Artificial Intelligence is surrounded by marketing hype, making it difficult to assess what's real and useful. In this episode, we talk with a venture capital investor and two software entrepreneurs to learn what's involved with creating products that rely on artificial intelligence and machine learning. Join us as we cut through the hype of AI.

For this show, we talk with a VC investor and two company founders developing pproducts that use AI to make work in the enterprise easier and better.

Ed Sim is the Founding Partner of Boldstart Ventures. Sean Chou is the CEO of Catalytic, which makes the Pushbot platform. Keith Brisson is CEO and co-founder of Init.ai, a venture-backed developer platform that enables companies to create conversational apps. Michael Krigsman is an industry analyst and the host of CXOTALK.

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Michael Krigsman: Now that's good, and large companies do the same thing. But seriously, when you say "process-bot," tell us what you mean by that?

Michael Krigsman: I think a good place to kick off this discussion is all three of you are very actively involved in the consideration of different types of AI and what does it mean. And, Sean, maybe you can begin by helping us understand what do we mean by the term, “AI,” and what does it actually encompass?
Sean Chou: Yeah. For sure. It certainly covers a lot of different things, and I think with all major new technologies, there’s always this retrospective period where people look back a little bit and say, “Hey. This really looks like it should be under this umbrella,” and you get a lot of repackaging of things that once maybe weren’t part of AI, but now, because it’s the hot, buzzy topic, now get rebranded under AI. But, I think generally, when we think about AI, we think about it in three different categories.
There's really a strong AI, which is to try to create basically machines that are able to think in a general sense, in the same way that you and I are able to think. So, "strong" or "general AI," there are only a handful of companies that really should be considering that. You need a ton of resources; it's the Google, Microsoft, Amazon, you know, of the world that are going to sort of win in that type of space.
The second category is really more "weak AI," or "narrow AI." And, that's not as difficult. It's still extremely hard, but now what you've done is you've set instead of a general, thinking machine, we're going to focus on a specific domain or a specific field. And so, you see a lot of that in virtual assistants like Siri or maybe Ingram, or Clara, you know; these are folks who are saying, "We're going to create AI," and its personality oftentimes, but it's only going to solve a very narrow set of problems.
And then the third category, which I believe Init.ai and I both fall into, which is: The users of the technology and the research has come out of all this primary research on AI. So, we are beneficiaries of the research that’s gone into natural language processes, sentiment analysis, machine learning; all the things that kind of power AI, we take them, we repackage it, we make it, at least in catalytic space, we make it available for the average business so that they’re able to use it in their processes. And then we’re using it for our product in a very, very applied setting. So, you know, it’s not machine learning to be able to act as humans, machine learning will figure out how to improve their business processes.
Ed Sim: Yeah, and Sean, I think that’s a great point. Kind of how we look at the world is applied AI. I mean, AI is such a buzzy word these days. Everyone has AI in their business plan, the kind of time that everyone had “.com” back in the day. And the reality of it is, what business problem are you solving? And applied AI is very exciting because you’re not going to out-Google Google in AI learning, or Facebook with that AI, but how do you leverage best what’s out there and then apply to enterprise data? That’s the data that they don’t have and can get, and that’s what I love about what you guys are doing and what other companies are doing as well is working on that private enterprise [...], and learning from that.
Michael Krigsman: What are some of the really interesting use cases for this type of applied AI that any of the three

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