Are absolutely coming for our jobs, you can see it already, take a look at the relatively low decline rate Labor Force Participation rates, and number two, it will allow for the creation of completely new jobs and industries, just as in previous revolutions, so there is nothing to worry about. In any event, occupational internment is at an alltime low. The truth is probably somewhere between those, maybe. That is what this panel will talk about, automation and ai, are they a substitute or complement to human labor . We have a great panel to discuss this today, and i will introduce. Hem very quickly first, we have diane daly, an associate professor at the school of information at ut austin, she studies technology and work. Her Current Research interests include engineering product design, data and health care, and economic development. Conductive material to studies conducts imperial empirical studies as well. James bessons from the Boston University school of law, he is an economist. He does research on how technology affects job skills and wages. His new book is learning by doing. Amarks km particularly focuses on realworld applications that can benefit from the competent three abilities of humans and machines. And the chief economist at google, how marion. He has been included in involving hal varian. Also a professor at uc berkeley in three departments. A good place to start off this panel is to talk about what we actually mean by artificial intelligence. And in brief preliminary discussions, this was propped up as would be a great place to start. What we mean we are talking about ai and where we are talking about it. Ece as a person who is studying ai and is not really involved in these discussions as much, it is a pleasure to be here and hear the discussion. It is a field that is 70 years old, it started with alan turning and his computing machine. Around 1950s, the fathers of ai came together and wrote a paper that said this is what ai is. I think we are going to be able to solve this i ai problem in three months. They had a meeting where they discuss all the different applications of ai we think about today. The problem ended up being that it is more difficult than they thought it was going to be. Some things turned out to be easier, some things turned out to be harder. That is why we are harder. Field, wey in the have been going through these where we kind of realized ok, i think we are Getting Better at this or know, i still think this is pretty hard and doing the back and forth. So what is ai . There are multiple definitions of ai. One definition i like very much talks about it is the activity of making machines intelligent. What we mean by intelligence is, for whatever the machine is best designed for, we expect the machine to react appropriately in its environment by sensing it, and acting on it. That is what we mean by an intelligent machine. However, intelligence is a term that we think of humans for. Humans are intelligent, other animals are not. A lot of the abilities that ai system we expected to have. Another definition that is more human focused is having these abilities that are special to humans. Our goal right now, why we think ai is so much in the press. I was a graduate student 10 years ago. Advice i got was if you are in the job market, do not say you are doing ai, because ai was a term that was poisonous and people thought nothing good ever comes out of ai. But google has ai and it was an ai company from birth. What we are really going on is there are two sectors in the past five years, five to 10 years that came together to make some of the algorithms we already had in the field very, very successful. Humans are very good at perception. We have a lot of data now coming from sensors, coming from crowdsourcing, coming from human activity and like that. We have more competition than ever before, and that is going to include those as well. And these made things like Neural Networks very successful. We now know how to train the algorithm. And we are now seeing great perception passes, image cognition, and that affect that success that deep mind had. Inare seeing paths perceiving the world, understanding with the objections are, and i guess machines are really getting intelligent this time. Scott before we go into the labor aspect of it, a. I. Has been around people have been working on a 70 years. It comes and goes in popularity. Is it the case that the last time it was popular, someone may have had a panel like this, where people were sitting, saying this is the time it is going to work . Or is is there really Something Different this time . Ece that is the question we are here for, to discuss. No but he knows the answer of that. However, when you look around there are, already, a lot of applications of a. I. People use every day, like search engines. All of the optimization all algorithms and management. It is not like a. I. Was not there. I think if a. I. Was good at some tasks that others were not good at, like understanding probabilities, making understanding decisions, now, with new techniques, a. I. Is inching into tasks a. I. Was not good at but humans were good at. That is creating, first, a discussion about what other tasks machines will be able to do now that we have these techniques. Second, what does this mean for the human job . That is something we need to see. However, there is another discussion, which is does this mean we are getting job loss because of intelligence . Meaning i will have a. I. Technique that will become so good that without much customization, i will be able to solve all a. I. Tasks . I do not think we are getting there. Scott jim, you have