17 Conference Report #techonomy17

IBM’s John Kelly on AI, Cognitive Computing & Security


  • IBM's Dr. John Kelly with David Kirkpatrick in a one-on-one conversation during Techonomy 2017. Photo Credit: Paul Sakuma Photography


John Kelly
Senior Vice President, Cognitive Solutions and IBM Research, IBM


David Kirkpatrick
Founder and CEO, Techonomy

A conversation with Dr. John Kelly of IBM and David Kirkpatrick at Techonomy 2017. The full transcript can be accessed here, with an excerpt below.

David Kirkpatrick: John is basically Ginni Rometty’s number two at IBM. He runs IBM Research. He’s been at IBM since 1980. He’s still going strong. He’s responsible for cognitive computing at IBM, which is one of their central strategic priorities. Oh, you wrote the book with Steve Hamm, Smart Machines: IBM’s Watson and the Era of Cognitive Computing.

John Kelly: Before it was a popular subject.

Kirkpatrick: With a great journalist, Steve Hamm, who spent many years at BusinessWeek. So anyway, we’re going to talk a lot about artificial intelligence and other issues that have been prominent throughout our discussions the last couple days. But maybe you should start, John, by talking about what the big picture is at IBM about what it’s trying to accomplish as a company.

Kelly: So at 106 years old, we’ve seen it all, David. We’ve reinvented ourselves many, many times. I’ve been part of three or four of these major transitions since 1980, beginning with the PC and services and software, and now cloud and cognitive AI. So we’re literally reinventing ourselves again, which I think makes us very unique. The companies that we’ve seen in the tech industry who have not reinvented themselves are gone. They’re in the bone pile.

So we’re doing it again, and we’re doing it around three imperatives. One of course is artificial intelligence and cognitive on a cloud platform and through an industry lens, which means enterprise. And so we have chosen to focus on enterprise, on AI, on cloud, as opposed to consumer. Many of our clients serve consumers, but that’s where we focused. The reason we believe that this is the right direction for us is we see an immense opportunity—we think it’s on the order of two trillion dollars, which is twice the size of the classic IT industry—

Kirkpatrick: The opportunity for what? Cognitive computing at large?

Kelly: Cognitive computing in the enterprise. So decision support is the goal. Having been around the industry for so long, I think there’s a reason why this is occurring right now, and we’ll talk more about it, and it has to do with exponential curves. The first one we know about is Moore’s Law—double transistor count every 18 months, double performance every 18 to 20 months—and look what that’s done. It took us from the original IBM System/360 through the compute power you have in your smartphone.

The second exponential, which has resulted in all the great networking companies, all the Internet companies that you talked about today, is Metcalfe’s Law. And Metcalfe’s Law basically says every time you add another node on a network, the value of that network goes up as the square of that number of nodes. So it’s an exponential curve. And that is why you see the Googles and the Facebooks and the social media companies gaining value, because of the expanding network. Now, the assumption is that the value and quality of everything on that network is good. We can discuss that later.

Kirkpatrick: That’s not the assumption anymore, but it has been the assumption.

Kelly: That’s been the assumption. As soon as that falls apart, by the way, Metcalfe himself said that exponential flattens out.

But the third one, which has me really excited right now, is that data is doubling every 12 to 18 months in the world. And if we can harness—

Kirkpatrick: That’s an amazing stat.

Kelly: Yes. If we can harness that data in some manner, shape, or form, and improve our decision making, then we can learn on an exponential as humans—to the point of this conference—and we can extract value from that data that today is hidden in 80 percent of that dark data. And the way to do that is through artificial intelligence. So if we can use AI to harness the capability and the knowledge in that data, then our decision making, and we as humans, go up that exponential curve with it.

So that’s why we’re so excited about AI. It’s not just another passing fad. It’s not often you get these exponential curves, but we are in one right now.

Kirkpatrick: One of the things that’s come up in a number of different ways on this stage, which I know is something you think about a lot, is this issue of, if that’s the case, which I think is not a controversial point of view, what is the intersection between actual thinking, breathing people and that? And we opened our conference with a session on what we call the convergence of man and machine with the chief scientist of Alexa and Mary Lou Jepsen and two other really big thinkers about this. And Mary Lou Jepsen is actually doing brain reading technology using ultraviolet light beamed into the brain to measure oxygen uptake in the brain cells and then ultimately do pattern matching with thoughts in the cloud so you can really figure out what people are thinking. And that’s a kind of scary idea. She’s working on more pragmatic things like MRI replacement, etcetera, too. But if we’re going to try to take advantage of that doubling of data every 18 months and retain our humanity, what are our challenges from your point of view from that perspective?

Kelly: First of all, we often think of man versus machine. But every study I’ve seen, and all of our experience with AI, is that man and machine always beats a man or a machine. And I’ve seen it time and time and time again with Watson, that Watson will be trained by humans, it will hit a roadblock, it will get more human input, some perspective, it will start learning again, and back and forth. And I’ll see the humans who are interacting with the system, whether it’s a doctor, a lawyer, a tax accountant, call center operator, get smarter at the same time. So it’s this back and forth between the man and the machine. So that says then that we have to do our best—and I always call it impedance match, the human and the machine, which says that we have to make the machine more human-like in the way it communicates.

For instance, the machine needs to understand is the human is understanding what it’s doing?  If not, it needs to train in a different way. It needs to explain in a different way. On a similar vector, the human has to understand what the machine is doing so that it can improve its intelligence going forward. So this impedance matching of man and machine is really critical. The way it manifests itself in our business—because we’re focused on the enterprise, so we’re focused on healthcare or legal systems or financial services—is getting AI and cognitive into the workflow of these professionals so that it becomes man and machine. And it doesn’t take a neat gadget to do that, but you have to get it into the workflow of what the humans are doing in order to really extract the value.

Kirkpatrick: Would you go so far as to say that, maybe down the road, pretty much everybody in their work is going to be intersecting with AI in some respect?

Kelly: Absolutely.

Kirkpatrick: From the CEO on down?

Kelly: I thought this several years ago as I started to see what our AI system, Watson, was doing. I cannot think of an industry or a human activity or a decision that we make that can’t be augmented by a machine. And I’ve seen this pattern now, sort of roughly speaking, where we as humans, a third of the decisions we make are good decisions.

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