In the last few years, we’ve become capable of building real-world AI products that automate tasks that were previously out of reach of computational systems. One of the challenges of developing these products is that while it is possible today to build a product to solve a single, specific instance of a problem, it’s much more difficult to solve a general version of the same problem. This is why AI product development is still specialized and relatively expensive, rather than just another service purchased from a vendor or built by simply plugging in an open source module.
Hilary Mason explores a framework for applied AI research, with a focus on algorithmic capabilities that are useful for building real-world products today. Drawing on real-world examples, Hilary outlines a system for thinking about which AI capabilities are ready to transition from pure research to applied products, explains how to make the transition from research paper to a working product, and demonstrates how this system applies to specific prototype products developed around several capabilities, including deep learning for image analysis, deep learning for text analysis, and probabilistic programming.
Hilary Mason is vice president of research at Cloudera Fast Forward Labs and data scientist in residence at Accel Partners. Previously, Hilary was chief scientist at Bitly. She cohosts DataGotham, a conference for New York’s homegrown data community, and cofounded HackNY, a nonprofit that helps engineering students find opportunities in New York’s creative technical economy. She’s on the board of the Anita Borg Institute and an advisor to several companies, including SparkFun Electronics, Wildcard, and Wonder. Hilary served on Mayor Bloomberg’s Technology Advisory Board and is a member of Brooklyn hacker collective NYC Resistor.
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