Archive for January, 2017

My former Intuit Innovation Lab boss, Jana Eggers, posted a tweet today about the IBM Watson X Prize. No surprise there since she’s the CEO of Nara Logics, an AI platform company here in Massachusetts. The X Prize is to encourage the use of AI to help solve society’s big problems. I spent a few minutes googling around to see what people were saying were society’s big problems, and some of the ideas around using big data for solving some of these things.

The problems and solutions I read about were interesting, but not related to my own areas of expertise. However, I did realize something that was, and it all goes back to what I learned in the iLab. I know how to approach a problem, even a seemingly insurmountable one, in ways that may have been overlooked.

So I don’t understand AI systems, although I did once write a learning algorithm to solve a magazine’s puzzle – a brute force path approach that remembered bad paths, so was less likely to try it again. (It did solve the puzzle in about 30 seconds on an old PC.) To me, AI is maybe looking for correlations in data? I realized it’s something we humans do all the time, just slowly and with only a little bit of data.

In the iLab, we looked for surprises – things that were wrong in the data. Everybody will choose the bank with the best rates … but they won’t switch. Estimating is about getting the prices correct using the most up-to-date databases … except it’s the data flow that’s hard, not the prices.

It got me to thinking about AI. Could it look for high-certainty conclusions that were wrong in real life? Could it model what we did in the iLab, such as finding “Creative Rainbow” approaches to resolving problems? I’ve taught creativity techniques that can be done by anyone, even us non-creative engineer types. Could those be taught to a system? What solutions do humans try today to solve some of these problems on the small scale? Could those solutions be applied to society at large? Maybe it’s not about finding solutions, but about applying solutions. Perhaps the AI can find analogues in the successes of other societal transformations, and suggest those analogues as methods for applying the small-scale solutions.

Could Pokémon Go solve poverty?

Maybe the X Prize will go to the team that can teach a computer how to look at data with a creative camera.