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:

AI for Enterprise Legal Departments - Contract Analysis and More

AI for Enterprise Legal Departments – Contract Analysis and More

AI has numerous use cases in legal, from document search to compliance and contract abstraction. This week, we speak with Lars Mahler, Chief Science Officer for LegalSifter, about what's possible with AI for legal departments today and how AI applications for legal teams, such as natural language processing-based contract analysis, work. In addition, Mahler discusses how lawyers at companies and data scientists work together to train machine learning algorithms.

Data Management Challenges in the Healthcare Industry

Overcoming Data Challenges for AI in the Healthcare Industry

Episode Summary: There's a lot of venture money pouring into artificial intelligence in healthcare. From pharma to hospitals and beyond, the potential applications in healthcare are promising. 

Success Factors for AI Business Models - A Venture Capitalist's Perspective

Success Factors for AI Business Models – A Venture Capitalist’s Perspective

Episode Summary: Saying that your company does artificial intelligence might still have a slightly cool ring to it if you're talking to one of your peers at a conference, but it doesn't mean very much to venture capitalists today, who've been battered with machine learning and artificial intelligence in every pitch deck they've seen for the last three or four years.

What Makes a Successful AI Company? - Perspectives from a Venture Capitalist

What Makes a Successful AI Company? – A Venture Capitalist’s Perspective

Episode Summary: If one wants to start a general search engine, they're going to have to compete with Google. If one wants to start a general eCommerce platform, they'll have to compete with Amazon. But the same dynamics play out on a smaller scale. There are going to be some established players, some big tech giant, be it IBM or someone else, who already has a product.

Why It's Exceedingly Difficult to Build and Adopt AI in Business

The Challenges of Building and Adopting AI in Business

Episode Summary: A lot of AI in the press is CMOs or marketing people talking about what a company can do in a way that really is aspirational. They're speaking about what they can do, but in reality, the things that they're talking about, the capabilities won't be unlocked for maybe a year or more. These are just things on the technology road map, but people speak about them like they exist now.

How to Build Data Science Teams for AI Projects in the Enterprise

Facebook’s Jason Sundram on How to Build Effective Data Science Teams

This week we interview a leader at Facebook. Jason Sundram is the lead of World.ai at Facebook, which is one of their efforts to work with public data around roads and population and other projects of that kind. But Sundram is also highly involved in the Boston office here, where Facebook will soon have around 650 employees. Many of them focus on data science and artificial intelligence.