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Pedro Domingos

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2016-08-30
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2016-08-30
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  1. “Sure, artificial intelligence or AI for short is the subfield of computer science that deals with getting computers to do those things that require human intelligence to do as opposed to just routine processing. So things like reasoning, common sense knowledge, understanding language, vision, manipulating things, navigating in the world”

    2016-08-30 · The Knowledge Project with Shane Parrish · #13 Pedro Domingos: The Rise of The Machines · IDENTIFIED FROM THE TRANSCRIPT

  2. “What is on my nightstand right now? Right now, I'm reading another book by Dagar Schafstad, which is a book about how analogy, you know, we were talking about the Five Tries of Machine Learning. He has actually written this book about how it's really all just analogy, right? So Douglas is the ultimate analogy, in fact. He coined the term. And I have actually not completely read the book to date. So that's one book that I'm reading. I'm also reading a book on symmetry group theory because I think this is something that has not been exploited in machine learning and might be the origin of that sixth paradigm. It's something that is used a lot in physics and in mathematics. In fact, some people say physics and mathematics.”

    2016-08-30 · The Knowledge Project with Shane Parrish · #13 Pedro Domingos: The Rise of The Machines · IDENTIFIED FROM THE TRANSCRIPT

  3. “Picture of human history that is very absent from most of what history does today, and I think it's very important to have books like that. And I could go on, but I think those are three of the main ones.”

    2016-08-30 · The Knowledge Project with Shane Parrish · #13 Pedro Domingos: The Rise of The Machines · IDENTIFIED FROM THE TRANSCRIPT

  4. “Well, one book that influenced me a lot was just the first AI textbook that I ever saw. I saw a book in a bookstore called Artificial Intelligence, and I was very intrigued what that might be. And reading that book is actually what set me on the path to AI. Another book that I've read 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 Davis Hofstadter's Gertell Lescherbach. It's an amazing book and very thought-provoking. And it speculates about all sorts of things that have to do with AI and computers, including things that we've been talking about. Another book that I've read more recently that I think is really amazing and really important is Jared Diamond's guns, germs, and steel. I think it gives this large, you know,”

    2016-08-30 · The Knowledge Project with Shane Parrish · #13 Pedro Domingos: The Rise of The Machines · IDENTIFIED FROM THE TRANSCRIPT

  5. “Uber is a company that has also invested heavily into self driving cars, and they of course have a very different and very clear business model, which is they just want to take what they're doing and have the cars not need human drivers anymore. And all of these different models imply different ways to go about doing it.”

    2016-08-30 · The Knowledge Project with Shane Parrish · #13 Pedro Domingos: The Rise of The Machines · IDENTIFIED FROM THE TRANSCRIPT

  6. “Yeah, exactly. And you know, if you're not driving the car, you can't take over in a fraction of a second because you need to build up context by driving the car over a certain amount of time. Also, and this happens with pilots, is if you're not piloting very often, you start to get rusty. And then when the time comes for you to take over, you're actually not as good at doing it as used to be before it was a computer doing the driving or the piloting most of the time. But I think that in the short term the approach of the Teslas and the Toyotas and whatnot will be the prevalent one. I think in the longer run, it will be the Google approach that prevails, right? There's another interesting thing in all this, which is what is the business model of each of these companies? It's one thing to be the companies that are selling cars today. It's not a thing to be Google which actually doesn't have a business model for cars, right? Maybe they're going to sell their software to car companies. It's yet another thing to be, for example, Uber.”

    2016-08-30 · The Knowledge Project with Shane Parrish · #13 Pedro Domingos: The Rise of The Machines · IDENTIFIED FROM THE TRANSCRIPT

  7. “Drive the car. So Google's reaction to this was to say no, we just have to go all the way and have the car be completely self driving.”