been writing about labor and a. I. Tell us your view. Are we going to be jobless . [laughter] and is that good or bad . [laughter] james not in the next 10 or 20 years. We have had a. I. In the workplace and marketplace since 1987. A. I. Systems were used to do fraud section in credit card systems. But we had computer automation, some of which is not so different from a. I. , since the 1950s. What is interesting is we are seeing an acceleration, we at computer automation, some of which is not the different from a. I. Since the 1950s. We are seeing an acceleration in the scope of things that a. I. Can handle, and maybe in the case in which it is addressing. It is about automation and the impact of automation. We perennially have a basic many people basically misunderstand what automation means for jobs. It is commonly assumed if a job if some tasks are automated, jobs are lost in the occupation. That is simply not true. We can look at manufacturing and we are well aware that lots of manufacturing jobs have been lost to automation. In the 1940s, half a million Cotton Textile workers were in the u. S. , now there are only 16,000. Most of that difference is from automation, something global some from global trade. But that has clearly had a dramatic effect on many communities, on many workers and their families. But the thing to remember is automation can increase jobs. We only got to have 500,000 textile workers because for the previous 100 years before 1940, automation was accompanied by job growth. This seems strange. How can automation sometimes create more jobs and sometimes eliminate jobs . What is going on and what does that mean . It comes down to demand. When you look at textile automation, in the beginning of the 19th century, the average person had one set of clothing. Automation met the price of cloth went down and people could afford more. Demand was very elastic, so they bought a lot more. In fact, they bought so much more that even though they needed fewer workers to produce the cloth, many were employed because they were buying that much more cloth. You come to the mid20th century, people have closets full of clothing, further price decline is simply not going to produce much more demand for cloth. Then you have automation and the net effect of eliminating jobs. If you look today what is , happening with computer automation, we see lots of evidence of examples where computer automation, just like the early textile automation is creating jobs. One of my favorite examples is the bank teller. There is a great untapped demand, or there was a demand for getting cash at remote locations. The atm machine came along and people assumed it will wipe out the bank teller. We have more bank tellers since the atm was installed. The reason is it made it cheaper to operate a bank branch. Banks could open up more branches and serve more people. There was a market demand for that. They built so many more branches that even though they needed fewer tellers per branch, they were employing many more. That is the pattern we are going to continue to see in many sectors of the economy. Not in manufacturing over the next 1020 years. That is basically what i think the Immediate Response is going to be to a. I. As well. Scott diane, do you say that even accelerating change in a. I. Will generate more jobs that is completely counter to what some others say. Eric free health and, for example, said eric, for example, said one of the differences is the rate of change is not allowing industries to catch up. Excuse me, by allowing the labor market to catch up. You are saying the opposite, right . The faster the change is, the better it will be . James yes and no. There are two things. A faster change will if you have elastic demand and you were making faster change, that productivity improvements will bring in job growth. If you make faster productivity improvements, you will have faster job growth. At least for the period of time where that is occurring. It is going to be disruptive in another sense. Wasnt mean to i maybe overly optimistic in the way i described things. When the question is, are we going to be seeing mass unemployment, the answer is no. Are we going to see individual jobs destroyed . Yes, but others will be created. At that peace accelerates, that is disruptive. You know, those textile workers in North Carolina need to find jobs, need to have skills. There may be, there are jobs opening up in the rest of the economy. And the same thing everywhere else. We are seeing and will continue to see jobs eliminated. So the acceleration will put more stress on our ability to transition people, to retrain them, to relocate them. Scott diane, i know you were much more less optimistic. Diane i would just ask this. In 2004, a man wrote a book called the jobtraining charade. What he talked about was that we were losing a lot of manufacturing jobs. The way we talked about that was that these people need to be retrained in other jobs. For example, we will train them to be bakers. They will work at a supermarket in the bakery. There is only so much bread and pastries we can eat and there were not enough of these other jobs for people to take. The language of job retraining started to change from teaching people new Technical Skills to enter different jobs, to focusing on what they call soft skills. People started to be told the reason you dont have a job is not because you are lacking Technical Skills, for your communication skills are not good, this kind of thing, you dont work well on a team. It started to put it on workers for their lack of soft skills rather than recognizing we had a structural shift in the economy