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Thursday, May 11, 2017

Generative Networks

Its all about learning.  But learning does not just mean saving away information.  Its about constructing an architecture that allows that information to be accessible and and used in context. Ontologies are one such approach.   Generative methods are another.  Ultimately a big challenge for advancing AI.

How generative artificial networks are accelerating AI learning  By Larry Alton  In VentureBeat:
" ... One of the biggest limiting factors of artificial intelligence (AI) systems is that they can’t think or conceptualize the world the way humans can. .... 

Google’s GANs

Google researcher and AI expert Ian Goodfellow is working on AI that belongs to a group of “generative models,” which are designed to create images and sounds comparable to those you’d find in the real world. This is a deceptively difficult task, as AI programs must first conceptually understand what it is they’re trying to replicate, a leap forward in intuitive thinking that has historically been reserved for human beings.

Goodfellow is attempting to accomplish this using something called generative artificial networks, or GANs, which are sets of two dueling, semi-competing AI algorithms designed to continuously one-up each other. For example, one AI may be programmed to generate imagery that looks realistic, while the other AI will be programmed to distinguish real images from machine-generated ones. Over time, the image generator will get better at generating realistic images, and the “judge” will get better at discerning them.  .... " 

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