    2016-08-30 · The Knowledge Project with Shane Parrish · #13 Pedro Domingos: The Rise of The Machines · IDENTIFIED FROM THE TRANSCRIPT

  8. “That's right. And in fact, it's not just Tesla. If you look at the big automakers like Toyota and BMW and whatnot, I think they're all following this more incremental approach. And I think it makes sense for them because, you know, they're selling cars today and they're not ready to deploy a fully self-driving car. So what they're going to do is they're going to introduce these things one by one. However, I think Google also has an important point, which is born of their experience, which is this notion of mixed control between the human and the car is actually very problematic. If someone is not driving the car and then suddenly the computer says like, oh, shoot, I'm confused. Take over. Then the person will not be very well able to take over. And in fact, they might well crash the car because they're not in the frame of mind”

    2016-08-30 · The Knowledge Project with Shane Parrish · #13 Pedro Domingos: The Rise of The Machines · IDENTIFIED FROM THE TRANSCRIPT

  9. “He was actually doing the wrong thing, except that nobody ever stopped at that red light. And that's the things that the cars have a little bit of trouble dealing with”

    2016-08-30 · The Knowledge Project with Shane Parrish · #13 Pedro Domingos: The Rise of The Machines · IDENTIFIED FROM THE TRANSCRIPT

  10. “Driving cars have no trouble at all dealing with each other. In fact, they can coordinate much better than we human beings can, as a result of which we can put many of them more on the road that spacing can be smaller, we can have fewer traffic jams and so on and so forth. What really makes life hard for self-driving cars is us, the humans. So, you know, it'll be interesting to see when this happens. You know, like the first time that the Google car got into an accident, it was because it stopped at a red light and it got rearrended.”

    2016-08-30 · The Knowledge Project with Shane Parrish · #13 Pedro Domingos: The Rise of The Machines · IDENTIFIED FROM THE TRANSCRIPT

  11. “Yeah, exactly. That part is hard to predict because, again, it involves so many unpredictable things. Again, the cars don't have to be perfect before we start using them instead of people. They just have to get better than people. I think when that will happen, it's hard to predict, but my guess is that five years from now that there will be a lot of self-driving cars around and maybe 10 years from now, 15, most cars will be self-driving. The other thing is that what really makes and ironically, if we took everybody off the roads today and it was all just self-driving cars, it would be much easier.”

    2016-08-30 · The Knowledge Project with Shane Parrish · #13 Pedro Domingos: The Rise of The Machines · IDENTIFIED FROM THE TRANSCRIPT

  12. “Once you're driving in the city, boy, you know, all sorts of stuff can happen. People will cross, you know, people will make strange maneuvers. So that's harder. And I think this is where the frontier is right now. The cars are starting to drive in the city. And I think we are going to get to the point where they are widely deployed, not necessarily because they have become as good as people at dealing with unexpected situations, but because we will also adapt to them, right? I think what's going to happen is that self-driving cars will have to clearly be identified as self-driving cars and that we human beings will know to deal with them differently than we deal with cars driven by people. Again, we will expect different eras or also different ways in which they're reliable, right? Self-deriving cars probably won't do stupid things that people do because they're drunk, but they might do stupid things because they just don't recognize.”

    2016-08-30 · The Knowledge Project with Shane Parrish · #13 Pedro Domingos: The Rise of The Machines · IDENTIFIED FROM THE TRANSCRIPT

  13. “It depends. So here's the crucial question how uncontrolled can the environment in which you're driving be? So why was it that the first thing that we have was self-flying planes long before there was self-driving cars 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 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. A freeway is less controlled than the air, but it's much more controlled than driving in a city.”

    2016-08-30 · The Knowledge Project with Shane Parrish · #13 Pedro Domingos: The Rise of The Machines · IDENTIFIED FROM THE TRANSCRIPT

  14. “Well, I think we could at this point actually, and it might actually win. I think in the past, the technology wasn't ready. And then once the technology is ready, the people have to let it happen, right? So the Indy 500 will have to let a self-driving car compete. I actually wouldn't be surprised if that happened in the next few or several years. I think we're at the point where it could. But sometimes people don't want the computers to or the machines to be competing with them on a field like this. They're actually games where the humans actually refused to play against the computers because actually, you know, it used to be that humans wouldn't play computers because the humans 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.”

