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The Future of Artificial Intelligence: Why the Hype Has Outrun Reality Posted on : Jul 23 - 2017

Robots that serve dinner, self-driving cars and drone-taxis could be fun and hugely profitable. But don’t hold your breath. They are likely much further off than the hype suggests.

A panel of experts at the recent 2017 Wharton Global Forum in Hong Kong outlined their views on the future for artificial intelligence (AI), robots, drones, other tech advances and how it all might affect employment in the future. The upshot was to deflate some of the hype, while noting the threats ahead posed to certain jobs.

Their comments came in a panel session titled, “Engineering the Future of Business,” with Wharton Dean Geoffrey Garrett moderating and speakers Pascale Fung, a professor of electronic and computer engineering at Hong Kong University of Science and Technology; Vijay Kumar, dean of engineering at the University of Pennsylvania; and Nicolas Aguzin, Asian Pacific chairman and CEO for J.P. Morgan.

Kicking things off, Garrett asked: How big and disruptive is the self-driving car movement?

It turns out that so much of what appears in mainstream media about self-driving cars being just around the corner is very much overstated, said Kumar. Fully autonomous cars are many years away, in his view.

One of Kumar’s key points: Often there are two sides to high-tech advancements. One side gets a lot of media attention — advances in computing power, software and the like. Here, progress is quick — new apps, new companies and new products sprout up daily. However, the other, often-overlooked side deeply affects many projects — those where the virtual world must connect with the physical or mechanical world in new ways, noted Kumar, who is also a professor of mechanical engineering at Penn. Progress in that realm comes more slowly.

At some point, all of that software in autonomous cars meets a hard pavement. In that world, as with other robot applications, progress comes by moving from “data to information to knowledge.” A fundamental problem is that most observers do not realize just how vast an amount of data is needed to operate in the physical world — ever-increasing amounts, or, as Kumar calls it — “exponential” amounts. While it’s understood today that “big data” is important, the amounts required for many physical operations are far larger than “big data” implies. The limitations on acquiring such vast amounts of data severely throttle back the speed of advancement for many kinds of projects, he suggested. View More