Thought Leaders in Big Data: Marc Alacqua CEO and Steve Davis CTO of Signafire (Part 1)

Complex search queries remains a tricky arena. Read on to learn more.

Sramana Mitra: If you could provide us with some background about yourself as well as Signafire to start us off, that would be great.

Marc Alacqua: I’m the CEO and Co-Founder of Signafire. Steve Davis is one of my partners and also Co-Founder. I come from the military world. The origins of our company are from that world. I’m an ex-army officer and worked in special operations. Signafire was born out of the post-9/11 world where there was a mandate in the intelligence world to break down the silos of data across various >>>

Thought Leaders in Artificial Intelligence: Steve Scott, CTO of Cray (Part 5)

Sramana Mitra: Do you have any thoughts on this problem that is being discussed nowadays? AI is a bit of a black box and all these biases that are creeping into AI are going to drive society in the next several decades. We don’t really have a very good understanding of what really the AI is doing in a lot of domains and a lot of AI applications. How does the world deal with that?

Steve Scott: That is a super good question and it’s very real. I mentioned before that the marriage of AI and traditional simulations can help address that. People are very leery to take what was done in simulations and just replace it wholesale with a deep neural net because you don’t really know if you can trust what’s going on and you lose some insight into what’s happening. You can’t peer inside and understand it. >>>

Thought Leaders in Artificial Intelligence: Steve Scott, CTO of Cray (Part 4)

Steve Scott: With the advent of GPU computing, deep neural nets started to become enabled to the point where you could get good enough performance so that you could really do useful things with them. GPU computing is the application of the processors that were designed for highly parallel tasks of painting triangles on the screen for rendering graphics in real time.

It turns out you can use all those parallel functional units for doing normal computation. GPUs are the first processors with a single processor level powerful enough to do meaningful deep neural net. We could previously do it on very large systems, but that limits you to a small number of people who have these large systems. GPUs were the first ones that could do it on a single desktop basis at a useful scale. >>>

Thought Leaders in Artificial Intelligence: Steve Scott, CTO of Cray (Part 3)

Sramana Mitra: Can you give an example?

Steve Scott: If you think about deep neural networks in particular, there’s training and there’s inference. Training is the learning part where you take a bunch of data and based on that, you train a model to be able to provide some function. Inference, of course, is using that model that has done the learning already to make decisions on new data. The inference problem is sort of a throughput problem.

Once you’ve got the model designed, you can run lots of data through the model and make ad decisions very quickly. The training problem itself takes a lot of compute and a lot of communication. This is the sort of thing that a Cray system >>>

Thought Leaders in Artificial Intelligence: Steve Scott, CTO of Cray (Part 2)

Steve Scott: The way people have used Cray and other high-performance supercomputers is, you have a bunch of equations that present a model for the natural world whether that’s equation of airflow across an airplane wing or equations dictating the molecular dynamics involved in drug discovery.

You iteratively solve these equations spread across these points in space. You’re figuring out what goes on by solving these large numbers of equations that represent the real world. In AI, we have a different way of calculating results. Deep neural networks are more of data-driven versus mathematically-driven techniques where you have these layers of of artificial neurons. They’re taking inputs in. >>>

Thought Leaders in Artificial Intelligence: Steve Scott, CTO of Cray (Part 1)

Steve takes us deep into the field of high performance computing and how AI is impacting it.

Sramana Mitra: Let’s start by having you introduce yourself a little bit as well as Cray’s activities in the domain of AI currently.

Steve Scott: I’m the Chief Technology Officer at Cray. I’ve been with Cray most of my career. I actually was a summer intern here for two years during grad school and have been here ever since except for a three-year walkabout I took a few years back. I went and spent two years as the CTO of the Tesla GPU computing Group at Nvidia and spent a year in the platforms group at Google and then came back in 2014. >>>

Thought Leaders in Big Data: Periscope CEO, Harry Glaser (Part 3)

Sramana Mitra: What do you see around your space that are open problems?

Harry Glaser: One of the fun things about our space is that every problem is an open problem. One of the biggest open problems that we see that we are working hard to solve is, there is so much going on and it all can be a little bit disconnected. You will have data scientists training production models. >>>

Thought Leaders in Big Data: Periscope CEO, Harry Glaser (Part 2)

Sramana Mitra: Let’s do more use cases. I’d like to understand how your product is being used in other contexts.

Harry Glaser: Another of my favorite customer is Crisis Text Line. They are first responders. Folks who are in personal crisis can text a trained professional who will go through that moment of personal crisis and get them to a better place. One of the things with Crisis Text is they use machine learning in order to suggest responses to their first responders. These responders are literally saving lives with Crisis Text Line. >>>