    2016-08-30 · The Knowledge Project with Shane Parrish · #13 Pedro Domingos: The Rise of The Machines · IDENTIFIED FROM THE TRANSCRIPT

  15. “I think we will if we find it interesting. So, you know, just like there are these robot contests and these days they're starting to have drone concepts and all that, I could imagine Facebook challenging AlphaGo for a game. I'm not sure that people will be interested in that. I think more likely what will happen is that people will still be playing each other even though the best players are computers in the same way that there's race cars that go way faster than people. But we still have people doing in the Olympics. What you have is people competing against each other to see who can do whatever 100 meters faster or the marathon fastest. And people are interested in race cars because they're driven by human pilots. If those race cars were self-driving, we probably would be less interested.”

    2016-08-30 · The Knowledge Project with Shane Parrish · #13 Pedro Domingos: The Rise of The Machines · IDENTIFIED FROM THE TRANSCRIPT

  16. “So, first of all, that type of computing power was not available back then. But DeepMind used enormous amounts of computing, not just while it was playing live in Seoul against Lisidol, but in the months that it spent learning by playing against itself. I don't know exactly how much servers Google use, but I've heard that it was a very large number continuously playing for months on end.”

    2016-08-30 · The Knowledge Project with Shane Parrish · #13 Pedro Domingos: The Rise of The Machines · IDENTIFIED FROM THE TRANSCRIPT

  17. “Doing the same thing. So number one alpha Go is using machine learning, whereas Deep Blue wasn't, and without machine learning, I don't think we would ever figure out how to win Etgo. And the second one is the amount of computing power that was used by AlphaGo completely dwarfs the amount of computing power that was used by Deep Blue.”

    2016-08-30 · The Knowledge Project with Shane Parrish · #13 Pedro Domingos: The Rise of The Machines · IDENTIFIED FROM THE TRANSCRIPT

  18. “Yeah, so number one, right? As I said, deep blue used no machine learning. AlphaGo used machine learning extensively. So AlphaGo, the first thing that AlphaGo did was it learned from all the existing, the entire existing database of Go matches played by human masters. That was the first thing it did was learn from those, right? 30 million moves or something like that is the figure I think I heard. And then the next thing that it did was actually it learned by playing against itself. Two versions of AlphaGo would play and then AlphaGo would learn from which one had won. This is actually a very, very old idea in machine learning. It's one of the oldest IDs. It goes all the way back to the 50s. And this researcher at IBM called Arthur Samuel, who actually wrote the first machine learning system to learn to play a game. And the system learned to play checkers by playing against itself. So AlphaGo was”

    2016-08-30 · The Knowledge Project with Shane Parrish · #13 Pedro Domingos: The Rise of The Machines · IDENTIFIED FROM THE TRANSCRIPT

  19. “Then maybe you can use deep learning as the pattern recognizer and then put that into the game search. And this is in essence what DeepMind did, and it worked amazingly well.”

    2016-08-30 · The Knowledge Project with Shane Parrish · #13 Pedro Domingos: The Rise of The Machines · IDENTIFIED FROM THE TRANSCRIPT

  20. “Exactly, it's completely infeasible to do that type of search for Go. And for a long time, people actually had no idea how to solve Go. And when you look at how human beings, how human experts play Go, it's almost like what they're doing is instead of having these few explicit features of the board, they're doing this kind of visual pattern recognition, right? They look at the board and they, again, they have a hard time explaining what it is that made them make that move, but somehow their visual system and their brain figured out what was the right thing to do. And so what DeepMind did with AlphaGo was to actually combine some of the classic AI game search with deep learning, with this type of neural network approach to do the evaluation. Since the evaluation of board positions is effectively a bit like a visual pattern recognition problem, and deep learning is very good at visual pattern recognition. In fact, that's what it's best at.”

    2016-08-30 · The Knowledge Project with Shane Parrish · #13 Pedro Domingos: The Rise of The Machines · IDENTIFIED FROM THE TRANSCRIPT

  21. “Sure, so deep blue was very much classic AI. There was no machine learning involved. Deep blue essentially it was just doing a very clever and very extensive search for the best moves to make. And the way, and this is the way classic game playing programs work is that they look at a board position and evaluate how good it is. And in things like chess and most other games, this is not that hard to do. For example,”

    2016-08-30 · The Knowledge Project with Shane Parrish · #13 Pedro Domingos: The Rise of The Machines · IDENTIFIED FROM THE TRANSCRIPT

  22. “Type of thing with machines. So to some degree, the machines are going to have to become better at doing this. To some extent, we humans are going to have to get more comfortable with the notion that even though we don't completely understand what's going on, it's actually good that the machines have a handle on these complex phenomena that without them we wouldn't.”

