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tv   Life 3.0  CSPAN  September 30, 2017 11:01pm-12:11am EDT

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this is a topic that interest you, go to our website at in the search bar type in mental health book with a archive of materials all of these prals are available to watch online. c-span where history unfolds daily. in 1979 c-span was create as a public service by america's cable television companies. and is brought to you today by your cable or satellite provider. [applause] >> the importance of discussing artificial intelligence may very well with be crucial to human
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destiny. try ares to assess everything that could go wrong is not being alarmist from the contrary deep thinking, analysis, and advance planning will allow us to think about what kind of future do we want, and ultimately enable us to create a future a.i. that is positive for u humans and machines. we're fortunate that these conversations are happening more now and that two of the press thought leaders driving these kftions are our special guests tonight. first, we will hear from eric, director of the m.i.t. initiative on dimmingal economy and author with andrew mcafee of two best selling books second machine age and machine platform crowd harnessing our dingal pooch. next we'll hear from max cofounder of the future of life institute, and author of the --
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of the new book just coming out life 3.0 being human in the age of artificial intelligence, and i understand it's in its second week with on "the new york times" best seller list. congratulations. then the two gentleman will have a conversation together and finally there will be time for questions with the audience before we conclude with a reception and a book signing. so without further adieu please join me in giving a warm welcome to eric nelson and max. [applause] >> thank you. >> thank you. thank you i'm so delighted to be here. a pleasure to be able to have a chance to share research we've been doing and work on the digital economy about artificial intelligence, and how it has changing society. i want to add my thank you, jack and suzie rebbe know for supporting this and thank you
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for inviting me and max we know each other about three years but he's become one of my best friends. he is as i say in the back cover of his book, has an absolutely joyful mind when he said let's do this, i jumped at the opportunity. mainly because it is just fun to talk to him you'll found out in a little while where we interact and i'm also looking forward to hearing all of your questions and comments because we'll open up and everybody will participate many it. you know we live in very unusual times right now. as you may have read and seen even, cars are beginning to drive themselves. people are walking down the street and they're talking to their phones and they're not talking to another person but expecting fen to understand what they're saying and talk bag to them. you know it is bumpy they're really are not that good at it but beginning to talk to us and we're many the i would say a ten year period where we're going from machine mostly not understanding us to machines
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understanding us pretty are well and that's kind of a unique time in human history and we're all very lucky to be able to parent many that to be part of it. it opens some amazing possibilities these could be the next, next ten could be best decade in the history of humanity or it could be one of the worst because the power being unleashed by artificial intelligence is unlike anything we've seen before. so let me first set this stage a little bit by defining a little bit what we're talking about, so you can think of artificial intelligence is this set of techniques to intimidate the human mind the classic the test, many of you know some would propose bit computer science alan could you speak a machine and not know whether or not you were interacting with a human or machine. machines aren't really passing that test yet but they're getting closer and closer especially in narrow areas within that is a category called
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machine learn egg and this is what is driving a lot of excitement recently is that good old fashioned a.i. was an area where we would teach machines what to do and write down symbols an say this is how you play checkers this is how you play chess these are the rules and this is how you prepare taxes and machines would follow those instructions so instead of us humans telling machines step by step what to do frankly department work that well it was okay but it ran into a lot of barriers now machines are learning themselves how to solve problems. they're figuring it out and way they do that mostly there's different techniques but main approach is -- that we give them lots of examples say this is a dog, this is a cat. this is a dog, this is a cat. and i won't do it many times as they do to the machine 10,000 or o a million times and eventually machine says i think i see pattern here and it will start learning and the nice thing is
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we have so much digital data now that we can show them lots and lots of examples of fraud or successful go moves or of faces that eventually they started learning patterns. statistically, and a particular sub category of that where really biggest part of the break through especially in the past five to eight years is in deep learning or deep neuronetwork loosely based on human brain and deep learning sub category called reenforcement learning, these machines can learn new strategies on their own so one example is -- group of people at a company called deep minds google bought and what they did was on the covers of nature the science magazine, about they gave this machine learning algorithm pixels how many have played space invaders how about breakout you know the game of breakout i'll show you the game
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of breakout they just gave the machine the raw pixel they didn't say this is a paddle, block, a ball the machine had to figure that out. learn it on its own. they gave it the raw pixels. they tbaif it a controller to move it left to right and they gave it a score and said here's the score look at the score. your job is to move around panel to get or move around the controller and try to make a score as high as possible. and so -- at first the machine wasn't all that good. it would sometimes get lucky and hit the ball other times it would completely miss it and it was basically randomly moving arranged but whef it was successful and hit got the score went higher, machine was like i have to do more of that. reenforcement learning feedback on what tods more and more and after 300 game it was really pretty good never missing good teenager, and playing along there pretty successfully.
