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Thought Leaders in Artificial Intelligence: Dave O’Flanagan, CEO of Boxever (Part 2)

Sramana Mitra: What are the parameters against which you do the personalization?

Dave O’Flanagan: There’s a number of different parts. There are three key things that feed into our algorithm. One is that we build a very deep transactional view or behavioural view of the customers. The interesting thing in travel is that I can participate or shop in a different context. I may be a business traveler all year long but this time when I’m on the site and I’m trying to purchase a flight for my wife and three kids, I have very different cost parameters and desired outcomes in terms of my trips.

It’s not just about understanding who the customer is from a historical perspective, but it’s actually about understanding the context that the customer is participating in right now. That’s the customer piece. >>>

Thought Leaders in Artificial Intelligence: Dave O’Flanagan, CEO of Boxever (Part 1)

Learn how the airline industry is using Artificial Intelligence to market to their customers in context, with personalization.

Sramana Mitra: Let’s start by introducing our audience to yourself as well as to Boxever.

Dave O’Flanagan: I’m the CEO and Co-Founder of Boxever. Our customer intelligence cloud powers one-to-one customer experiences for some of the biggest travel brands in the world including Emirates, TigerAir, and over 20 airlines around the world. We help them create unique differentiated experiences in the digital and the real world to help them personalize for each of their individual customers. We were formed in 2011. We’re about 70 people and we’re based in Dublin, Ireland. We have offices all over the world.

Sramana Mitra: Pick some of your customers and let’s do some use cases of exactly what these experiences look like and what you do to power these experiences. >>>

Thought Leaders in Cyber Security: Amir Husain, CEO of SparkCognition (Part 5)

Amir Husain: Within our own area of cyber security, one of the things that’s happening at the large-scale level is that cyber security is being weaponized. This is very sad but it’s true. Cyber security is now becoming a weapon of warfare. You’ve seen where digital weaponry was used to rollback the Iranian nuclear program by almost two years. In that two-year period, space was created for a negotiated diplomatic solution to a crisis which, otherwise, would have resulted in a shooting war. God knows how many unknown examples that are not in the public domain exist.

Lately, there has also been an attack on the Ukrainian power grid. At the end of the day, nobody disputes the fact that that was a cyber attack. It brought 200,000 individuals off the power grid. These are large-scale attacks now and this is happening in the real world. The consequences and chances of digital threat resulting in actual physical damage are increasing.

They’re also becoming much more diverse. There’s already 500,000 cars in the US that could be remotely hacked. As they’re going down the highway at 70 miles an hour, you can call them to turn left or right. With self-driving cars, that will be taken to a whole different level. As this burgeoning industrial Internet >>>

Thought Leaders in Cyber Security: Amir Husain, CEO of SparkCognition (Part 4)

Amir Husain: There are lots of examples where we found binaries that were not registering on any one of the 60 different anti-virus engines and yet our machine learning anti-virus capability gave them threat rating as high as 80%. As we actually investigated the envelope manually, we discovered that there was an embedded threat, and that it was a mutation. Therefore, a signature-based system was not able to catch it. There’s lots of these examples. Now, we’re also starting to see in the cyber-physical domain where you have large physical systems where both natural problems as well as potential cyber threats can be tracked and discovered before they can cause any damage.

Sramana Mitra: Can we get to the last segment where the question is essentially, what is your view of emerging trends in the industry and open problems?

Amir Husain: I’ll first take a higher-level view above cyber security for a moment. One of the things that’s happening that is very revolutionary right now is >>>

Thought Leaders in Cyber Security: Amir Husain, CEO of SparkCognition (Part 3)

Sramana Mitra: I have a question in that context. There’s a lot of processing going on midstream of traffic coming in. Is it all happening in real time? How do you deal with delays and latencies?

Amir Husain: First of all, we’re not blocking things until the final answer arrives. In other words, we’re not inserting ourselves as a delay in the servicing of whatever requests our clients or customers are looking to service. All this data exhaust is going into our system and there’s a growing level of confidence being built up as deeper and deeper research is happening. You clearly don’t want to go to real-time NLP research query while you’re waiting on the customer to get their web page back.

Sramana Mitra: That’s right.

Amir Husain: We can do a lot of stuff in real-time, which is quicker. It might be knowledge that we have learned that we can apply. Still, there are things that might look fishy while you may continue to do what the system would have done as long as the action falls in the range of things >>>

Thought Leaders in Artificial Intelligence: Jimi Crawford, CEO of Orbital Insights (Part 2)

Jimi Crawford: Another use case that we’ve been working on is simply counting cars. It seems simple. It’s not actually all that simple because the satellite images have relatively low resolution—about a meter or so per pixel. There’s not very many pixels. We’ve been able to train to actually count cars accurately in parking lots, roads, and bridges. One of the things we can do is track how many people are going to shop in all the different retailers in the US so we can give you an idea at the end of the Christmas season as to how many people were shopping versus the year before.

We can look across the US and see major effects of cold weather in the northeast. We can also get an idea, and we’ve been tracking this the last couple of months, whether the US economy is slowing down or speeding up. There are other applications as well. For instance, we’ve heard from a lot of insurance companies we’ve talked to that car density is one of the biggest and most important determinants of car accident rates. >>>

Thought Leaders in Artificial Intelligence: Jimi Crawford, CEO of Orbital Insights (Part 1)

Using satellite images to predict trends is Orbital’s unique offering. Read on to see how they do it, and where they are finding applications.

Sramana Mitra: Let’s start by introducing our audience to you as well as Orbital.

Jimi Crawford: I’m originally a PhD in Artificial Intelligence. I spent the first part of my career doing relatively basic research in Artificial Intelligence. I had the opportunity, about 15 years ago, to move here to Silicon Valley to lead a team in robotics at NASA Research Centre where we had fantastic projects in schedulers for the Mars rovers and genetically-engineered spacecraft antennas. Of course, being in the middle of Silicon Valley, I eventually got opportunities in startups that couldn’t be turned down. >>>

Thought Leaders in Artificial Intelligence: Jana Eggers, CEO of Nara Logics (Part 4)

Sramana Mitra: We’ve talked in the context of retail. Is there any other finding in other verticals?

Jana Eggers: I mentioned healthcare before. A quick example that everyone understands is doctor and patient matching. When someone who just moved has a specific condition that he/she wants a right doctor for, we help them by giving suggestions on which doctor in their network fits that patient. I mentioned oil and gas. What they’re working on is lots of knowledge in the enterprise. When someone searches their knowledge base, it should give a different answer to a VP Operations as opposed to a Process Engineer.

Sramana Mitra: That’s a much smaller parameter set that you’re personalizing against. In an enterprise oil and gas context, that number of parameters is much smaller. >>>