The Exchanges

Q of theory or not. And we also discuss the problems of data and optimization, as well as the pros and cons of machine learning as a service and touch on the theme of the API economy. But we begin by quickly reflecting on where we are right now. What are we seeing with companies adopting AI beyond R&D? The first voice you'll hear is Scott followed by Joe. Why now?

A So I think AI is kind of this, in this unique position that it hasn't been in historically before. All the pieces are coming together. People have the data sets now. They have the tooling and the open source community has been huge in that with tools like MXNet and TensorFlow being widely adopted and productionalized. And now they have the infrastructure readily available with things like AWS and all these new Nvidia chips. In addition to a whole bunch of APIs to make a lot of the hiccup and like difficult parts of the system easier and easier. And so the combination of all these things together means that instead of spending a decade in the R&D lab to try to come up with something, now a couple of data scientists can make real business impact almost immediately with the AI go-to-market.

AI assessment note: “All the pieces are coming together. People have the data sets now.”

Q You've painted a three-level taxonomy of supervised, unsupervised, and reinforcement learning. So where are you then on the end of theory?

A I think there's going to be need for all of it, to be honest. Um, when it comes down to solving a very specific business problem like fraud detection, you don't want the algorithm to learn on its own. Just let a lot of fraud through as you slowly come up with an idea of what the world looks like. You want to solve this very specific supervised learning algorithm. Or when you're training a car or something like that, uh, how to drive a car, you don't necessarily want it just to, like, go get into a million accidents as it slowly learns, like, what does steering even mean? Um, it needs to be somewhat more directed. That being said, in the security space, um, if it's more like anomaly detection, that might be something different, because you don't necessarily know what all different breaches could look like, and so you need to kind of do this more, uh, unsupervised, like clustering-based approach.

AI assessment note: “I think there's going to be need for all of it, to be honest.”