and what kind of jobs were available. I was asked about two weeks ago to sit on a National Academies of Engineering Panel to talk about engineering workforce and how they needed to be more adaptive to survive in this new economy we are going to be seeing. I work at a large public institution, the university of texas. So getting an invite to sit on a National Academy of Engineering Panel is a big deal. Cvt means i can put it on my and get a 3 raise set of my 2 raise. I have some skin in the game. But i turned it down. And i turned it down because i told them i do not believe in the premise of the panel. They thought they were putting the onus on engineers to become more adaptable, be a quick learner. They are telling all of us these things rather than saying you know what . We are going to start seeing fundamental shifts in the economy and maybe we ought to start planning for that. As all of us, not just us individuals running around, slowly becoming more adaptive, quicker learners, moving up the scale, because there is only so much room at the top. If what we think about a. I. Might be true, there will be and all ofs of their us are quick enough to go up the ladder. That worries me, what will be left for everyone. I think there are a lot of problems with job training. And i think it is even more complex than that. We have issues with geographic relocation. Where you see a lot of the jobs appearing is not where the unemployment is. You also have a great difficulty my book is called learning by doing. One of the themes is a lot of new Technology Related skills have to be learned on the job. It is not a matter of the classroom entirely. We have to come up with new ways of getting people experience. But i think we do see plenty of sectors where there are midskilled jobs emerging. It may be different for instance nursing jobs have been , in great demand for a long time. Yet it is often very difficult for people to transition into those. Maybe we dont have we dont maybe we do not have enough. I think we dont understand what is involved in making all these transitions. Scott you are bringing in the longer term talk. Hal i want to say a word about this jobtraining issue. I think this is very interesting. If the demand for the jobs are here and the skills are here, there are two way to solve that problem. You bring the skill to the demand or you bring the job down. In fact, there is a lot of that going on through technology because it used to be to be a cashier you had to know how to make change. Well, to be a taxi driver, you had to know how to navigate around town. Not necessary anymore. To be a veterinarian you had to identify 150 breeds of dogs. Not necessary anymore. You can do that with a. I. Or your phone for that matter. So this cognitive assist is a big deal because it allows the onthejob training you are talking about. You drive around town and learn your way. You learn how to make change because that is what the machine tells you. You learn the breeds of dogs on the job. And there are a lot of delivery mechanisms that are extremely efficient in the onthejob delivery of education. Look at you two. There are 500 million video views per day of howto videos on youtube. I will bet you almost everyone in this audience has asked fixed something in their house by going and looking at a youtube video. So these are not just highlevel cognitive skills like solving quadratic equations or areas, they are actually important, manual labor skills that people can learn how to weld, how to replace a screen door, how to hang the window. Scott if i were to play devils advocate, that is a lot to repair people who did not get called in to do work. Hal thats right, but when you look at the next part of my talk we talk about what happens to them. I want to talk about the theme that relates to this discussion. We talked about the demand for labor and what the theory is, as some suggest, that ai will reduce the demand for labor. On the other hand, if you look at the supply of labor, we can get quite a different story, because there is only one social science they can predict 10 years ahead. That is tomography. Everything kind of pales besides that. Lets look at tomography. 1946, that is when the baby boom started. Basically 1946 to 1964. After the baby boom there was a , baby bust. Then there was the echo of the baby boom. You can look through this whole series of population changes and basically add 65 years to it and we see what is happening now. All of those baby boomers are retiring, that is followed by the baby bust. Now, what does that mean . Right now, the labor force is growing at half the rate of the population. 20he decade of the 20s, you will see the lowest growth in the labor force since world war ii. You look at the labor force, if you restrict immigration, it is actually going to decline. All those baby boomers are retiring. They expect to continue consuming. You need some workers somewhere to be producing this stuff they need to consume. You have this race going on between automation, which is increasing productivity, and you have the supply of labor, which is very, very low to decline. And we have it good in the u. S. Go look at china, japan, korea, germany, italy. They are seeing outright declines in the labor force. It is very, very worrisome from the point of view of the future of their economies. Now look at robots. What countries of the most investment in robots . Guess what . China, japan, korea, germany, italy. They have to have those robots. They have to have some improved efficiency and productivity to produce the stuff their population is going to be demanding. That is true as a worldwide phenomenon. By