You going to kick off this weekend with the book the master algorithm where he takes a look at the future. This is booktv. On tonights guest. For the Computer Science and engineering is a fellow Association Association from the advanced Artificial Intelligence board of the learning journal and cofounder of the learning society and the winner of the intervention award. Hes the highest author on Machine Learning data mining and other areas. Tonight joining us for the purchased new book the master algorithm how it will remake our world. Please join me in welcoming. [applause] thank you all for coming. I wanted to you about the case that is happening today. The intranet, the personal computer and in fact it goes to all of them and touches every part of society and affects everybodys life and touches your life right now in ways you are probably not aware of. Fortunes are being made because of it and there are also jobs in many cases unnecessarily. Children have been born because of this change. This change. Machine learning as computers, learning things by themselves. Its the mission of discovery and its computers Getting Better over time the same way that humans do. Its a Scientific Method on steroids making observations and forming hypothesis refining the hypothesis and there is a result in any given period of the time they can discover more knowledge than any scientist could. The knowledges and the relativity so far it is more modernday knowledge that this is what our lives are made up. What do you search for on the web. When you go to amazon what do you buy . If you are a company that helps you. If youre an individual, Machine Learning helps you find books to read and movies to see, a job, a new house and even a date. A third of all relationships that lead to marriage today began on the internet and its Machine Learning algorithms that pose potential dates to use with there are children alive today who wouldnt have been born if not for the Machine Learning so perhaps it isnt so mundane after all. Let me give an example, the smart phone in your pocket right now is chockfull of algorithms. It uses is to correct your spelling errors, to predict what your going to do and make suggestions and used as the gps to figure out what the routines are and can combine that with the calendar whether you are doing it for meetings to act accordingly. All of this is happening today with the hope of Machine Learning and as we progress, things like cell phones are learning more and more about you and as a result hopefully to better serve you as well. With all of this economic value in Machine Learning its no wonder that Tech Companies are all over it. They are investing heavily no customers and no products just because it has been a learning algorithm that if you google and alerts you predict what people will click on 1 better than before, that alone is worth over half a billion dollars every year. As another example, just this month ibm paid a billion dollars for a medical Imaging Company not because of what it does, but because they want to have access to his medical images so they can train learning algorithms to automatically diagnose Breast Cancer. The kind of thing thats today takes highly paid doctors to do and Machine Learning experts are very highly sought after. The character of research at microsoft says that the package would you pay them a deep learning being a hot area in Machine Learning is similar to what you pay for the top quarterback. So theyve won, finally. Now, well and good as all of that is it often has a downside which we need to be aware of. Machine learning is what is behind increasing automation so it could cost your job. Some people say they use Machine Learning to spy on you. We dont know, but it could lobby. Machine learning may lead to the terminator, evil robots taking over the world. So Machine Learning could be your best friend or worst enemy depending what you do with it which is why we are at the point that everybody is to have a basic understanding of Machine Learning is and what it does and thats why i wrote a book about it and im going to try to talk about them tonight. Its not that you need to understand the details of learning algorithms. You need to understand how the engine works, for engineers and mechanics, but you need to understand what to do with the Steering Wheel and pedal. So today you dont even know where the Steering Wheel is aware that such a thing exists. So we as a society have a lot of decisions to make regarding Machine Learning into the decision is to be informed more so than it has been in the past. So how is it that computers do this thing of learning from data . It may come as a surprise to you thats even possible. Computers are supposed to use these machines we just programmed to do the same task over and over again into discovering new knowledge to require a lot of intelligence. Picasso said computers are useless because they only give us answers. Machine learning is what happens when computers start asking questions coming into the questions 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 into the computer is trying to learn is to say there is a tumor were no there is no tumor there and it does this by looking at a lot of data. In the old days before there was Machine Learning this is how things worked. The data might be a Breast Cancer example or the output might be the diagnosis. And the algorithm is the sequence of instructions that tells the computer exactly what it needs to do. Its like a recipe from the for meatloaf, except it has to be more detailed than a recipe. Machine learning, Machine Learning turns this around. The output instead of coming out also goes into figures up