Ben Vigoda – The Power of Probabilistic Programming

800 800 The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

My guest for this third episode in the O’Reilly AI series is Ben Vigoda. Ben is the founder and CEO of Gamalon, a DARPA-funded startup working on Bayesian Program Synthesis. We dive into what exactly this means and how it enables what Ben calls idea learning in the show. Gamalon’s first application structures unstructured data — input a paragraph or phrase of unstructured text and output a structured spreadsheet/database row or API call. This can be applicable to a wide range of data challenges, including enterprise product and customer information, AI or digital assistant, and many others.

Before Gamalon, Ben was co-founder and CEO of Lyric Semiconductor, Inc., which created the first microprocessor architectures dedicated for statistical machine learning. The company was based on his PhD thesis at MIT and acquired by Analog Devices. In today’s talk we are discussing probabilistic programming, his new approach to deep learning, posterior distribution, and the difference between sampling methods and variational methods and how solvers work in the system. Nerd alert: We go pretty deep in this discussion.

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Thanks to Our Sponsor

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About Ben

Mentioned in the Interview

1 comment
  • Massimo

    What a great interview. Never heard of probabilistic programming before. Every interview opens a new world to discover. Thank you!
    Love the part where you talked about how humans learn via inception. And this is a goal for ml algorithms. My quote of this episode is. “How can your build modern civilization when humans can train each others only through stimulus and response? It’s not, it’s crazy, this can’t work.”

    Best from Germany

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