all accounts, unless there is a really big surprise on the automation side, you will see a tight labor market inch developed countries for the next 2530 years, and that is reading off the demographics. Scott how far down the line you see that . Seeing the labor force become more consumed . The when you look at figures of the labor rates around 2060, the labor force is at the same rate as the population. It is interesting to think this is all because of this huge shock of world war ii. It created this gigantic demographic event that does not work itself out for 100 years. Scott it is the baby boomers fault . Hal of course. [laughter] scott it seems like so far there are four issues. One is sort of a general shortterm versus longterm. The second is, is there anything you can do for people who might be im not sure what the right word is displaced in the short run . Does jobtraining play a role given what we learned about the effectiveness of that . And the distributional effects, which are both short and longterm, and overtime, the demographic and the demand for labor, which will swamp everything. So, diane and the inequality issue, whether it benefits just accrued to a small group. Diane, you turned down this position at the academy. But what do you think their project should have focused on to address your concerns . Diane we should be paying attention to power dynamics. So you hear a lot if you read about books on a. I. And predictions about jobs, look at the bureau of labor Statistics Data that describes jobs. They describe the tasks and jobs. Based on that description we will tell you some percentage of jobs are going to be automated or replaced by a. I. Within some period of time. Right . So they are doing that based on on a description of what people do. No job is just what you do. Every job takes place in an environment that is surrounded by, for example, all kinds of occupational norms and perhaps regulations. I spent a decade studying how engineers are using new computational techniques and software. Things like finite element analysis. Because i wanted to understand how it was changing design and analysis in that field and what it was doing to the workforce. I will just give you two quick examples that point out issues of power and when workers have power and how they control Technology Choices made for them and when they are not in power. So the example that shows some power is actually power that is held by the government somewhat. If you look at Civil Engineers who design Building Structures like the one we are in, their solutions are governed by a strict laws and regulation that involves things like county peter peerreview and review of plans for building. Because of this building were to fall down, those of us who survived would sue. The person responsible is the Senior Engineer who put his professional stamp on the drawings for this building. And because that person faces professional liability, they are very careful about using automation in their work, because they know Computer Software programs can yield Unrealistic Solutions based on easible in substance assumptions in the first place. Or Civil Engineers, everything is about those assumptions how a load travels through buildings, for example, that guides designs. They rejected a lot of automation. It is not like they use the techniques, but they dont use any automation between the steps of Engineering Design and analysis that we see in other fields. Now i go to automotive engineers. Scott are you portraying that as a positive thing about engineering . Does that necessarily make us safer or less safe . That they are using new technology diane i think it zero technologies. Diane i think it makes as much safer. I would hesitate to write in an elevator that a computer had designed, and all the Civil Engineers i watched would tell you the same thing. It is because you have to watch i can go to countless examples. I study things at the micro level. I spent hours sitting at the elbow of engineers while they are designing. While they are designing, i talk to them. The computer crashes, we have time for a short interview. I asked why did they do this thing . At the Automotive Firm i was in, they had a secret laptop with secret software. They were not supposed to use it anymore. They had it locked in a desk. The boss did not know about it, because they were restricted in the Software Programs they were supposed to use. These are the kinds of things, i ask them why you picked these technologies, use these technologies . I think its a good thing that the legislation was there. Civil engineers took safety they did not take it as a harness. They took it as an ethical obligation and something they were proud of. Something that made them different. Automotive engineers do not have the same kind of things guiding their work. Yes, they have the National Transportation safety board, but there is no stamp that has to be put on the vehicles, the vehicles have to pass a government test. Their work has been rationalized. Their work has been digitized. Their work has been computerized in ways changing what is happening with the workforce. Very quickly i will just say the engineers i have studied in that firm since about 2004 have a hiring freeze on analysis engineers in the u. S. They only hire analysis engineers, simulation engineers in india. Because they built a big center there, and my team spent months at that center and months in michigan. What they do in that center is they have offshored the work of building the fea models. The reason they were able to offshore that work was because we had digitized the models. We digitized and mathmetized. That