And unemployment is at an alltime low, so there is nothing to worry about. And probably the truth is somewhere between those, inclusive maybe. And so that is what this panel is talking about. And whether itai is a substitute or complement to human labor. I will introduce our guests weekly. Diane bailey is an associate professor at the university of coverswhere she engineering product design, economic development, and she conducts large scale empirical studies. Essen is from the Boston University school of law. His latest book is learning by. Ng ece kamar is a researcher at Microsoft Corporation and focuses on Artificial Intelligence. Rian is a chief economist at google. Been involved in many aspects of the Company IncludingCorporate Strategy and is also an emeritus professor at the university of california berkeley in three departments, business, economics, and information management. A good place to start is to talk about what we actually mean by Artificial Intelligence. Ag brought this up in the preliminary discussions that this would be good place to start so where are we today with ai . First of all, thank you for is aing me to this panel person who is studying ai and is an involved in policy discussions, im glad to be here to hear the discussion. Artificial intelligence, is a field that is around 17 years old and started in england. Then the father of ai came together and wrote a paper that wed, this is what ai is a going to be able to solve this ai problem in three months . And they had a meeting where they discussed the different applications of ai, him and several applications were discussed. And they realized the problem was going to be a bit more difficult than they thought was going to be. Some things turned out to be easier, andb some things turned out to be harder. And then they realized ok i think we are Getting Better at this. Oh no, i think this is really hard. Im kind of going through this back and forth. So what is ai . There are multiple definitions of ai. When definition i like very much talks about, its the activity of making machines intelligent. And what we mean by intelligence is, for whatever domain you are designing for, we expect the machine to act appropriately within this environment, by testing its environment and acting on a. Thats what we mean by intelligent machines. However, intelligence is a term that we think of humans for. A lot of the abilities humans ai system toct an have. So another definition that is more human focus is having the ability that is very specific to humans. I want to go into why we think ai is so much in the press. When i was a student 10 years ago the advice that was given me was, if you are entering the job market, dont say you are united because ai was a term that seemed like it was poisonous. However that was a time that google was booming and google is in ai company. So what is really going on is , there were two things in the last five to 10 years that came together to make things happening in the field very successful. Are, we have a lot of data now coming from sensors coming from crowdsourcing, coming from human negativities. We have more competition than human activities. We have more competition than ever before. Through this we are now seeing great advances in perception like facial recognition, image recognition. See these paths that are associated with humans, understanding what the objects we say that machines are really getting intelligent. Before going to the labor has been it, ai around, people have been working on for years, and comes and goes in popularity. The last case that time it was popular, someone might have had a panel like this , saying that this is the time is going to work . Or is it really Something Different this time . I think thats the question we are here for to discuss. Nobody knows the answer to that. Around, when you look there are a lot of applications of ai people use every day. All of the optimization of algorithms, but he does not like ai wasnt there. I think ai was good for some werent goodmans at, like understanding probabilities, making trending decisions, making optimization with this and now technique, ai is moving into tasks that, i wasnt good at. Is creating a perception about what other tasks machines may be able to do now. And second, what does this mean for the human jobs . And that is something we need to say. However there is another discussion which is, does this mean we are getting to general Artificial Intelligence . Much customization, i am going to be able to stop all ai tasks . I dont think were getting there, thats my opinion. Jim, you been writing about labor and ai. Tell us your views. We have actually had ai in the workplace, in the marketplace, since 1987, when ai frauds were used to do detection and creditcard systems. Computerve had automation, which isnt so different from ai, since the 1950s. Whats interesting is that we are seeing an acceleration, within the scope of things that the handle, and maybe in the pace of how it is addressing it. So its in the automation. And we perennial have i think, a basic, many people have a basic misunderstanding about what automation means for jobs. Its commonly assumed, some tasks in a job are automated, that jobs are lost in that occupation. And thats simply not true. We can look up manufacturing and we are very well aware that a lot of manufacturing jobs have been lost to automation. In the 1940s there were nearly half a million Cotton Textile workers in the u. S. And now there are only 16,000. Most of that difference is from automation. Some is from global trade, but that is clearly having a dramatic affect on many communities and many workers and their families. But the thing to remember is, automation can also increase half ad we have only got million textile workers because for the previous 100 years automation was accompanied by job growth. So this is strange. How can automation sometimes create more jobs, and sometimes eliminate jobs, and was going on what is that me . And it comes down to demand. At Something Like textile automation at the beginning of the 19th century, the average person had one set of clothing. Automation meant the price of cloth went down, which meant people could afford more, and they bought more, infected but so much more that even though they needed fewer workers to produce the clock, many more workers to produce the cloth, many more workers were employed because there was much more demand for the clock. Him to an a for the price that and a for the price decline would not produce more demand th. Clo if you look at today and what is happening with computer automation, we see lots of examples of how computer automation, just like textile automation, is creating jobs. One of my favorite examples is the bank teller. There was a great untapped demand for getting cash at and the atmions, machine kim along and people assumed that was going to wipe out the bank teller. In fact, we have more bank tellers since the atms were installed. And the reason is, it made it cheaper to operate a bank branch, thanks could operate more branches and serve more people. There was a market demand for that and they built so many more branches that, even though they needed fewer tellers per branch, they were employing many more. Pattern i think is the we are going to continue to see, and many sectors of the economy, not manufacturing, over the next 10 or 20 years. And i think with the Immediate Response is going to be to ai, as well. You,ane, before we go to accelerating changes in a will generate more jobs more quickly. But that is counter to what some others say. One of the differences that the rate of change isnt allowing industries to catch up, allowing the labor market to catch up. Then you are saying the opposite, right . That the faster the change, the better will be. There are two things, yes and no. If you have elastic demand and you are making faster change, productivity improvements are bringing job growth, if you make faster productivity improvements you are going to have faster job growth. It is going to be disruptive, though, in another sense. Maybe overly optimistic the in the way i described things. When the