Kris HammondWe recently had the opportunity to sit down with Kris Hammond, the Chief Scientist for Narrative Science. Narrative Science focuses around automating text generated from data, turning raw data into insightful accounts. Hammond has spent over 20 years working in and developing the AI labs at the University of Chicago and Northwestern University, making him uniquely placed to offer perspectives on the past, present and future of AI. In this first part of our discussion, Hammond discusses three game-changing technologies that he considers to be key in the future of machine learning.

In the near future, I think what we’re going to see is the acceleration of certain ideas that will actually have a commercial and societal impact. The three areas where I think we’re going to see amazing accelerations are everything associated with automated driving and auxiliary uses of the same technology. So an automated factory that can control automated robotics. We’re going to see an explosion of that.

With the Watson model, I think IBM has to get a handle on two things: One, how to quickly configure Watson for new domains, which is still a bottleneck for them, and how to actually present their results not just as answers, but the answers and explanations. I think that once those two nuts are cracked, we’re going to see a massive flow of decision support systems that are based upon the Watson technology. I think, for lack of a better term, disruptive in the white collar world, in a way that we can’t imagine. People think that the white collar world is somehow protected, but I don’t see it as being protected from the incursion of highly intelligent systems.

Third is the communications layer (and that is me, talking about my company). I think that one of the things that hold people back in terms of understanding the world is an inability to look at data directly and understand what’s happening in the world based upon the data alone. What we’re going to see is a rise of applications based upon Narrative Science technology that allow people to understand everything that’s going on in the world that the machine has been capturing for all these years, and how it keeps expanding in terms of capturing that information, and understand that not because they’re data scientists, not because they know how to do analysis, not because they know how to read a spreadsheet, or look at a visualisation, they’re going to be able to understand it because they can read something that has been indirectly making them understand it. And that thing will be written by Quill.

So I think that super organisation around the process of moving things around, the cars, the further development of Watson technology to really push into the white collar world of decision-making, and then communication coming from the data layer that has been interpreted by Quill or other similar or identical technologies.

(Image credit: Pascal)

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19-21 December, 2014- 17th IEEE International Conference on Computational Science and Engineering, Chengdu, China