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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. “Naive, but it's actually quite powerful. Again, you can actually prove that if you give an approach like this enough data, it can learn anything. So those are the five main schools of machine learning.”

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

  2. “The new malaria drug. And once you have one robot scientist like this, there's nothing keeping you from making millions and then science will progress correspondingly faster. And then finally, the last major school of machine learning is inspired by several fields, but probably most importantly by psychology. And this is the idea of learning by analogy. So there's a lot of evidence, and this to most people is actually quite intuitive, that we do a lot of learning and reasoning by analogy. When we're faced with a new situation, what we do is we retrieve from memory similar situations that we experienced in the past, and then we try to extrapolate from one to the other. The solution that applied in the previous situation, we apply it or transform it to applying the new one. So for example, if you want to do medical diagnosis in this way, what you would do when you have a new patient to diagnose, you look for the patient in your file with the most similar symptoms, and then you assume that the diagnosis will be the same.”

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

  3. “Data and then you either throw out those hypotheses or refine them and you keep going like that. So it's very much the way the scientific method works except it's being done by machines instead of by human scientists and therefore it's much faster and can discover a lot more knowledge. And in fact one of the applications of this that's quite exciting is in the UK they developed this complete robot scientist it's a robot biologist It actually does this whole process, including carrying out the experiments using microarrays and gene sequences and whatnot and it starts out with basic knowledge of biology, molecular biology like DNA, proteins, regulation and so on and then it develops models of the cells that it's looking at and in fact a couple years ago the robot is called Eve there was a previous one called Adam a couple years ago Eve actually discovered”

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

  4. “In the beginning, you have what's called your prior probability, which is how much you believe in each hypothesis before you see any evidence. And then as you see more evidence, your belief in the hypothesis evolves. So the hypothesis that I consistent with the evidence that you're seeing become more likely, and the ones that are inconsistent become less likely, and hopefully at the end of the day, some one or a few hypotheses shine through. But even if they don't, you're always in a position to make decisions by letting those hypotheses vote with the weight that's proportional to how probable they are. So this is vision learning. Another more first principles approach is symbolic learning. The idea in symbolic learning is to learn in the same way that scientists and mathematicians and logicians do induction. You look at the data, you formulate hypotheses to explain the data on you.”

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

  5. “They work better than the ones that were developed by human engineers. They typically completely different. So, the things that no one would ever think of, but they're good enough that the US Patent Office actually granted them patents for these things. So that's another approach. Both of these are inspired by biology in one way or another. Most machine learning researchers actually think that taking inspiration from biology is not a great idea, even if it's superficially appealing, because biology is random and who knows if it's actually doing the best thing. So most machine learning researchers, they believe in doing things more from first principles. And one way of doing things from first principles, which gets back to this theme of uncertainty, is Bayesian learning. So the idea in Bayesian learning is that you start out with a large number of hypotheses, and they always insert, so you quantify how much you believe in each hypothesis using probability.”

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

  6. “In Chinese and vice versa to things like image recognition and whatnot. So, this is one approach. Another approach is to emulate not the brain, but evolution. So, you know, your brain is great, but if you think about it, evolution made the brain in the first place and made it. Body and made all creatures on earth. So that's a heck of a learning algorithm. So maybe what we can do is simulate evolution on the”

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

  7. “Yeah, so there are five main ones, all of them quite interesting because each one of them has its origins in a different field of science. So one that is very popular today is learning by emulating the brain. So the greatest learning algorithm on earth is the one inside your skull by definition has learned everything that you know and everything that you remember. So we can take inspiration from the neuroscience, see how brain circuits work, how neurons work, how they're put together, how they learn, which is by strengthening synapses, and then develop algorithms that try to do the same thing in a simplified form. And indeed, there are some very, very successful applications today of this type of learning. Like, you know, for example, speech recognition on Android phones and, you know, the kind of simultaneous translation that Skype can do where you speak in English and somebody might hear you.”

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

  8. “Decide whether their business is becoming better or worse. So I think as time goes forward, the machine learning will get better using a broad spectrum of information. I think for a long time there will still be types of common sense knowledge that people have. So I don't think for most things the human element is going to become unnecessary very quickly. But maybe ultimately it will.”

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

  9. “And human beings can try to factor this in, whereas the algorithms couldn't, or the Fed just said that it's going to raise interest rates or something like that. So human beings can bring a lot of knowledge to bear that the algorithms don't have. Having said that, what we see even from the 80s to now is that the machine learning algorithms are starting to use a lot of these things. So, for example, there's hedge funds that trade based on things like what's being set on Twitter, right? If you can pick up certain themes on Twitter, then maybe this is a sign that something is going to happen or has happened or a recession has become more likely or whatever. And they can, you know, learn from things that wouldn't occur to people. Like, for example, I know there's one company that they use real-time traffic data and they use satellite photos. I'm not kidding, of parking lots to figure out how many people are shopping at Walmart, let's say, and other stores.”

