Ron so there are children alive today that would not have been born if not for machinery so maybe it is not so monday and after all. Another example. The smart phone in your pocket right now is chock full of dirty algorithms using a Machine Learning to address to read you are saying to correct your spelling errors, predicted he will do and make suggestions using gps and you can compare that with your calendar to see if you will be late for meetings. All this is happening today with the help of Machine Learning and as we progress cell phones and what Companies Develop will learn more and more and as a result with all of this economic value is no wonder that Tech Companies are all over it they are investing heavily in Machine Learning so google recently paid half a billion dollars for a company that has no customers and no product pitches had better learn the of the rape of rhythms that led to predict what people will click on. That alone is worth half a billion dollars every year as another example of just this month with the medical Imaging Company so they can trade in those learning algorithms just like for Breast Cancer today it takes doctors to do initiate a and Machine Learning with the director of research at microsoft says what you pay is a hot area in Machine Learning is comparable to a top nfl quarterback. Also Machine Learning has a dark side which we need to be aware of. Increased automation of whitecollar jobs it could cost you your job. Some people say nsa uses that to spy on you. We dont know because it is secret but also it may lead to a the terminator of evil robots taking over the world. Mitt could be your best friend or worst enemy depending on what you do with that which is why were at the point of Everybody Needs to have a basic understanding of what it is and what it does and why i have wrote a book about it. You dont have to under state and the gory details it is like driving a car even after a understand how oh the injured works the you do need to understand what happens when you push the pedal. But today you dont even know that it exist. So we as a society have a lot of decisions that we need to make if we need to be informed. Even more so than in the past. So how is it the computers do this thing . It may be surprising it is even possible. Reprogram them to do the same repetitive task over and over and discovering new knowledge is supposed to require intelligence and creativity they said computers are useless because they cannot they give us answers the Machine Learning is what happens when you ask questions. It continues to ask is how to return input into output . For example, so pretend the input is the xray of a prestigious trying to learn there is a tumor here. And it does this by looking at data. In the old days before a Machine Learning data and the algorithm went into the computer again think of the Breast Cancer example the output may be the diagnosis. And the algorithm is a secret a sequence of instructions telling it exactly what it needs to do. It is like a recipe for rigo. Use these ingredients. Except it has to be more detail because computers are down but with Machine Learning it turns around. Now the al point also goes in and with the Machine Learning of rhythm does is figured out once they learn that algorithm that i need need, if this is the image of the breast how do i get to one from the other . The amazing thing of machine and learning is when you program the traditional way it takes human programmers and for every application to write a Different Program one of her soaring, a diagnosis, etc. , etc. But Machine Learning algorithm can learn all knowledge that have been learned by learning of a rhythm so the whole holy grail is to discover the master algorithms one that makes others the single algorithm to discover of knowledge if you give it the appropriate data if it is about credit risk it will run through credit scoring press cancer diagnosis it will do that. What could this possibly be . Then we can talk about the future consequences next part of what makes this interesting these algorithms covered interesting origens for each of these there is the school of thought the five kinds of Machine Learning what idea is it is built, and what the algorithm dust does. There is the single list they have their origins and logic and their master algorithm of induction as the inverse then those connection is to use big idea is to reverse engineer the brain so they tried to implement the brain on a computer and then that is called back propagation. But there grid in the rest is you. A redo regard evolution as a kind of the algorithm than there are those that have origins is statistics in may will see what that is as well. And those that have their roots and all learning are intelligence am probably the most famous is well see what those do. So here are three of the most prominent in the world from carnegiemellon. He was also on my ph. D. Committee so what is the idea of basing the list . The most direct version by filling the gaps in our knowledge by hypophysis in the basic idea is Interest Deduction and with the idea how do we deduce things . Deduction goes from the general to the specific this goes on the specific to the general so think of thats lower integration is part of differentiation. For example, edition is the answer to an end to what do i get . Attraction is the answer what do i need to ask in order to get for . And in exact parallel with that deduction follows the you go to humans are more toll to guess what . This comes from general knowledge here but the inverse says it is it my data then what am i missing . And of course, the answer is i need to know that humans are mortal. Here the rules are written in english but for the computer is written in formal logic so the idea is the same then you run these rules and you can do something that you cannot combine them and arbitrary ways that has never been done before so this is the basic idea behind symbolism. Here is an example