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Wednesday, February 08, 2017

Gaussian Processes for Modeling with Uncertainty

Gaussian Processes, like Bayesian methods introduce natural uncertainty to modeling.   And provide more transparency than deep learning neural nets to the models being built and used.  Already being used by many practitioners.   Article provides a good, largely non-technical introduction  and examples of their current use.

In Wired: AI is About to Learn more like Humans, with a little Uncertainty   by Cade Metz

A broader view of artificial intelligence (AI) seeks to equip deep-learning neural networks to better deal with uncertainty via a Bayesian approach that feeds new evidence into existing models, performing functions at which neural networks do not excel. "There are problems in the domain of language and in driverless cars where you're never going to have enough data to use brute force the way that deep learning does," says Geometric Intelligence founder Gary Marcus. Startups are designing neural networks along a Gaussian process (GP) of statistical modeling to identify uncertainty. "Knowing that you don't know is a very good thing," says University of Edinburgh researcher Chris Williams. "Making a confident error is the worst thing you can do." One company is applying GPs toward building AI systems that can learn to navigate massively multiplayer games and other digital environments, with a long-term goal of eventual real-world navigation capability. ... " 

Gaussian Process in WP.

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