means you can probably go on a computer, internet lines, and people in india are able to do the work for a fraction of the cost of a u. S. Engineer. That was my point about adaptable. How can u. S. Engineers who wanted to do that work be adapted in the u. S. When it would have to move to india. You cant do that job anymore in the u. S. That was a change. It came about because those engineers did not have the same type of power to control what they do. I will conclude with a group that does have power, radiologists. Guess what . All your medical scans can be read abroad for reading and they are. They are often sent at night in when youre radiologist does not want to be woken up. They send it to india. In india a radiologist will examine it and it comes back to the u. S. Why do we still have radiologists employed in the u. S. . The ara, their professional Association Lobby for legislation that sent those scanned have to be signed off in the morning by a Board Certified u. S. Trained radiologists. Scott hal, a response to that . Hal i think the radiology not only can be done in india, but now can be done automatically. That has been actually true not just recently, but it has been true for a decade or two. In a lot of cases, recognizing the malignant cell is really pretty straightforward. It can be done by even relatively untrained labor. There are border cases and lots of things where you might want have some adult supervision you were describing, but it can be turned into basically exercising exclusionary power to keep a privileged position. I think that is true. I have often said we would have driverless vehicles, Autonomous Vehicles on the right now if it on the road now if it were not for those darn human drivers. Not to mention the pedestrians , which are even worse. You have a controlled environment like a freeway, and expressway, a controlled environment is really possible to have Autonomous Vehicles right now. It was possible to have Autonomous Vehicles at least a decade ago. In that context, it is dealing with all the exception handling that is the problem in many cases. Scott feel free to jump in. Ece let me hear some discussions about a. I. , there is discussion about super intelligence or taking jobs away. I worry about something more shortterm than that, which is as ai applications enter society, these applications a. I. , algorithms have their problems too. How are they going to be handling these shortcomings of ai if this will be the determinants how we get value out of this technology. I mention at the beginning that deep learning is one of the reasons there is some excitement about a. I. However that excitement comes with the downside. These are hard for people to understand. So when they are making a prediction and this relates very much to the comments about if i know when my algorithm is oh, doing the wrong thing, i can override it. So the statistical techniques when the algorithms are learning from large amounts of data, it is quite impossible to understand what these algorithms are going to be doing for each case. On top of that the algorithms , get updated pretty often. For example, tesla car. There are questions about how they will be driving if they are updated pretty much every week. And the driver is not expected to understand how i trust my tesla . So there is a transfer problem between the ai algorithm and the user or the controller or the supervisor of the algorithm. We need to do a lot of work to make that layer transparent in the sense we can actually create transfer in partnership between the human and the machine so they can work together. So we can get to that case where, ok, i know my algorithm is not doing the right thing. I should override. There is a certain amount of responsibility that should happen. For example one of the cases , where a. I. Algorithms have been used in public space is decisions that happen in the legal system. For one decade now, it is quite amusing, statistical learning algorithms have been employed in courts for making sentencing and federal decisions. Article was a beautiful that discusses issues by having such a system working with judges. Thats a great thing for all of understand, some of the socialist use come from the use of ai in public space. So if youre a judge under time pressure and need to make these decisions, wouldnt it be easier for you just to agree with the machines decision . What you gain by overriding it . In most cases if you override and a person commits a crime, you are at fault. You need to really think hard so so you need to really think hard about the balance of opinions and who was responsible for these decisions when you actually have a team of a. I. Making these decisions who is responsible in that case . And research has shown algorithms can be quite biased. For instance they were analyzing , statistical techniques for making federal decisions and for africanamericans and whites in society. Scott sorry to interrupt. These are huge issues. Lets try to stay more narrowly focused. We could expand but still, to yourthat theme, how in work or the work of others do you try to make humans and a. I. Complementary, whether in labor or understanding . Ece i think we need to move away from the thought that a. I. Will automate and humans will adapt to it. That is not what needs to happen. I think what we need to work on is yes, we are going to be able be working, we will on automation for getting these capabilities to a. I. , only have to think hard about the design of the thing in the middle later layer in terms of the ai explaining itself to the human, humans having some transparency with this machine, and working