Artificial intelligence may be one of the technology world’s current obsessions, but many people find it scary, envisioning robots taking over the world.

Two top experts in the field — Andrew Ng, a Stanford University adjunct professor and former AI scientist at Alphabet Inc.’s GOOG, +1.16% Google and Chinese internet giant Baidu Inc., BIDU, +0.02% and Tong Zhang, executive director of the AI Lab at Tencent Holdings Ltd. 700, -0.29% — sat down with The Wall Street Journal’s global technology editor, Jason Dean, to explain why they believe the opportunities associated with this technology far outweigh the bad.

Edited excerpts follow.

The new electricity

WSJ: Let’s start with the scary stuff because that’s the most fun. The title of this panel refers to “the singularity,” or the idea that artificial intelligence will become so powerful that robots will take over. Andrew, I know you’re skeptical of that. What should we be worried about with AI and where are the biggest opportunities?

Andrew Ng: Worrying about evil AI robots today is a lot like worrying about overpopulation on the planet Mars. You know, some day we might get to Mars and we might actually overpopulate it and we’ll need to worry about it then. But today, we haven’t even landed on Mars yet, so I don’t know how to productively work on that problem.

Having gotten the scary stuff out of the way, I think AI is the new electricity. Whatever industry you work in, AI will probably transform it, just as 100 years ago the rise of electricity transformed industry after industry—everything from transportation and communications to manufacturing to health care.

Artificial intelligence is set to revolutionize every sector, and will likely eliminate whole categories of jobs in the next few years, says Andrew Ng. Getty Images

So I hope that whatever industry you’re in, you’ll figure out how to leverage AI, because I think it will create new winners and losers in almost every category.

WSJ: Tong, do you share that basic optimism?

Tong Zhang: Yes. Nowadays, AI can solve a lot of specialized problems, in some cases even better than humans. But there is no single solution for AI to solve all problems simultaneously.

It takes a lot of human effort to deliver a specialized AI system for medicine, for example. And that doesn’t translate to other problems as easily as some people might think. That isn’t singularity. That’s far from it.

WSJ: Part of the hype around AI is that every company in the technology industry and even some that aren’t are saying, “Oh, we’re doing great stuff with AI.” It can be hard to tell how much of that is real and how much of that is baloney. Where do you think there is more malarkey than actual substance in the AI talk?

Ng: Here is one pattern that might be useful for spotting when AI might affect your industry or some other industry you care about. First, the industry is digitized, meaning activity is moved to computers. That creates data, which gives AI an opportunity to come in and eat the data and automate decisions or do things more intelligently.

The single most lucrative application of AI today might be online advertising, or deciding what ad to show people. Because the online advertising realm has always been a digital realm, there is tons of data, and as a result the AI for that is very sophisticated today. The fintech industry is another area where a lot has moved to the digital realm. Health care is a bit further behind. So is construction. But you see this pattern where first comes digitization, which creates data, and then comes the AI.

Lifelong learners

WSJ: Let’s be predictive. My nine-year-old twins are in the phase now of coming to me and saying what they want to be when they grow up. What jobs should I tell them probably won’t exist once they are adults? Radiology is one you’ve mentioned before. What else is in the target zone?

Ng: If any of you have friends or children in medical school—AI is getting much better at reading radiology images, frankly. So if any of your friends are going through medical school and graduating with a degree in radiology, I think they might have a perfectly fine five-year career as a radiologist. Maybe 10 years.

Again, the broader pattern is that in any task in which a lot of people are doing relatively routine, repetitive work, that creates a very strong incentive for AI teams to come and automate that task.

There is one other rule of thumb, which is that almost anything a typical human can do with less than one second of mental thought, we can probably now or in the near future automate using AI.

Take a security guard looking at a video feed and saying, “Are there people in this? Are they doing something suspicious?” That task is actually a lot of one-second judgment thoughts strung together, so I think a lot of it can be automated.

I do think we’re entering an era in which lifelong learning is no longer optional. The old model of education, where you go to college for four years and coast through the next 40 makes no sense in today’s rapidly changing world.

In IT, half the stuff we invent today will be obsolete in five years. The sands are constantly shifting. You have to keep learning. This is becoming much more pervasive elsewhere in our economy, too.

So tell your children: “Learn to be lifelong learners.”

WSJ: It seems like the U.S. and China are the two places where the most is happening with AI in terms of development and deployment of the technology. You both have worked in both places. What are the strengths and weaknesses in each?

Ng: The U.S. is very good at inventing basic technologies, a lot of the latest AI innovation.

I think the China ecosystem is very strong at taking things to market—it moves shockingly fast. In the U.S., when you launch a new product, it feels like you always have to fight market segment by market segment for users. And there are so many market segments.

China is a relatively homogenous society, so once you find that beautiful product market fit, you can scale really quickly.

The flip side of that, of course, is that if your competitors find product market fit, you can also lose share more rapidly than in the U.S.

Zhang: I think the U.S. has more innovations nowadays than China.

But Chinese companies, as well as universities, are trying to do more innovative research in terms of both quantity and quality. So I think it’s happening, but the U.S. on this particular aspect is definitely leading.