figures up to 10 p. M. Put into the output is the algorithm that i need to turn the input into the output if this is the image of the breast and this is the diagnosis, how do i get the answer and the amazing thing about Machine Learning is when you program a traditional way it takes human programmers to do it in and for every application to write this program, one program to do Credit Credit scoring and wanted a Breast Cancer diagnosis etc. But in Machine Learning the same algorithm as all different kinds of knowledge that have been learned by learning algorithms. So the holy grail of the Machine Learning researchers is to discover this master algorithm. The single algorithm by which you can discover all knowledge if you get the appropriate data. If you give it without credit risks if a lender the diagnosis and so on. What could this possibly be . Then i will talk about the future consequences if we succeed in this enterprise is what is going to make it possible . Part of what makes Machine Learning interesting is that all of these algorithms come from interesting origins and for each of these there is a school of thought who pursued at. Theres five of them and you will see what each one does. With ideas is built on. So its going to do a lot of the different fields. So theres the symbolist who in some ways they have their origin was learning as the induction. Then theres the connection us whose big idea is to reverse engineer the brain so what they do is try to implement the brain on the computer and they get that from science into the maps are something very famous and well see what the idea behind it is. The evolutionary say no, no. Of the greatest algorithm the greatest algorithm on earth is not the greatest evolution. The evolution meet of made the brain and the rest of you and all of life on earth and biologists to regard the algorithm and then its called genetic programming. Then theres those that have their statistics and dniester algorithm and well see what that is as well and then finally there are the analyst risers. That have the idea that all learning and intelligence works by an illogical reasons by looking at similarities and so on. And the most famous in this line of work we will see what those do. Lets start with a visit. Here are three of the most prominent in the world. Ross is actually the alumni on the committee by the way. He was the First Teacher of Computer Science that was awarded. So what is the idea of the symbolist . Its this idea that we are going to learn in the same way that scientists discover things by trying to fill the gaps in the knowledge by reasoning and formulating hypothesis into the basic idea is this notion of inverse induction and how do we do things. It can be deduction goes from the general to the specific and induction is from the specific to the general so we can think of it as an inverse of deduction as subtraction is that deduction of addition or the square root is the inverse of the square. For example, addition gives the answer to the question just by asking what do i get. This obstruction is the answer to the question what do i need to add in order to get to four . Deduction is how you go from human and humans are mortal this is going from the general knowledge about humans to the specific knowledge about properties and the inverse sense if i know its an idea that socrates is human and i also know that he is mortal, what am i missing in order to go from one to the other and of course the answer to that is i need to know that humans are mortal. This is written in english and computers dont understand english just so they are written in formal logic but the general idea is the same venue can learn these rules from the different data which is now combining the ways to answer new questions to things youve never seen before. Heres an example of what you can do with symbolic learning today. Guess who the biologist is a picture is. Its not that i in the lab coat, that is a Computer Scientist i was just talking with the other day and the other guy is not a biologist either, its a machine that the robot that knows about microbiology in the dna and protein and is making discoveries about metabolism and so on and so forth and runs the whole process by itself. He uses this method of inverse direction to make hypothesis and then literally carries out the extent with dna sequencing and whatnot to test hypothesis and like the scientists may be maybe they throw them away and keep going. Right now theres only two in the world they call them at him and eve. They discovered a drop that isnt being tested and once you have two computers its like having a Million People doing medical research so this is a very powerful thing to be able to do. Lets see what the connectionist s are doing. Here are some of the most prominent. He is someone that started as a psychologist and became a Computer Scientist. Ever since the 70s his goal in life has been to understand how the brain works and he has persisted through thick and thin and made a lot of progress although as hes had. He tells the story of coming home from work one day very excited and say yes i figured out how the brain works and his daughter says not again. [laughter] another famous one is very much in the news on the front pages of the New York Times because the success that its happening and keep learning. Its a modern name of connecting or networks as they are also known because the idea of a connectionist is to be inspired by the human brain. So how do we build learning based on how the brain works . The brain is made up of neurons and vendor roots are dendrites and it branches off. So its a little bit like a forest with one big difference the roots