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they decide to let it run for a little while and guys at google deep don't play breakout and didn't know there was a strategy here. look it seppedz the ball around behind and they're like oh, we didn't know you could do that. so -- [laughter] machine had not only learned how to play but learn how to play better than designers imagine, a newborn baby being born and -- in the hospital and you handed a game and by the end of the day it is feeding surgeons and doctors at the ghaims kind of how fast it is learn now that's sort of a cool little example. you know, a scientific progress. and by the way, this same technique works for a whole set of target it did work on space invaders eventually on pac-man and other games not on all of them but a quick peedback loop if you did something the score would change quickly. it was able to learn those on its own quickly with no control programming.
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now you can use these same techniques for other things, though, not just games you could think of a data center where google has all of their computers. running as a big video game, these all of this data coming in and temperatures and the score is try to make it a deficient as possible let's lower cooling bill as much as possible, and your controller is you can adjust a little valve to move them left and right now they have a bunch of smart working on optimizing this so they thought they pretty much had it running as efficiently as possible but once they put machine learning all l gore rhythm against it got dramatically better. this is better of machine learning ftion on and turn machine learning own and 40% more efficient, and it was pretty and then they turn off again and the way it was beef. so machine figured out ho run data center better than all of
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these geniuses at google were running it and it doesn't take imagination to say do it for data centers and all kinds of factories for steel finishing lines and makers of any kind of an october so there's room to apply these things to improve all sorts of categories. many of the there's three big break throughs where machine learning has made a big difference that weren't important as recently as about 10 or 15 years ago and we write about them in our book to some extent vision lag wage interacting with a physical world and problem solving so for instance, you may want to get some snacks gaff wards be care what you're reefing for. there's sol muffins here not all are muffins, though. [laughter] sometimes we can mistake when is we're seeing things and at stanford they have a large database called image net with
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14 million images and had many painstakingly labeled by humans as to what they are, and back in 2010 when they tried to have machines see what they were machines were not very good they were wrong about 30% of the time. today, they're wrong about 2.6% of the time. so got dramatically better. the steep curve which when they startedded using deep machine learnings algorithms as a reference point humans are about 5% they haven't improved a whole lot. [laughter] so we still have pretty much the same hardware and software, and so machine have crossed that threshold now many tasks better with humans do them it is better to have a machine do them at least more accurate to have a machine do them. and that shows up in a number of areas for instance you can help them diagnosis diseases with that same and show examples of patients that don't have cancer and patients that do have kerns and machine carts figuring out
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as well with or better than a human pathologist it was a paper just published by sebastian throwing company looking at skin cancer and it did better than human so this is just happening this past few months. past few years, i mentioned voice recognition, you can see progress there 8.5% to in the past year that's not like over the past ten years that's just the past year. since july 16th, july of 2016. still humans are about 5% error rate too so sort of in that ballpark right now not quite better than humans. and that is opening up a lot of economic possibilities. interacting with the physical world so once you can see and recognize things, very are us to recognize a peds or bicycle i-it starts becoming feasible to give control of the car to a machine. when they first started doing these they made error one per 30 frame one per second not what
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you want to have in a car. now, it's once per 30 million frames so that's -- years you can go without making a mistake again better than human and so -- very is soon we'll see more and more of these on the road i have privilege of ridsen many a bunch of them now and more comfortable driving down the road being driven down the road making a left turn through traffic waiting and ultimately i think it will be much safer there are 30,000 deaths by humans. drivers today, we can drop that by 90 or o 99% when machines probably not 100% so we'll have to -- face some ethical issues when machines drivers still make mistake, but it will be dramatically safer than what we have today. and they're beginning to work in factories brod brooks when used to be computer science and run the lab has a company in boston called rethink robotics baxter works for about $4 an who are
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doing simple task you don't have to do any computer programming. you show baxter what you want it to do pick this up, put it in the box after a couple of example it is says i get what you want to do and it does that task and, of course, baxter can work seven days week. 24 hours a day, i was just on thursday watching another robot a little bit like this sort sorg thing hads soft objects like clothing with faster than humans did, and that will replace a great deal of work in those areas last but not least all sorts of problem solving medical diagnosis i already showed in legal area which tack to guys at jpmorgan down in new york they see a lot of relatively routine legal work with a set of 360,000 hours worth of legal work. so what does it mean nor the economy let me briefly touch on what before i toughs it over to max. first off there's good news go there's also some big challenges. it makes the pie bigger.