    2016-08-30 · The Knowledge Project with Shane Parrish · #13 Pedro Domingos: The Rise of The Machines · IDENTIFIED FROM THE TRANSCRIPT

  23. “And there's no reason why they can't be something that you hear a lot of today is like, oh, learning organs are black boxes, we're just going to have to learn to live with the metro. The learning guns don't have to be black boxes. There's actually no reason why I shouldn't be able to say to the Amazon recommender system, why did you recommend that book to me? Or, you know, I just bought a watch. Please don't recommend more watches because I don't want to buy a watch now. That's the last thing I want to buy. So you should be able to have this type of richer interaction with a learning algorithms. And in fact, it's what we already do with other people. So when you decide, when your brain decides to do something, and then let's say you're the doctor and just tell someone, you know, you probably should get surgery because blah, blah, and then somebody asks why, you're able to tell them why in natural language. The things that you tell them are not a complete explanation of what went on in your brain. In fact, you don't even know what is a complete explanation of what your brain. But it's enough for the people to understand and for trust the result. And you can do the same.”

    2016-08-30 · The Knowledge Project with Shane Parrish · #13 Pedro Domingos: The Rise of The Machines · IDENTIFIED FROM THE TRANSCRIPT

  24. “Well, so that is exactly the key advantage of machines is that they can take an unlimited number of variables into account, very much unlike humans that are much more limited. Our brains are very good at things like vision, where we do take millions of variables into account and motion and whatnot. But for other problems, we are very, very limited and the machines aren't. So what's going to happen is that the machines are going to be able to learn much more complex models of the phenomena than human beings ever could. And this is good, right? Because with those better models, we can make better decisions, with the better model of the cell, we can cure cancer, so on and so forth. Having said that, it'll still be important for people to trust what the computers are saying. And if they don't understand it, they won't trust it. I think what's going to happen is that partly the learning algorithms are going to have better, to get better at explaining to people what they're doing. And again, some of them are better than others.”

    2016-08-30 · The Knowledge Project with Shane Parrish · #13 Pedro Domingos: The Rise of The Machines · IDENTIFIED FROM THE TRANSCRIPT

  25. “Not clear. So we do have, for example, an era 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 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 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 we don't know.”

    2016-08-30 · The Knowledge Project with Shane Parrish · #13 Pedro Domingos: The Rise of The Machines · IDENTIFIED FROM THE TRANSCRIPT

  26. “Yeah, so there's again just looking, just extrapolating from the past, what happens is that there are these phase transitions and the phase transitions build on each other. So one capability makes other capabilities possible. So electricity makes computers possible and then computers make AI possible and whatnot. And these things do build on each other. But when they happen and how large they are is extremely hard to predict. So no one knows exactly, for example, the current surge of progress in AI, how long it's going to last before it flattens out. Will it be 10 years? Will it be 20? Will it, you know, how far will it go? We can't assume that it will just run away from here without any more plateaus or interruptions. That would be very unusual, actually.”

    2016-08-30 · The Knowledge Project with Shane Parrish · #13 Pedro Domingos: The Rise of The Machines · IDENTIFIED FROM THE TRANSCRIPT

  27. “And that's their argument. I think that argument is actually very dubious because, in reality, no exponential goes on forever. Because there's always a limit because the world is finite. So actually what happens with all of these technology curves is that in the beginning they look like exponentials, but then they flatten out. So they're what are called S curves. Technology growth curves are always S-curves. The first part of an S curve is actually mathematically indistinguishable from an exponential, so it's easy to look at that part and say like, oh, exponential growth, we're headed for a singularity. Actually, what we have is one of these S-curves and we're headed for a phase transition. Once the phase transition is done and things flatten out again, things could be very, very different from what they are now, and I think they will be in the case of AI, but I don't think we're going to see this infinite growth that goes completely beyond what humans can imagine.”