of one tree connect with the branches of others and then theres this electrical impulse like this big electrical storm happening. Its an electrical storm encloses continually happening. The connections might be stronger or weaker. The stronger the connection between the two is the more likely the first is to help the second fire. When the amount of charge with the amount of electricity coming in and exceeds a certain threshold and then the fires it can make others fire. The brain is a network of 10 million of these all working together. So the way we are going to do this is number one, we are going to do the symbolist mathematical model that we can out of their neuron works and for example this could be the pixels on an image of a breast for Breast Cancer diagnosis and then what we do is multiply to represent how strong the synopsis is and then if this average is the input if it exceeds the threshold and the output is gone the deep learning comes from the idea that they have manual input how did you learn those . If you think about it its a tricky problem because here is the image but say and its supposed to be one but its not porn, its pleased to so it needs to go up into the question is theres a certain era and now how much was in each of these responsible which is accordingly so there is the function of the input for the also have to change into the air waiting for it to go down and others to go up and so back as we saw in the call itself in this problem and there is some evidence that some parts of the brain it is innocence of the Machine Learning that credits the science problem which is when something goes wrong who do you blame if this is the right answer nothing needs to change but when its the wrong answer something needs to change and what that does is propagates the network to figure out where the synopsis needs to change. Now deep learning is responsible for the processing of the visions for this type of Machine Learning. And for example this is how google does the image search both image understanding of speech understanding and they all use this type of learning. But but one famous example that was in the first page of the New York Times is what has come to be known as the google cab network which was the Largest Network of her belt and it has on the other end of a billion which is still small and then what they did is trained to network on the videos. Basically just sit there watching videos for a long time so maybe you should call it the couch potato network. Then people to upload into beginning is the Golden Network and also the center recognize dogs and a lot of other things. The evolutionary say where did the training come from . It was evolution that accomplished that and your in your body and Everything Else is low. John holland died recently for the first couple of decades people used to say that evolutionary computing than in the 80s it took off. They proposed genetic component that evolutionary learning. Its to simulate the process of evolution. We understand ever since darwin and so forth so what happens in evolution in a very schematic way each individual has a genome and they go out in the world and get killed or you have many offspring and so the world will assist tremendous of some individuals turned out to maybe the drafts of a longer neck because it can help them reach and so the individual with the highest fitness gets to produce what do crossover sexual reproduction genes of two successful individuals and the more likely you are up to participate. It also happens because the dna that turned out to be good so after that happens you have the process that repeats again and then it takes you to a human being. They implement us on the computer and this is just on a computer so its like having dna. Otherwise the process is very smart and theyve been able to quite Amazing Things with it or between el paso to her and this is often taken in patterns for the circuits instead of the conversation we would already have passed. The more advanced type of genetic algorithm is genetic programming and the idea here is that why you do this with strength this is a low level of representation. And the program is like multiply c. And we can do the very rich programs except now the crossover and you start with one string and then cross over to the other. You do that with the program but for example if you take this point and this point for crossover than one of the offspring will be the tree in white with a tree in black. Its the formulation as a function its a constant sign of the square roots and you can discover this and much more complicated things using genetic programming. These days perhaps the most interesting and in vicious and scary application of genetic algorithms is not to do this in the computer but to do it in the real physical world so there is an area of robotics where you literally have these robots and this is from harvard and cornell performing whatever function you want them to perform, crawling around doing things and then they get to the three d. Printer to create the next image. Hopefully we will keep these robots under control which brings up a whole host of other issues. Now the visions are known as the most fanatical of all. They are fanatical for good reasons. The paradigm comes from statistics that they were persecuted minority and its a good thing they did because they have a lot to contribute and these days they are very much on the right even in the statistics within the Computer Science he won the award a few years ago that had the other famous visions is actually at Microsoft Research and mike jordan at berkeley so what do they do . They take their name from the base of europe. They see them