silicon and have an algorithm to run near human-level general AI, humans can maintain the comparative advantage as long as the cost of hardware is more than the cost of raising and educating a human, and the operating cost is more than wages for the same task.

vlovich123 5y ago
Human-level intelligence is highly variable. If we’re talking about something that can intelligently and independently make discoveries in math science and engineering, then the comparative advantage can’t stay long because you’d just pose the problem of “make yourself but faster” to it. It doesn’t even need discoveries. Imagine an AI that could manually transcode your high level code into absolutely optimal assembly, simplifying your design, removing unnecessary code, optimizing the code in response to observed behavior, etc. I would guess we have several orders of magnitude of power efficiency loss just from building a system that has understandable and flexible layers of abstraction. A sufficiently powerful AI could work to automatically remove those abstractions and even operate at a higher level of abstraction. The real question is whether we’re at all on the right track. ¯\_(ツ)_/¯
- adrianN 5y ago
  > Imagine an AI that could manually transcode your high level code into absolutely optimal assembly The Halting Problem prevents such an AI from existing, but your point still stands of course.
  - vlovich123 5y ago
    How so?
    - adrianN 5y ago
      Suppose you write a program that simulates a TM and then prints "Halt" when the TM halts. The magic AI could optimize this program to just a print (or an infinite loop). This requires solving the halting problem.
      - hyper_dynamics 5y ago
        It might not be able to do it for any turing machine/ a universal turing machine – but it might quickly figure out what a turing machine will do without executing all steps of it.
        simiones 5y ago
        It might get it right some of the time, but it will be necessarily wrong some of the time. It's also very possible that for many (possibly even most) TMs, the most efficient algorithm for predicting the output is that TM itself.
        vlovich123 5y ago
        I think the problem is you're assuming that general AI = Turing machine, but there's no indication that needs to be the case. "General AI" to me means human intelligence running on an artificial system (silicon, simulated brain, etc), so the optimization I'm thinking of is more akin to having an assembly expert translate your code into assembly than a compiler optimization pass. Given that I have optimized my fair bit of code by removing abstraction layers or simplifying code, by definition a general AI should be similarly capable & can handle even ambiguous tasks like "refactor this codebase in this way". Obviously this gives up accuracy, but humans make mistakes writing code as well & it would be much easier to say "I've observed a fault that has this properties. Figure out the problem". It should do an even better job than I can on problems like that because for complicated problems it should be able to follow complex codebases with greater ease than I. Again, I'm defining a tautological definition of "general AI" as one that's capable of doing all that. If it's not capable of doing that then it's not general AI.
        5y ago
freemint 5y ago
Intelligence might not be scalar between intelligences based on different computational substraits. Computers beat humans at symbolic integration and differentiation since forever but don't beat humans in other areas. A near human-level intelligence will be vasly supperior in many other areas.

vlovich123 5y ago

Human-level intelligence is highly variable. If we’re talking about something that can intelligently and independently make discoveries in math science and engineering, then the comparative advantage can’t stay long because you’d just pose the problem of “make yourself but faster” to it. It doesn’t even need discoveries. Imagine an AI that could manually transcode your high level code into absolutely optimal assembly, simplifying your design, removing unnecessary code, optimizing the code in response to observed behavior, etc. I would guess we have several orders of magnitude of power efficiency loss just from building a system that has understandable and flexible layers of abstraction. A sufficiently powerful AI could work to automatically remove those abstractions and even operate at a higher level of abstraction. The real question is whether we’re at all on the right track. ¯\_(ツ)_/¯

adrianN 5y ago
> Imagine an AI that could manually transcode your high level code into absolutely optimal assembly The Halting Problem prevents such an AI from existing, but your point still stands of course.
- vlovich123 5y ago
  How so?
  - adrianN 5y ago
    Suppose you write a program that simulates a TM and then prints "Halt" when the TM halts. The magic AI could optimize this program to just a print (or an infinite loop). This requires solving the halting problem.
    - hyper_dynamics 5y ago
      It might not be able to do it for any turing machine/ a universal turing machine – but it might quickly figure out what a turing machine will do without executing all steps of it.
      - simiones 5y ago
        It might get it right some of the time, but it will be necessarily wrong some of the time. It's also very possible that for many (possibly even most) TMs, the most efficient algorithm for predicting the output is that TM itself.
        vlovich123 5y ago
        I think the problem is you're assuming that general AI = Turing machine, but there's no indication that needs to be the case. "General AI" to me means human intelligence running on an artificial system (silicon, simulated brain, etc), so the optimization I'm thinking of is more akin to having an assembly expert translate your code into assembly than a compiler optimization pass. Given that I have optimized my fair bit of code by removing abstraction layers or simplifying code, by definition a general AI should be similarly capable & can handle even ambiguous tasks like "refactor this codebase in this way". Obviously this gives up accuracy, but humans make mistakes writing code as well & it would be much easier to say "I've observed a fault that has this properties. Figure out the problem". It should do an even better job than I can on problems like that because for complicated problems it should be able to follow complex codebases with greater ease than I. Again, I'm defining a tautological definition of "general AI" as one that's capable of doing all that. If it's not capable of doing that then it's not general AI.
        5y ago

freemint 5y ago

Intelligence might not be scalar between intelligences based on different computational substraits. Computers beat humans at symbolic integration and differentiation since forever but don't beat humans in other areas. A near human-level intelligence will be vasly supperior in many other areas.