    2016-08-30 · The Knowledge Project with Shane Parrish · #13 Pedro Domingos: The Rise of The Machines · IDENTIFIED FROM THE TRANSCRIPT

  28. “So, what happens is that we start with machines that are not very intelligent, but if each one of them can produce a machine that's more intelligent than the previous one, then maybe the intelligence will just take off and leave human intelligence in the dust. And the first people to speculate about this were, you know, John von Neumann, who's the one of the founders of computer science. Now, the basic evidence that people like Kurtzwhile deduced in support of this is they show all these curves of exponential progress. You see the progress just getting faster and faster and you're extracting this into the future and it's just going to a singularity mathematically is a point at which a function goes to infinity.”

    2016-08-30 · The Knowledge Project with Shane Parrish · #13 Pedro Domingos: The Rise of The Machines · IDENTIFIED FROM THE TRANSCRIPT

  29. “So, the singularity is this notion that if machines can learn, right, think of what we've been talking about. So you have an algorithm that makes another algorithm, right? My machine learning algorithm makes an algorithm to do medical diagnosis or play chess or whatever. But by the same standard, we can actually have a learning algorithm, make another learning algorithm. And if the learning algorithm is able to make a better learning algorithm, then that learning algorithm makes it even better learning algorithm potentially.”

    2016-08-30 · The Knowledge Project with Shane Parrish · #13 Pedro Domingos: The Rise of The Machines · IDENTIFIED FROM THE TRANSCRIPT

  30. “But that decision, even though it was partly made with recommendations from a computer, I probably will always want to make it myself. I'm not just going to go to New York because the computer told me to. So I think, you know, what we see today is already this very intricate mesh of what's decided by humans and by computers. So, you know, somebody wants to find a date while they may have a dating site to help them find the date, but then they decide to go to dinner with them. And then they help, you know, so that's their decision. But then maybe they use Yelp to decide where to go to dinner. And then they drive the car to dinner, but it's GPS that's telling them where to turn, although it's still them driving, right? So there's this very intricate mesh of the human and the machine. And I think it's only going to get more intricate in the future. But ultimately, I think most things will be done by machines except the really key decisions that people will always want to retain, even though they make them with advice from the machine.”

    2016-08-30 · The Knowledge Project with Shane Parrish · #13 Pedro Domingos: The Rise of The Machines · IDENTIFIED FROM THE TRANSCRIPT

  31. “Trusting them that they are making the right calls and that they would do what we would do 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 those are the highest level decisions. I think the decisions on how to accomplish our goals, they can be taken by, you know, like I want to get from here to New York. I made that decision, but now how I get flown there? Well, sure. I'm perfectly okay with the plane being flown by an algorithm or maybe the car that drives me to the airport also being an algorithm. And maybe I decided to go to New York because of something that some computer advised me about. I said like, oh, there's this great whatever thing that you should do in New York that's going to be this festival that you should attend or there's these people that you need to meet.”

    2016-08-30 · The Knowledge Project with Shane Parrish · #13 Pedro Domingos: The Rise of The Machines · IDENTIFIED FROM THE TRANSCRIPT

  32. “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. 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. Pilots tend to take the controls that landing and takeoff, which are actually the more dangerous moments. And they make more errors than the computers do. But people feel comfortable having a pilot in the copy. 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 making these calls.”

    2016-08-30 · The Knowledge Project with Shane Parrish · #13 Pedro Domingos: The Rise of The Machines · IDENTIFIED FROM THE TRANSCRIPT

  33. “Yeah, and a lot of the way I think is, and this is part of why people need to become aware of machine learning, is that we should actually be our own gatekeepers. Ideally, we wouldn't rely on third parties to be our gatekeepers. And often the way a lot of change comes about is because people take on that role. So, for example, doctors initially were not very interested in the web or in computers. These days they have to be because patients will come to them and say, well, you said A, but I actually looked on the web and the web says that B, so what is it? And now this forces the doctors to start looking at the web. And once, for example, these machine learning systems become more widely available as they are becoming, people will start using them and the doctors will be forced to catch up. Same thing in a lot of large companies, right? The IT department says, no, we're going to use A. But then everybody starts using B and after a while the IT department just has to fix reality and start using B as well. So I think the same kind of thing can and will happen with machine learning.”

