Editorial board of the Machine Learning journal the cofounder of international Machine Learning society and the winner of the ctk dd innovation award. He is the author of over 200 technical publications on Machine Learning data and datamining and other areas. Tonight he joins us and what ive learned as a launch event for his new book the master algorithm how the quest for the alternate learning machine will remake our world. Please join me in welcoming to town hall dr. Pedro domingos. [applause] thank you all for braving the traffic to be here. I will try to make this special preview part i want to tie about the change happening in the world today. This change is as big as the internet, the personal computer. It touches every part of society. It affects everybodys life. It touches your life right now in ways that you are probably unaware of. Fortunes are being made because of it and fortunately jobs in many cases are necessarily being lost because of it. Children have been born because of this change. This change is the rise of Machine Learning. So what is Machine Learning . Machine learning is a computer learning to do things by themselves. Its the automation of discovery. If computers Getting Better over time the same way that humans do. Despite the Scientific Method making observations from leading hypotheses testing them against data refining the hypothesis more than any scientist ever could. As a result in any given period of time machines can use more knowledge than any scientist could. Now the knowledge is neither is deep as newtons laws or the theory of relatively at least so far. It tends to be more knowledge but knowledge is what our lives are made up, things like what you search for on the web . When you go to amazon what do you by . Machine learning helps us understand customs. Machine learning helps define books to read, movies to see, a job, new house. A third of all relationships lead to marriage today begin on the internet. And Machine Learning algorithms could pose potential problems. There are children alive today that wouldnt have been born without Machine Learning so perhaps perhaps is not so mundane after all. The mcgiffen of example. We use Machine Learning to understand what you are saying. He uses it to correct spelling errors, to prevent what you are going to do and to make suggestions. They can even compare and act accordingly. All of this is happening today with the help of Machine Learning. And as it progresses things like cell phones and other systems need to learn more and more about you and his results better serve you as well. With all of this economic value in Machine Learning its no wonder that tech companies, all the Major Tech Companies are examining Machine Learning. Google recently spent half a Million Dollars her company that has customers and products because it has better learning algorithm. That alone is worth a half a billion dollars every year. As another example ibm just this month paid a billion dollars in a medical imaging company. Not because of what it does, but the cause they want to have access to its vast library of medical images so they can train learning algorithms to things like automatically diagnosed her as cancer. The kind of thing that today takes highly paid doctors to do. Machine learning expert are very highly sought after. The director of research at microsoft says that what you pay to atop steep learning expert these days, deep learning being a hot area in Machine Learning is similar to what you pay for a top nfl quarterback so the geeks have one, finally. Now well and good is all that is Machine Learning has a downside which we need to be aware of. Machine learning is increasing automation of whitecollar jobs so it could cost you your job. Some people say the nsa uses Machine Learning to spy on you. We dont know because what nsa is the secret. In the space allotted talked to the media is that Machine Learning me lead to the terminator evil robots taking over the world. Machine learning could be your best friend or your worst enemy depending on what you do with it which is why i think we are at the point where Everybody Needs to have a basic understanding of what Machine Learning is and what it does and thats why i wrote a book about it and thats what im going to talk about tonight. Its not that you need to understand the gory details about the rhythms. Its a little bit like driving a car. You need to understand how the engine works. You need to understand what to do the Steering Wheel and pedals and today Machine Learning as we saw so we need to think we as a society have a lot of decisions to make regarding Machine Learning and i think more so than in the past. So how is it that computers do this thing of learning from data . It may come as a surprise to you that thats even possible. Computers are supposed to be machines that we program them to do the same repetitive task over and over again and discover new knowledge is supposed to require a lot of intelligence and creativity. Picasso famously said computers are useless because they can only give us answers. Machine learning is what happens when the computer starts asking questions. And the question that a computer asks when its learning is how do i turn this input into this output . For example lets say the input is an xray, thats the input and what the computer is trying to learn is to say oh yes theres a tumor here or now there is no tumor here. And it does this by looking at a lot of data. Now in the old days right before there was Machine Learning this is how things worked. Data and algorithm went to the computer and out came the output. Like the data might be oppressed cancer example it could be an image of an