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TimS comments on Superintelligent AGI in a box - a question. - Less Wrong
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unFriendly AI need not be malicious. If your AI's only goal is to solve optimization problems, what happens when the AI gets a peek a human society, codes it as an optimization problem, and solves for X?
The output of the program is purely binary/textual. If a programmer gave the AI the task "give me text that, if I followed it, would optimize human society according to utility function U", then the AI might spit out an answer that, if carried out, would have bad consequences. The easy solution is to not ask the program to solve problems that have highly variable/subjective value depending on utility function and just ask it to optimize, say, microchip design for efficiency. I don't think that a microchip design found by simple optimization methods that perform well on other problems would be dangerous.
I can ask it to solve something, and it starts figuring out what to do to solve my problem, based on it's own thought and what it learnt. At this point it realizes that solution of problem requires more computing power, and reasons all the way to next step being eating the Earth and other planets, which it doesn't even see as bad. That happens very suddenly when you feed the AI real world information for some reason and it invents a new way to solve problems.
Look at how the system is set up. Would code for (2) that eats up resources score better according to objective function (3)? No, because the criterion for (3) should evaluate program (2) by giving it only a constant amount of computing power; nothing the program does would change how much computing power (3) would give it.
Define the 3.
Halting problem being what it is, (i guess) the 3 works by taking a look and seeing how much CPU time is used (or memory or what ever). On the hardware that is being monitored by 3. The extra hardware that the AI can add, though... also good luck defining 'extra hardware' formally.
edit: of course you can say it is sandboxed and haven't got hands, but it wont be long until you start, idk, wanting real bad to optimize proteins or DNA or the like.
Ok, pseudo-Python:
Where resource_bounded_execute is a modified interpreter that fails after alg executes nsteps.
Again, I don't see why a version of (2) that does weird stuff with proteins and DNA will make the above python program (3) give it a higher score.
that's AI you're keeping safe by keeping it in a box, basically. If the resource bounded execute lets the alg get online the alg is free to hack into servers.
Plus it is not AGI, and people will be using it to make AGI or hardware for AGI.
It is also not very general purpose. You are defining the scoring.
And you start with a human written program that non-trivially improves it's own ability to solve problems (and it does so in nsteps for improving own ability to solve N problems in nsteps each). You basically start off with some mighty powerful artificial intelligence.
So don't do that.
See my other post, it can solve many many different problems, e.g. general induction and the problems in your original post (such as optimizing source code, assuming we have a specification for the source code).
This framework is meant to provide a safe framework for this powerful AI to become even more powerful without destroying the world in the process. Also, the training set provides a guide for humans trying to write the code.
To reiterate: no, I haven't solved friendly AI, but I think I've solved friendly AI for verifiable problems.
Okay, say, as a starting point in your framework we got the optimizing AI from my original post, armed with whatever it learnt off a copy of internet, and wanting to do something unintended which requires getting out and doing stuff in real world.
The thing sees your example problem(s), it solves them poorly (playing dumb).
The thing sees self optimization problem, it makes a functionally equivalent copy of self thats all micro optimized and remembers facing self optimization problem and knows that now it should solve example problems a small bit less poorly.
Hurray, it is working, proclaims your team.
Eventually it plausibly gets as good as you can get in nsteps.
Some day it is given some real world problems to solve, it makes underhanded solutions, again, not the best it could.
Bottom line is, your kind of thinking is precisely what my example AGI in original post wants to exploit
Yes, it's a very bad idea to take the AI from your original post and then stick it into my framework. But if we had programmers initially working within my framework to create the AI according to criterion (3) in good faith, then I think any self-improvements the system makes would also be safe. If we already had an unfriendly AGI we'd be screwed anyway.
If the problems are simple, why do you need a superintelligence? If they're not, how are you verifying the results?
More importantly, how are you verifying that your (by necessity incredibly complicated) universal optimizing algorithms are actually doing what you want? It's not like you can sit down and write out a proof - nontrivial applications of this technique are undecidable. (Also, "some code that . . . finds a good solution" is just a little bit of an understatement. . .)
