Good evening everyone and welcome to books and thank you for supporting your local and Employee Owned bookstore. Cspan is here to make sure your phones are in. [silence] i do want to like you know about a couple of other events coming up as my friend reading from his new novel, on tuesday Stephen Kinzer is going to present the history of the cia. We also have some offsite events tickets are Still Available for mondays conversation between selecting a norcal and talking wednesday. Tonight we welcome gary, author of rebooting ai which argues the computer began human in jeopardy does not signal that we are on the dorsum of fully Autonomous Cars are super intelligent machines. Taking inspiration from the human minds, the book explains that if we need to advance our intelligence to the next loophole, and suggest that if we are wise on the way, we wont need to worry about the future of machine overlord. He says that finally book that tells us what ai is and its not in what ai could become if only it were ambitious and creative enough. The called elusive and warmed account. Gary marcus is the founder and ceo of a robust ai and was founder and ceo of geometrics intelligence. Hes published journals in science and nature and perhaps the youngest and nyu is the author at heart zero in on thursday this comment rebooting ai. Thank you. [applause] [background sounds] we had some technical difficulties here. I am here to talk about this new book rebooting ai some of you might have seen as the head of the new york times. But how to build Artificial Intelligence we can trust. I say we all should be worried about the question because people are building a lot of Artificial Intelligence they dont say that we can continue trust. The way we put it in the base was visual intelligence as a trust problem we are relying on ai for more but hasnt yet earned our confidence. We also suggest and i also want to suggest that there is a hype problem. A lot of ai is overhyped these days. Often my people who are very prominent in the fields. So a group angst, deep learning, a major approach to ai these days and he said the typical person into a mental task with less than one second of thought, we can probably automate it using the ai narrative are in the new future. As a profound claim. Anything that you can do it a second, we can get ai to do. If they were turned in the world work be on the verge of changing altogether. It may be true some days 20 years or 50 years or hundred years from now but im going to try and persuade you that is not remotely true now. The trust problem is this. We have things like Driverless Cars and people say that they can trust but that shouldnt actually trust it and people die in the process. This is the pitcher from a few weeks ago in which a tesla crashed into a stopped emergency vehicle. That actually is happened five times in the last year. The tessler on autopilot has crashed into a vehicle by the side of the road. A systematic problem. Heres another example in a murky and the robot industry and hope this never happens to microbus. This night robotics is the security robot and it basically committed suicide by walking into a little puddle. [laughter] and machines can do anything a person can do in a second will a person in a second, can look at puzzles i maybe i shouldnt go in there. Thereabouts cant. That other kinds of problems you like bias on people been talking about lately. See can do a google image search for the word professor. [speaking in native tongue] Something Like this. Or almost all of the professors are white males even though the statistics are in the United States that only 40 percent of the professors are white males and if you look around the world, its even much lower than that. Yes systems that are taking a lot of data that they dont know if the data is any good and theyre just reflecting it back out and thus perpetuating false stereotypes. The underlying problem with Artificial Intelligence right now is the techniques that people are using are simply is it too brittle. So everybodys excited about something called deep learning. Slippery good for a few things. Actually for many things. Object recognition for example. Get them to recognize that this is a bottle and maybe this is the microphone or you can get it to recognize my face may be distinguished from my uncle teds face for example. I hope it will be able to do that. Deep learning can help some with radiology but it turns out that all of the things that is good at, all into one category of human thought or in intelligence. In the category that they fall into several things called perceptual things. So you see for example something and you have to identify it further examples of things that look the same percent the same as a fourth. But that doesnt mean that that one technique is useful for everything. I wrote a critique of deep learning a year and have a go. Can find it online. But deep learning is critical bravery printing find a live for freight and a summary of it is that the deep learning is really brittle of vacant shower that theyre downsized to be learned. Even though everybody is excited about it it doesnt mean things are perfect. First i will give you a real counterpart to his claim. If you are running a business and wanted to use ai for example, you need to know okay i actually do for you nor can it not do for you. Or if you are thinking about ai ethics and wondering machines might be able to do sooner what they might not be able to do soon, i say its important to realize your limits. Heres my counterpart. The typical person can do a mental task less than one second of luck thought and we can gather enormous and added data that are directly relevant, we have a fighting chance to get ai to work with that. Song is the test data and the things that they work on their different from the things that we taught the system on. Theyre not is it too terribly different. In the system doesnt change very much over time. The problem they try to change ourselves doesnt change much over time. So this is the recipe for gain. This is what ai is good at right now is fundamental things like games. Alf ago, is the best no player in the world and is better than any human has vincent exactly with what they are good at. System hasnt