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but there's no law there's no economic guarantee that everyone is going to benefit. it is possible for some people to be -- that none of them share or to be made worse off than they were before, and saturdayly that's part of what's been happening the past decade and i think it could get worse if we're not careful. productivity has continued toughs grow and gdp is higher and family income is lower. good news is they have reports up for 2016 in the last year, there was a up tick over 3% in last year and depending on how you do adjustment it may have matched previous high although if you -- normalize it it is still lower than the previous high back in 199 so we're roughly flat during that period. now, meeting how can meeting be so much lower than that per person and that's because median you guys museum of science here is 50% per percentile but person
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right in the middle half of the people are higher half of the people are lower, so medium can stay plat if you have a bunch of wealth going to the top 1% where the top one tenth of one percent that's basically what happened as kiewrts kicked in there's been biased technical change -- and you've heard about 1%, the 1% had their own 1%. the pnt 01% up here, and the share of income going to them is at a new record high only time it was close was back right before the great depression it that's any consolation. so we're having a pie getting bigger and one happening but distribution is become more and more skewed and there are many reasons for that. part has to do with tax policy part of it international trade but most economists including me see the way that technology is being used as number one driver of that.
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now that's not inevitable. ultimately, we have an opportunity to rethink how we organize our economy. where pie is getting bigger creating more wealth that means it is theory for even to get richer at the same time we can make the rich ripper, middle class richer, poor richer and better off at the same time that math mathically adds up but choices we have to make as a society of what we want to do many terms of -- taking advantage of some of this bounty. business as usual is not going to solve the problem. we're doing a number of things that m.i.t. to address it and trying to understand, the drivers of this, do some research on it. we've also organized something called inclusive innovation challenge and i invite you to another event if you don't get tired out by this one october 12th boston city hall plaza the governor eric schmidt other people are coming to talk about how we can use technology to create shared prosperity for many and not just for the few.
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so with that, let me leave it -- leave you with a closing thought that these technology its are wondering but they give us all sorts of opportunities to be used for good. they can be used to create vast wealth but they don't lead to a distribution that makes everyone else better off. so it is very important i'm glad you're all here because it is important for all of us to think hard about what we can do to change the kinds of society we have towards a better one and what we want to do to use these technologies for broadly shared prosperity so thanks very much with that let me turn it over to max. [applause] thank you so much for inviting me here and thank you so much eric if for your friendship and for your all too kind introduction and also for setting me up. so wonderful here. see technology -- cooperates if i switch over to --
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this -- so i'm going to continue a little bit further forward in time. and talk about what will happen if it gets even smarter. what it will be like being human in the age of a.i. and what it should be like. so let's -- first go far become and look at thing big picture so starting years ago -- you know, i have to start here since physicist universe was very boring with just almost uniform plasma everywhere and nobody there to even witness it or enjoy it. gradually, the law of the physic clump this into galaxy stars, planet and so on and about 4 billion years ago first life appeared here on earth. satellites was it is pretty dumb, though, i call it life.0 couldn't learn like bacteria here.
