AI is transforming every industry from healthcare and customer support to professional services and beyond. Founders are launching startups that tackle real problems using generative AI, machine learning and automation. Others are exploring the shifting mindset of investors as they evaluate opportunities in this fast-moving space. If you’re curious about the practical steps and real-world
Sramana Mitra: I don’t buy all of Kurzweil’s points, but there are certain things that I think machines can do better than humans. For example, if you look at medicine and diagnosis, if you really have all the data and images set up, I think machines would do a better job with diagnosis. David Schmaier:
Sramana Mitra: How did things progress from there? What’s the next major milestone after this? Joe LeCompte: We turned from website development into custom development jobs—not so much of web applications, but more of dealing with local companies and helping them solve business problems. We worked with Delta Airlines and Kimberly-Clarke. Robert Castles: We found
Sramana Mitra: Let’s say in the 10-year horizon we are seeing really significant adoption of still people-driven but a lot of data-driven behavior and optimization. It would be probably reasonable to say that in another 20 to 30 years, the people component will become less and less and everything becomes fully automated. Joe Shamir: Certain
Sramana Mitra: If you were to predict the horizon, what time frame are we talking about in which decisions will be driven by data – more scientifically as opposed to approximation? Joe Shamir: It’s very hard to predict that. You have to be a social guru for that. Today, the phenomenon is driven from the
Sramana Mitra: Sounds like your customer base is largely in retail and consumer packaged goods. Is that accurate? Joe Shamir: Yes, we are also in other verticals but this is the first verticals where you have this phenomena happening by nature of things.
Sramana Mitra: You’re basically using past promotions and modeling those past promotions in your machine learning algorithms to see how the behavior changes when you run certain kinds of promotions. When another promotion is being run in a similar geography by a similar channel partner, then you’re able to predict demand using those models.
Sramana Mitra: Joe, I’d like to do a couple of use cases because a lot of the things that we are talking about here are fairly complicated. The best thing to do is to just go through a couple of use cases. You can pick whatever customers best illustrate the concepts that we have discussed.