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