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life 2.0 just what i call us, however, we can learn. and if we use eric, we metaphor of thinking as a computer of sorts then learning is uploading new software into our minds if i want to learn spanish i can spend a bunch of time starting and uploading it and have a new skill and -- [inaudible conversations] bacteria can't do that, and it's frankly ability to learn to change their own software which enabled culture evolution which mades us the most powerful species on this planet. life 3.would be if you can also design your own hardware, we humans are kind of trying to head into that correction and life 2.1 right now to talk implant or artificial needs or artificial pacemakers but true life 3.0 certainly doesn't it exist artificial intelligence might help pus get there, though.
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we heard from eric that a.i. is getting smarter and we heard from eric that traditionally artificial intelligence used to work like when a character star of -- the world chess champion over 20 years ago used to work by people. taking their own intelligence and coding it into a computer program which then beats simply because it could think faster an remember more than he could. with the recent progress as we heard from eric has been driven instead by machine learning where you just have very simple machines often simulated neuronetwork inspired by human brains and you just train them with massive amount of data you see it in pixels and out come this is caption. a group of young people playing game of frisbee and this image and tell you that's the elephant walking across the grass field. and it is just even more striking if you look at playing computer games as we saw many
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this nice example that eric showed. now, once a computer can learn to play a party game, that's telling you that there's a lot of room for growth there because you can start to think if you're a robot for example of life itself is a game when you get rewarded for certain things and try toe learn and, in fact, same company did this, came out to -- came out with the follow result with a train little robots to see if they can learn to walk they just gave them points when they're able to move forward but never seen video of someone walking and didn't know about the concept of walking. they can did this insimulation cheaper than doing it with objects that kept falling down but the idea the same and this is what happened. ♪ nobody thought to do that.
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learned by itself. tried variety, they learn to jump, walk, and so on. and anything almost can in life where we have a goal thought as a game where it is playing the stock market or -- playing a sport. so where is this all taking us? i like to think of all -- intellectual tasks as forming a landscape like this where the height is is how hard it is from machine to do. and the ocean level being how good machines are doing it at present. so --
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our chess play human chess playing skills have long sense submerged by machines ability to multiply number fast even longer ago with calculators so on and worst career advice to give to our kid is to encourage them to do jumps right on boundary here which are about to get submerged. but the sea level is rising -- in this metaphor landscape and machines keep getting better and fascinating to wongd where will happen. some think machines can never do all we can do but a lot of computer scientists think that, in fact, machines will soon eventually be able to do everything we can do maybe many 30 years or so. sub emergencying highest here, and then what? we have very interesting choices to make here. and these are choices we shouldn't make deliberately after thinking about them so in that spirit i found with a bunch
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of colleagues mia and lucas you want to stand up cofounders of the future of life institute are here, and -- [applause] eric also so very happy to have on our board here, the idea of the organization is simply that -- help create the best possible future with technology by speaking hard in advance what we need to do to get thing right i'm optimistic to create a wonderful future as long as we win this race between growing power of the technology and the growing wisdom in which we manage the technology. but in order to win this race, we need to change strategies i feel. and the past we always stayed ahead in this -- wisdom by learning from mistake we have fire, crewed up a bunch of times and then we invented fire extinct, ier and car messed up on invengted seat belt and all in all a good strategy but --
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as technology gets more and more powerful eventually you reach a threshold where technology is so powerful you no longer want to learn from mistake but plan ahead and get thing right the first time because it might be the only time you have. i would argue that nuclear weapons is already in that category where we don't want to learn from mistakes and have accidental nuclear war like oopsy maybe should have handled that better, and superhuman intelligence is certainly in that category also. so we need to shift from being active to being proactive. some people call that being alarmist scare monger i call it safety engineering. when nasa -- very systemically thought through everything possibly could go wrong with the first mission to the moon they within the scare amongers, precisely what they did that is openly led to success of the mission they thought will you all of the things that could have gone wrong and made sure they didn't. so what does my suggestion for
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what we should dosome let me tell you four things i think qowb good. first of all we should try hard to make sure that we international treaty against alarmist weapon so biologist and chemists are very happy if i ask you, what is your first -- not buy weapons and chemistry you think of new materials rather than chemical weapons. why is that in because those scientists came out in force, and persuaded politicians of the world please make international ban on weapon and chemical weapons. we physicists have more -- iffy scorecard here right when you read about kim jong-un and putin and trump and nuclear button so on you're like we physicist feel pretty strong for this. and -- [laughter] the a.i. researchers of the world today feel very strongly that they want to be like the
