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  • Today I thought I'd talk about a paper fairly recent.

  • It was last year

  • A paper called "Concrete Problems in AI Safety"

  • Which is going to be related to the stuff I was talking about

  • before with the "Stop Button". It's got a bunch of authors; mostly from Google Brain

  • Google's AI research department, I guess..

  • Well a lot of it's AI research, but specifically Google Brain and some

  • people from Stanford and Berkeley and opening iEARN. Whatever... it's a

  • collaboration between a lot of different authors

  • The idea of the paper is trying to lay out a set of problems that we are able to

  • currently make progress on like if we're concerned about this far-off sort of

  • super intelligence stuff.. Sure; it seems important and it's interesting and

  • difficult and whatever, but it's quite difficult to sit down and actually do

  • anything about it because we don't know very much about what a super

  • intelligence would be like or how it would be implemented or whatever....

  • The idea of this paper is that it... It lays out some problems that we can tackle now

  • which will be helpful now and that I think will be helpful later on as well

  • with more advanced AI systems and making them safe as well. It lists five problems:

  • Avoiding negative side effects, which is quite closely related to the stuff we've

  • been talking about before with the stop button or the stamp collector. A lot of

  • the problems with that can be framed as negative side effects. They do the thing

  • you ask them to but in the process of doing that they do a lot of things but

  • you don't want them to. These are like the robot running over the baby right?

  • Yeah, anything where it does the thing you wanted it to, like it makes you the

  • cup of tea or it collects you stamps or whatever, but in the process of doing

  • that, it also does things you don't want it to do. So those are your negative side

  • effects. So that's the first of the research areas is how do we avoid these

  • negative side effects.. Then there's avoiding reward hacking, which is about

  • systems gaming their reward function. Doing something which technically counts

  • but isn't really what you intended the reward function to be. There's a lot of

  • different ways that that can manifest but this is like this is already a

  • common problem in machine learning systems where you come up with your

  • evaluation function or your reward function or whatever your objective

  • function and the system very carefully optimizes to exactly what you wrote and

  • then you realize what you wrote isn't what you meant. Scalable oversight is the

  • next one. It's a problem that human beings have all the time, anytime you've

  • started a new job. You don't know what to do and you have someone who does who's

  • supervising you. The question is what questions do you

  • ask and how many questions do you ask because current machine learning systems

  • can learn pretty well if you give them a million examples but you don't want your

  • robot to ask you a million questions, you know. You want it to only ask a few

  • questions and use that information efficiently to learn from you. Safe

  • exploration is the next one which is about, well, about safely exploring the

  • range of possible actions. So, you will want the system to experiment, you know,

  • try different things, try out different approaches. That's the only way it's

  • going to find what's going to work but there are some things that you don't

  • want it to try even once like the baby. Right, right.. Yeah you don't want it to

  • say "What happens if I run over this baby?" Do you want certain possible things

  • that it might consider trying to actually not try at all because you

  • can't afford to have them happen even once in the real world. Like a

  • thermonuclear war option; What happens if I do this? You don't want it to try that.

  • Is that the sort of thing that.. Yeah, yeah.. I'm thinking of war games.. Yes, yeah.. yeah. Global

  • Thermal Nuclear War . It runs through a simulation of every possible type of

  • nuclear war, right? But it does it in simulation. You want your system not to

  • run through every possible type of thermonuclear war in real life to find

  • out it doesn't work cause you can't.. It's too unsafe to do that even once. The last

  • area to look into is robustness to distributional shift. Yeah

  • It's a complicated term but the concept is not. It's just that the

  • situation can change over time. So you may end up; you may make something.

  • You train it; it performs well and then things change to be different from the

  • training scenario and that is inherently very difficult. It's something

  • humans struggle with. You find yourself in a situation you've

  • never been in before but the difference I think or one of the

  • useful things that humans do is, notice that there's a problem a lot of current

  • machine learning systems. If something changes underneath them

  • and their training is no longer useful they have no way of knowing that. So they

  • continue being just as confident in their answers that now make no sense

  • because they haven't noticed that there's a change. So.. if we can't

  • make systems that can just react to completely unforeseen circumstances, we

  • may be able to make systems that at least can recognize that they're in

  • unforeseen circumstances and ask for help and then maybe we have a scalable

  • supervision situation there where they recognize the problem and that's when

  • they ask for help. I suppose a simplified simplistic example of this is when you have

  • an out-of-date satnav and it doesn't seem to realize that you happen to be doing

  • 70 miles an hour over a plowed field because somebody else, you know, built a

  • road there. Yeah, exactly. The general tendency of unless you program them

  • specifically not to; to just plow on with what they think they should be doing.

  • Yeah. It can cause problems and in a large scale heavily depended on , you know , in

  • this case, it's your sat-nav. So it's not too big of a deal because it's not

  • actually driving the car and you know what's wrong and you can ignore it

  • As AI systems become more important and more integrated into

  • everything, that kind of thing, can become a real problem.

  • Although, you would hope the car doesn't take you in plowed field in

  • first place. Yeah. Is it an open paper or does it

  • leave us with any answers? Yeah. So

  • the way it does all of these is it gives a quick outline of what the

  • problem is. The example they usually use is a cleaning robot like we've made this.

  • We've made a robot it's in an office or something and it's cleaning up and then

  • they sort of framed the different problems those things that could go

  • wrong in that scenario. So it's pretty similar to they get me a cup of tea and

  • don't run over the baby type set up. It's clean the office and, you know, not knock

  • anything over or destroy anything. And then, for each one, the paper talks about

  • possible approaches to each problem and

  • things we can work on, basically. Things that we don't know how to do yet but

  • which seem like they might be doable in a year or two and some careful thought

  • This paper. Is this one for people to read? Yeah, really good. It doesn't cover

  • anything like the range of the problems in AI safety but of the problems

  • specifically about avoiding accidents, because all of these are

  • these are ways of creating possible accidents, right? Possible causes of

  • accidents. There's all kinds of other problems you've been having in AI that

  • don't fall under accidents but within that area I think it covers everything

  • and it's quite readable. It's quite... It doesn't require really high-level because it's

  • an overview paper, doesn't require high-level AI understanding for the most

  • part. Anyone can read it and it's on archive so you know it's freely

  • available. These guys now working on AI safety, or did this then

  • They've hung their hat up. They've written a paper and they're hoping

  • someone else is gonna sort it all out. These people are working on AI

  • safety right now but they're not the only people. This paper was released in

  • summer of 2016, so it's been about a year since it came out and since then there

  • have been more advances and some of the problems posed have had really

  • interesting solutions or well.. Not solutions, early work, that looks like it

  • could become a solution or approaches new interesting ideas about ways to

  • tackle these problems. So I think as a paper, it's already been successful in

  • stirring new research and giving people a focus to build their AI safety research on

  • top of. So we just need to watch this space, right? Yeah, exactly..

Today I thought I'd talk about a paper fairly recent.

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人工智能安全的具體問題(論文) - Computerphile (Concrete Problems in AI Safety (Paper) - Computerphile)

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    林宜悉 發佈於 2021 年 01 月 14 日
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