Autonomous Autonomous car Driverless Car Robo Cars

The Future Of Artificial Intelligence: Why The Hype Has Outrun Reality

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.

Get The Full Ray Dalio Series in PDF

Get the entire 10-part series on Ray Dalio in PDF. Save it to your desktop, read it on your tablet, or email to your colleagues.

We respect your email privacy

Venture Capital Investing Process Improvement Through “Machine Learning”

What It Takes To Make A Product Viral

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.

Artificial Intelligence
geralt / Pixabay

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.

In other words, many optimistic articles about autonomous vehicles overlook the fact that it will take many years to get enough data to make fully self-driving cars work at a large scale — not just a couple of years.

Getting enough data to be 90% accurate “is difficult enough,” noted Kumar. Some object-recognition software today “is 90% accurate, you go to Facebook, there are just so many faces — [but there is] 90% accuracy” in identification. Still, even at 90% “your computer-vision colleagues would tell you ‘that’s dumb’…. But to get from 90% accuracy to 99% accuracy requires a lot more data” — exponentially more data. “And then to get from 99% accuracy to 99.9% accuracy, guess what? That needs even more data.” He compares the exponentially rising data needs to a graph that resembles a hockey stick, with a sudden, sharply rising slope. The problem when it comes to autonomous vehicles, as other analysts have noted, is that 90% or even 99% accuracy is simply not good enough when human lives are at stake.

Exponentially More Data

“To have exponentially more data to get all of the … cases right, is extremely hard,” Kumar said. “And that’s why I think self-driving cars, which involve taking actions based on data, are extremely hard [to perfect]…. “Yes, it’s a great concept, and yes, we’re making major strides, but … to solve it to the point that we feel absolutely comfortable — it will take a long time.”

So why is one left with the impression from reading mainstream media that self-driving cars are just around the corner?

To explain his view of what is happening in the media, Kumar cited remarks by former Fed chairman Alan Greenspan, who famously said there was “irrational exuberance” in the stock market not long before the crash of the huge tech stock bubble in the early 2000s. Kumar suggested a similar kind of exaggeration is true for today for self-driving cars. “That’s where the irrational exuberance comes in. It’s a technology that is almost there, but it’s going to take a long time to finally assimilate.”

“To have electric power and motors and batteries to power drones that can lift people in the air — I think this is a pipe dream.”–Vijay Kumar

Garrett pointed out that Tesla head Elon Musk claims all of the technology to allow new cars to drive themselves already exists (though not necessarily without a human aboard to take over in an emergency) and that the main problem is “human acceptance of the technology.”

Kumar said he could not disagree more. “Elon Musk will also tell you that batteries are improving and getting better and better. Actually, it’s the same battery that existed five or 10 years ago.” What is different is that batteries have become smaller and less expensive, “because more of us are buying batteries. But fundamentally it’s the same thing.”

Progress has been slow elsewhere, too. In the “physical domain,” Kumar explained, not much has changed when it comes to energy and power, either. “You look at electric motors, it’s World War II technology. So, on the physical side we are not making the same progress we are on the information side. And guess what? In the U.S., 2% of all of electricity consumption is through data centers. If you really want that much more data, if you want to confront the hockey stick, you are going to burn a lot of power just getting the data centers to work. I think at some point it gets harder and harder and harder….”

Similar constraints apply to drone technology he said. “Here’s a simple fact. To fly a drone requires about 200 watts per kilo. So, if you want to lift a 75-kilo individual into the air, that’s a lot of power. Where are you going to get the batteries to do that?” The only power source with enough “power density” to lift such heavy payloads is fossil fuels. “You could get small jet turbines to power drones. But to have electric power and motors and batteries to power drones that can lift people in the air — I think this is a pipe dream.”

That is not to say one “can’t do interesting things with drones, but whatever you do — you have to think of payloads that are commensurate what you want to do.”

In other areas, like electric cars, progress is moving along smartly and Kumar says there is lots of potential. “The

The post The Future Of Artificial Intelligence: Why The Hype Has Outrun Reality appeared first on ValueWalk.


Leave a Reply

Your email address will not be published. Required fields are marked *