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:

Tuning Machine Learning Algorithms with Scott Clark 1

How to Raise Money for Your AI Startup – Canvas Ventures’ Ben Narasin

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?).

Tuning Machine Learning Algorithms with Scott Clark

How to Learn Machine Learning – an Investor’s Perspective

Episode Summary: There’s been lot of hype around AI and ML in business over the past five years. Even among investors exist a lot of misconceptions about using ML in a business context, and how to get up to speed on and learn machine learning as it applies to utility in industry. Recently, I talked with Benjamin Levy of BootstrapLabs in San Francisco, whom I met through an investment banking friend in Boston.

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Machine Learning in Infosecurity – Current Challenges and Future Applications

Episode Summary: Uday Veeramachaneni is taking a new approach to machine learning in infosecurity aka infosec. Traditionally, infosec has approached predicting attacks in two ways: 1—through a system of hand-designed rules and 2—through anomaly detection, a technique that detects statistical outliers in the data. The problem with these approaches, Veermachaneni says, is that the signal-to-noise ratio is too low. In this episode, Veermachaneni discusses how his company, PatternEx, is using machine learning to provide more accurate attack prediction. He also discusses the cooperative role of man and machine in building robust automated cyberdefense systems and walks us through a common security attack scenario.

Deep Learning Applications in Medical Imaging 3

How to Hire Machine Learning Talent – with HIRED’s Parshu Kulkarni

Episode Summary: When it comes to finding an expert who knows how to hire machine learning  talent, Parshu Kulkarni may just be the guy to ask. Not only is Kulkarni one of a small subsegment of the global population with an advanced degree in data science who has also been hired to work in tech companies like eBay, but he's been on the unique side hiring of ML and AI talent.
Today, Kulkarni works full-time as Head of Data Science at Hired, Inc., a giant platform for hiring top talent in tech and other areas. In this episode, Kulkarni provides insight into what executives with experience in data science look for in potential hires, alongside what businesspeople get wrong about machine learning. He also gives his insights on the supply-and-demand landscape for data scientists now and in the future. Kulkarni's is an interesting interview for anyone looking to hire or be hired as a data scientist in the ML and AI space.

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How Algorithms Improve Advertising – AI for Marketing Optimization

Episode Summary: In marketing, there are lots of applications in AI and machine learning (ML), from recommendation engines to predictive analytics and beyond. At the company Albert, there are even more ambitious projects underway - like automating the process of marketing altogether by having a machine run and generate ads, or test and spend the marketing budget of a company. Or Shani, CEO of Adgorithms, focuses on the quantitative aspects and optimization of marketing, using algorithms to improve advertising processes. In this interview, Shani talks about how Adgorithms' smart marketing platform "Albert" meshes with humans in marketing, and also discusses how these roles might change over the next 5 to 10 years as we move towards an ever more automated marketplace.

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Automating White Collar Work – Two Examples and a Look Forward

Episode Summary: Not all knowledge work can be crunched by a program, but there are some hard-to-automate business processes that a select few entities are making an attempt to automate now. Boston-based Rage Frameworks, Inc. is one such company, and in this episode we speak with Senior Vice President (SVP) Joy Dasgupta about specific applications of automation technologies applied to white collar environments. Rage Frameworks has developed intelligent machines that have been able to take over process that, prior to the emergence of AI and automation technologies, would have required thousands of people to accomplish. These developments are a microcosm of what is to come, and the process is not without its ethical considerations (as discussed in a previous interview with Yoshua Bengio). But Dasgupta's insights provide a concrete glimpse into how these processes are being automated in the knowledge workplace today and what that might mean or look like decades from now.