changed. The domain in the game hasnt a change, in 2500 years. In a perfectly six set of rules and you can gather as much data as you like for freight. Promos for freight coming up the computer by itself or different versions of itself which is what deep minded in order to make the player and you can just keep playing it so we can keep gathering more data. Lets compare that to a robot that is eldercare. You dont wonder about the does elder care to collect and then set the amount of data through trial, nara and work some of the time and not some of the other time. So if it works 95 percent of the time, putting grandpa in bed and drops grandpa 5 percent of time, theyre losses of bankruptcy. Its not going to fly for the ai that would drive an old eldercare robot. When it works, the way the deep mourning works is something called a Neural Network that depicted up at the top it is fundamentally taking big data and making statistical effect nation. And its taking label data. A little about the pitcher affect what, a bunch of pitcher of couples and a bunch of pitcher of Angelina Jolie and he shared a new pitcher of tiger woods that isnt is it too different from the old pitcher of tiger woods and it seems and it correctly identifies that it is tiger woods and a angelou eat chili. This is the sweet spot of departing. The people got really excited about it when he first got get popular. Deep learning will send him a supersmart robot. Believe ernie seen some examples are an example of a robot thats not really all that smart. Mostly smart later. The promise has been around for several years but has not been delivered on. In fact there are lots of things that deep learning is for. In osaka most of the switches rating. On the right, are some training examples and he would teach a system these things are elephants. Eddie shared another element that looked a lot like those on the right, the system would have no problem at all. It knows wouldnt know if it. But suppose you shared a pitcher of the left will be sure the left, the way deep learning system responses says, person. It mistakes the silhouette of an elephant for a person. And its not able to do what you would be able to do which is personal recognize it as a silhouette and second of all that truck is really an elephant. This is what you might call extrapolation or generalization deep learning cant really do this. So interesting deep learning everyday more more seating uses systems that make judgments about whether people should stay in jail but whether they should give particular jobs and so forth. Its really quite limited. As another example. In making the same. About unusual cases. He joined the pitcher of the school bus, but hillside and snowbank, it is with great confidence while that is a snowplow. What that tells use the system cares about the texture of the road and still is no idea numbers between snowplow and school bus with a fork. Fundamentally mindless statistical summation and correlation the doesnt know whats going on. The state the right was made by some people at mit if you are a deep learning system, you see its an espresso because there is phone there. Is that super visible because of the landing here but it picks up on the texture of the foam and says the espresso because another example is if you show deep learning systems banana, and this super in front of the banana not worried that this is starting to control our society and you are not paying attention. And we get the next side of this. Maybe not. Were going to have to go that went out size of that becomes of technical difficulties. I will continue though. Theres nothing i can do. All right. Once again here. Beckman sounds okay im certain next going to show you a pitcher of a parking sign with stickers on it. It would be better if i can show you the actual pitcher but presenting size over the web, theres not going to work so with the stickers on if you can imagine that in the departing system calls it a refrigerator still filled with a lot of food and drink so is just completely off it it started something about the colors and textures but it doesnt really understand whats going on. That is going to show you a pitcher of a dog that is doing bench press with barbell. Yet something has gone wrong. [laughter] say of that. What is that of down. Beckman sounds i would need it back laptop and i just couldnt do it fast. I dont know, i dont take theyre going to be wheeling to edit it. So is gone. If so xo you pitcher a dog with barbell and it is lifting barbell. With deep learning system concilio theres barbell there and dog but he cant you hey that is really how did that dog is the rep that i could lift the barbell. He has notes because of things and is looking at. Currently ai is out of his depth when it comes to reading so going to redo a short little story that Laura Ingalls wilder wrote. It is about a manzo the nine yearold boy who finds a wallet full of her name this truck in the street and then amandas father guesses at the wall it might belong to somebody whos names as sir. He finds mr. Johnson. So heres the thing that wilder wrote in a manzo terrace mr. Thompson f he said to do those of popular. As of the weve god of in those days any jobs in aesop news answers pocket and says, yes, i have, 1500 to two. What you know about it. Is this a. Yes thats it. Snatching the public document. And he hurriedly cast the her name in account all of the bills were twice as many in a recent basis i have relief. He said the boy didnt steal any of it. You are a mental image of it. Might be very vivid or not so limited but you know in your different a lot of things and why hasnt stolen a her name or where the her name might be and you understand why hes reached in his pocket looking for the wallet because you know the wallet is occupied physical space and that if your wallet is in the pocketbook, art is in the pocket, youll recognize it and if you dont feel anything there that i will be there and so forth. In all of these things pretty can make it a lot of inferences about things like how everyday objects work and how people work. Second answered questions of what is going on. Theres no ai system yet they can actually do that. Gpt two is the closest. Some of you might have heard about it because ai is