What you wanted to know about AI • Hacker News Board

Re: What you wanted to know about AI

#161

Post by JoeAltmaier » Tue, Mar 17, 2015, 4:24 PM UTC

davmre wrote: post #158

JoeAltmaier wrote: post #157

Earlier quoted context omitted.

All that talk about maximizing utility, isn't about intelligence at all. Its about a sensory feedback loop maybe. Intelligence is what lets you say "This isn't working. Maybe I should try a different approach. Maybe I should change the problem. Maybe I should get a different job". Until you're operating at that meta level, you're not talking 'intelligence' at all, just control systems.

That's not the definition the mainstream AI community has taken, for what I think are largely good reasons, but you could define intelligence that way if you wanted. It's only a renaming of the debate though - instead of calling the things we're worried about "intelligent machines", you'd now call them "very effective control systems". The issue is still the same: if a system that doesn't perfectly share your goals i…

Yeah you can imagine a lizard brain being introduced into a biomechanic machine to calculate chess moves. It doesn't make a lizard more intelligent, or even add intelligence to the lizard.

If we don't regard intelligence as something different from control, then I guess birds are the most intelligent because they can navigate complex air currents. Etc. That is a poor definition of intelligence, because its not helpful in distinguishing what we normally mean by 'smart' from mechanistic/logical systems.

And the discussion of rogue AIs is all about intelligence gone awry. Does anybody fear a control system that mis-estimates the corn crop? No, its about a malicious non-empathetic machine entity that coldly calculates how to defeat us. And that requires more that the current AI's are delivering.

Re: What you wanted to know about AI

#162

Post by eli_gottlieb » Tue, Mar 17, 2015, 8:35 PM UTC

maaku wrote: post #160

eli_gottlieb wrote: post #149

Earlier quoted context omitted.

I just want to point out: the history of software, just regular software, has been typified by the New Jersey approach and the MIT approach. The former consists in just hacking together something that kinda-mostly works, releasing fast, and trying to ameliorate problems later. The latter consists in thoroughly considering what the software needs to do, designing the code correctly the first time with all necessary fu…

No one is making "world optimizations" engines. The concept doesn't even make sense when he wheels hit the road. No AI research done anytime in the foreseeable future would even be at risk of resulting in a runaway world optimizer, and contrary to exaggerated claims being made there would be plenty of clear signs something was amis if it did happen and planty of time to pull the plug .

I think you missed the last sentence: your software doesn't need to be a "runaway world optimizer" to be a very destructive machine merely because it's a bad machine that was put in an important job. Again: add up the financial and human cost of previous software bugs, and then extrapolate to consider the kind of problems we'll face when we're using deterministically buggy intelligent software instead of stochastically buggy human intellect.

At the very least, we have a clear research imperative to ensure that "AI", whatever we end up using that term to mean, "fails fuzzily" like a human being does: that a small mistake in programming or instructions only causes a small deviation from desired behavior.

Re: What you wanted to know about AI

#163

Post by eli_gottlieb » Wed, Mar 18, 2015, 10:25 AM UTC

cousin_it wrote: post #153

eli_gottlieb wrote: post #150

Earlier quoted context omitted.

>That said, it's indeed very hard to directly specify a utility function involving paperclips. If our universe were a Game of Life universe and we knew exactly which configuration corresponds to a paperclip, I'd be able to do that right now. But since we don't know the true laws of physics, the "hard way" involves encoding some kind of Solomonoff prior over all possible physical universes, and a rule for recognizing…

AIXItl isn't really the kind of AI that I like, because it's reflectively inconsistent. In any case, the time complexity of AIXItl is kind of irrelevant at this stage, because we're trying to figure out what is the right thing to optimize. Only then we should start figuring out how to optimize that thing efficiently, because we really don't want to optimize the wrong thing efficiently. I'm very skeptical that approac…

> I'm very skeptical that approaches based on "conceptual abstraction" can tell us the right thing to optimize, as opposed to my preferred approach (defining a utility function over mathematical objects directly).

And I'm very skeptical that mathematical Platonism is useful for AI: "mathematical objects directly" do not exist in the real world, and it is very much real-world things on which we want our software to operate. "Conceptual abstraction" simply refers to a learning algorithm that possesses a representation of, for instance, a chair, that is not composed entirely of a concrete feature-set (visual edges, orientations, and colors) and can thus be deployed to generatively model chairs in general.

Computational cognitive science is working towards this sort of thing, and the results should start to hit the machine-learning community fairly soon.

Re: What you wanted to know about AI

#164

Post by me1010 » Wed, Mar 18, 2015, 5:34 PM UTC

themgt wrote: post #84

jallmann wrote: post #39

When I started school, my dream was to figure out a theory to underpin a grand unified model of artificial intelligence. Imagine my disappointment once I started studying the subject in detail. Most functional AI nowadays consists of algorithms that are carefully tuned to solve a very specific problem in a narrowly defined environment. All the research nowadays is pushing the boundaries of a local optimum. Right now,…

The core dilemma, and I'm paraphrasing I can't remember who, is that for every other problem given to computer scientists to solve, there already exists an understanding of what the problem space is and what a solution is required to do. The computer scientist just architects and implements the solution in software, but they're implementing business logic, or physics equations to guide a spacecraft, or "route this ca…

> the ultimate unanswered question

Answer: 42.

Re: What you wanted to know about AI

#165

Post by cousin_it » Wed, Mar 18, 2015, 6:01 PM UTC

eli_gottlieb wrote: post #163

cousin_it wrote: post #153

Earlier quoted context omitted.

AIXItl isn't really the kind of AI that I like, because it's reflectively inconsistent. In any case, the time complexity of AIXItl is kind of irrelevant at this stage, because we're trying to figure out what is the right thing to optimize. Only then we should start figuring out how to optimize that thing efficiently, because we really don't want to optimize the wrong thing efficiently. I'm very skeptical that approac…

> I'm very skeptical that approaches based on "conceptual abstraction" can tell us the right thing to optimize, as opposed to my preferred approach (defining a utility function over mathematical objects directly). And I'm very skeptical that mathematical Platonism is useful for AI: "mathematical objects directly" do not exist in the real world, and it is very much real-world things on which we want our software to op…

> "mathematical objects directly" do not exist in the real world

Well, there's an influential minority that thinks mathematical objects are all that exists (Tegmark multiverse). I don't necessarily agree with them, but that's one way to rigorously define the domain for a utility function, in a way that is not obviously exploitable. Another way is to define utility in terms of an agent's perceptions, but that is exploitable by wireheading, and IMO that flaw is unfixable as the agent gets more powerful. I'm not aware of any other approaches that are different in principle from those two, so I'll stick with the lesser evil for now, and hope that someone comes up with a better idea.