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biologist and chemist and keep welfare stunts power of a.i., focused on things like cures cancer doing wonderful stuff that eric mentioned rather than just making -- making a dramatically cheaper to murder people anonymously as good economist like eric can tell you if you take something like that, like anonymous murder and drive the price to disaster law of economics will put us lead us to it a place we don't want to be so number one on my list try to -- keep this focused on -- civilian things and i think this is real hope for that actually because superpower is all have a lot to lose there. second, i think as eric said with try our best ensure that this growing pie that a.i. can create is used to make everybody better off, and look forward to talking more with you right after this about what is something we can quickly did and third, i think we have to invest
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in a.i. safety research. what do i mean by that? there are a lot of nerdy technical problems to solve in order to transform today's hackable computers into robust a.i. systems that we can really trust. raise your hand if your computer has ever crash ared on you. oh, boy that's a lot of hands. how would that feel in frustrating maybe. but frustrating isn't the word that you would use if what crashed was -- a.i. that was controlling the u.s. nuclear arsenal for example. ...
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.... .... >> then to retain the goal also. my kids are less interested in logos than they were when they
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were little. there are a lot of technical challenges there. just to summarize why i think we should take seriously the possibility we should get machines smarter than us and why this ai features are so important. let me summarize in this short video. >> will artificial intelligence every replace humans is a hotly debated question. some people argue computers will be able to outperform humans and others say it will be another tool we can control like current commut computers.
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>> machines have been better at tasks like weaving. whytient i believe there are simply things computers can do that are impossible like a human like consoling a friend? >> from the perspective of modern physical science, intelligence a just an information reaction formed by particles moving around and there is no law of physics that said it is impossible to do it better than humans do. it is not a stretch to say earthworms process information better than rocks and in many areas machines are better than humans. this might suggest we have only seen the tip of the intelligence icebe iceberg. >> how do we keep ourselves on the right side of the flourish or flounder balance?
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what if anything should be we concerned about with super intelligence ai? >> here is what has people concerned. not super intelligence turning evil but that it will not share our goals. if a missile is honing on you you would not think it was evil but what matters is the what the missile does and how well it does it. not what it is feeling. the real worry is competence. super intelligence ai is very good at achieving goals. if we want to build whatever it an anthill even.
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>> when is super intelligence going to arrive and when should we start panicking? >> it doesn't have to be something negative. ai might be the best thing to happen to humanity if we get it right. we need to start right away. for example, we will need to figure out they learn the goals of humanity and adopt them and remain the goals as they get smarter.
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>> thanks, max. how do i get involved to make sure we don't live in a super powered intelligence dictatorship? >> on that note i am interested to carry the conversation to hear what the future you want to create with this technology. thank you so much. sgh so max and i are going to start the conversation and after 30 minutes or so we will ask you to join in. let me pick up.
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that tender was asking a question when do you think artificial general intelligence going to be here and you daunl dodged the question. we did holes of this and you can be precise. what are the people saying when we might have machines that are intelligent. what did the other aspects say? where do you put yourself on that? >> what do we mean by intelligence. different people quibble aabout this. we have machines that have narrow phels and b-- else and
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that is intelligence that is as broad as ours. we ask a bunch of leading ai researchers by when did they think machines would be able to outdo us at all goals and the answer is interesting. there was a violent disagreement which means we really don't know. if smbld sells you it is sgoeg to happen -- if somebody tells you it is happening soon they are exaggurating. but if someone says it will not happen they are also doing the same. we have had a lot of top ai researchers who say maybe 2040, 2050 including this group that thinks they will get here soon include the google deep mind leaders who work with showcase here. my own feeling is i think the
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average is about 2055. the other was 3047. >> two years later they thought it was coming eight years sooner because the progress was quick. in a matter of decades i think that is a reasonable guess. we put a lot of thoughts into what would happen if we planned for retirement. >> what i thought was compelling is what if someone told you an alien intelligence was going to land on earth in 2025? in some ways this an alien intelligence. >> except we are better off here because if it is just space ship that arrives from the vega system we will just figure it doesn't matter what we do because their technology is so far beyond us.