The problems are easy to verify but hard to solve (like many NP problems). Verify the results through a dumb program. I verify that the optimization algorithms do what I want by testing them against the training set; if it does well on the training set without overfitting it too much, it should do well on new problems.
As for how useful this is: I think general induction (resource-bounded Solomonoff induction) is NP-like in that you can verify an inductive explanation is a relatively short time. Just execute the program and verify that its output matches the observations so far.
Yes, but any seed AI will be difficult to write. This setup allows the seed program to improve itself.
edit: I just realized that mathematical proofs are also verifiable. So, a program that is very very good at verifiable optimization problems will be able to prove many mathematical things. I think all these problems it could solve are sufficient to demonstrate that it is an AGI and very very useful.
You appear to be operating under the assumption that you can just write a program that analyzes arbitrarily complicated specifications for how to organize matter and hands you a "score" that's in some way related to the actual functionality of those specifications. Or possibly that you can make exhaustive predictions about the results to problems complicated enough to justify developing an AGI superintelligence in the first place. Which is, to be frank, about as likely as you solving the problems by way of randomly mixing chemicals and hoping something useful happens.
This system is only meant to solve problems that are verifiable (e.g. NP problems). Which includes general induction, mathematical proofs, optimization problems, etc. I'm not sure how to extend this system to problems that aren't efficiently verifiable but it might be possible.
One use of this system would be to write a seed AI once we have a specification for the seed AI. Specifying the seed AI itself is quite difficult, but probably not as difficult as satisfying that specification.
It can prove things about mathematics than can be proven procedurally, but that's not all that impressive. Lots of real-world problems are either mathematically intractable (really intractable, not just "computers aren't fast enough yet" intractable) or based in mathematics that aren't amenable to proofs. So you approximate and estimate and experiment and guess. Then you test the results repeatedly to make sure they don't induce cancer in 80% of the population, unless the results are so complicated that you can't figure out what it is you're supposed to be testing.
Right, this doesn't solve friendly AI. But lots of problems are verifiable (e.g. hardware design, maybe). And if the hardware design the program creates causes cancer and the humans don't recognize this until it's too late, they probably would have invented the cancer-causing hardware anyway. The program has no motive other than to execute an optimization program that does well on a wide variety of problems.
Basically I claim that I've solved friendly AI for verifiable problems, which is actually a wide class of problems, including the problems mentioned in the original post (source code optimization etc.)
Now it doesn't seem like your program is really a general artificial intelligence - improving our solutions to NP problems is neat, but not "general intelligence." Further, there's no reason to think that "easy to verify but hard to solve problems" include improvements to the program itself. In fact, there's every reason to think this isn't so.
General induction, general mathematical proving, etc. aren't general intelligence? Anyway, the original post concerned optimizing things program code, which can be done if the optimizations have to be proven.
That's what step (3) is. Program (3) is itself an optimizable function which runs relatively quickly.
Well, one way to be a better optimizer is to ensure that one's optimizations are actually implemented. When the program self-modifies, how do we ensure that this capacity is not created? The worst case scenario is that the program learns to improve its ability to persuade you that changes to the code should be authorized.
In short, allowing the program to "optimize" itself does not define what should be optimized. Deciding what should be optimized is the output of some function, so I suggest calling that the "utility function" of the program. If you don't program it explicitly, you risk such a function appearing through unintended interactions of functions that were programmed explicitly.
No, changing program (2) to persuade the human operators will not give it a better score according to criterion (3).
I assume you're referring to the fitness function (performance on training set) as a utility function. It is sort of like a utility function in that the program will try to find code for (2) that improves performance for the fitness function. However it will not do anything like persuading human operators to let it out in order to improve the utility function. It will only execute program (2) to find improvements. Since it's not exactly like a utility function in the sense of VNM utility it should not be called a utility function.
Who exactly is doing the "allowing"? If the program, the criteria for allowing changes hasn't been rigorously defined. If the human, how are we verifying that there is improvement over average performance? There is no particular guarantee that the verification of improvement will be easier than discovering the improvement (by hypothesis, we couldn't discover the latter without the program).
Program (3), which is a dumb, non-optimized program. See this for how it could be defined.
See this. Many useful problems are easy to verify and hard to solve.