When you have “a really hot, frothy space” like AI, even the most basic questions — like what is it good for, how do you make sure your data is in shape, and so on — aren’t answered. This is just as true for the companies eager to adopt the technolog...
When you have “a really hot, frothy space” like AI, even the most basic questions — like what is it good for, how do you make sure your data is in shape, and so on — aren’t answered. This is just as true for the companies eager to adopt the technology and get into the space, as it is for those building companies around that space, observes Joe Spisak, Head of Partnerships at Amazon Web Services. “People treat it like magic,” adds a16z general partner Martin Casado.
This magical realism is especially true of AI, because by definition — i.e., machines learning — there is a bit of a “black box” between what you put in and what you get out of it. Which may be fine… Except when you have to completely change the data being fed into that black box, or you’re shooting for a completely different target to come out of it. That’s why, observes Scott Clark, CEO and co-founder of SigOpt, “an untuned, sophisticated system will underperform a tuned simple system” almost every time.
So what does this mean for organizations going from so-called “toy” problems in R&D to real business results tied to KPIs and ROI? In this episode of the a16z Podcast, Casado, Clark, and Spisak (in conversation with Sonal Chokshi) share their thoughts on what’s happening and what’s needed for AI in practice, given their vantage points working with both large companies and AI startups. What does it mean for data scientists and domain experts? For differentiation and advantage? Because even though we finally have widely available building blocks for AI, we need the scaffolding too… and only then can we build something powerful on top of it.