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we get the opportunity to decide what kind of intelligence is going to be. i think this is an incredible opportunity we should take advantage of. >> you convinced elon musk of this. he came by and found this to be a really important challenge. he spoke at mit and said we are summoning the demon and that doesn't go well. tell us the fascinating story. it is in the book but tell us the story of how you got elon to make donation. i will not give away what all we did but it was kind of interesting. >> yeah, it is fascinating. not too long ago i could have
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dreamed i would have been doing a project with elon musk. since we were planning to do the conference and wanted to bring ai researchers into the conversation i reached out to him. it became clear quickly not only did he care about this but he really gets it. he is maligned in the media and i feel nothing could be further from the truth. you know him, too. he has this incredible optimism and the potential of human kind. that is why he actually is spending his energy and money on things a lot of other people think are impossible like spreading life beyond our planet
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or making cars all electric and having solar panels throughout the world. he is not going to want to dismiss the risk that might ruin everything. what i tried hard to persuade him of was what we really needed to do was get the ai community engaged in this conversation and show them this wasn't about trying to stop ai research but i talk about winning this race between the power of the research of ai and wisdom. we didn't need to trito slow down the power but instead help the other one win the race. develop the wisdom by investigating in that research. i talked about various kind of safety research. they were all completely unfunded.
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he kindly agreed to be the first person to fund that kind of research. after he pled the $10 million -- >> that is a big number. >> it is a small number compared to what the u.s. government spends on research but the first ever dollar spent on this research. we decided to give it out to anyone in the world who has the study. we have '00 -- 300 teams. >> you almost didn't make the announcement. you kept going over the corner and having these discussions. it is like something big is brewing there. your heart palpitations. >> what was the reason why he almost didn't do it? >> the reason was he was trying to attempt the first ever landing of a first stage rocket
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booster on his barge. his has been a dream for so long. you cannot distract the media from this by having another dig announcement from the day before. we came up with this. you would make the announcement you would do this and you were not allowed to tell the world and after the rocket landed and a few days passed -- >> just to make sure he kept the secret he didn't tell us the number. >> exactly. >> that number is the headline. >> right. i feel happy in hindsight how this felt. it transformed the way research friends think about it. when they go to a conference see their colleagues working on and think it is cool and something
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they can help with rather than just feeling under attack. >> right. you have a project to reduce nuclear proliferation. today the north korean sent a missile over japan again? >> do you have a concrete think week do about that or anything we should be thinking about? >> evening just bringing it up. i think it is very much the same phenomenal we are talking about an ai. humans through science understand the world better and better and use that to build technology to amplify our own power. we have to make sure we have the
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wisdom to be friday it. you would never walk into a kindergarten and say here is a box of hand grenades. why don't you play with it? when i listen to statements by kim jong-un and read tweets by a certain people i feel that is what we have done. take this box of 400 nukes and play with them. in the hindsight this could have been handled better. >> how? >> if we want to have russia and the u.s. obs abu dhabi hazardously want to have iron clad deterance. how many nuclear weapons do you need to deter putin? maybe a 100? 500? if you take up the largest 500 russian cities there is not much left. i went down the list of the
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largest issues cities and got to woburn, massachusetts. putin doesn't have 900. he has 5,000. why does russia have 7,000? we have 7,000. why do we have 7,000? because russia does. if putin and trump got together and agreed to cut down to 1,000 each the deterrent would be unchanged. it would not in any way reduce our deterance against kim jong-un. it would just be a step in the right direction. getting more awareness of the risk that our technology poses so we can do this. >> and as fizz cysphysicist you
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this. the next wave may be existent l existential. we talk about their technology, and ai and biological and others, are like giving a red button that says will destroy humanity. please do not push. >> right. >> suppose we handed out one of those to each one on the planet and how long do you think we would survive? >> exactly. i bet people during the stone age thought it was a good idea to kill a bunch of other people. how many people can you take out with a rock and club? not so many. as a society, we have to get together and make sure this stuff, relief of the ai-based weapons don't fall into everybody's hands. i think that this is why we
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should make sure we don't get into a military arms race. nuclear weapons are expensive to build but these things don't cost much more than an amazon delivery zone delivering something else. even physical things. if someone can put in the address and photo of the ex-girlfriend they are upset with. this will help terrorist and non state actors and weaken china and the u.s. i saw your five minute warning. i want to make sure before we turn it over.
