The Exchanges

Q And so normally we're used to natural and this kind of begs the question, um, that we had talked about over dinner, which is where does knowledge come from? Yeah.

A So the knowledge that we human beings have that makes us so intelligent, uh, comes from a number of different sources. The first one, which people often don't realize is just evolution, right? We actually have a lot of knowledge encoded in our DNA that makes us what we are. Uh, That is the result of a very long process of weeding out the things that don't work and, you know, building on the things that do work. And then there's knowledge that just comes from experience. Uh, that's the knowledge that you and I acquire by living in the world, and that's encoded in our neurons. And then, um, equally important, there's the knowledge that, the kind of knowledge that only human beings have, which is the knowledge that comes from culture, from talking with other people, from reading books, and, and, and so on. So these are the sources of knowledge in natural intelligence. The thing that's exciting today is that there's actually a new source of knowledge on the planet, and that's computers. Computers discovering knowledge from data. And I think this emergence of computers as a source of knowledge is going to be every bit as momentous as the previous three were. And also notice that each one of these sources of knowledge produces far greater quantities of knowledge far faster than all the previous ones. So for example, you learn a lot faster from experience than you do from evolution an…

AI assessment note: “comes from a number of different sources. The first one... is just evolution”

Q And then the master algorithm is one view of where they all come together?

A Exactly. So each of these schools has its own master algorithm in, for example, the master algorithm, the connection, this is called backpropagation, because it's based on propagating errors from the output back to the input. And the Bayesian's is called, um, Uh, probabilistic inference. The evolutionaries have genetic programming. The, um, the symbolists have inverse deduction, and the analogizers have what are called kernel machines. Uh, the master algorithm would actually be a single algorithm that unifies all of these into one. Again, think of the analogy with physics. You know, so Maxwell unified electricity and magnetism and light into one set of equations. And now the standard model has actually unified those with, you know, the strong and weak nuclear forces. So the idea here is we should be able to have a single machine learning algorithm that can actually do what each of these five can.

AI assessment note: “The master algorithm would actually be a single algorithm that unifies all of these”

Q It seems like everybody's getting into artificial intelligence, artificial intelligence, machine learning from Facebook and IBM to Amazon and Google. Do you see, do you envision a world, like, 10 years out, I have a bit of a mischievous mind, so, where people are trying to feed other people's algorithms false signals to change the machine learning, or is that just crazy?

A Oh, this is already happening, and it's going to happen even more in the future, right? So what happens whenever you deploy a machine learning system is that the people who are being modeled Change their behavior in response to the system. Sometimes in benign ways, but sometimes in adversarial ways. A classic example of this is spam filters. The first spam filters were extremely successful. They were, you know, 99% accurate. They were very good at tagging an email as being spam or, or, or being a legitimate email. But then guess what? Once those spam filters were, were deployed, the spammers figured out ways around them. They figured out how to exploit the weaknesses of the spam filters and do, do things that would get through. And there's been this, you know, ongoing arms race ever since then, where the spammers come up with new tricks, the machine learning, you know, algorithms together with the data scientists come up with ways to defeat those tricks. And this just keeps going. And I think the same thing is going to be true in many other areas. In fact, two other areas where you can already see things like this very much happening. One of them is actually the stock market, right? The stock market is largely a bunch of algorithms trading against each other. And in fact, what these algorithms are doing Whether or not they know it is modeling each other. And what typically happ…

AI assessment note: “Oh, this is already happening, and it's going to happen even more in the future”

Q This is fascinating. I could go on for hours. I know we're coming up to our time limit here, so I just have two questions left, uh, switching subjects a little bit here. What, um, books would you say had the most impact on you, uh, in your life in terms of, do you keep coming back to, or that changed the way you see the world?

A Well, one book that influenced me a lot was, uh, you know, just the first AI textbook that I ever saw, you know, I saw a book in the bookstore called Artificial Intelligence, and I was very intrigued what that might be, and reading that book is actually what, what set me on the path to AI. Another book that I've read, uh, you know, that is related to AI, and that I know has influenced a lot of people into becoming AI researchers, in my case, I was already on that path before I read it, is, is Douglas Hofstadter's Gödel Escherbach. Uh, it's an amazing book and, you know, very thought provoking and, you know, it speculates about all sorts of things that have to do with AI and computers, including things that we've been talking about. So that has been another very influential book. Another book that I've read, you know, more recently that, that I think is really amazing and really important is Jared Diamond's, um, uh, Guns, Germs, and Steel. I think it gives this large, you know, you know, picture of, of human history that is very absent from, from most of what history does today, and I think, uh, you know, it's very important to have books like that, and, you know, and I could go on, but I think those are three of the main ones.

AI assessment note: “Well, one book that influenced me a lot was, uh, you know, just the first AI textbook”

Q So this is fundamentally different, machine learning, than traditional computer science, which is you have an input Um, you give it to an algorithm which generates an output, and now we have, I think, the output and the data going into the algorithm, which is creating another algorithm, or am I misunderstanding that?

