[applause] the importance of discussing Artificial Intelligence may very well be crucial to human destiny. Destiny. Trying ttry and to assess everyg that could go wrong is not being alarmist. On the contrary, deep thinking coming analysis and advanced planning will allow us to think about what kind of future do we want, and ultimately enable us to create a future with ai that is positive for humans and machines. We are fortunate that these conversations are happening now, and that two of the press and thought leaders driving these conversations are our special guest tonight. First we will hear from eric director on the Digital Economy and author with Andrew Mcafee of two bestselling books the second machine age and machine platform crowd harnessing the digital future. Next we will hear from max tegmark cofounder of the culture of Life Institute and author of the new book just coming out, life 3. 0 being human in the age of Artificial Intelligence, and i understand it is in its second week on the New York Times bestseller list. Congratulations. And the gentleman will have a conversation together and finally, there will be time for questions with the audience. Before we conclude with reception and a book signing. Without further ado, please join me in giving a warm welcome to eric and max tegmark. [applause]. Thank you jack and susie for supporting us. Especially think its a mark, we only known each other about three years that he is one of my best friends. He has a joyful mind ten when he said lets do this, jumped at the opportunity mainly because its fun to talk to him. Well have a phone conversation. Also i hear your questions and comments. We live in very unusual times now. Assuming have read and seen cars are driving themselves, people are talking to their phones, its a little bumpy, theyre not really that good at it but theyre beginning to talk to us. Run a ten year time burger from machines not understanding us to them understanding us very well. Thats a unique time. We are lucky to participate in that. It opens amazing possibilities. They could be the best decade in the history of humanity, or one of the worst because the power being unleashed by Artificial Intelligence is unlike anything before. Lets define what were talking about. You can think of Artificial Intelligence is techniques to imitate the mind. Could you speak to machine and not know whether or not youre interacting with the human or machine . Machines are not passing a test to that theyre getting closer. Within that is a category called Machine Learning. This is was driving a lot of excitement. Oldfashioned ai is an area where we would teach the machines what to do and say this is how you play checkers or chest synthesis the rules and machines would follow those instructions. Now the learning revolution is taking over. Instead of us telling machine stepbystep what to do which frankly didnt work that well, machines are learning themselves how to solve problems. The way they do then is we give them many examples. This is a dog, this is a cat, this is a dog, this is a cat. They usually do it 10000 are million times. Eventually the machine says i see the pattern. We have so much digital data that we can show them many examples of fraud were successful moves are faces and eventually they start los learng the patterns. A particular subcategory the biggest part of that is in deep learning their deep neural networks. Loosely based on the human brain. This a category called reinforcement learning. They can learn new strategies on their own. One example is a people the Company Called deep line. The cover of the Science Magazine they gave this machine algorithm the pixels for atari games. How many have ever put played breakout . They gave the machine the raw pixels and they didnt say what it is the machine had to figured out. They gave at the raw pixels a controller to move it left or right in the score and said look at the score, your job is to move around the controller and try to make the score as high as possible. At first, the machine was a very good. The sometimes get lucky and hit the ball other times it would miss it. Whenever it was successful in the square one higher the machine is like i have to do more of that. After 300 games is pretty good, almost never missing. As good as a good teenager. So they let it run for a while. They dont play breakout a lot and theres a strategy here to figured out how to send the ball behind the liquid in the you could do the so the machine learned how to do better than the designers. Imagine a newborn baby being born in the hospital and hand it again by the end of the day its beating all the surgeons. Thats a cool little example. In the same technique looks for others it worked on Space Invaders and pacman. Once that had a quick feedback loop for the square change quickly was able to learn that on its own very quickly. You can use these techniques for the things. You could think of a data center where google has their Computers Running as a big videogame. This data is coming in and temperatures in the score is tried to make it as efficient as possible. Your controllers that you can adjust the valves. They had a bunch of smart phds working on this and they had thought they had it running as efficiently as possible but when they put the machine against it got dramatically better. This was before, the term Machine Learning on the cap 40 more efficient. Then they turned it off and went back to before. The machine figured out how to run the Data Center Better than the geniuses at google. So then you figure out why dont we do for all kinds of factories, for still finishing lines and makers of any object. Theres room to improve all sorts of categories. Theres three breakthroughs when Machine Learning made some big breakthroughs. We talk about them in problem solving and you might want to get snacks afterwards be careful what youre reaching for. There are some muffins, not all are muffins sometimes we can make mistakes and at stanford they have 14 million images. Each has been labeled by humans what they are. In 2010 the machine see what they were they were wrong about 30 of the time. Today there wrong about 2. 