    2016-08-30 · The Knowledge Project with Shane Parrish · #13 Pedro Domingos: The Rise of The Machines · IDENTIFIED FROM THE TRANSCRIPT

  34. “For those who are just starting. For example, because you could develop something that is enough of a major innovation that it outweighs those things in the same way that Google had enough of an innovation with Pedrank and so on that even though they were just starting, they actually did a lot better than the dominant search engines of the time like, for example, Alta Vista. So this is one aspect. And the other one is that precisely because machine learning is something that can be used just about everywhere in every single industry, in every single part of what a company does. So far it's only been used for a small fraction of the things that it could be used for. So you could do a startup that comes in and does machine learning for X where nobody has really done machine learning for X before and they could just run away with it even if initially their learning algorithms are not the most advanced ones. They're just picking the low hanging fruit. You could actually get a lot of mileage that way.”

    2016-08-30 · The Knowledge Project with Shane Parrish · #13 Pedro Domingos: The Rise of The Machines · IDENTIFIED FROM THE TRANSCRIPT

  35. “I think some of the advantages permanent in the sense that there's this network effect of data where if you have a good product and people start using it, then you have a lot of use. This, for example, how Google has built up such an unassailable position in search, right? It's like you use their search engine, therefore they have a lot of data to learn from, therefore, the search gets better. Therefore, more people use the search engine. And so you have even more data to learn from. So someone coming in from scratch trying to learn from initially no data or very little data will have a very hard time competing with Google or Bing. So I think in some aspects, this first mover advantage is extremely important, you know, because you have more data and also because you have, you've hired a data scientist, you've developed the algorithms, there is a race to develop better machine learning algorithm, and there is certainly an advantage to coming first. Having said that, there are also lots of opportunities.”

    2016-08-30 · The Knowledge Project with Shane Parrish · #13 Pedro Domingos: The Rise of The Machines · IDENTIFIED FROM THE TRANSCRIPT

  36. “Have in front of you when you see a page of results from Google or when you go to a web page. And Google has models of what people will click on. The advertisers have models. There's companies like Rocket Fuel who basically work for the advertisers to model the users for them. The content providers have models of what will be clicked on and whatnot. So basically everybody is modeling everybody. And all of these models are evolving in tandem. So I think we're already starting to see this, but we will see it even more in the future. Another example is fraud detection. Obviously, another example is things like law enforcement and counterterrorism. So the examples are Legion.”

    2016-08-30 · The Knowledge Project with Shane Parrish · #13 Pedro Domingos: The Rise of The Machines · IDENTIFIED FROM THE TRANSCRIPT

  37. “In fact, what these algorithms are doing, whether or not they know it, is modeling each other. And what typically happens when somebody deploys a neural network to predict a certain stock, like for example, you might have 3,000 networks each predicting one stock in the Russell 3,000, is that it works for a few weeks and then it stops working gradually because somebody else has started to model what those algorithms were doing and therefore now those people are making the money and this is never ending. And in fact what some of these people are doing now is they are combining connectionist learning with evolutionary learning because you actually in order to be able to learn faster and more broadly than the neural networks could. And another area where you already see this happening is online ads. So the whole online ad market is, you know, there's these auctions among advertisers to put the”

    2016-08-30 · The Knowledge Project with Shane Parrish · #13 Pedro Domingos: The Rise of The Machines · IDENTIFIED FROM THE TRANSCRIPT

  38. “Many other eras. 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.”

    2016-08-30 · The Knowledge Project with Shane Parrish · #13 Pedro Domingos: The Rise of The Machines · IDENTIFIED FROM THE TRANSCRIPT

  39. “Oh, this is already happening, and it's going to happen even more in the future. 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 99% accurate. They were very good at tagging an email as being spam or being a legitimate email. But then guess what? Once those spam filters were deployed, the spammers figured out ways around them. They figured out how to exploit the weaknesses of the spam filters and do things that would get through. And there's been this ongoing arms race ever since then where the spammers come up with new tricks. The machine learning 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”

    2016-08-30 · The Knowledge Project with Shane Parrish · #13 Pedro Domingos: The Rise of The Machines · IDENTIFIED FROM THE TRANSCRIPT

  40. “With you, all of these things computers are not yet able to do today. Maybe they will in the future, and certainly the boundary between what is best done by the machines and what is best done by humans will keep changing. But I think for the foreseeable future, in most jobs, it will be a combination in human and computer that works best.”