on the coordination. Thats the only people get to a situation that is right. I think there are some other issues as well. Think about the exceptions in self driving vehicles. Black swan events are hard to predict. You need this huge amount of data to come up with a reliable estimate about how safe the vehicle is. So you think about self driving cars. Tesla has an enormous amount of proprietary data. It is guiding us algorithms, but an Insurance Company or government regulators do not have access, or the public in general. It is hard to understand what the actual risk is without some sort of data sharing. There is a new set of issues emerging in terms of data transparency. Hal i think you are exactly right. There will be demands for interoperability and data sharing for safety reasons. Look at the Airline Industry as an example. When there is an airline event, there is immediately an investigation by several different parties with different constituencies and interests and they try to resolve the cause and make sure it doesnt happen again. I think that same set of infrastructure, that same set of procedures will be carried over into this context without much objection. Who is going to stand up to object to that kind of investigation . One thing about the news, when you look at the journalism on ai, they were always picking the exceptional kind of interesting cases go, poker, all of these games. If you want to get an idea of what is really done in the ordinary sorts of cases, go look at kagel. Thats a company that sets of Machine Learning competitions. One Company Might say we will offer a prize of 1 million to the party best able to predict hospital readmission within 90 days using this data set. I have to say, two qualifications one, i was an Angel Investor in it and it was recently acquired by google, so nothing to do with each other, those two facts. Quite interesting, because you see things like housing appraisal. Zillow put up a data set of houses, features, values, and came up with the best model for pricing houses. Youtube videos, google put up a labeled 4. 5 million videos and has a contest to predict what people are doing those videos. Even those still images, we have a Good Technology for that now. Goode do not have technology for videos, active people moving around. Are they exercising, dancing, fighting, whatever . Readmission to hospital example i mentioned, recognizing leaves, counting plankton. There are all sorts of applications, 230 something applications 230something applications to get an idea of what is going on in ordinary business practice. It is quite interesting. I think we will see this diffusing. There will be the really exciting cases like a driverless eating the human go champions. Ordinary is a lot of activity being automated as well. Say one thing, ai has a long fail problem. This comes to your point we use a there are a lot of cases. In the real world, a. I. Has to do with a lot of edge cases. If you look at all the cases where you have a lot of data, the distribution of the data, for some cases we have a lot of data, but as they get more to the edge we have less data. The common techniques they use, the statistical learning techniques are very good to learn we really need to think hard about how to get it right. It could be for collection of edge cases, creating data sets of those edge cases, but we need to think about some kind of collection of techniques working together. Not only sticking to one technique, but a collection of techniques working together. Under human supervision to get those cases right. Just saying we are getting 95 accuracy on the data set does not necessarily mean the application will be providing value to you and the shortterm. That comes to your question about why we had nothing productivity from a. I. Yet. Because when you think about those curves you need to think , about, what is the point where im going to be able to get value from this technology . That is a different question. Scott i want to go to questions so get them ready. Diane i want to return one thing to bear in mind is to Pay Attention to the rhetoric used around a. I. I agree the media focuses on the fascinating cases. But i also think the Tech Companies put out a lot of rhetoric other on. Self driving vehicles, i bet you can tell me how many Motor Vehicle deaths per year we expect to save. 1. 2 million lives. You might not note that as the number of Motor Vehicle deaths per year worldwide. The number in the u. S. We it is not we dont care about the rest of the world, the number in the u. S. Is 35,000 deaths per year. To let you know what the number said i was situated between what is right below it and right above it. Right below it, 5 lower, deaths by falling down. Right above it, 25 higher, death by poisoning. We dont see a. I. Solutions for falling down or poisoning. We see solutions for Motor Vehicle deaths. Lets talk about Motor Vehicle deaths. Motor vehicle deaths reached a peak in the 1970s and we have been decreasing ever sent. Why have they been decreasing . Because mechanical and electrical improvements in vehicles that have been on grind and because of regulations and laws things like dui laws and , sobriety checkpoints in and mandatory seatbelt. You have to ask yourself why are we therefore so interested in selfdriving vehicles . The thing about saving lives is not really it. That problem is solving itself. If you look at commercial truck drivers, the first group scott the number of deaths from automobiles is going down, but that is not me selfdriving cars would not further reduce that. Diane they might help to reduce the number, but the number we are talking about