YouSaid · the spoken record

Peter Norvig

lines on the record: 74
first: 2019-09-30
most recent: 2019-09-30
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“Right. Humans don't know what color the dress was. And so they're vulnerable to certain attacks that are different than the attacks on the machines. But the attacks on the machines are so striking. They really change the way you think about what we've done And the way I think about it is I think part of the problem is we're seduced by our low dimensional metaphors. So”
2019-09-30 · Lex Fridman Podcast · Peter Norvig: Artificial Intelligence: A Modern Approach · IDENTIFIED FROM THE TRANSCRIPT · source
“Was the decision really based on my collateral, or was it based on my religion or skin color or whatever? I can't tell if I'm only looking at my case. But if I look across all the cases, then I can detect a pattern. So, you want to have that kind of capability. You want to have these adversarial testing. So we thought we were doing pretty good at object recognition and images. We said, look, we're sort of pretty close to human level performance on ImageNet and so on. And then you start seeing these adversarial images and you say, wait a minute, that part is nothing like human performance.”
2019-09-30 · Lex Fridman Podcast · Peter Norvig: Artificial Intelligence: A Modern Approach · IDENTIFIED FROM THE TRANSCRIPT · source
“Right, so the bank can tell me, well, you didn't get the loan because you didn't have enough collateral. And that may be true, or it may be true that they just didn't like my religion or something else. I can't tell from the explanation. And that's true whether the decision was made by a computer or by a person. So, I want more I do want to have the explanations and I want to be able to have a conversation to go back and forth and said, well, you gave this explanation, but what about this? And what would have happened if this had happened? And what would I need to change that? I think a conversation is a better way to think about it than just an explanation as a single output. And I think we need testing of various kinds. So in order to know”
2019-09-30 · Lex Fridman Podcast · Peter Norvig: Artificial Intelligence: A Modern Approach · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah. So I prefer to talk about trust and validation and verification rather than just about explainability. And then I think explanations are one tool that you use towards those goals. And I think it is important issue that we don't want to use these systems unless we trust them and we want to understand where they work and where they don't work. And an explanation can be part of that, right? So I apply for a loan and I get denied. I want some explanation of why. And you have, in Europe, we have the GDPR that says you're required to be able to get that. But on the other hand, an explanation alone is not enough, right? We were used to dealing with people and with organizations and corporations and so on, and they can give you an explanation, then you have no guarantee that that explanation relates to reality.”
2019-09-30 · Lex Fridman Podcast · Peter Norvig: Artificial Intelligence: A Modern Approach · IDENTIFIED FROM THE TRANSCRIPT · source
“And secondly, we could somehow learn, yeah, there's this rule that you can remove one grain of sand. And you can do that a bunch of times, but you can't do it near infinite amount of times. But on the other hand, when you're doing induction on the integer, sure, then it's fine to do it an infinite number of times. And if we could, somehow we have to learn when these strategies are applicable rather than having the strategies be completely neutral and available everywhere.”
2019-09-30 · Lex Fridman Podcast · Peter Norvig: Artificial Intelligence: A Modern Approach · IDENTIFIED FROM THE TRANSCRIPT · source
“Which in some sense is good, but it also means there's no guidance as to where to apply. And so you started getting these paradoxes like, well, if I have a mountain and I remove one grain of sand, then it's still a mountain. But if I do that repeatedly, at some point it's not. And with logic, there's nothing to stop you from applying things repeatedly. And I don't really know what the right name for it is, we could separate out those ideas. So one, we could say, you know, a mountain isn't just an atomic notion. It's some sort of something like word embedding that has a more complex representation.”
2019-09-30 · Lex Fridman Podcast · Peter Norvig: Artificial Intelligence: A Modern Approach · IDENTIFIED FROM THE TRANSCRIPT · source
“So certainly, I think the idea of representation and reasoning is crucial that sometimes you just don't have enough data about the world to learn de novo. So you've got to have some idea of representation, whether that was programmed in or told or whatever, and then be able to take steps of reasoning. I think the problem with the good old-fashioned AI was one we tried to base everything on these symbols that were atomic. And that's great if you're trying to define the properties of a triangle. Because they have necessary and sufficient conditions. But things in the real world don't. The real world is messy and doesn't have sharp edges atomic symbols do. So that was a poor match. And then the other aspect was that the reasoning was universal and applied anywhere.”
2019-09-30 · Lex Fridman Podcast · Peter Norvig: Artificial Intelligence: A Modern Approach · IDENTIFIED FROM THE TRANSCRIPT · source
“Think we'll gain a better understanding of what you can do there. I think we'll need to incorporate all the things we can do with the other technologies, right? So deep learning started out, convolutional networks and very close to perception. And it's since moved to be able to do more with actions and some degree of longer-term planning. But we need to do a better job. with representation and reasoning and one-shot learning and so on. I think we don't know yet how that's going to play out.”
