Artificial intelligence is shaping up as the next industrial revolution, poised to rapidly reinvent business, the global economy and how people work and interact with each other.
Andrew Ng, chief scientist at Chinese internet giant Baidu Inc. and co-founder of education startup Coursera, and Neil Jacobstein, chair of the artificial intelligence and robotics department at Silicon Valley think tank Singularity University, sat down with The Wall Street Journal’s Scott Austin to discuss AI’s opportunities and challenges.
Here are edited excerpts.
MR. AUSTIN: Andrew, there’s a lot going on with artificial intelligence. What is Baidu focused on?
MR. NG: For large enterprises like Baidu, AI creates two big pockets of opportunities. One is our core business. Web search, advertising—all of that is powered by AI today. For example, we run a very large food-delivery service, and when you order food, we use AI to predict how long the food will take to get to you. That includes deciding which motorcyclist to dispatch to pick up the food, so they arrive just as the food is cooked and fresh, and they can get it to your door while it’s as hot as possible.
In addition to strengthening our core business, AI is creating a lot of new opportunities. Just as about 100 years ago electrification changed every single major industry, I think we’re in the phase where AI will change pretty much every major industry.
So part of my work at Baidu is to systematically explore new verticals. We have built up an autonomous driving unit. We have a conversational computer, similar to Amazon ’s Alexa and Google Home. And we’re systematically pursuing new industries where we think we can build an AI team to create and capture value.
MR. AUSTIN: Let’s talk about speech recognition. I believe someone in your program has said that the hope is to get to the point where it is 99% accurate. Where are you on that?
MR. NG: A couple of years ago, we started betting heavily on speech recognition because we felt that it was on the cusp of being so accurate that you would use it all the time. And the difference between speech recognition that is 95% accurate, which is where we were several years ago, versus 99% accuracy isn’t just an incremental improvement.
It’s the difference between you barely using it, like a couple of years ago, versus you using it all the time and not even thinking about it. At Baidu we have passed the knee of that adoption curve. Over the past year, we’ve seen about 100% year-to-year growth in the daily active use of speech recognition across our assets, and we project that this will continue to grow.
In a few years everyone will be using speech recognition. It will feel natural. You’ll soon forget what it was like before you could talk to computers.
MR. AUSTIN: Neil, at Singularity what sort of trends are you seeing from an AI perspective?
MR. JACOBSTEIN: Just since the beginning of 2017 we’ve seen a team at Northwestern develop an AI that could solve the Raven Progressive Matrices Test, an intelligence test of visual and analogical reasoning, better than the average American.
Focus on Analytics
Percentage of surveyed companies using or planning to use artificial intelligence in the following ways
We also have seen a team at Imperial College London develop an AI that could diagnose pulmonary hypertension better than cardiologists typically do. Cardiologists have about 60% accuracy. This system does 80% accuracy. And in January of this year, Tuomas Sandholm and Noam Brown from Carnegie Mellon University developed a poker player called Libratus, which beat four of the world champion poker players, and not by just a little bit. They played 120,000 hands of poker, and Libratus ended up with $1.77 million in poker chips. This is a big deal, because it signals the ability to deal with incomplete information and to deal with situations that require bluffing and an opponent that generates misinformation. That is a really important set of skills. It will lend itself to negotiation, to strategy development, and perhaps even to policy analysis.
MR. AUSTIN: For decades there has been a cycle of hype and rapid progress and then you have an AI winter and it goes away. Does everyone agree it’s different this time?
MR. NG: Modern industries go through winter, winter, winter, and then eternal spring. I do think we’re in the eternal spring phase of AI, because unlike the earlier waves of maybe overhype, today AI is creating tremendous value for firms like Baidu and Google.
This creates a very clear revenue stream with which to keep investing in and improving AI technology.
JOHN BUSSEY: How might corporations that maybe haven’t thought much about AI use it to augment their strategy right now?
MR. NG: Right now, AI technology is this magical thing, right? It’s useful for so many different things. But the reality is, AI technology needs a lot of customization for your business context.
So I recommend that business leaders hire a senior AI leader—a chief AI officer or a VP—to sort this out for them.
Recruiting AI talent is so difficult that having a centralized AI function would be the best way to have consistent hiring and promotion and management standards for an AI team. This team can then work cross-functionally to figure out how to fit these technologies into your business.
MR. JACOBSTEIN: I have a different perspective on this. I believe in the power of small, interdisciplinary teams that have support high up in the corporation. It’s very important to match the speed of the technology with the nimbleness of the teams. And having a centralized AI guru at the top, where everybody has to ask questions of that person, is unlikely to be as fast and effective as having a decentralized organization with powerful teams, with real talent.
One second of thought
MR. AUSTIN: Do you think almost any job can be automated? Early on, we were talking about manufacturing jobs, blue-collar jobs, truck drivers. Now we’re talking about white-collar jobs.
MR. NG: Things may change in the future, but one rule of thumb today is that almost anything that a typical person can do with less than one second of mental thought we can either now or in the very near future automate with AI.
This is a far cry from all work. But there are a lot of jobs that can be accomplished by stringing together many one-second tasks.
Consider a security guard monitoring security footage. They have a pretty complex job. But the job maybe can be broken down into a lot of smaller tasks, which involve one second of cognitive thinking. So a lot of the art and skill in figuring out where to insert AI is to recognize the business opportunities where you have a complex system but a lot of these one-second tasks that you might be able to string together automatically.
MR. JACOBSTEIN: I think people are going to be surprised at how fast machine learning is going to displace routine jobs.
MR. AUSTIN: How fast are we talking?
MR. JACOBSTEIN: We’re talking about a transition that’s going to occur over the next 10 to 15 years that is really significant.
For that reason, we need to invest heavily in free education and explore various ways to provide a basic income [to those who are displaced].
MR. NG: Just as AI will destroy jobs, it will create new jobs that we can’t yet imagine. The challenge is the skills mismatch.
MR. JACOBSTEIN: The good news is that AI and robotics are going to generate massive amounts of new wealth. Our responsibility is to make sure that in addition to having our companies be successful, people who get displaced have a reasonable quality of life. So yes, we need to make education affordable because there will be new jobs.
But the real question is, “What’s the ratio of jobs destroyed to new jobs?” I think at least in the short term, that could be an unfavorable ratio.
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Appeared in the March 7, 2017, print edition as ‘How AI Will Change Everything.’