How Will Technology Affect Employment?

Live-blogging from the IBM Watson University Symposium at Harvard Business School and MIT Sloan School of Management. Additional coverage is on the Smarter Planet Blog. .

Panel discussion: How Will Technology Affect Productivity and Employment?

Moderator: Erik Brynjolfsson – MIT Sloan, CDB

Panelists: David Autor – Economics, MIT; Irving Wladawsky-Berger, MIT, IBM Emeritus; Frank Levy, MIT

The Next Big ThingThis is one in a series of posts that explore people and technologies that are enabling small companies to innovate. The series is underwritten by IBM Midsize Business, but the content is entirely my own.
David Autor

David Autor

Autor: The idea that machines eliminate jobs is a fallacy. A century ago, 38% of the US population worked on farms. Today it’s 2%. But we don’t have 36% unemployment. We’re in a period where the scope of what can be done by machinery is expanding rapidly. If we look at 10 categories of occupation (shows a chart), there are three categories: Low-paid positions like food service work; mid-level, relatively low-paid positions like clerical jobs; and relatively highly paid jobs like professional, technical and managerial.

What we see is a decline in operative production jobs and clerical/administrative support jobs. The middle third are the jobs that are declining most quickly. Should we be worried about that? Probably, because it can lead to policies that are intended to preserve these positions instead of moving toward the jobs that are growing.

Employment Polarization, 1979-2009

Changes in Employment Share by Job Skill Tercile, 1993-2006

Wladawsky-Berger: About 80% of the job growth is in information-intensive service jobs. We’re living in a time of sustained high unemployment and this is concerning. Who will pick up the challenge of providing these jobs? People are looking to large businesses, but they are shedding these jobs along with everybody else. Others look to government, but in my experience government won’t do that.

Irving Wladawsky-Berger

The top-down approaches aren’t going to work, but neither do I want to tell people that they’re on their own and that they have to take a more entrepreneurial approach. The world is becoming more entrepreneurial.

Levy: Everything we see here is colored by the recession, but this recession doesn’t have much to do with computers, it has to do with housing bubbles. The mid-skill decline is very real. Development is very uneven. Natural language processing has improved a lot, machine vision hasn’t and technologies like judgment and practical sense really haven’t gone anywhere.

People look at the Google truck and say it’s remarkable that it’s gone 2,000 miles without an accident. What really happened was that Google made detailed maps of the infrastructure it would be traveling. Without that infrastructure, this car doesn’t have the driving ability of a 16-year-old who just got a permit. So while this technology is promising, the Teamsters shouldn’t be protesting yet.

Brynjolfsson: Is there a future for the people who have those kinds of jobs?

Wladawsky-Berger: It has to be more entrepreneurial than top-down. The kinds of jobs that MIT and Stanford graduates have don’t scale very well. Small businesses don’t tend to create many jobs.

Can we apply technologies that have traditionally been available only at the high end and make them easier to use? Can there be new retail services, trades, sustainability-oriented businesses where these skills can be applied?

Frank Levy

Frank Levy

Levy: I can give you an example of one of our graduates who is now running a business making high-end stationery. It’s a good living, but it’s a small piece of the market.

Autor: in a lot of countries there are businesses that we might call entrepreneurial but which are really people just getting by. Most people want to be employed. When the economy booms, people tend to stop working for themselves and go to work for other people. Asking people to create new jobs is asking a lot.

What are the advantages of humans? Common sense, judgment, physical flexibility, understanding. It’s solving novel problems. Positions like cleaning driving actually require  those capabilities.

Wladawsky-Berger: Will global enterprises create these jobs? they’re becoming more distributed and moving a lot of tasks to the supply chain. A lot of people in the supply chain could be these mid-skilled people.

Autor: Cleaning restrooms requires a lot of flexibility, but it’s not entrepeneurial.

Erik Brynjolfsson

Erik Brynjolfsson

Brynjolfsson: So what skills should we be training people for?

Levy: One of the problems is you’re problem-solving by analogy. In the old world, where you were problem-solving by algorithm, it was pretty simple. Now you need to understand how things are similar and how you would use analogies to make decisions.

Autor: Germany has done a good job by training for needed skills and by reducing wages and increasing flexibility. It was painful, but when the shock hit, they were able to handle it better.

The US has a very good system for elite education. We don’t have a particularly good way to handle the people who can’t go to college. The traditional feeders like unions and apprenticeships aren’t as available today. The jobs that are emerging are those that require some level of post-high-school education. We have an incredibly big for-profit post-high-school education sector, but the only guarantee you have is that you’ll come out with a lot of debt. We’re squandering a lot of mid-level talent.

Levy: When you’re talking about a lack of training for people oer 30, you also have to look at where we are in training people under 18. That’s a problem in the pipeline.