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>> you spoke about ai can grow the pia and then you said shame on us. i would like to ask more specifically how would you like to see this happen? i know there are a lot of people that are against any kind of wealth redistribution in this country. >> my longer term isn't as long as your long term. i am thinking the next 5-10 years versus 20-30 years. in the short room there is only things humans can do. most things humans can only do. we need to think about as the old jobs get automated how do we get people to do the new jobs? three broad things you can do there.
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the first one most economists love is education and helping people learn now skills. is not just spending more on it but a matter of reinventing thing. machines are bad at creativity, emotional intelligence, team work, leadership, motivating people. a lot of schools try to stamp creativity out of adults and kids and we want to reinvent how we do schools. it will have to be a life long thing where people continuously learn now things. it is mainly not just throwing technology but a conceptual change and learning to follow instructions which is what machines are good at and more playing how can we discover. we do action learning where people are involved in projects and figure out how to solve them.
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>> you have look at entrepreneur and jobs for the united states, it has gone down. everyone things it has gone up. but it is down. we talked about entrepreneurs and over all there is less new business formation, less new jobs created, less new companies started. that makes it difficult to invent the new jobs and tasks that are there to replace the old ones. it is always a loosing strategy to try to freeze the old economy in place. the successful strategy has been as technology auto mates all things like it did with agriculture and many other things. we didn't say let's see if we can hold on to the jobs. we, and i mean entrepreneurs, invented goods and services. it was called creative d destructi destruction. we have to do more to encourage that. there has been more and more
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regulations and barriers. in boston, there was a law a special tash on uber they took to give the money to taxi drivers to try to slow the transition. in toronto, they banned uber all another. those are small examples of people feeling unxhcomfortable with change. we have a tax system that shifted more money toward richer people. >> in other countries like sweden where you were born they
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have been successful and in norray in investing in health, education, infrastructure, in child care, and people having a lot of vacation and not necessarily giving people money but making life a little bit more pleasant and hard edge. those are three things we can do to soften the blow. in the long run, we mean eventually -- i share your optimism that machines will be able to do about just everything humans can do and do all the work we do. my reaction to saying machines take jobs and shame on us if we talk about that being a bad thing. it should be great we are able to spend our time playing and doing sports.
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the problem is the income distribution. >> to put you more on this, i agree with all things you said. i also agree if we can have a life long vacation i expect it will be great. i know a lot of people who never worked a day in their life and feel plenty of meaning and purpose. will be be able to do it? i want to ask you. do you think we will actually be able to do this? this seems like it is going in
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the opposite direction. >> i think one reason we are happy you are having this discussion is it isn't a matter of us making predictions. it is a matter of us making choices. the way it works in a democracy is we the people decide. i have talked to the folks in congress and more senior than they. they say look, you know, i like some of your ideas but unless the citizens and the voters are clamoring for it. what values do we have? i have no doubt about these abilities. it is political feasibility that is a choice. that is up to us ultimately.
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>> that is a good note. very interesting to hear all your questions and comments. my understanding is there is a couple microphones and we would be delighted to hear what questions you have. >> try to keep the questions brief and make sure they impact our questions. >> all right. first question right here over here on this side. >> good evening, i have a question about the artificial intelligence and machines taking over our jobs. we know the economic consequences of that is a lot of people will be out of jobs. taxi drivers and millions of truck drivers. you say it is political but nobody but us are talking about
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it. on the television and public media you don't hear about it. you hear maybe ownership in the data. what are you ideas of forwarding that whole thing in society? this is a huge issue and part of the frustration we have in the economy, part of it, is driven by the economic trend. median income is basically stagnant so half the people have gotten worse off even as the economy is going. i don't blame people for being angry. you would be surprised if they were not angry because you see things are fought not getting getter. under hundre -- tens of million people are going to lose their jobs. the question is whether there will be tens of millions of hundreds of new jobs coming into
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place. we have done an okay job of creating jobs. they are not good jobs. part of that depends on employment is basically higher now than it used to be. unemployment is not super high. the issue is more around wages than unemployment. wages are stagnating even though the total number of people working isn't suffering as much. can we create new and better jobs? i think there is lots of work to be done in child care, in healthcare and elder care and creative work and in teaching, and in science, and in art. and we can afford to spend more on that if we want to. the money is there. we can enlist the private sector more. there is over 300 companies and organizations that announced the winners and are doing creative things.