xray image in the output might be the diagnosis. The algorithm is just a sequence of instructions that tells the computer exactly what it needs to do. Its like a recipe for meatloaf. How do you make meatloaf . Used use these ingredients and you combine it and you have meatloaf. And now gore has to be much more precise and detailed because computers are not. With Machine Learning, Machine Learning turns this around. In Machine Learning the output instead of counting out also goes in and let what the Machine Learning algorithm does is given the input and the output what is the algorithm that i need to turn the input into output . If this is the image of the press and this is the diagnosis have i get from one to the other . The amazing thing about Machine Learning is that when you Program Computers the traditional way it takes human programmers to do that and for every application you need for the program want to do credit scoring one to do diagnosis and want to play but the same learning algorithm can learn all sorts of different knowledge, all of these things that i just described that have been learned so the holy grail of machinery of whom i am one is to discover this master algorithm, and algorithm that makes other algorithms. A single algorithm by which you can discover all knowledge if you input the appropriate data. If you enter credit risk and if you get the data about Breast Cancer diagnosis it will look a Breast Cancer diagnosis and so on. Now what could this possibly be . Thats what most of the rest of my talk is going to be about and then i will talk about the consequences if we succeed in this enterprise. What is going to make possible . Part of what makes Machine Learning interesting is all of these algorithms actually come from very ingesting origins. They usually have their origins in fields of science. For each of these candidate master algorithms there is a set , school of thought is that the people in Machine Learning who pursue that. I call them the five kinds of Machine Learning and we will see what each one does. What ideas its built upon and with the algorithm does. We are going to do a run through a lot of different fields. What are the five tribes as a preview . There is the simplest door until my soul does try. They have their origins in philosophy and mathematics and their master algorithm is interested section so learning is induction and they think of induction as being the inverse. Then there are the connection is as ideas to reverse engineer the brain. The human brain is the best learning algorithm around so they try to implement the braynon computer. They think their inspirations from science. We will see the idea of mine that. Of evolutionary say no, now the greatest learning algorithm on earth is not the brain is evolution. Evolution made the brain and the rest of you and all of life on earth so its an amazing learning algorithm and its inspired evolution is called genetic programming. The mayors of vision is to have their origins and statistics and something famous called the base theorem and their master algorithm is and we will see with that is as well. Finally there are the analogize hers who have their roots in a lot of different fields but mainly in psychology. Their ideas about learning and some say of all intelligence works by and logical reasoning by looking at similarities and so on. Probably the most famous of the algorithms in this line of work is and we will see with those two. Was this that the symbol is. Here are three of the most prominent symbolists in the world. Tom Mitchell Carnegie mellon. Ross is actually an alumni. He was also in my teaching community. He was the first ph. D. In Computer Science that was awarded. What is the idea of the sub for . The idea of the symbolistss in some ways the most direct vision of this idea that we learn when scientists discover things by trying to fill gaps in our knowledge by formulating hypotheses and refining them in the basic idea is this notion of inverse deduction. This is the idea that how do we induced things . We induced things. Induction can be thought of being the inverse is deduction. Deduction goes from the general to the specific and deduction goes from the general to the specific. In the same way for example subtraction is the inverse of addition. The square root is the inverse of the square. For example addition gives is the answer to the question when i add two plus two what do i get . Attraction is the answer to the question what do i need to add to in order to give for . An exact parallel with that inverse deduction, deduction is how you from doing that is the human and humans are mortal. This is going from the general knowledge here about humans to the specific the inverse of that is saying if its in my data that is human and i also know that socrates is mortal but am i missing an order to be able to go from one to the other . Of course the answer to that is i need to know that humans are mortal. These rules are written in english and computers dont understand english at supper the computer the rules are written in formal logic. The general idea is the same. Then you can learn a bunch of these rules from different data and then you can do something that none of the other paradigms can do which is even combined rules in arbitrary ways to answer new questions to things that you have never seen before. This is the basic idea behind symbolism. Here is an example of what you can do with symbolic learning. Guess who the biologist in this picture is. Its not this guy here in the labrum. This is the Computer Scientist and they affect on the left is not a biologist either. The biologist is this machine. This machine is a robot that knows