    2016-08-30 · The Knowledge Project with Shane Parrish · #13 Pedro Domingos: The Rise of The Machines · IDENTIFIED FROM THE TRANSCRIPT

  41. “Yeah, I think exactly. This is true in the medical field, and I think in most fields, and as you mentioned, chess is a great example because it's not like, you know, when deep blue beat Kasparov, well, now the world chess champion is a computer, and ever since then computers have been the world chess champions. Actually, that's not the case. The best chess plays in the world today are what are called centaurs in the community. They're a team of a human and a computer. So human and a computer can actually together can actually beat a computer. And this is precisely because the human and the computer have complementary strengths and weaknesses. And the same thing that I think that is true of chess, I think, is true of medical diagnosis. It's true of a lot of other things. So for example, there's more, of course, to being a doctor than just doing diagnosis, right? There's interacting with the person, there's reading how they're feeling from how they interact.”

    2016-08-30 · The Knowledge Project with Shane Parrish · #13 Pedro Domingos: The Rise of The Machines · IDENTIFIED FROM THE TRANSCRIPT

  42. “And they're not very interested in replacing themselves of their job that they like best by machines. But eventually it is going to happen and it is starting to happen. For example, in situations where doctors are not available, and so nurses can use this or for patients that need constant monitoring or in low resource situations where people can't afford the doctors and so on.”

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  43. “Yes, so machines are remarkably better than human doctors at doing all types of medical diagnosis, not just from x-rays, but from symptoms. You have a patient, you have their symptoms, what is the diagnosis? And even very simple machine learning algorithms running on fairly small databases of patients, like maybe with only hundreds of thousands of patients, typically do better than human doctors. And part of the reason is that algorithms are very consistent, whereas human beings are very inconsistent. They might be given the same patient in the morning and the afternoon and have different diagnosis just because they're in a better mood or they forgot something. So human beings are very noisy in that regard. And, you know, and if you're the patient, that's actually not a good thing. So I think for things like this, machine learning is a very desirable thing to use. In the particular case of medicine, it's not used more already because, of course, the doctors are also the gatekeepers of the system.”

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  44. “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 precisely because they do not come naturally to human beings, but machines don't have that type of difficulty. So in some ways, the jobs that are easy to automate are different from the ones that people often think are.”

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  45. “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 things that are more 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, 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 lawyers, doctors, et cetera, right? We've already talked about medical diagnosis as an example, where something like, for example, construction work is very hard to automate, right? Because that type of work takes advantage of abilities that evolution took 500 million years to develop. They seem easy because we take them for granted.”

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  46. “There's a couple of implications. One of them is great. Well, now, because of that, we can have better radios and better amplifiers and better filters and whatnot. So it's a gain. The other side of this is that, well, maybe we don't need all”

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  47. “Yeah, not just the patent, but a whole series of them that I think they have dozens or more of patents for typically things like electronic devices at this point.”

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  48. “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 track.”

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  49. “Well, there's certainly a lot of people working on these things. So 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. There's a lot of work on combining connectionist learning and Bayesian learning or connectionist and evolutionary. In essence, all of these combinations are things that people are working on. And these days we have Gondas Fries and 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. Others say that yeah no none of these really has everything that it takes. It's going to take some new ideas. It's going to take maybe some entirely new paradigm. And my gut feeling is that actually it's more the latter. I do believe that we have made a lot of progress, but I think we are still missing some important ideas. And in fact, part of my goal in writing the book was to try to get people.”

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  50. “Exactly. So each of these schools has its own master algorithm. For example, the master algorithm connection, this is called backpropagation because it's based on propagating errors from the output back to the input. And the Bayesians is called probabilistic inference, the evolutionaries have genetic programming, the symbolists have inverse deduction, and the analogizers have what are called kernel machines. The mass of algorithm would actually be a single algorithm that unifies all of these into one. Again, think of the analogy with physics. So Maxwell unified the 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 naturally do what each of these five can.”

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