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the government can help support that. we get a little nerdy. there is something called the earned income tax credit. it is basically like the basic income but tied to work. if you work more low income jobs they will supplement it and it takes an $8 an hour job and turns it into a $12 wage not by having a minimum wage but having us contribute to it and the employer has no disincentive to hire the person. the person gets more money and gets back to the question about meaning. a lot of people feel luke they want to contribute to society. if you just write people checks, a lot of people don't feel satisfied about that. i talked to my friends and they
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said contribute to society is important. >> next question to the left. >> thank you both for being here. many people have taken to the streets by asking people to solve it and every non-violent revolution is stimied because people have buttons they can push to stop it. i see most technological advances as preventing that. what do you see as the most seamless, least violent way to overthrow the hierarchical self-reinforcing nature tof -- of the election of power? can we use artificial intelligence for that?
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how would you apply that? >> all intelligent is a double-edge sword. even fire. certainly one can use these for positive social change. i feel what is happening is it growing. you have lot of angry people who have not had the opportunity to learn the full details of what is happening. that is a dream come true for demi gods and populists. and i think what we have been seeing in a number of recent elections is that people are
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very hungfry change and just are going to vote for whoever promises the most change. barack obama's slogan was change and in a sense donald trump's slogan was also change. they can build a social movement and tap into this anger and so people. eric's new book, i would love to see the same ideas used to transform society.
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a third of the newest book was about whether it was social media. we use wikipedia and other things. i am not sure the answer is to overthrow what we have. we have institutions that have been valuable for creating power to the people and other countries used them effectively and if we could restore some of those it might be helpful. if we fail to live up to the promise of what the institutions were designed for i think we will have them overthrown. i don't think the united states is immune to that and i think the revolution we have seen in other countries and the united
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states even 200 years ago could happen here. the people in power should think about this. >> we have our next question in the front. >> i think we can address the fears of the artificial intelligence turning against us by looking at the movie oregon. the only way to win is not to play. if we look at simple game there oh we know the secret is almost cooperate and not compete. any intelligence computer is going to learn that. very good question.
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i think one of the most powerful ways to make people cooperate is to have them all think about why people get married. it is because it will in collaboration we can do great stuff. if we look at movies it is like the terminator and my wife likes to point out that is the opposite of what we have to do. eric and i get students asking about career advice and if i say
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where do you want to be and i will say maybe i will have cancer or stabenowed. that is terrible. i wantmies sparkling. we as human kind need to do that. that is the key reason i wrote my book. i want to encourage people to think about what future you were excited about. people saying if we work together we can get there. >> that seems like the perfect place to end actually. working at the idea of cooperation and how we can work together to envision a positive future. i will take the proi priority
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saying we will continue this at a reception and book signing. we will have desserts. eric and max are there to talk to. thank you, gentlemen. this has been so eye opening and amazing.
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investigative journalist art levine is here talks about his bo book and is intervied by jeffrey liberman >> mental health care as offered by providers and clinicians continues to be portrayed as an alied good and supposedly more people need to receive more treatment regardless of the quality. unfortunately, the political debate over repealing obamacare has obscured the truth that even having insurance doesn't insure good, safe care. we are saying it has to be good quality care.
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how do we have outcomes and they keep offering new measures. it is not implemented. there is no culture of enforcement. and that is why i argue what we are facing in this country is what amounts to an epidemic of malpractice even if it isn't acknowledg acknowledged. the reality is the attorneys don't take the case unless someone dies. >> and you are watching booktv on c-span2. joinin i


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