question is, are we going to see mass unemployment in the next 10 or 20 years, the answer is no. Are we going to see lots of individual jobs destroyed . Yes. We are going to see lots of jobs destroyed and others created. Those textile workers in North Carolina need to have skills, need to find jobs, and there are jobs opening up in the rest of the economy. Arelike everywhere else, we saying 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. I know you are much less optimistic. Let me just push a little bit by asking this. A book called the job talked about,de we were losing a lot of manufacturing jobs and we were talking about how these people need to be retrained into other jobs. For example, we will train them to be bakers and work in a bakery. Is onlyturned out there so much bread and pastries that we can eat and there werent enough for these other jobs for these people to take. And then the language of job retraining started to change from teaching people knew Technical Skills to enter different jobs, to focusing on what they called soft skills. Told,ople started to be the reason you dont have a job is because your Communications Skills arent very good, or this type of thing, you dont work well on a team. As started to put the onus on workers for their lack of self skills, rather than on recognizing that we had a structural shift in the economy and what kinds of jobs were available. Sits asked to weeks ago to on an Engineering Panel to talk about engineering workforce and how they need to be more adaptive to survive in this new economy that we are going to be seeing. 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. It means i can put it on my cv, and maybe get a 3 raise instead of a 2 raise. Have some skin in the game. But i turned it down. And i turned it down because i told them i didnt believe in the premise of the panel. Ok . Putting theanel was onus on engineers to become more adaptable, learn more skills, become a quick learner. There telling all of us these things rather than saying, we are going to start seeing some fundamental shifts in the economy and made we start planning for that. All of us. Not just us individuals, running around suddenly becoming more adaptive, better, quick learners, moving up the scale, because there is only so much room at the top. If we think about ai might be true, there is only going to be so many jobs up there and not all of us are equipped to go up that conceptual letter to take at the top youre doing worse worries me, what is going to be left for everyone. There are a lot of problems with job training, and i think could see more complex than that. We got issues by geographic location. Because where you see a lot of the jobs of hearing is. You also have great difficulty, my book is called learning by doing and one of the themes is that a lot of new technologyrelated skills have to be learned on the job. So its not a matter of the classroom, entirely. We have to come up with new ways of getting people experience. But he think we do see plenty of sectors where there are midskill jobs emerging in numbers. Nursingance, nursing, jobs have been in great demand for a long time. Yet it is still very difficult for people to transition into those, and maybe we dont have enough. I think we dont understand what is involved in making these transitions. You are going to bring in the longerterm process. Aboutant to say a word the jobtraining issue which i think is interesting. If the demand for the job this year the skills are here, there are two ways to solve the problem. You can bring the skills up to the demands he can bring the job down. Going on is a lot of through technology because it used to become if you were a catcher you had to know how to make change. They used to be a few were a taxi driver he had to know how to navigate around town. Necessary anymore. It used to be if you were a veterinarian you had to be able to identify 150 breeds of docks. And you can do that with a i come are with your phone for that matter. So this is a big deal because it allows for the kind of onthejob training you are talking about. You drive around town, you learn your way. You learn to make change because thats what the machine tells you. And theres a lot of delivery mechanisms now which are extremely efficient in the onthejob delivery of education. Look at youtube. There are 500 million video views per day of how to videos on youtube. And all that she almost everyone in this audience has picked something in their house has fixed something in her house by going and looking at that youtube video. And these arent solving quadratic equations, their important manuallabor skills the people can learn, how to weld, how to replace a screen door, how to hang a window. If i were going to play devils advocate i might point repairt that is a lot of people that didnt get called in to do work. Hal 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 released to this discussion. We talk about the demand for a. I. Will reduce the demand for a. I. Will reduce the demand for labor. On the other hand, if you look at the supply of labor, we can find a different story because there is only one social science that can predict 10 years ahead, and that is demography. Everything kind of pales besides that. 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 add 65 years to it and we see what is happening now. Namely all those baby boomers who are retiring, followed by the baby bust. What does that mean . Right now the labor force is growing at half the rate of the population. In the decade of the you will 20 20s, see the lowest growth in the labor force since world war ii. When they started measuring it. When 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. Right . But you need some workers somewhere to be producing this , stuff they need to consume. So 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 a good in the united dates. 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. Look at robots. What countries of the most investment in robots . 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 will be demanding. So that is true as a worldwide phenomenon. By all accounts, unless there is a really big surprises on the automation side, you will see this in the next 25 years. A tight labor market in developed countries, for the next 25 to 30 years. And that is just reading it off the demographics. Scott how far down the line you see that . The labor force becoming more constrained . Hal when you look at the figures around 2060 bc the labor , force growing 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 so it seems like so far there are force leopard issues. There are four separate issues. First, a general shortterm versus longterm. 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 when we learned about the effectiveness of that . Then the distribution affects, both shortterm and longterm. And overtime, the demographics and demand for labor, which will swamp everything. So, diane, o sorry, and the inequality issue. Whether it benefits just accrue to a small group. Diane, you turned down this position at the academy. What should their project have focused on to address your concerns . Diane i think what we ought to be paying attention to is power dynamics. 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. And it describes 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 simply based on the 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. Are doing that simply based on the description of what people do. No job is just what you do. Becad how it was changing design and analysis in that field and what it was doing to the workforce. I will 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. The people who chose power, it is power held by the government somewhat. If you look at Civil Engineers to design Building Structures design Building Structures like the one we are in their solutions are governed , by strict laws and design regs that involves things like here review and countyreview of plans for building. Because of this building were to because if this building were to fall down, because in 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 thei