realistically is 35,000 in this country. Then you look at other countries. The university of michigans Transportation Institute put out a map of the 25 countries with the most Motor Vehicle deaths per year. If you look at that map, you get a sense of what the roads in these countries look like. There is no way a becuase things like hal brought up, they are chaotic roads. I go to india a lot. I have for the last two decades. There is no way you will have self driving vehicles in india unless the chairman and ceo of marudi suzuki says they will have it because there is chaos on the roads. We have other ways we can bring these numbers down. I dont think the real motivation for why we are going after selfdriving vehicles is to reduce Motor Vehicle deaths. I think there is another motivation and i would like to have a conversation. We could really redesign our cities and improve and some and end some Climate Change problems. Would not that be wonderful . Lets have it conversation. Hal i will say if you look at the examples you gave, they have this common theme. Advances in are monitoring. People can fall over, be monitored, and i just saw a pill bottle that warns you about what you are taking and whether you already took this this morning and all sorts of other things. There are a lot of cases. I dont know if i would call it a. I. , but i would say technology. We see technological advances in one form or another that help people live safer, more productive lives. In a way this issue about selfdriving cars, the problem is dealing with all these exceptions. I asked the team do you break for squirrels . That is a decision. Avoiding a squirrel could cause lots of damage elsewhere. They say no. Do you break for dogs . They say how big is the dog . [laughter] do you break for deer . Of course because there are huge number of accidents involving deer. What we need is an automated deer to avoid the cars in the we will have a safer road. [laughter] scott we have a little time left. Larry . I was a cheerleader in high school. Is it on . Lets come back to the topic, demand for labor. And back in my graduate school days, a Nobel Prize Winner threw out to the class the horse. Or horse story. Pointed out between 1900 and the population of horses and gone way down. Why . Because of the internal combustion engine. We had horseless vehicles rather than personless vehicles for personal transportation outside of urban areas, for shortterm haul, hauling freight in urban areas. Cars, trucks, tractors. Did not totally reduce or eliminate all horses. There was still a small population left, but clearly the marketclearing wage for horses went way down. Below reproduction sustaining levels. Fortunately horses have shorter lives. Fortunately we feel differently about how to deal with surplus horses. So the question is could we are we confident, and hal, i think your point about labor supply may modify, but the issue of the demand for labor and arguably we have been so lucky over the past two centuries that Technology Shifts have not really diminished the demand for labor. If anything it pushed the demand for labor, but we cant relet rule out the possibility of a Technology Change doing to humans what the Technology Change between 1900 and 1940 did to horses. Scott what happens if we are horses or the population have not decreased . Right. That the marketclearing wage falling way low. And then what . It is difficult to retrain horses. [laughter] james it is not a direct comparison. We can look at occupations that have largely gone away. We have taken those people and absorbed them and other capacities. That has generally been true. Why has it been true . If you look at individual occupations, it is a story about demand. Demand is elastic. There is job growth, but when demand starts getting inelastic, further automation leads the job declines. They can be dramatic. In the 10 or 20 year timeframe, we will not see a dramatic change in the nature of demand. But it is a good question further out. Are we going to basically be able to satiate demand in one market after another so there are no jobs left . That really raises a broader philosophical question about what is it that humans want. People have john maynard the 1930s and leonti talked about by this time we were going to have huge amount of leisure time and technological unemployment. If you look at what has happened to leisure time, yes, the work week has declined fairly dramatically since the 1800s when you had a 72 hour work week. Now we are down to 34 hours. But it has been remarkably slow. You have to ask because some people are getting value. There is demand for things they will either consume or demand for how they obtain their leisure. The technology of leisure has dramatically increased in terms of video games, movies, and all these various things. The question comes down to, in 50 years, will there be anything humans want their machines cant deliver . I think we are at a philosophical level. Do humans want interactions with other humans . Do humans want is the personal role an important aspect of what humans want, both in terms of what they are doing for gainful employment and in terms of how they want to consume . I dont think i would dain to answer that, but i can point a very bright people. I have a hard time imagining all the additional things humans might want that might cause them not to have a 10hour work week. Hal i just wanted to say a couple of followups on that. 80 of the u. S. Economy is service. Clearly people want to be involved with other people. There are lots of services you could automate pretty easy. I dont really need it person to lead me to my table