In their groundbreaking book The Second Machine Age, Erik Brynjolfsson and Andrew McAfee pointed to employment trends to illustrate a workforce that is inarguably affected by automation–and how companies must transform in order to remain relevant.

Brynjolfsson, director of the MIT Initiative on the Digital Economy (IDE) and McAfee, principal research scientist and co-director at the MIT IDE, have follow-up book out in June. And this one pinpoints specific qualities of the “second machine age,” which the authors argue is maturing to a point at which technologies are now replacing workplace tasks once considered routine. We are now, they write in Machine, Platform, Crowd: Harnessing Our Digital Future, at the “second wave of the second machine age.”

SEE: Why automation in the age of AI will change the way we think of work

In a keynote panel at MIT’s CIO Symposium in Cambridge, MA, Jason Pontin, editor in chief and publisher of the MIT Technology Review, moderated a session with Brynjolfsson and McAfee that addressed questions such as: How can businesses harness AI and machine learning to stay ahead of the curve? What is the importance of platforms in a company’s overall strategy? And what is the role of the CIO in ensuring the smooth transition towards an innovative future?

So, what is the “second wave” of the second machine age? It’s when machines get smart enough to learn on their own.

“We don’t have to specify step-by-step how to recognize a face, or how to understand speech,” said Brynjolfsson. “Instead, machine learning systems are beginning to open up a much broader set of activities for machines to be able to do. This is the most important thing affecting the economy and society over the coming decade.”

McAfee believes the power behind these forces are currently underestimated. “Even though we’re all really enamored of machine learning and artificial intelligence and autonomous vehicles, I think we’re still low-balling what’s actually coming at us,” he said. For example, McAfee brought up Go–the highly-complex strategy game, based mainly on intuition, that is now mastered by a machine.

“Go has been intently studied by people for 3,000 years,” he said. “And after playing AlphaGo, the Chinese Go champion said, ‘I don’t think that a single human has touched the edge of the game of Go,'” said McAfee. “Basically, what he’s trying to say is that 3,000 years of accumulated knowledge and study have got us to this level, and the machines are telling us that there is this entire additional stakes up above over here.”

Why is machine learning success in Go so important? Because, according to McAfee, this game isn’t the only domain where that’s the case.

Over the last ten years, “we basically went from talking to machines, to them routinely talking to us,” said McAfee. At Google I/O recently, the company said voice recognition has improved from 8 1/2% error rate to about 4% error rate, he said. The catch? This time, it wasn’t over a span of ten years–it was over the past ten months.

The panel also touched on the problem of biases in machine learning (LINK). The algorithms, based on massive data sets, have “biases that appear in the system, and are hard to disentangle,” said Bryjolfsson. “It’s hard to get a machine to explain what it’s doing,” he said, which is a primary reason so many researchers are working on explainable AI.

As always, the crowd was interested in the experts’ take on which jobs might remain safest from being replaced by automation. McAfee and Brynjolfsson pointed to jobs involving “personal connections,”–like advising, coaching, nursing, etc.

Still, it’s hard to predict the future.

“For the first time in history,” said McAfee, “We have machines that we can’t explain.”

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