2019-09-30 · Lex Fridman Podcast · Peter Norvig: Artificial Intelligence: A Modern Approach · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I think we don't know yet how it's all going to play out. So in the new edition, we have a chapter on deep learning. We got Ian Goodfellow to be the guest author for that chapter. So he said he could condense his whole deep learning book. Into one chapter. I think he did a great job. We were also encouraged that we gave him the old neural net chapter and said have fun with it, modernize that. And he said, you know, half of that was okay. That certainly there's lots of new things that have been developed, but some of the core was still the same.”
2019-09-30 · Lex Fridman Podcast · Peter Norvig: Artificial Intelligence: A Modern Approach · IDENTIFIED FROM THE TRANSCRIPT · source
“We certainly thought that learning was important. I guess we missed it as being as important as it is today. We missed this idea of big data. We miss that the idea of deep learning hadn't been invented yet. We could have taken the book from a complete machine learning point of view right from the start. We chose to do it more from a point of view of we're going to first develop different types of representations and we're going to talk about different types of environments. Is it fully observable or partially observable and is it deterministic or stochastic and so on? And we made it more complex along those axes rather than focusing on the machine learning axis first.”
2019-09-30 · Lex Fridman Podcast · Peter Norvig: Artificial Intelligence: A Modern Approach · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I guess we did an outline and then we sort of assigned chapters to each person. At the time, I had moved to Boston, and Stuart was in Berkeley, so basically we did it over the internet. And that wasn't the same as doing it today. It meant dial-up lines and telnetting in. You know, you tell Netted into one shell and you type cat file name and you hoped it was captured at the other end.”
2019-09-30 · Lex Fridman Podcast · Peter Norvig: Artificial Intelligence: A Modern Approach · IDENTIFIED FROM THE TRANSCRIPT · source
“And that was in two ways. So, you know, the good old fashioned AI was based primarily on Boolean logic, and you had a few tricks to deal with uncertainty. And it was based primarily on knowledge engineering, the way you got something done is you went out and interviewed an expert and you wrote down by hand everything they knew. And we saw in 95 that the field was changing in two ways. One, we were moving more towards probability rather than Boolean logic, and we were moving more towards machine learning rather than knowledge engineering. And the other books hadn't caught that wave. They were still in the more in the old school. So certainly they had part of that on the way. But we said if we start now completely taking that point of view, we can have a different kind of book and we were able to put that together.”
2019-09-30 · Lex Fridman Podcast · Peter Norvig: Artificial Intelligence: A Modern Approach · IDENTIFIED FROM THE TRANSCRIPT · source
“So, I guess it came about, I would go to lunch with the other AI faculty at Berkeley and we'd say, you know, the field is changing seems like the current books are a little bit behind. Nobody's come out with a new book recently. We should do that. And everybody said, Yeah, yeah, that's a great thing to do. And we never did anything And then I ended up heading off to industry. I went to Sun Lab. So I thought, well, that's the end of my possible academic publishing career. I met Stuart again at a conference like a year later and said, you know, that book we were always talking about, you guys must be half done with it by now, right? And he said, Well, we keep talking, we never do anything. So I said, Well, you know, we should do it. And I think the reason is that we all felt it was a time where the field was changing.”
2019-09-30 · Lex Fridman Podcast · Peter Norvig: Artificial Intelligence: A Modern Approach · IDENTIFIED FROM THE TRANSCRIPT · source
“They're letting me have some fun in the short term, but they're also helping me in the long term. Rather than competing against me.”
2019-09-30 · Lex Fridman Podcast · Peter Norvig: Artificial Intelligence: A Modern Approach · IDENTIFIED FROM THE TRANSCRIPT · source
“philosophical area. I think that's a really important issue too. Certainly thinking about that. I don't think about that as an AI issue as much. But as you say, the point is we've built this society and this infrastructure Where we say we have a marketplace for attention and we've decided as a society that we like things that are free. And so we want all apps on our phone to be free. And that means they're all competing for your attention. And then eventually they make some money somewhere through ads or in-game sales or whatever. But they can only win by defeating all the other apps by stealing your attention. Build a marketplace where it seems like they're working against you rather than working with you. And I'd like to find a way where we can change the playing field so you feel more like, well, these things are on my side.”
2019-09-30 · Lex Fridman Podcast · Peter Norvig: Artificial Intelligence: A Modern Approach · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, so at least we now we're arguing in an informed way. We're not asking for something impossible. We're saying here's where we are and here's what we aim for. And this strategy is better than that strategy.”
