Worried about AI? Relax it's dumber than you think(2)
The Human Brain Conundrum
This led to the panelists to note a second underappreciated divide: the scope of projects that AI can currently master. Kumar pointed out that tasks like translation are relatively narrow. We have “figured out how to go from data to information to some extent, though … with deep learning it’s very hard even to do that. To go from information to knowledge? We have no clue. We don’t know how the human brain works…. It’s going to be a long time before we build machines with the kind of intelligence we associate with humans.”
Not long ago, Kumar noted, IBM’s supercomputer Watson could not even play tic tac toe with a five-year-old. Now it beats humans at Jeopardy!. But that speedy progress can blind us to the fact that computers today can best handle only narrow tasks or “point solutions. When you look at generalizing across the many things that humans do — that’s very hard to do.”
Still, the stage is being set for bigger things down the road. To date, getting those narrow tasks that have been automated have required humans to “learn how to communicate with machines,” and not always successfully, as frustration with call centers and often Apple’s Siri suggests, noted Fung.
Today, the effort is to reverse the teacher and pupil relationship so that, instead, machines begin to learn to communicate with humans. The “research and development, and application of AI algorithms and machines that will work for us,” cater to us, is underway, Fung said. “They will understand our meaning, our emotion, our personality, our affect and all that.” The goal is for AI to account for the “different layers” of human-to-human communication.
“We look at each other, we engage each other’s emotion and intent,” said Fung, who is among the leaders worldwide in efforts to make machines communicate better with humans. “We use body language. It’s not just words. “That’s why we prefer face-to-face meetings, and we prefer even Skype to just talking on the phone.”
Fung referenced an article she wrote for Scientific American, about the need to teach robots to understand and mimic human emotion. “Basically, it is making machines that understand our feelings and intent, more than just what we say, and respond to us in a more human way.”
Such “affective computing” means machines will ultimately show “affect recognition” picked up from our voices, texts, facial expressions and body language. Future “human-robot communication must have that layer of communication.” But capturing intent as well as emotion is an extremely difficult challenge, Fung added. “Natural language is very hard to understand by machines — and by humans. We often misunderstand each other.”
So where might all this lead when it comes to the future of jobs?
Machines Are Still ‘Dumb’
“In the near future, no one needs to worry because machines are pretty dumb….” Kumar said. As an example, Fung explained that she could make a robot today capable of doing some simple household chores, but, “it’s still cheaper for me to do it, or to teach my kids or my husband to do it. So, for the near future there are tons of jobs where it would be too expensive to replace them with machines. Fifty to 100 years from now, that’s likely to change, just as today’s world is different from 50 years ago.”
But even as new tech arrives it is not always clear what the effect will be ultimately. For example, after the banking industry first introduced automatic teller machines [ATMs], instead of having fewer tellers “we had more tellers,” noted Aguzin. ATMs made it “cheaper to have a branch, and then we had more branches, and therefore we had more tellers in the end.”
“With blockchain technology, eventually the cost of doing a transaction will be ‘like sending an email, like zero.’ Imagine applying that to trade finance.”–Nicolas Aguzin
On the other hand, introducing blockchain technology as a ledger system into banking will likely eliminate the need for a third-party to double-check the accounting. Anything requiring reconciliation can be done instantly, with no need for confirmation, Aguzin added. Eventually the cost of doing a transaction will be “like sending an email, it will be like zero … without any possibility of confusion, there’s no cost. Imagine if you apply that to trade finance, etc.”
Already, Aguzin’s bank is about to automate 1.7 million processes this year currently being done manually. “And those are not the lowest-level, manual types of jobs — it’s somewhere in the middle.” In an early foray in affective computing, his bank is working on software that will be able to sense what a client is feeling and their purpose when they call in for service. “It’s not perfect yet, but you can get a pretty good sense of how they are feeling, whether they want to complain or are they just going to check a balance? Are they going to do x, y — so you save a lot of time.”
Still, said he remains confident that new jobs will be created in the wake of new technologies, as was the case following ATMs. His view about the future of jobs and automation is not as “catastrophic” as some analysts’. “I am a bit concerned about the speed of change, which may cause us to be careful, but … there will be new things coming out. I tend to have a bit more positive view of the future.”
Fung reminded the audience that that even in fintech, progress will be throttled by the available data. “In certain areas, you have a lot of data, in others you don’t.” Financial executives have told Fung that they have huge databases, but in her experience, it often is not nearly large enough to accomplish many of their goals.
Kumar concedes that today we are creating more jobs for robots than humans, a cause for concern for the future of jobs for humans. But he also calls himself a “pathological optimist” on the jobs issue. AI and robotics will work best in “applications where they work with humans.” Echoing Fung, he added that “it’s going to take a long time before we build machines with the kind of intelligence associated with humans. When it comes to going from “information to knowledge, we have no clue. We don’t know how the human brain works.”
Security at the Top — and Bottom
Picking up on Fung’s point that many lower-skill level jobs likely will be preserved, Kumar added that the jobs most likely to be eliminated could surprise people. “What is the one thing that computers are really good at? They are good at taking exams. So, this expectation of, oh, I got a 4.0 from this very well-known university, I will have a job in the future — this is not true.” At the same time, for robots “cleaning up a room after your three-year old is just very, very hard. Serving dinner is very, very hard. Cleaning up after dinner is even harder. I think those jobs are secure.”
The panel’s consensus: The jobs safest from robot replacement will be those at the top and the bottom, not those in the middle.
What about many years down the road, when robots become advanced enough and cheap enough to take over more and more human activities. What’s to become of human work?
“You will still want to read a novel written by a human even though it’s no different from a novel written by a machine someday. You still appreciate that human touch.”–Pascale Fung
For one thing, Fung said, there will be a lot more AI engineers “and people who have to regulate machines, maintain machines, and somehow design them until the machines can reproduce themselves.”
But also, many jobs will begin to adapt to the new world. Suppose, for example, at some point in the distant future many restaurants have robot servers and waiters. People will “pay a lot more money to go to a restaurant where the chef is a human and the waiter is a human,” Fung said “So human labor would then become very valuable.”
She added that many people might “become artists and chefs, and performing artists, because you still want to listen to a concert performed by humans, don’t you, rather than just robots playing a concerto for you. And you will still want to read a novel written by a human even though it’s no different from a novel written by a machine someday. You still appreciate that human touch.”
What’s more, creativity already is becoming increasingly important, Fung notes. So, it’s not whether AI engineers or business people will be calling the shots in the future. “It’s really creative people versus non-creative people. There is more and more demand for creative people.” Already, it appears more difficult for engineering students “to compete with the best compared to the old days.”
In the past, for engineers, a good academic record guaranteed a good job. Today, tech companies interview applicants in “so many different areas,” Fung added. They look beyond technical skills. They look for creativity. “I think the engineers have to learn more non-engineering skills, and then the non-engineers will be learning more of the engineering skills, including scientific thinking, including some coding….”
Kumar agrees. Today, all Penn engineering students take business courses. “The idea of a well-rounded graduate, the idea of liberal education today, I think includes engineering and includes business, right? The thing I worry about is what happens to the anthropologist, the English majors, the history majors … I think those disciplines will come under a lot of pressure.”