famous because elon musk has founded and has a premises at the giveaway all their ai for free. Thats what makes the story so interesting. He gave it away for free until the maid this thing called gpt two. And they said and is so dangerous that we cant give it away. It is an ai system that was a good a human language that they didnt want the world to have it. Some people figured out how it works and make copies of it and i can use it on the internet and so my collaborator ernie davis and my coauthor and i sent him the amount the story into it. A manzo is found the wallet and he given to the guys i scanned the her name and he now is super happy. But you do as a feeding a story continues it. It took a lot of time many hours, and it continues it for him to get the her name from the safe place where he had it. This just makes no sense. It is perfectly grammatical but if he found his wallet what is it doing in a safe place. But the words are correlated and some asked database but is completely different understanding. In the second of the talk which one i will do that went out visuals is called looking for clues. The first clue that i say we need to do as we develop ai further is to realize the perception which is what deep learning does woa, its just part of what intelligence is present some of you especially in cambridge know how the theory of multiple intelligences there is Global Intelligence and musical intelligence and selfworth. In the cognitive psychologists, there is also common sense in planning and attended many different components and what we have right now is the forum of intelligence that is just one of those. And it is good at doing things that did that went out. Good perception, certain time of a pain. Doesnt mean it will do everything else. The way i say about this is that deep learning is the great hammer and we have a lot of people looking around staying, because i have a hammer, everything must be in l. And some things actually work that went out like jazz and so forth but in much less progress on language so theres been, exponential progress in how will computers play games but there has been zero progress in getting them to understand conversations that that is because intelligence itself has many different components no Silver Bullet can solve it. The second thing i wanted to see is no substitute for common sense. We need to build that into our machines. I wanted to show you pitcher of a robot on a tree with a chainsaw and is cutting down the wrong side if you can pitcher that. So is about to fall down. This would be very bad. You would want to solve it with the popular technique called reinforcement learning we had many trials. Would not want that. All of these mistakes that would be bad. But ghostbusters. I was finishing this relic will pitcher something called a yarn theater which is like a little bowl with some yard and strength comes out of the hole. As soon as i describe it to you, you have enough common sense about how physics work but i might be wanting to do the hard they can understand. And then really ugly one beacon recognize this one even though it looks totally different because you get the basic concept. That is what common sense is about. Daves going to show you a pitcher of obama. Youll note this vacuum cleaner robot. That is going to show you the tele and a dog doing his business you might see. So the robin doesnt know the difference between the two and that is going to syria the who apocalypse. Something that happened not once but many types which is that none of the difference between what they should clean up and dont waste. And they spread the dog waste all the way through people news houses and it has been described as Jackson Pollock of Artificial Intelligence and commonsense disaster. Then what i really wish i could show you the most is my daughter climbed two chairs sort of like what you have now. My daughter at the time was four years old you guys are sitting in chairs where there is a space between the bottom of the chair in the back of the chair. And when she far years old she was tall enough to pick cancer. And she didnt do reinforcement learning she didnt do it by imitation i was never able to climb through the chairwoman today and even if im in good shape and exercising a lot, as you remember those who know it watched a Television Show dukes of hazard and climb through a window to get inside the car. She never seen that so she invented for herself a goal. I say this is the essence of how human children learn things. They see goals that live cannot do this. What effect do that. Can i walk on the small ridge on the side of the road. Potential of their five and six and a off and they all day long, make against when it was like this are counted out. She tried this as you learn it essentially unlimited. So she squeezed juice and a little sack and she did a little problem solving. This is very different from collecting a lot of data with a lot of labels away that deep learning is working right now. I would suggest that the day i want to move forward is that we take simplest from kids do this. The mixing i was going to do is to quote hell live silvey who teaches at harvard down the street and she made the argument than if you are born knowing that there are objects incessant places and things like that, and you can learn about particular objects but if you just know about pixels and videos so you cant really do that. You need a starting. Is this what people call a native hypothesis the opposite of life site hypothesis. Now a video about the baby let me down the mountain. Nobody wants to say that a few months have anything other than the temperaments are not shy. People who say that human a few months are built with notion of space and that is still arguing. Nobody has any problem thinking that animals might do this. So i should have a beehive explaining, mountain a few hours after it was born. Embracing this view has to realize that something built into the brain of the baby read there has to be for example, an understanding of threedimensional geometry from the minute that the ibex comes out of the moment. Similarly it must know something about physics. And if somebody did doesnt know that it can calibrate it and figure out just how strong his legs are and