about molecular biology, knows about i need and its making discoveries about metabolism, diseases and so on and so forth. It was the whole process all by itself or it uses this method of inverse deduction to make hypotheses and literally carries out the experiments with dna microwaves in order to test those hypotheses and just like human scientist it takes these hypotheses and keeps going that way. Right now there are only two computers in the world. They call them at them and even to keith has discovered a new malaria drug that is now being tested. And once you have two of these computers you can make a lot of them. Then there are a Million People doing medical research so this is a very powerful thing to be able to do. Now the connectionist im not big fans of this approach. Its too abstract and too rigid and making the directions on paper. So lets see what the connectionists are doing. The most prominent is someone who started as a psychologist but gradually became a Computer Scientist. Ever since his ph. D. In the 70s is colon life has been to understand how the brain works and he has persisted through thick and thin and made a lot of progress although he has also seen a lot of failure. In fact he tells the story of coming home one day from work very excited and saying yes ive done it that i figured out how the brain works and his daughter says oh dad not again. [laughter] another famous connectionist. Its literally on the front pages of newspapers because of the success of tapping. Deep learning is the modern name of connection is him or Neural Networks as they are known. The idea of the connectionist is to emulate what is inspired by the human brain. How do we do this . How do we build learning algorithms based on how the brain works . The brain is made of neurons and neurons are these interesting cells that look kind of like trees. There is a cell body and the roots are called dendrites and then the trunk is called and asked him and the branches are more dendrites. Its a little bit like a forest with one big difference. The roots of one tree connect with the branches of others and then there are these electrical impulses like this big electrical storm. As you are sitting listening to this talk theres this electrical storm of impulses being fired on neurons. Basically what happens is the connections between the neurons might be stronger or weaker. The stronger the connection between two neurons for more like it the first neuron is able to make the second one fire. When they come onto electricity coming into a neurons exceeds a certain threshold that neuron and turn fires and that can make other neurons fire. The way we are going to do this with the computer is number one we need to build the simplest mathematical model we can up our neuron works and here come the inputs and these inputs good for example be the pixels on image of a for diagnosis or on the face and then what we do is we represent how strong the synopsis and then if this innocence the average of the input if it exceeds a threshold than the outcome is one meaning benaron aspiring and otherwise at zero meaning benaron is not firing. Now we take the semiconnected into a big network with layers. The name deep learning comes from the idea that these are not works that have one layer that feeds into the next later layer. But they probably have to solve is how did you learn those . This is a tricky problem because you assure image of a cat lets say and its supposed to be one because its a cat but its. 2. So it needs to go up. The question is how do we make it go up . This neuron at the output, there is a certain error. Now what we try to see is how much are these responsible and then we exchange them accordingly. Then you turn each of these so the result is a function of this from the previous layer. Those guys also have to change. The ones that are causing it to be too low maybe because theyre negative their weights need to go down, so that propagation as this is called a solving this problem. There is some evidence that some parts of the brain seem to work by this algorithm. When something goes wrong who do you plain . If this is giving the right answer than nothing needs to change but when its giving the wrong answer something needs to change and what that does is it propagates the errors that the network to figure out where in the brain if you will the synopsis needs to change. Deep learning is responsible for just in the last few years an amazing process and things like vision and speech Rec Commission translation etc. Due to this type of Machine Learning. For example this is how google does a search and its how these companies in essence these days do image understanding. The skype simultaneous translation system that you can talk live with somebody in a different language. They all use this type of learning. One very famous example that was on the first page of the New York Times is what has come to be known as the Google Network. The Google Network at the time was the Biggest Network ever built that it has on order from alien stimulus which is still small compared to your rain but humongous compared to they train this network on youtube videos. We sat there watching it for a long time. Maybe we should call it the couch potato network. Because i dont know if you know this but people like to upload pictures of their cats of a single concept this this network was a concept of cats is why it became known as the network but it also knows how to nice dogs and people and other things. The evolutionary have a different idea. The illusionary say literature training come from . You were just tweaking the synopsis on the brain but someone had to invent the brain. It was evolution