of what you cant do. Guess to the biologist in this picture is . Not just a guy in the lab coat. The other guy is not a biologist either. The biologist is the machine. This machine is a robot that news about dna and molecular biology and is making discoveries about metabolism , diseases and runs the process all by itself with inverse deduction to make a hypothesis and it literally carries out the experiment with dna and sequencing to test the hypothesis. Then maybe it throws them away or keeps going. Right now there only to appease computers in the world there called adam and eve and he just discovered a new malaria drug that is being tested. And is like having 1 Million People doing medical research. This is a very powerful thing to do. I am not big fans of this approach if it is too abstract or too rigid for them to be making deductions on paper here are the most prominent one the steadied as a psychologist his goal in life is to understand how the brain works he has persisted through thick and thin and has made a lot of progress in and in fact, he tells a story of coming home one day from work very excited to say i have done it i have figured out how the brain works and his daughter said not again. [laughter] another famous connection test it is very much in the news literally on the front page because of the success under the name of deepening. That is the name of connectionism is the name of deep learning. So how do we do this . How do we build these learning algorithms . The brain is made of marylands better kind id like trees then their roots are called dendrites and branches into more dendrites so it is a little bit like the forest with a difference the roots of one tree connect with the branches of others then it is like a of a big electrical storm happening. They are fired by the neurons and it is happening. So the connections might be stronger or weaker. The structure the connection the more like the the first run can make the second fire. With that amount of electricity coming in as one fires it could make others fire that is a network of 10 billion. So number one we will build the simplest mathematical model that we can it comes as it and put it could be a pixel on an image on a of a graph of a cat or of a face and that represents how strong it is. What is the weighted average of the input . If it is the threshold of the output is one. And now we take this to connected to the deep network with layers deep learning comes this way because it is all done in layers. But another problem you have to solve is how did you learn that . It is tricky because here is an image of a cat it is supposed to be one but it is 0. Two. The question is how do make it go up . There is the search and errol and now i tried to seek damages responsible . And then you turn each of these as it comes from the previous letter. The one that causes it to be too low so that propagation that this is some evidence some parts of the brain work like this algorithm. When something goes wrong who do you blame . When it gives the wrong answer something needs to change so it gets a errors back to the network to see where the synapses need to change. Now the states in the last few years it has amazing processing like recognition and translation to this type of Machine Learning. El google does the image schurzs search to understand the simultaneous translation you can talk live in a different language theyll use this. But a famous example of the first pages of the New York Times was the Google Network at the time was the Largest Network ever built and has of the order of 1 billion synapses which is still small compared to your brain but human best compared to those who went before. And basically its that theyre watching you to videos for of longtime media should a couch potato. [laughter] then people like to upload videos of their cats it could be known as the google cat Network Although it could recognize dogs and people and others. The of the missionaries have a different idea that say where does that trading come from . But somebody had to invent the brain to have that accomplishment and evolution hope to accomplish that. The big pioneer of revolutionary learning just died recently and in fact, for the first couple of decades people would say evolutionary computing was john holland and his students then in the 80s he proposed genetic programming to the master algorithm and then there were doing fun things with evolutionary learning. So the basic idea of evolution there relearning . The process of the evolutionary nature. Ever since darwin because know how genetics work so it happens in this thematic way anytime you have a population that has a genome then they get killed or not and they have a logger neck so the individuals in particular cross over sexual reproduction of to a successful individuals when the evolution also happens if the dna turns out not to be good. So after that happens you have a new generation of genome and the process repeats again that they transfer from the envy but to a human being but now it is just bits from the computer it is like having dna. But otherwise the process could do quite Amazing Things like have circuits to do radio reception or to invent a low pass filter these are better than design by the cuban ventures for those circuits so they could make the path for the invention. The bar of this genetic algorithms or genetic programming why do this with strings . It is low level representation that the program is really a treat of operations except that to cross over into two strings so if you pick this point for crossover then one of the offspring is the tree in white the other is the tree in black the one in white is kept alive the formula for the duration of a planet here ebullition of the sun. It is the square root of the distance and you can actually discover this using genetic programming. Now perhaps the most interesting and ambitious area of application of genetic algorithms is actually not to do this in the