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Here's the problem with the "Let's just agree not to do this research!" plan that everyone seems to suggest when they start thinking about existential risks: wh

Here's the problem with the "Let's just agree not to do this research!" plan that everyone seems to suggest when they start thinking about existential risks: when we're sitting around in 2030 with a million times more computing power at our fingertips than we have today, constructing a workable AI just isn't going to be that difficult of an engineering problem. We already know the equations that we'd need to use do general intelligence, it's just that they're not computable with finite computer power, so we'd have to do some approximations, and at present it's not realistic because the approximation schemes we know of would work too slowly. Pump up our computer power a million times and these schemes start to become a lot more realistic, especially with some halfway decent pruning heuristics. It's bad enough that (IMO) by 2040 or so, any reasonably smart asshole in his basement could probably do it on his laptop with access to only the reference materials available today; I have no idea how you avoid that risk by making some political agreement. Hell, ban the research altogether on pain of death, and there's still going to be some terrorist team working on it somewhere (and that's even if all the governments actually stop work on it, which they won't). The only positive way out of this is to go to great pains to figure out how to design safe (friendly) AI, and to do so while it's still too difficult for random-dude-with-a-botnet to achieve (and preferably we should do it before the governments of the world see it as feasible enough to throw military research dollars at). We need to tackle the problem while it's still a difficult software problem, not a brute-force one that can be cracked by better hardware.

anonymousguru 15y ago
"when we're sitting around in 2030 with a million times more computing power at our fingertips than we have today, constructing a workable AI just isn't going to be that difficult of an engineering problem." Sorry, but bruteforcing "creativity" is a p==np problem. If you have a proof of p==np then please submit it right away and collect your turing award. "We already know the equations that we'd need to use do general intelligence, it's just that they're not computable with finite computer power, so we'd have to do some approximations, and at present it's not realistic because the approximation schemes we know of would work too slowly." Unless there have been some MAJOR discovery within the past couple of days, this sentence is completely wrong. We don't have equations to build an AGI because if we did, we would already have a working AGI. If we had these so called equations, we would have a more fundamental understanding of humans, how they work, and how they "learn". I will now stop thinking about this ridiculous sentence before I get an aneurysm. P.S. I would love it if you could prove me wrong with links to any academic papers.
- elehack 15y ago
  P?=NP doesn't have anything to do with possibility - it has to do with feasibility. The point is, with sufficient hardware, we might be able to just do the NP problem. Also, brute-forcing creativity is not P==NP. Brute-forcing is NP. If P=NP, then there's a way to do it without brute-forcing.
alextp 15y ago
> We already know the equations that we'd need to use do general intelligence, it's just that they're not computable with finite computer power, so we'd have to do some approximations, and at present it's not realistic because the approximation schemes we know of would work too slowly. If you do know the equations, please tell me, as all models that I know of have fundamental failures that prevent them from generalizing to arbitrary problems even without considering computation.
arethuza 15y ago
"We already know the equations that we'd need to use do general intelligence" Not only am I pretty sure we don't know how to build a general intelligence I'm pretty sure that nobody really knows what kind of approach would be most likely to succeed. Having said that, I would love to be proved wrong on this one - so as you specifically say that the necessary techniques have already been published perhaps you could give the relevant references?
- Retric 15y ago
  There are projects that simulate neurons at the biological level not just the neural network approximation which have demonstrated great results. Theses simulations work for simple organisms and scaling things up is not really a CS problem. PS: It's assumed that there are shortcuts to AI, but the absolutely worst possible case is a QM simulation of each cell in a body and it's environment and we do have the math for that even if the computational power is hundreds or even thousands of years in the future at current computing growth rates.
  - cma 15y ago
    Link to an example of a simple organism simulation, based on that organism's nervous system. I have never seen one.
    - Retric 15y ago
      An example of a nematode simulation: The purpose of this web site is to promote research and education in computational approaches to C. elegans behavior and neurobiology. This tiny animal has only 302 neurons and 95 muscle cells, making an anatomically detailed model of the entire body and nervous system an attainable goal. Physiological information is still incomplete, but computer simulations can help direction experiments to questions which are most relevant for understanding the neural control of behavior. http://www.csi.uoregon.edu/projects/celegans/ http://www.csi.uoregon.edu/projects/celegans/ As to scaling things up: The IBM team's latest simulation results represent a model about 4.5% the scale of the human cerebral cortex, which was run at 1/83 of real time. The machine used provided 144TB of memory and 0.5PFLop/s [petaFLOPS per second]. Turning to the future, you can see that running human scale cortical simulations will probably require 4 PB of memory and to run these simulations in real time will require over 1 EFLop/s [exaFLOPS per second]. If the current trends in supercomputing continue, it seems that human-scale simulations will be possible in the not too distant future. http://www.theregister.co.uk/2009/11/18/ibm_closer_to_thinking_computer_chip/ http://www.theregister.co.uk/2009/11/18/ibm_closer_to_thinki... PS: A counter argument on the IBM simulation: http://www.popsci.com/technology/article/2009-11/blue-brain-scientist-denounces-ibms-claim-cat-brain-simulation-shameful-and-unethical http://www.popsci.com/technology/article/2009-11/blue-brain-...
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anonymousguru 15y ago