2019-09-30 · Lex Fridman Podcast · Peter Norvig: Artificial Intelligence: A Modern Approach · IDENTIFIED FROM THE TRANSCRIPT · source
“What you can aim for and how much you can get. You can't have everything. But the analysis certainly can't tell you where should we make that trade-off point.”
2019-09-30 · Lex Fridman Podcast · Peter Norvig: Artificial Intelligence: A Modern Approach · IDENTIFIED FROM THE TRANSCRIPT · source
“So, I want to have a 60% chance of recurring regardless. And the makers of one of the makers of a commercial program to do that says that's what we're trying to optimize. And look, we achieved that. We've reached that kind of balance. And then on the other side, you also want to say, well, if it makes mistakes, I want that to affect. Sides of the protected class equally. And it turns out they don't do that, right? They're twice as likely to make a mistake that would harm a black person over a white person. So that seems unfair. So you'd like to say, well, I want to achieve both those goals. And then it turns out you do the analysis, and it's theoretically impossible to achieve both those goals. So you have to trade them off one against the other. So that analysis is really helpful to know”
2019-09-30 · Lex Fridman Podcast · Peter Norvig: Artificial Intelligence: A Modern Approach · IDENTIFIED FROM THE TRANSCRIPT · source
“Then another big part of it is just kind of theoretical of saying, what can we accomplish? And so you look at like this. Work on the programs to predict recidivism and decide who should get parole or who should get bail or whatever and how you're going to evaluate that. And one of the big issues is fairness across protected classes, protected classes being things like sex and race and so on. And so two things you want is you want to say, well, if I get a score of, say, six out of 10, then I want that to mean the same, whether no matter what race I'm on.”
2019-09-30 · Lex Fridman Podcast · Peter Norvig: Artificial Intelligence: A Modern Approach · IDENTIFIED FROM THE TRANSCRIPT · source
“So there is no one answer. Yes, there are techniques to try to learn that. So we talk about inverse reinforcement learning. So reinforcement learning, you take some actions, you get some rewards, and you figure out what actions you should take. And inverse reinforcement learning, you observe somebody taking actions and you figure out, well, this must be what they were trying to do. If they did this action, it must be because they wanted it. There's restrictions to that, right? So, lots of people take actions that are self-destructive, where they're suboptimal in certain ways. So you don't want to learn that. You want to somehow learn the perfect actions rather than the ones they actually take. So that's a challenge for that field.”
2019-09-30 · Lex Fridman Podcast · Peter Norvig: Artificial Intelligence: A Modern Approach · IDENTIFIED FROM THE TRANSCRIPT · source
“I guess it is philosophical, right? So we've always had a philosophy chapter, which I was glad that we were supporting. And now it's less kind of the Chinese room type argument and more of these ethical and societal type issues. So we get into the issues of fairness and bias and just the issue of aggregating utilities”
2019-09-30 · Lex Fridman Podcast · Peter Norvig: Artificial Intelligence: A Modern Approach · IDENTIFIED FROM THE TRANSCRIPT · source
“Then I think another thing that we especially note is this time around is in all three of the first editions, we kind of said, well, we're going to find AI as maximizing expected utility. And you tell me your utility function, and now we've got 27 chapters worth of cool techniques for how to optimize that. I think in this edition, we're seeing more, you know what? Maybe that optimization part is the easy part. And the hard part is deciding what is my utility function. What do I want? And if I'm a collection of agents or a society, what do we want as a whole?”
2019-09-30 · Lex Fridman Podcast · Peter Norvig: Artificial Intelligence: A Modern Approach · IDENTIFIED FROM THE TRANSCRIPT · source
“Right, so 95 was the first, and then 2000, 2001 or so. And then moving on from there, I think we're starting to see that again with the GPUs and then more specific type of machinery like the TPUs. And you're seeing custom ASICs and so on for deep learning. So we're seeing another advance in terms of the hardware.”
2019-09-30 · Lex Fridman Podcast · Peter Norvig: Artificial Intelligence: A Modern Approach · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, so it's been a lot of years, a lot of changes. One of the things changing from the first to maybe the second or third Was just the rise of computing power, right? So I think in the first edition, we said here's predicate logic, but that only goes so far because pretty soon you have millions of short little predicate expressions and they couldn't possibly fit in memory. So we're going to use first order logic that's more concise. And then we quickly realized, oh, predicate logic is pretty nice because there are really fast SAT solvers and other things. And look, there's only millions of expressions, and that fits easily into memory, or maybe even billions fit into memory now. So that was a change of the type of technology we needed just because the hardware expanded.”
2019-09-30 · Lex Fridman Podcast · Peter Norvig: Artificial Intelligence: A Modern Approach · IDENTIFIED FROM THE TRANSCRIPT · source