AI 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: 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.
Episode Summary: There is, in fact, a dark side to AI. Although we’re certainly not at the point where we need to fear terminators, it’s certainly been leveraged toward malicious aims in a business context. In data security, tremendous venture dollars are going into preventing fraud and theft, but this same brand of technology is also being use by the “bad guys” to try and steal that information and break into machine learning-protected systems. In this episode, I speak with Justin Fier, director of cyber intelligence at Darktrace, who speaks about the malicious uses of AI and how companies like Darktrace have been forced to fight these “AI assailants.” Fier provides valuable insights into the role of unsupervised learning, an addition to the full list of AI for data security applications that we've covered in the past.
Episode Summary: Most of our recent investor interviews have been Bay area investors, like Accenture and Canvas, and we don't usually get to speak with investors overseas, particularly in Asia. This week, however, we interviewed Tak Lo, a partner with Zeroth.ai, an accelerator program and cohort investing firm based in Hong Kong and focused on startup artificial intelligence (AI) and machine learning (ML) companies. Lo speaks about when he saw AI take off in Hong Kong and the differences in that rise compared to the U.S. He also gives valuable insight on consumer differences in how the two populations interact with technology (a topic echoed in an earlier Emerj interview with Baidu's Adam Coates), and how these differences in the Asian market drive different business opportunities in Hong Kong than in the U.S.
Episode Summary: If you're going to apply machine learning (ML) in a business context, you need a lot of data, and algorithms across the board perform better with more recent, rich, and relevant data. Today, there are companies whose entire business models are predicated on helping others make sense of and use of this type of information, as more entities look for the first place to apply ML in their organization. In this episode, we speak with the CTO and Co-Founder of one such company—Palo Alto-based Cloudera. CTO Amr Awadallah, PhD, speaks with us this week about where he sees "data lakes" (or "data hubs", Cloudera's preferred term) and warehouses play an important role in ML applications in business. Based on his experiences helping a variety of companies in many countries set up data lakes, Amwadallah is able to distill and communicate these uses in three broad categories that apply across industries as companies look to apply ML applications to solve tough problems and ask more complex questions using unstructured data.
Episode Summary: One facet of business that nearly any industry has in common is the need to stay on top of news in their respective market, including competitor strategies or understanding changes in news related to the field. Media monitoring is a domain that machine learning (ML) is well suited for, with it's ability to coax out headlines, contextual information, and financial data from the seemingly endless stream of social, blog, and other information on the web today. Signal is a company that uses ML specifically for these purposes. In this episode, we speak with Signal's Chief Data Scientist and Co-founder Dr. Miguel Martinez, who dives into real business use cases illustrating the use of machine learning for media monitoring across industries.
Episode Summary: What does it mean to tune an algorithm, how does it matter in a business context, and what are the approaches being developed today when it comes to tuning algorithms? This week's guest helps us answer these questions and more. CEO and Co-Founder Scott Clark of SigOpt takes time to explain the dynamics of tuning machine learning algorithms, goes into some of the cutting-edge methods for getting tuning done, and shares advice on how businesses using machine learning algorithms can continue to refine and adjust their parameters in order to glean greater results.
Episode Summary: In this episode, recorded live at Canvas Ventures in Portola Valley, I speak with Ben Narasin, a general partner with Canvas and an avid venture investor in AI and ML companies. Ben doesn't look to invest in AI; instead, he looks for solid companies in which to invest, a subtle but important difference in a startup world that is increasingly caught up in the explosion of AI and ML technologies. Besides making important distinctions on where investments can make a return and how to raise money for your AI startup, this interview is also chock full of great analogies (give me golden dragons all day long—anyone?).