at a restaurant. You can flash little arrows on the floor to make it happen. But we want that. People want those services. The work week, nothing is written in stone about a fiveday work week. Mexico, the work week in mexico is 45 hours. In the u. S. , 37 hours. In the netherlands, 30 hours. France is lightly behind at 33. You can take some of that increased productivity and leisure. Absolutely. No question about that. You would get very few objections if people said, hey, let that every weekend be a threeday weekend. Which is pretty much what is technologically possible today. That could easily happen if people chose to move in that direction. One last word about the first invasion of the robots. The first invasion was around the 1880s to 1910 or so. Domestic robots. Washing machines, dryers, dishwashers, lawnmowers, sewing machines. All those mechanical innovations that made homework far more productive. You saw women in particular shifting from the household into the labor market. And you see this tremendous increase in output from a household basis because of that kind of innovation and automation. I introduced myself as an economist historian. And besides the secret handshake which we have, we are supposed to tell people about how things were the same in the past. I dont think a. I. Is distinct from say the bow and arrow, which is a substitute for spears or the horse, which is a substitute for messengers on foot. Of course these technologies have implications. The horse was an instrument of the aristocracy because they were expensive to maintain for a while. I think on the whole these robots, which is what a shovel is. A shovel is a robot. Size are to be viewed with optimism. Here is a number which ought to be in everyones mind. It is not true the number of jobs created or lost in the United States is what is reported every month. About 200,000 jobs when things are good. Minus 200,000 jobs when things are bad. Every year 20 million jobs are lost in the United States. This is in a workforce of 150 million. 160 million. There is a tremendous amount of turning. In 2000, 130,000 people employed in video stores. I urge you all to be of good cheer. [laughter] scott probably have time for one more question. That comes back to something ece said at the beginning. Whether we are moving towards a general artificial intelligence. It is a generalpurpose technology or a shovel . There is a huge difference. Diane they have argued that if we look back at other Technological Innovations like the mccormick reaper, it transformed agriculture and threw a lot of people out of work. But because we do not have simultaneous innovations going on in other industries, there were places for them to go pretty quickly. His argument is that what we will see with a. I. Is simultaneous innovations across all industries so there is never nowhere left to run. Would you say you dont agree with that idea . No, and i would like to know how diane how do know they were happening across i think he is looking at the present. Had his you know the present [indiscernible] [laughter] its a highly unpredictable thing. No one knew the invention of gunpowder in china would radically change the aristocracy. Scott we need to wrap up. We will take a short break, 10 minutes and start the next panel. If the next panel goes over we will miss the eclipse and they will not let anyone out to see it. Join me in thanking the panel. [applause] [crowd noise] today is Small Business saturday, which is held thanksgiving weekend. He was created to encourage shoppers to support local Small Businesses. Linda mcmahon, administrator for the Small Business administration sent out a fleet out shopping. She said next out a taste of holland. I cant wait to try the streets. 97 of Connecticut Businesses are Small Businesses. Pennsylvania Republican Congress and Ryan Costello tweeted, proud to support local Small Businesses on Small Business saturday and every day. Aude into john dr downtown lebanon for helping to pick the perfect train set to run under the christmas tree. Oregon democratic senator john merkley posted this. Happy small biz saturday. Cap small and support local businesses like tender loving empire, which sells locally made goods like records press that cascade pressing that create local jobs, power local economies and add so much hard to our communities. Tonight on cspan, david brooks and historian ronald white discuss character and the presidency. Comparing past administrations to the trump white house. David brooks talks about dwight eisenhower. Character you build is to identify your core sin and fight it. We all have some weakness, and for one of my characters, dwight eisenhower, is weakness was his temper. The story i told was ike at eight or nine going trickortreating. His mom would not let him. He got said that he punched a tree in the front yard and rubbed all the skin off his fingers. His mom sent it to his room and let them cry for an hour and then came up to bind the wounds and recited from proverbs. He who conquers his own soul is greater than he who conquers a city. He said it was the most important conversation of his life. It taught him he had a problem with this temper, and if he wanted to be a leader of any kind he had to conquer it. He spent the next 60 years working on his own weakness. Isnew York Times Company david brooks and historian ronald white discuss character and the presidency, comparing past administrations to the truck white house tonight at 8 00 eastern on cspan. President rmer Vice Joe Biden in Ohio Governor john kasich talk about partisanship. This event was hosted by the Vice President s alma mater at the university of