News & Interviews


Edge Executive Insight – Chess Stetson, CEO, ∂RISK – Edge Innovator of the Year FINALIST

In the lead up to Edge Computing World, we’re taking some time to speak to key Executives from the leading companies. Today we’re talking with Chess Stetson, CEO of ∂RISK


Tell us a bit about yourself – what led you to get involved in the edge computing market and ∂RISK

My background is in computational neuroscience, and I spent a long time realizing that most of the value in data was in the unexpected places or edge cases. dRISK builds tools for training and testing autonomous vehicles on edge cases, and perhaps ironically does so with a Knowledge Graph called dRISK Edge, which both stores knowledge, edge cases, and works on a distributed database, all of which have have either a semantic or technical relationship to edge computing. But in the strictest sense of edge computing, our heavy use is coming up, as the object detection feeding our knowledge graph starts to run on edge devices rather than centrally. Moreover, the advent of AVs will see more V2V, X2V and V2X communication requiring super low-latency, which will move decisionmaking to the edge.

What is it you & your company are uniquely bringing to the edge market?

We are bringing a dramatically new way to train and test autnomous vehicles on edge cases and, as I say above, it’s not just window dressing to say that all AVs will have an edge computing component in the near future.


Tell us more about the company, what’s your advantages compared to others on the market, who is involved, and what are your major milestones so far?

We are by far the most comprehensive resource for training and testing on edge cases, and our customers enjoy a 6x or greater improvement in safety performance.


How do you see the edge market developing over the next few years?

I don’t think it’s controversial to say that centralized heavy compute is expensive and subject to burdensome latency. That will change as a large variety of distributed computing resources take over the load where it’s needed.

How do you see the edge market developing over the next few years?

We focus on AVs, so I’ll keep my answers contained there. See above — V2X, V2V, X2V all mean edge computing.  But we also work on large distributed graph databases, which are ideal for distributed computing, and it’s easy to see how edge compute could take graph computing all the way to the periphery of the computing process with ease.