Thought Leaders in Artificial Intelligence: Josh Sullivan, SVP and Angela Zutavern, VP of Data Sciences at Booz Allen Hamilton (Part 2)

Sramana Mitra: Can you parse that out? What are the tasks that machines do better and what are the tasks that humans do better in that context?

Josh Sullivan: They were having people look over their existing loyalty club members and trying to figure out how to upsell or get them to use their points that they have accumulated in different ways. They were missing something even bigger that said, “How do I go and entice people who are not already loyalty club members? Build me a predictive model of what it would take in order to covert those people.” >>>

Thought Leaders in Artificial Intelligence: Josh Sullivan, SVP and Angela Zutavern, VP of Data Sciences at Booz Allen Hamilton (Part 1)

Josh and Angela have written a book called The Mathematical Corporation, based on their exposure to various customer use cases of Machine Intelligence at Booz Allen. They discuss a few here.

Sramana Mitra: You can decide who goes first. Please introduce yourselves as well as frame this conversation in the context of Booz Allen as well as your book.

Josh Sullivan: Ladies first, of course. >>>

Man and Superman: Impact of the Science Revolutions

By Guest Author Frank H. Levinson

In the previous two segments, we looked at what a Singularity was and as part of evidence for the possibility of a technology singularity, we studied the frequency of five scientific revolutions. Let us now analyze the impact of these revolutions.

It is perhaps hard for us to realize that the impact of each of these last five revolutions, measured by their pervasive effects which underpin modern life, is as important as the domestication of agriculture or creation of geometry millennia ago. But the modern ones have a much greater social impact because they arise, become widely available and approach near ubiquitous use in vastly shorter adoption times. >>>

Man and Superman: Modern Science Revolutions

By Guest Author Frank H. Levinson

In the first segment of this series, we saw what a singularity was and were looking at the evidence for the possibility of a technology singularity. So, let’s now fill in the revolutionary landscape (remember that the agricultural revolution which started about 10,000 BCE was the first one!).

The next major scientific revolution was led by Greek science and Roman engineering when systematic structure (e.g., modern mathematics was born) and methodology in terms of engineering occurred.  We see this reflected in such diverse fields as geometry, >>>

Man and Superman: Life Near An Approaching Technology Singularity

By Guest Author Frank H. Levinson

Ray Kurzweil began writing more than 25 years ago about the possibility of a technology singularity occurring sometime in the late 20^th century. He defines “Singularity” as “a future period during which the pace of technological change will be so rapid, its impact so deep, that human life will be irreversibly transformed.”    >>>

Thought Leaders in Artificial Intelligence: Mike Flannagan, SVP of Analytics, SAP (Part 6)

Sramana Mitra: In your estimate, how are we in getting to that kind of usability of being able to use AI in a highly-leveraged way without having rocket science capabilities in the individuals?

Mike Flannagan: I think we’re doing a great job on the consumer side. Amazing work has been done to provide those sorts of technologies and recommendation engines to consumers. The lagging factor is business applications. That’s where SAP is focusing on.

How do we begin to integrate those capabilities in every one of our business applications so advanced analytics becomes something that not just 10% but 90% of your workforce can take advantage of. I would challenge other business applications providers to think about whether they will be able to maintain relevance if they don’t incorporate those technologies into their business applications in the process. >>>

Thought Leaders in Artificial Intelligence: Mike Flannagan, SVP of Analytics, SAP (Part 5)

Sramana Mitra: Unintended bias has been very intentional in the past.

Mike Flannagan: That’s the thing. If you look at the way compensation is generally determined, it’s based on salary history. If you consider the fact that, historically, there was an intended bias, it’s based on a history. If we want to remove that inherent bias, it’s very difficult for people to do it because they’re biases they don’t even realize they have. That’s one of the things that something like machine learning gives us.

Sramana Mitra: I agree with that. >>>

Thought Leaders in Artificial Intelligence: Mike Flannagan, SVP of Analytics, SAP (Part 4)

Sramana Mitra: In that scenario, if SAP strikes a deal with Spotify and develops that intelligence and then sells it to a variety of retailers, and the retailers help enhance that model, and then if you go back and sell that enhanced model to every single retailer, that becomes a very questionable scenario.

Mike Flannagan: I think that’s right because you start getting down to data that is very specific to that individual retailer. Back to the example of targeted advertising, that is about trying to pull you into my store versus into another store.

If you consider the example of music playing in the store increasing the propensity to buy or increasing the total dollar value of >>>