computer but in the real physical world. So where you literally have these robots at cornell there performing whatever perform the function we want them to perform them the robot to the highest fitness can program with a three the computer to create the next generation of robots finally may have robots under very robust to crawl around that can adapt. Traditionally they did not. So the next nine eupepsia spider it could be one of these. Hopefully we would keep this robot under control so then that brings up a bunch of other issues. The most fanatical of all learning their fanatical for good reasons but that paradigm comes from statistics the they were a persecuted minority so they have to give very hard core to survive and it is good that they did. But now but now the most famous who actually won the award of Computer Science other famous bayesians of those pictured. So bayesians takes its learning they think there is a formula for the world war Machine Learning like e e mc2. For so it actually has a base pair of. Than they put the picture outside of their office. So what is this gobbledygook . Let me give you basic intuition of what is going on. They have a problem of the Machine Learning that it tries to solve. For missing the last of compensable knowledge or the problem of uncertainty that you never know anything for sure. But a moment is never certain proposed else could have been the case so we consider all possible hypothesis that the same time the net changes have much to believe in each hypophysis. So you start off with a prior that is how much you believe in each hypothesis before you see any data. Even before you know, anything then when you see data there is the likelihood if the hypothesis is true how likely a by to see this . Then the idea is more likely make the world that use the bend the world make sure hypothesis value multiplied at by a the likelihood that is how much you believe in your hypothesis after you have seen the data if you see more in board data then they should become very unlikely that eventually theres just one hypothesis but in the meantime you may consider a bunch at the same time. There is also marginal probability but we will not worry about that. Doing this in practice can be very difficult the computations can become exposed to inexpensive but there are clever ways to do this and very interesting applications exist one they were familiar red the first most prevalent way is to use a vision of learning with that tries to do is to decide when a log that email if this ban or not. The priority is even before it sees your in box how likely . Ninetynine . Take your pick then the evidence is the words in the mail if it contains more exclamation marks in sequence it is likely to be spam with it contains the words your mom or your boss you just want to be sure. Said is one big application another the google ads the algorithm is uses is part of the Vision Network with millions of notes to predict if you will . Of that or not. Then the more money rigo would make some of the happier you should be of the advertisers. Finally talked about the analogize hours theyre not as cohesive is a loose grouping of people of i analogy this is they have extensive evidence people do a lot i needed to solve a problem i will look for a similar problem in my memory look for the most similar symptoms if they have the flu then this patient could have the flu. This is just the simplest form one of the of pioneers of the of leading analogy based until it cable long this was the most important and also a Douglas Hofstadter probably the most famous analagizers of all also a scientist and he coined the term analagizers and in his most recent book is 700 pages arguing everything all intelligent thinking is just analogy. He very much believes in a logical reasoning is the math of it all. So let me propose up a simple puzzle it ended is the simplest machine running that there is it was invented many years ago but it is still pretty good i have a map the two countries over here and show with the plus and every year with a negative sign. Just of the location of the cities can you tell where the border is . That is a hard problem but probably a good idea is the following any point on the map closer to aplus city getting dash city is probably part of the positive country. They are exactly the same distance so this is just reasoning by similarity. Now instead of being a backup it could be Breast Cancer imaging. It could be used for all sorts of different things. This border is a little bit jagged too many sharp lines and straight quarters another problem if you think about it you could throw ladies examples if you throw away this guy and this does not change. Then you have millions of these examples and then it is a heavy expense. They only keep the support factors with the following exercise you will always want to walk with the positive cities on your rights day as far away as possible think of it as a land mine while step eight very far from any land mine with a margin of safety and this is the machines do. It is just the technical name how you measure a the similarities between two objects in the world. What me just give an example of the message are at work in the world. They use all sorts of algorithms is still one of the most powerful if i figured if you like a certain movie one of the best things i can do is look for a similar taste to yours. Are the ones they did not like the and this idea worked extraordinarily well i have seen in the several places onethird of amazon business comes to recommend and three chords recorders come from the recommendation and system. This is the basic idea. Is quite interesting to notice those similarities and they have all been successful of what they do. Here is the big picture that it has worked on for a long time with the symbolist knowledge competition and so forth. People believe they have the master algorithm. To say that is all we need. And then we will be done. All of them are real. The