Wladawsky-Berger: For these mid-skill jobs you need post-high-school education. I’m not saying a BA in English – in fact, that might be a bad idea – and I’ve been hoping that government agencies would decide that this is better than paying welfare and unemployment.

Autor: Health care will grow and there will be opportunities. If I were asked what people should study for, I’d say a health care worker. I don’t think we’re over-investing in college, I think we’re under-investing in other areas. The high school graduation rate is falling for males in the U.S. We ought to think carefully about how we would use that talent for a set of opportunities that’s appropriate. They need skills beyond the generic skills they find in high school. They need vocational education.

Levy: In the case of medical care, the whole issue of judgment is very important. When you’re talking about eliminating unnecessary procedures, there’s quite a bit of judgment involved. These are not problems that machines can address.

Autor: Look at an example of something that’s been automated out of value: Horses used to be our main form of locomotion but now they’re hardly needed. The difference between people and horses is that horses don’t accrue wealth from the internal combustion engine and we do. We’re getting wealthier collectively but not individually.

Audience question: I’m concerned with how we communicate these changes who aren’t economists so we can avoid reactions like what happened with stem cell research?

Wladawsky-Berger: The consensus of everything I’ve read is that when we transitioned from the agriculture to the industrial age, literacy went way up. High school became the ticket to the mid-skill, mid-pay class. In today’s world you need the next level of education: information-based literacy. You need to be comfortable working with information and you need social skills. This prepares you to be much more flexible in the new working environment. People who learn to use these tools can make a good living.

Audience question: It seems that our society fails people who need to change careers. Our unemployment system doesn’t encourage people to try new things for fear that they may lose benefits. Our education system also doesn’t foster skills training.

Autor: We have very little of what other countries call activation systems for people who have lost their jobs. We have a trade-adjustment system that does a terrible job. The problem is that the Republicans hate trade adjustment and blame everything on trade, and the unions hate re-skilling. So we have trade adjustment, which does very little.

Audience question: What about the possibility of trading off standard of living for other benefits, such as fewer work hours?

Autor: There’s a societal choice to trades off work for standards of living. You can work two days a week and make less money and some people might choose that. But we want to work less and have higher standards of living. We have more and more, but the rewards are concentrated in fewer hands. Having more rewards doesn’t solve the skill problem.

Wladawsky-Berger: I think we need more collaboration between the private and public sector. So the government does more to help people while they’re training for jobs, but the jobs are provided by the private sector.

How Will Computers Serve Us in 2020?

Live-blogging from the IBM Watson University Symposium at Harvard Business School and MIT Sloan School of Management. Additional coverage is on the Smarter Planet Blog. .

Panel discussion: What Can Technology Do Today, and in 2020?

Moderator: Andrew McAfee – MIT Sloan, CDB

Panelists: Alfred Spector, Google; Rodney Brooks, MIT, Heartland Robotics, David Ferrucci,IBM

Alfred Spector, Google
Alfred Spector, Google

Spector: We focused in computer science for many years on solving problems where accuracy and repeatability was critical. You can’t charge a credit card with 98% probability. We’re now focusing on problems where precision is less important. Google search results don’t have to be 100% accurate, so it can focus on a bigger problem set.

When I started in computer science, It was either a mathematical or an engineering discipline. What has changed is that the field is now highly empirical because of all of that data and learning from it. We would never have thought in the early days of AI how to get 4 million chess players to train a computer. You can do that today.

The Next Big ThingThis is one in a series of posts that explore people and technologies that are enabling small companies to innovate. The series is underwritten by IBM Midsize Business, but the content is entirely my own.

Brooks: Here at MIT, all students take machine learning because it’s that important.

McAfee: Was there a turning point when you decided the time was right to take these empirical approaches?

Brooks: It was in the 90s. The Web gave us the data sets.

Ferrucci: Watson was learning over heuristic information. Plowing through all those possibilities through sheer trial and error was too big. You have to combine inductive and deductive reasoning.

Brooks: It’s easy to get a plane to fly from Boston to Los Angeles. What’s hard is to get a robot to reach into my pocket and retrieve my keys.

McAfee: Why does the physical world present such challenges?

Brooks: In engineering, you have to set up control loops and you can’t afford for them to be unstable. Once a plane is in the air, the boundaries of differential equations don’t change that much. But when reaching into my pocket, the boundaries are changing every few milliseconds.

McAfee: The things that 2-year-old humans can do machines find very difficult, and the things that computers can do humans find very difficult.

Rodney Brooks, MIT

Rodney Brooks, MIT

Brooks: One thing we have to solve is the the object recognition capabilities of a two-year-old child. A child knows what a pen or a glass of water is. There is progress here, but it’s mainly in narrow sub-fields. Google cars are an example of that. They understand enough of road conditions that they can drive pretty well.