6 . This is when they started using these deep Machine Learning algorithms. Humans are about 5 , they havent improved a lot. We are pretty much the same hardware and software. Its important because many task these better to have humans to sputter to have a machine do it or more accurate. For instance you can use them to diagnose diseases nick have examples of patients that dont have cancer in patients the two. The machine will figure out as well as a pathologist. It looked at skin cancer and did better than the human. I mentioned voice recognition. Thats just in the past year. Since july 16, 2016. Humans are about 5 error rate. Its in the ballpark not quite better than humans. Thats opening up economic possibilities. Once you can see and recognize things like a pedestrian or bicyclist starts to become feasible to give control of the car to a machine. When they first started doing them they made air about once per second. Now what youd want to have a car. Now its once per 30 million. Thats years without making a mistake. Very soon well see more of these on the road. Ive written in them and i feel quite comfortable being driven down the road making a left turn turn through traffic and ultimately i think it will be much safer. 30,000 deaths by human drivers today to drop that by 90 or 99 . Machines are probably not 100 so will have to face some ethical questions when the spec mistake. Rod who used to be at the lab has a company in boston called rethink robotics. Baxter works for about 4 an hour doing simple tests. No computer programming. You show baxter what you wanted to do. After a few examples it gets it. Of course i can work seven days a week, 247. Is watching another robot sorting things and soft objects like clothing faster than humans and that will replace work in those areas. Also medical diagnosis in the legal area they see a lot of routine legal work they had 360,000 hours for the legal work. What does this mean for the economy . Theres good news and big challenges. It makes the pie bigger than no economic guarantee everyone will benefit. Its possible for some people to be made worse off than they were before. Thats part of whats been happening. It could get worse. Productivity is growing in gdp is on a high that the median Family Income is lower than the 90s. They just had reports out there was an uptick over 3 last year and depending on the adjustment and may have match the previous high. If you normalize it its still lower than 97. How can median be so much lower because media is the 50th percentile not the average. Have the people are higher in floor. That can stay flat if you have a bunch of wealth going to the top 1 . Thats what is happened as computers kicked in. 1 have their own 1 . The share of income is at a new record high. The only time is close was at the great depression. Were having an economic challenge of a pie getting bigger. The distribution is becoming more skewed. Part has to of tax policy or International Trade most economist see the Way Technology is being uses number one driver. Thats not inevitable. We have an opportunity to rethink how we reorganize the economy. We could get everyone richer at the same time. We could all be better off at the same time that their choices will need to make as a society in terms of taking advantage. This was his usual will not solve the problem. Were trying to address understand the drivers oven, do Research Live looked at the inclusive innovation challenge. You can go to another event, the governor and other people will come and talk about how we can use technology to create shared prosperity for the many not just the few. Let me leave you with the closing thought these technologies are wondrous and they give us opportunities. They can be used for good and create basketball but they dont automatically lead to a distribution that makes People Better off. Its important for us to think hard about what we can do to change it to a better society. Thank you. Thank you. Thank you eric for your friendship and your kind introduction. Well see if the technology cooperates but we switch over. Ill continue further in time. Talk about what will happen if it keeps getting smarter. First lets go back and look at the big picture. 13. 8 billion years ago, it was very boring with almost uniform plasma everywhere and nobody there to witness or enjoy it. Gradually clamped into galaxies, stars and planets. About 4 billion years ago life appeared on earth. Life is dumb. They couldnt learn anything in the lifetime. Left to pronoun like us, we can learn. If we use the metaphoric thinking of a computer of sorts than learning correspond stop loading the software in our minds. I can upload the spanish model if i want to learn spanish but bacteria cant do that. Its this ability to learn the changes the software that allow cultural evolution that made us most powerful species on the planet. If you can also design your own hardware. Humans are trying to head in that direction. We can get implants or artificial knees, but true life threepoint doesnt really exist. We heard from eric of getting smarter that traditionally Artificial Intelligence used to work when they look at the world chess champion used work by people taking their own intelligence and coding it into a program simply because it could think faster and remember more. The recent progress has been driven by learning. You have simple machines inspired by human brains and train them with data. You feed them pixels and outcomes this. Will put it here and theyll say its a herd of elephants walking across the grass feel. If you look at the Computer Games as we saw, now once a computer can learn to play atari games that are ready tells you theres room for growth. You can think if your robot of life itself is a game where you get rewarded for certain things. The same company came out with a trained toy robot to see if they could learn