A Exactly. So what happens in traditional computer science, and really, you know, everything that we know about the information age was created that way, is that somebody has to write down an algorithm that turns the input into the desired output. So for example, if I want to, um, I don't know, diagnose x-rays of, of, of people's chest to decide whether they have lung cancer or not. I have to write an algorithm that takes in the pixels of that image and outputs a prediction saying, you know, here's where the tumor is or there's no tumor. And this is very, very hard to do. And in fact, for some things, we don't even know how to teach the computer to do them. The difference with machine learning is, is that The computer doesn't have to be programmed by us anymore. The computer actually programs itself. You give it the examples of the input and the output, like, for example, a lot of pairs of here's the x-ray, here's the diagnosis, here's the x-ray, here's the diagnosis. And by looking at that data, the computer figures out what is the algorithm that would turn one into the other. And the thing that's amazing is that often just by taking a basic machine learning algorithm and applying on a database of, for example, x-rays and diagnosis, you actually wind up with something that is better. That, for example, uh, you know, pathology than a highly trained human being would be. And the o…

AI assessment note: “Exactly. So what happens in traditional computer science... The difference with machine learning is”

Q And are they working together to kind of combine them? Or is it going to be like a sixth one that is created in your mind that kind of supersedes these?

A Well, there's certainly a lot of people working on these things. So, um, there's a lot of people, for example, working on combining two of these paradigms. There's a lot of work, for example, on combining symbolic learning with Bayesian learning. Uh, there's a lot of work on combining, you know, connectionist learning and, and vision learning or connectionist and evolutionary. In essence, all of these combinations are people, are things that, that people are working on. And these days we have, you know, Gondas, you find three, four, maybe even all five of them. So some people believe that we will solve the problem this way, and that in fact we're very close to solving it this way. Uh, others say that, that yeah, no, none of these really has everything that it takes. It's gonna take some new ideas. It's gonna take maybe some entirely new paradigm. And my gut feeling is that, is that actually it's more the latter. I, I do believe that we have made a lot of progress, but I think we are still missing some, some important ideas. And in fact, Part of my goal in writing the book was to try to get people from outside the field interested in these problems, because in some sense they are more likely, ironically, to have these new ideas than the people who are already professional machine learning researchers and are thinking along a specific track, and then it's hard to jump out of that…

AI assessment note: “And my gut feeling is that, is that actually it's more the latter.”

Q But just to be clear, we do have algorithms that are spitting out better versions of algorithms that are generating algorithms, or is that a futuristic kind of statement?

A Um, not clear. So we do have, for example, an error of machine learning. It's called the meta learning, which is precisely the learning algorithms learning to make better learning algorithms. And this type of meta learning in certain basic forms is actually already widely used today. Like for example, Netflix uses this type of thing, uh, to recommend movies. It doesn't just use one learning algorithm. It uses a whole bunch of them. And then another algorithm on top of that, that is learning how to use their results. And for example, the way IBM Watson wanted Jeopardy was using this type of learning. Having said that, this is still quite limited in what it can do, and it's not, we don't have enough at this point for this thing to set up this loop where it just keeps getting better and better. That hasn't happened yet, but it will be very interesting to see if we can make it happen. You know, for all I know, some kid in the garage today has actually invented that algorithm, but, but we don't know.

AI assessment note: “meta learning, which is precisely the learning algorithms learning to make better learning algorithms”

Q Do you think that the input that we have to make the decisions changes in the sense of the quantity of data, the reliability of the observations we're making from machine learning would be higher or?

A Yes. So all of those things are factors. I think where machine learning has a big advantage over human intelligence is Is that it can, it can take in vastly larger quantities of data. And as a result of which you can learn more and it can also be more certain if that data, you know, is very consistent with this piece of knowledge. Where it has the disadvantage is that machine learning is very good. Machine learning today is very good at learning about one thing at a time. The thing that humans have is that they can bring to bear knowledge from all sorts of directions. So, you know, take for example, the stock market, the traditional machine learning algorithms and people started using neural networks to do this in the eighties. They just learned to predict the time series from the stock itself and maybe other related time series in a way that human beings couldn't, but human beings could know that, oh, you know, today, you know, they began a war between Russia and, you know, the Ukraine and, you know, human beings can try to factor this in. Whereas, you know, the algorithms couldn't, or, you know, the fed just said that it's going to raise interest rates or something like that. So human beings can bring, can bring a lot of knowledge to bear. That the algorithms don't have. Having said that, what we see even from the eighties to now is that the machine learning algorithms are st…

AI assessment note: “Yes. So all of those things are factors. I think where machine learning has”

Q What are the limitations on the decisions that we'll let machines make? Do you think we'll let machines, uh, do you think we'll get to a place soon where machine, there's no pilots in airplanes, where a machine can, you know, sentence someone to death, where, um, boardroom mergers and acquisitions are made solely based on algorithms?

