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.
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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
A big thank you to Intel Nervana, who is supporting this podcast series. I’m very grateful to them for helping make this series possible, and I’m excited about the cool stuff they launched at the O’Reilly AI conference, including the Neon framework and Nervana Graph announcements. Be sure to check them out at intelnervana.com, and let them know via Twitter @IntelNervana how much you appreciate their support of the podcast.