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Machine Learning with Less Training Data - Approaches and Trends

Machine Learning with Less Training Data - Approaches and Trends

FromThe AI in Business Podcast


Machine Learning with Less Training Data - Approaches and Trends

FromThe AI in Business Podcast

ratings:
Length:
28 minutes
Released:
Dec 3, 2017
Format:
Podcast episode

Description

Expert systems and machine learning are two ends of a spectrum working to solve similar problems quite differently. One one hand you have if-then scenarios and a logical approach, and on the other you have vast neural networks and a big data approach. Some companies exist to try and bridge the gap between the if-then rule systems and the massive piles of data. They hope to find a middle ground of sorts, one that mitigates their individual disadvantages. One such company is Montreal’s fuzzy.ai. In this episode, we interview its founder, Evan Prodromou about the state of the middle ground, so-called hybrid systems. The middle ground is an elusive, still mostly theoretical concept, but businesses can take steps to prepare for when it becomes accessible to them. What exactly would a hybrid system provide to businesses in terms of automation? How accessible are they now, and what can businesses do to best integrate them when they’re ready? Find out in this episode of the podcast. For more interviews about the business applications of AI, visit: www.TechEmergence.com
Released:
Dec 3, 2017
Format:
Podcast episode

Titles in the series (100)

Learn what's possible - and what's working - with artificial intelligence in the enterprise. Each week, Emerj founder Daniel Faggella interviews top AI and machine learning-focused executives and researchers in sectors like Pharma, Banking, Retail, Defense, and more. Discover trends, learn about what's working now, and learn how to adapt and thrive in an era of AI disruption. Be sure to subscribe to "AI in Industry."