podcast interviews Articles and Reports
Our podcast interviews feature the best and brightest executives and researchers in artificial intelligence today, and each episode highlights current and near-term AI use-cases of value for business leaders. Explore our full list of AI podcast episodes below:
Episode Summary: Venture investing in AI healthcare applications has been on the uptick and is directly related to the subject of this week's episode: just how the healthcare industry is (and isn't) being impacted by innovations in AI technology. Guest Steve Gullans of Boston-Based Excel Venture Management talks about some of the various healthcare-related ML and AI applications that he sees being brought to light, and touches on which innovations have a better chance of getting blocked and redirected by parties of interest and those that have more promise in being accepted and rolled out sooner.
Episode Summary: At Emerj, we like to look around the corner at where AI is impacting industries and how people can make better business decisions based on that information. AI and data-driven software for enterprise is an emerging topic of interest, and in this episode we get a venture capitalist's perspective on where AI will play a vital and necessary role with real results in software and industry.
Episode Summary: In some ways, investors in AI have to do a lot of what we do at Emerj, which is sort through marketing fluff and determine what's actually working and what's more of a pipe dream, as well as what's coming up in the next five years that seems inevitable and what's more likely to flop. In this episode we're joined by Li Jiang, a venture capitalist with GSV Capital whom I was connected with through BootstrapLabs. This week, Jiang speaks about the current areas of AI that he sees driving business process automations, as well as what technologies he believes will make a long-term impact in terms of automation. His insights on where AI automations are generating cost savings and increased efficiency, as well as what roles might be completely replaced or significantly augmented by AI, are useful nuggets for companies who are thinking through some of their own business processes and are eager to identify low-hanging fruit.
Episode Summary: As it turns out, survival of the fittest applies as much to algorithms as it does to amoebas, at least when we're talking about genetic algorithms. While we've explored other types of machine learning algorithms in business in past articles, genetic algorithms are newer territory. We recently interviewed Dr. Jay Perret, CTO of Aria Networks, a company that uses genetic algorithm-based technology for solving some of industry's toughest problems, from optimization of business networks to pinpointing genetic patterns that are correlated with specific diseases.
Episode Summary: Getting beyond the marketing and jargon on the home pages of AI companies and figuring out what's actually happening, what results are being driven in business, is part of our job at Emerj. Shaking those answers out of founders is not always easy, but we didn't have to do much shaking with Yohai Sabag, chief data scientist for Optimove, a marketing AI and automation company in Israel. In this episode, he speaks about what humans are needed for in the optimization process, and what facets can be automated or distributed to a machine. Sabag gives an excellent walk-through of how marketers can use the "human-machine feedback loop" for artificial intelligence in marketing optimization at scale.
Episode Summary: You might be aware that some of the articles online about sports or financial performance of companies are article written by machines; this machine learning-based technology is the burgeoning field of natural language generation (NLG), which aims to create written content as humans would—in context— but at greater speed and scale. Yseop is one such enterprise software company, whose product suite turns data into written insight, explanations, and narrative. In this episode we interview Yseop's Vice President Matthieu Rauscher, who talks about the fundamentals of NLG in business, and what conditions need to be in place in order to drive business objectives. Rauscher also addresses the difference between discover-oriented machine learning (ML) and production-level ML, and why different industries might be drawn to one over the other.