A And yeah, that's a very interesting question. It's really not so much a technological question as a sociological one. I think what will limit, I think over time we will see more and more things being done by machines. And as we get comfortable with it, we will have no problem handing control to machines. Like for example, airplanes is an example, right? Every commercial airline is actually a drone. It's flying itself. And in fact, it would be safer if it was completely flown by a computer. You know, pilots tend to take, you know, the controls at landing and takeoff, which are actually the more dangerous moments. And they make more errors than the computers do. But you know, people feel comfortable having a pilot in the cockpit, but we already have two people in the cockpit instead of three, and then we'll have one and eventually we'll have zero. So I think there are a lot of decisions that we will gradually become more comfortable. It's partly a matter of just psychologically adapting ourselves to this notion that the machines are, are, are making these calls and, and trusting them that they are making the right calls and, and, and that they would do what we would do, uh, if we were making the calls ourselves. I think at the end of the day, there will be some things that we will always reserve the right to make our decisions about. And I think, um, you know, those, those, those…

AI assessment note: “there will be some things that we will always reserve the right to make”

Q before we get into the different ways of machine learning, I believe there's a couple different schools of thoughts there that I really want to hone in on and get some information. From you on, I want to better understand how you see this kind of propagating in the sense of we don't understand how the algorithms are working anymore. At some point, they're just self evolving. Are they not?

A Well, um, we, it's different from traditional programs, right? With traditional programs, we understand every little detail of how they work because we created it and we debugged it until it did exactly what we wanted. And certainly with machine learning, things are very different. Because to some extent, we don't fully understand what the algorithm is doing, and in some way that it's, that's, it's power, right? It can actually know way more than any of us could. Having said that, you know, we, the machine learning researchers and the data scientists, we actually have a good understanding of how the learning algorithm itself works. You know, what is it, what is it that it does to learn, and how could you make it learn better? And, and then, you know, there's the, there's a different issue, which is the understanding of what the algorithm produces.

AI assessment note: “to some extent, we don't fully understand what the algorithm is doing”

Q With self-driving cars, we're there now. Do you think from a technological point of view, just not a psychological one, or do you think we're, you know, a couple years away from that?

A It, it depends. So the, the, here's the crucial question is how uncontrolled can the environment in which you're driving be, right? So why was it that the first thing that we have was self-flying planes? Long before there was self-driving cars, there were already autopilots. It's because in the air, there's very little unexpected that can happen. So for that, you don't even need AI, just, you know, classic control systems and software engineering will actually do that for you. And then the next step is, well, what about driving a car on the freeway? Driving a car on the freeway is something that I think the technology to do that is there. The freeway is less controlled than the air, but it's much more controlled than driving in the city.

AI assessment note: “Driving a car on the freeway is something that I think the technology to do that is there.”

Q What do you see as the implications to algorithms being able to patent the algorithms that they effectively have created almost without human intervention or human understanding?

A There's a couple of implications. One of them is, is great is, well, now because of that, we can have better radios and better amplifiers and better, you know, filters and whatnot. So it's, it's a game. The other side of this is that, well, maybe we don't need all those engineers as much as we did before, which kind of touches on an interesting aspect of all this, which is as much as there's a huge shortage of computer scientists and engineers and so on today, in the long run, Things like this are easier to automate than, than things that are more, you know, from the humanities and social science and so on. So people often think that the easiest jobs to automate are like the blue collar ones. Uh, but actually our experience in AI is that it's actually more the opposite. It's often white collar jobs that are easy to automate. For example, things like engineering and, you know, and, and, um, you know, lawyers, doctors, et cetera. We've already talked about medical diagnosis as an example. Where something like, for example, you know, construction work is very hard to automate, right? Because that type of work takes advantage of, Abilities that evolution took five hundred million years to develop. They seem easy because we, we take them for granted. But things like being a doctor or an engineer or a lawyer that you have to go to college to do. Well, you have to go to college precis…

AI assessment note: “There's a couple of implications. One of them is, is great is, well, now”

Q Why haven't we had a self-driving race car in like the Indy 500?

A Well, I think we could at this point actually, and it might actually win. Uh, I think in the past, the technology wasn't ready. Uh, and then once the technology is ready that people have to let it happen, right? So the Indy 500 would have to let a self-driving car compete. I actually wouldn't be surprised if that happened in the next, you know, few or, or, or several years. I think we're at the point where it, where it could. Uh, but, but, you know, um, sometimes people don't want the computers to be, uh, or the machines to be competing with them on, on a field like this. You know, there are actually games where the humans actually refused to play against the computers because in some, actually, you know, it used to be that humans wouldn't play computers because the humans were, were sure to win. There's also other areas where the humans won't play the computers because the computers are sure to win, so it depends.

AI assessment note: “in the past, the technology wasn't ready. Uh, and then once the technology is ready”