to walk. They just gave them points to move forward. But they didnt know anything about the concept of walking. They did it in simulation. This is what happened. Nobody taught it to do that. Learn by itself. Local local. A try to friday of different types to run and jump and so on. Anything wears a bit of a game plan the stock market or a sport. I like to think of the says intellectual tasks as forming a landscape like this where the height is how hard it is for the machine to do. And the ocean level being how good machines are doing at present. Arch human chess playing skills have long since been submerged. And of course the worst kind of career advice to give torches is to encourage them to do jobs that are about to get some urge. The sea level is rising. Machines keep getting better. Its fascinating to wonder whats gonna happen. Some people think machines wont be able to do something machines will be able to do it all. And then what . We have interesting choices to make. A few other choices we should not make deliberately. So i found a bunch of collects will will the two cofounders here will in erics son is also some more happy to have. The organization help create the best possible future with technology for thinking hard in advance of what we need to do. Im optimistic we can create a wonderful future science we win this race between the growing power and the growing wisdom with which we manage. To win the race to change strategies. In the past we stayed ahead but learning by mistakes. When we invented fire, screwed up and then we invented the fire extinguisher. Will as Technology Gets more powerful we reach a threshold for technology is so powerful you know longer want to learn from mistakes. You want to plan ahead and get things right the first time. Nuclear weapons is in the category where we dont want to learn from our mistakes and have an accidental war and say whoops and superhuman intelligences in that category. Some people call that being an alarmist. I call it safety engineering. When nasa thought through everything that could possible go wrong with the First Mission on the moon. What they did was what led to the success of the mission. They thought through what could have gone on to make sure it didnt. What am i suggesting we should do . First of all, i think we should try hard to make sure we get a treaty against lethal weapons. The biologists and chemists are happy if i ask. [inaudible] if i asked about chemistry you probably think of new materials rather than chemical weapons because of scientist came out of force and persuaded the politicians of the world to make a band. We physicists have a more iffy scorecard here when you read about kim jongun and putin and trump. We feel pretty responsible for this. Ai researchers feel strongly they want to be like the biologist and chemist keep the power of ai will focused on things like your cancer and doing wonderful stuff rather than making it cheaper to murder people anonymously. If you take Something Like that will drive the price to zero will the law of economics will lead us to place we dont want to be. Will that be number one on my list will i think theres hope for that. Will because superpowers have a lot to lose. Second, we should try her best to make sure this growing time is used to make everybody better off. I look for to talking to more about this. And third, i think we have to invest in safety research. What i mean . There many to nerdy problems to solve to transform todays computers into ai systems. Raise your hand if your computer has ever crashed. Thats a lot of hands. How does that feel . Not good, frustrating maybe the frustrating its what used if ai was controlling the u. S. Nuclear arsenal for example or other key infrastructure. Its incredibly important we upper game in terms of making things work. Another challenge is to make sure the goals are aligned with ours. It doesnt have to be a frightening things to be another entity smarter than us. When we were all about this big were all in the presence of more intelligence agencies can our parents. But if you tell your future self driving car will to go as fast as possible in your being chased by Police Helicopters and they say thats what u. S. For will then you begin to see how hard it is to get them to understand our goals. We all know how tough it can be to get children to adopt our goals when they understand where we want. Also my kids are less interested in legos south and when there were little. We dont want machines to get bored with us as our teenagers are with legos. Just to summarize why we need to take this seriously that machines may be smarter than us will summarize in the short video here. Will Artificial Intelligence ever replace humans is a hotly debated question. Some claim it will gain and out before humans and destroy humanity. Others say dont worry be another tool we can use to control her current computers. So here we are going to share the takeaways of the conference. Will separate myth from fact. First off machines have long been better than us at arithmetic or weaving. Their mechanical and repetitive. Why should i believe that there some things that are impossible for machines to do. Weve thought of intelligence is mysterious that can only exist in biological organisms. Theres no law that says its impossible to do that Information Processing better than humans do. And some machines do things better than humans. This suggests that weve only seen the tip of the iceberg and long track to unlock the intelligence to help you for sure flounder. How do we stand the right side . What should we be concerned about . This would have been a concern. Super intelligence that doesnt ensure goal. If you think of muscle is coming in on you what matters is what it hes seeking missile dozen how well it doesnt. Its competence. Super intelligent ai is very good at attaining goals and the most important thing press to do is make sure the goals are aligned with ours. Cats and dogs have done a great job of lining their goals with the goals