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

  10. “Yeah, so all of those things are factors. I think where machine learning has a big advantage over human intelligence is that it can take in vastly larger quantities of data. And as a result of which it can learn more and it can also be more certain if that data 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, think, for example, the stock market, the traditional machine learning algorithms when people start using neural networks to do this in the 80s, they just learn 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, there began a war between Russia and the Ukraine.”

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

  11. “But actually, it turns out that that's wrong or it's outdated, then there's a better way to do it. So there's uncertainty on all sides of this, and it could be more or less depending on the problem.”

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

  12. “Well, it's certainly quite uncertain. So, any knowledge that you induce from data is necessarily uncertain because you never know if you generalized correctly or didn't. But sometimes you can actually machine learn knowledge that is actually quite certain because if you know well how the data was generated and you've seen enough data, you can say that with very high probability, the knowledge that you've extracted is correct. Conversely, a lot of the knowledge that we have from evolution and from experience and from culture, we often tend to think of it as much more certain than it really is. We have this great tendency that's been well studied by psychologists to be overconfident in our knowledge. And a lot of the things that we take for granted actually turns out that they just ain't so. So evolution could have evolved into a local optimum when there's actually a much better one a little bit farther away. And you might have learned something from your mom that told you to do things this way.”

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

  13. “This may be harder or easier. With some types of machine learning, like, for example, neural networks and deep learning, it's very opaque. What is learned is this big jumble of lots of parameters and non-year functions and nobody really understands what's going on, which in fact often precludes it from being used. But then there's other types of machine learning where what the algorithm produces is very easy to understand. It's a bunch of rules or it's a decision tree or it's some kind of graph connecting variables that you can actually look at and understand. So there's a spectrum of degrees to which the results of learning are understandable or not.”

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

  14. “Well, 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 power, right? It can actually know way more than any of us could. Having said that, we, the machine learning researchers and the data scientists, we actually have a good understanding of how the learning algorithm itself works. What is it that it does to learn and how could you make it learn better? And then there's a different issue, which is the understanding of what the algorithm produces, right? If this algorithm has produced a model of how tumor cells work in cancer, can I understand what the algorithm is doing? And depending on the type of machine learning,”

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

  15. “So, in essence, every major machine learning algorithm is a master algorithm in the same sense that a master key is a key that opens all doors. A master algorithm is an algorithm that works for all different problems. And that is very much the goal of all machine learning is to develop such master algorithms. And there are several major such algorithms today that have mathematical proofs that if you give them enough data, they can learn any function. Now, of course, the whole question is, can you do it with realistic amounts of data and computing power? And then different algorithms tend to be better for some things than others. But what I and others believe is that”

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

  16. “Would turn one into the other. And the thing that's amazing is that often just by taking a basic machine learning algorithm applying on a database of, for example, X's and diagnosis, you actually wind up with something that is better at, for example, pathology than a highly trained human being would be. And the other thing that's remarkable about machine learning is that in traditional computer science, you need to write down a different algorithm for everything that you want to do. So if you want the computer to do diagnosis, you need to explain to it what are the rules of that diagnosis. If you wanted to play chess, you need to write a completely different program. And if you wanted to, you know, drive a car or invest in the stock market, you need to write yet a completely different program. With machine learning, the same learning algorithm, a single learning algorithm can learn to do all of these different things just depending on the data that you give it. So if the data is chess games, it learns to play chess.”

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

  17. “Exactly. So, what happens in traditional computer science and really 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, I don't know, diagnose x-rays 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 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.”

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

  18. “There's seven directors on the board, and one of them's an algorithm. So their algorithm doesn't decide anything all by itself, but it does have a vote as much as any one of the humans. And of course, you can easily imagine if this tends to work well, then maybe tomorrow there will be two votes that are algorithms, and maybe then there'll be a majority and maybe eventually it'll be all algorithms, or it'll be some mix of the two.”

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

  19. “Yes, in many cases, for example, there's this venture fund recently that announced that one of their directors is now going to be an algorithm.”

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

  20. “They will be both discovering it and applying it, and in fact, both of those things will generally be done in collaboration with human beings. In some cases it will be the computers doing it all by themselves. So for example, these days there are hedge funds that are completely run by machine learning algorithms. For the most part, a hedge fund will use machine learning as one of its inputs, but there are some where the machine learning algorithms, they look at the data, they make predictions, and they make buy and sell decisions based on those predictions. So there's going to be the full spectrum.”

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

  21. “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 and so on. And it's going to be the same thing with computers. So in the not too distant future, the vast majority of the knowledge on Earth will be discovered and will be stored in computers.”

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

  22. “Yeah, so the knowledge that we human beings have that makes us so intelligent comes from a number of different sources. The first one which people often don't realize is just evolution. We actually have a lot of knowledge encoded in our DNA that makes us what we are. That is the result of a very long process of weeding out the things that don't work and building on the things that do work. And then there's knowledge that just comes from experience. That's the knowledge that you and I acquire by living in the world. And that's encoded in our neurons. And then 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 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.”

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

  23. “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, and learning. These are all subfields of AI, and if you add them all together, what you have is an intelligent entity, which in this case would be artificial instead of natural.”

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