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 Cyber Security: Manoj Leelanivas, CEO of Cyphort (Part 3)

Sramana Mitra: Let’s do another couple of examples like this. You said your main differentiation and where you’re innovating the most is in unearthing threats that are unknown to the enterprise. I’m trying to understand what kinds of threats are unknown to the enterprise that your work has helped you figure out.

Manoj Leelanivas: Definitely, the threats unknown is the most important thing. The second part of it, which is probably interesting for you, is that in this modern world, we definitely want to have a solution that is very easy to deploy. We were talking about differentiation, I want to cover how it is a differentiator.

Sramana Mitra: Ease of use, as a differentiator, is not interesting to talk about. We will not extract any insight out of that differentiation. >>>

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. >>>

Thought Leaders in Artificial Intelligence: Jana Eggers, CEO of Nara Logics (Part 3)

Sramana Mitra: Can you do one more level of double-clicking? You were distinguishing between clustering algorithms versus one-on-one recommendation and treating each individual as an individual. Can you explain how that works technically?

Jana Eggers: We take all of the product that you have. Let’s take retail. You may have groups of products and offerings. All of those products are connected to each other in a weighted graph to show how similar they are to each other across all of their attributes. Most people think of a product as a molecule. Think of our products as a bunch of atoms.

Those can be connected to each other in different ways and they’re not all the same type of atoms. One may be an iPad that has a certain level of memory. There’s a bunch of information about our product that we’re using and putting all of that information in a very large weighted graph to identify the product. How kid-friendly is a product? That’s part of the weighting in our graph that we figure out using lots of information that we have. >>>

Thought Leaders in Artificial Intelligence: Jana Eggers, CEO of Nara Logics (Part 1)

Most of the Artificial Intelligence recommendation engines are based on clustering algorithms and a limited number of parameters. Nara Logics is pushing the envelope on releasing both constraints, and going to a more granular level of personalisation. Read on to learn more.

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

Jana Eggers: I’m the CEO of Nara Logics. I’m a mathematician and a computer scientist. I started in research and really enjoyed the business side of technology and science. I went into technology—most of the time, in emerging tech. That was my career. Nara and I found each other, I would say, because I was looking for an opportunity that married science and business. That was what I was excited about when I spoke with the team about the work they are doing in artificial intelligence, but also the neural science that’s behind the idea. >>>

Thought Leaders in Big Data: Greg Moran, CEO of OutMatch (Part 3)

Sramana Mitra: If you were to synthesize that, what makes people successful in a dental office?

Greg Moran: This is for one company. I think there is a universality to this. What makes somebody successful in a fast-paced retail environment is nothing that isn’t intuitive. It’s really about the customer service focus. It’s about their judgement under stress. When they have irate customers, how are they going to deal with that? It’s obviously about their trustworthiness.

We’re really looking at a very rounded picture of an individual there to get a sense of, not only how they will interact with customers, but also how they’re going to interact with their co-workers. A lot of the work that we do is focused on very high-volume hiring. In companies like Subway, Aspen Dental, or Disney, thousands of people are getting hired for the same jobs over and over again. It’s really about understanding those characteristics. >>>

Thought Leaders in Big Data: Greg Moran, CEO of OutMatch (Part 2)

Sramana Mitra: You described the outcome but you haven’t described how you do it.

Greg Moran: The way that we would work with organisations like that is, we would go in and basically collect data around job and culture fit, and to help determine why somebody is successful in a given role in an organisation. What is it about the personality? What is it about the skills? What is it about their cultural match to the organisation? Once we have that profile built, we can then install that at a very early stage of the screening process and work with candidates applying against that profile by having them fill out a very simple questionnaire.

We are able to provide the hiring manager a score, way past the point of application. That score basically represents that candidate’s fit to the job and the culture of the organisation. You’re matching that candidate against that profile, and that’s how you really predict the performance of that hire. It also accelerates the hiring process as well. It really makes the job of the manager much easier. That’s a typical use case for us. >>>