"when we're sitting around in 2030 with a million times more computing power at our fingertips than we have today, constructing a workable AI just isn't going to be that difficult of an engineering problem." Sorry, but bruteforcing "creativity" is a p==np problem. If you have a proof of p==np then please submit it right away and collect your turing award. "We already know the equations that we'd need to use do general intelligence, it's just that they're not computable with finite computer power, so we'd have to do some approximations, and at present it's not realistic because the approximation schemes we know of would work too slowly." Unless there have been some MAJOR discovery within the past couple of days, this sentence is completely wrong. We don't have equations to build an AGI because if we did, we would already have a working AGI. If we had these so called equations, we would have a more fundamental understanding of humans, how they work, and how they "learn". I will now stop thinking about this ridiculous sentence before I get an aneurysm. P.S. I would love it if you could prove me wrong with links to any academic papers.

elehack 15y ago
P?=NP doesn't have anything to do with possibility - it has to do with feasibility. The point is, with sufficient hardware, we might be able to just do the NP problem. Also, brute-forcing creativity is not P==NP. Brute-forcing is NP. If P=NP, then there's a way to do it without brute-forcing.

alextp 15y ago

> We already know the equations that we'd need to use do general intelligence, it's just that they're not computable with finite computer power, so we'd have to do some approximations, and at present it's not realistic because the approximation schemes we know of would work too slowly. If you do know the equations, please tell me, as all models that I know of have fundamental failures that prevent them from generalizing to arbitrary problems even without considering computation.

arethuza 15y ago

"We already know the equations that we'd need to use do general intelligence" Not only am I pretty sure we don't know how to build a general intelligence I'm pretty sure that nobody really knows what kind of approach would be most likely to succeed. Having said that, I would love to be proved wrong on this one - so as you specifically say that the necessary techniques have already been published perhaps you could give the relevant references?

Retric 15y ago
There are projects that simulate neurons at the biological level not just the neural network approximation which have demonstrated great results. Theses simulations work for simple organisms and scaling things up is not really a CS problem. PS: It's assumed that there are shortcuts to AI, but the absolutely worst possible case is a QM simulation of each cell in a body and it's environment and we do have the math for that even if the computational power is hundreds or even thousands of years in the future at current computing growth rates.
- cma 15y ago
  Link to an example of a simple organism simulation, based on that organism's nervous system. I have never seen one.
  - Retric 15y ago
    An example of a nematode simulation: The purpose of this web site is to promote research and education in computational approaches to C. elegans behavior and neurobiology. This tiny animal has only 302 neurons and 95 muscle cells, making an anatomically detailed model of the entire body and nervous system an attainable goal. Physiological information is still incomplete, but computer simulations can help direction experiments to questions which are most relevant for understanding the neural control of behavior. http://www.csi.uoregon.edu/projects/celegans/ http://www.csi.uoregon.edu/projects/celegans/ As to scaling things up: The IBM team's latest simulation results represent a model about 4.5% the scale of the human cerebral cortex, which was run at 1/83 of real time. The machine used provided 144TB of memory and 0.5PFLop/s [petaFLOPS per second]. Turning to the future, you can see that running human scale cortical simulations will probably require 4 PB of memory and to run these simulations in real time will require over 1 EFLop/s [exaFLOPS per second]. If the current trends in supercomputing continue, it seems that human-scale simulations will be possible in the not too distant future. http://www.theregister.co.uk/2009/11/18/ibm_closer_to_thinking_computer_chip/ http://www.theregister.co.uk/2009/11/18/ibm_closer_to_thinki... PS: A counter argument on the IBM simulation: http://www.popsci.com/technology/article/2009-11/blue-brain-scientist-denounces-ibms-claim-cat-brain-simulation-shameful-and-unethical http://www.popsci.com/technology/article/2009-11/blue-brain-...

deleted 15y ago

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