Presented By O’Reilly and Intel Nervana
Put AI to work
September 17-18, 2017: Training
September 18-20, 2017: Tutorials & Conference
San Francisco, CA

When machines have ideas: A new approach to AI

Ben Vigoda (Gamalon)
11:05am–11:45am Wednesday, September 20, 2017
Implementing AI
Location: Imperial B Level: Beginner
Secondary topics:  Algorithms, Data and training
Average rating: ****.
(4.20, 5 ratings)

Prerequisite Knowledge

  • A general knowledge of AI (including the currently available technologies) and how it can be applied to different industry verticals

What you'll learn

  • Gain a deeper understanding of idea learning


Funded since 2013 as one of DARPA’s largest investments in next-generation machine learning, Ben Vigoda introduces a new approach to deep learning called Idea Learning—teaching the machine with ideas instead of labeled data—and demonstrates use cases with state-of-the-art performance on applications in unstructured enterprise data.

Photo of Ben Vigoda

Ben Vigoda


Benjamin Vigoda is the CEO of Gamalon Machine Intelligence. Previously, Ben was technical cofounder and CEO of Lyric Semiconductor (acquired by Analog Devices), a startup that created the first integrated circuits and processor architectures for statistical machine learning and signal processing whose products and technology are being deployed in leading smartphones and consumer electronics, medical devices, wireless base stations, and automobiles. The company was named one of the 50 most innovative companies by Technology Review and was featured in the Wall Street Journal, New York Times, EE Times, Scientific American, Wired, and other media. Ben also cofounded Design That Matters, a not-for-profit that for the past decade has helped solve engineering and design problems in underserved communities and has saved thousands of infant lives by developing low-cost, easy-to-use medical technology such as infant incubators, UV therapy, pulse oximeters, and IV drip systems that have been fielded in 20 countries. He has won entrepreneurship competitions at MIT and Harvard and fellowships from Intel and the Kavli Foundation/National Academy of Sciences and has held research appointments at MIT, HP, Mitsubishi, and the Santa Fe Institute. Ben has authored over 120 patents and academic publications. Ben holds a PhD from MIT, where he developed circuits for implementing machine learning algorithms natively in hardware.