Spector: We’re looking to attack everything that breaks down barriers to communication. Example: With Google Translate, we eventually want to get to every language.

Another is how to infer descriptions from items that lack them. How do you infer a description from an image? We’re at the point where if you ask for pictures of the Eiffel Tower, we’re pretty good at delivering that.

A third thing is to make sure that information is available always from every corpus, whether it’s your personal information, information in books or information that’s on the Web. We want to break down those barriers while also preserving property rights. How many times have you searched for something and you can’t find it? I turns out it’s in a place where you weren’t looking. When you combine that with instantaneity of access, you can be on the street and communicate with someone standing next to you in the right language and the right context. You can go to a new city where you’ve never been before and enjoy that city no matter where it is.

McAfee: You think in five years I’ll be able to go to Croatia and interact comfortably with the locals?

Spector: Yes.

Brooks: We think manufacturing is disappearing from the US, but in reality there is still $2 trillion in manufacturing in the US. What we’ve done is go after the high end. We have to find things to manufacture that the Chinese can’t. What this has led to is manufacturing jobs getting higher tech. If we can build robotic tools that help people, we can get incredible productivity. The PC didn’t get rid of office workers did; it made them do things differently. We have to do that with robots.

We can take jobs back from China but they won’t be the same jobs. That doesn’t mean people have to be engineers to work. Instead of a factory worker doing a repetitive task, he can supervise a team of robots doing repetitive tasks.

My favorite example is automobiles. We’ve made them incredibly sophisticated but ordinary people can still drive.

Spector: It’s machines and humans working together to build things we couldn’t build separately. At Google, we learn how to spell from the spelling mistakes of our users.

Ferrucci: This notion that the collaboration between the health care team, the patient and the computer can result in a more effective diagnostic system as well as one that produces more options. Everyone is well informed about the problems, the possibilities and why. I think we’re capable of doing that today much better than we did in the past. This involves exploiting the knowledge that humans use to communicate with each other already. This gets you as a patient more involved in making better decisions faster. It’s collaborating better with the experts.

McAfee: Don’t we need to shrink the caregiver team to improve the productivity of the system?

Ferrucci: The way you make the system more productive is to make people healthier. Does that involve a smaller team? I don’t know, but I do know you get there by focusing on the right thing, which is the health of the patient.

Andrew McAfee, MIT Sloan

Andrew McAfee, MIT Sloan

McAfee: If you could wave a wand and get either much faster computers, much bigger body of data or a bunch more Ph.D.’s on your team, which would you want?

Brooks: Robotics isn’t limited by the speed of computers. We’ve got plenty of data, although maybe not the right data. Smart Ph.D.’s are good, but you’ve got to orient them in the right direction. The IBM Watson team changed the culture to direct a group of Ph.D.s the right way. I think we’d be better off if universities were smaller and did more basic research that companies like IBM would never do.

Spector: When many of us in industry go to the universities, we’ve often surprised that the research isn’t bolder. Perhaps that has to do with faculty reward issues. We envision that there’s going to be need for vastly more computation. I’m sure Google data centers will continue to grow. If you stay anywhere near Moore’s law, these numbers will become gigantic. The issues will relate to efficiency: Using the minimum amount of power and delivering maximum sustainability.

With respect to people, there’s a tremendous amount of innovation that needs to be done. Deep learning is a way to iteratively learn more from the results of what you’ve already learned. Language processing is a way to do that. We learn from the results of what we do. Finally, data is going to continue to grow. We bought a company with a product called Freebase where people are creating data by putting semantic variables together. Just learning the road conditions in New York from what commuters and telling us is crowdsourced data, and that’s enormously useful.

David Ferrucci, IBM Research

David Ferrucci, IBM Research

Ferrucci: We need all three, but in order, it’s researchers, data, machines. Parallel is processing is important, but it’s less important than smart people.

McAfee: Do computers ultimately threaten us?

Brooks: The machines are going to get better, but for the foreseeable future we’ll evolve faster. There’s a lot of work going on in the area of putting machines into the bodies of people. I think we’re going to be merging and coupling machines to our bodies. A hundred years from now? Who the hell knows?

Spector: There will be more instantaneity, faster information. We can embrace that, like we did central heating, or reject it. I think we’re on a mostly positive track.

Audience question: What’s the next grand challenge?

Ferrucci: I think the more important thing is to continue to pursue projects that further the cause of human-computer cooperation. We tend to go off after new projects that require entirely different architectures, and that hurts us. I’d rather we focus on extending and generalizing architectures we’ve established and focus on applying it to new problems.

Brooks: I’d like to see us focus on the four big problems we need to solve.

  • Visual object recognition of a 2-year-old
  • The spoken language capabilities of a four-year-old
  • The manual dexterity of a six-year-old. Tying shoelaces is a huge machine problem
  • The social understanding of an eight-year-old child.