Learning to See [Part 4: Machine Learning]

Learning to See [Part 4: Machine Learning]

Last time, we left off wondering how to improve on our knowledge engineering approach to identifying fingers in images. Fortunately for us, there is a better approach, and its roots go all the way back to the beginning of AI. In the late 1940s, IBM engineer Arthur Samuel took on an interesting side project, writing

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I used Machine Learning to hack baseball

I used Machine Learning to hack baseball

[indistinct shouting] – No steal. There’s no steal there. I predict not a steal, watch this: …and he stayed. [excited] He’s gonna steal! This is a steal! If this app works, this kid’s about to steal! [excited squealing] There he goes! There he goes! Freakin’ works! It works. Two years ago I came up with

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SAS Machine Learning and AI

SAS Machine Learning and AI

STEVE HOLDER: Fundamentally, the math and the techniques that people are using with machine learning have been around for a long time. What’s interesting about it is the ability for organizations to now use some of the technical advancement, whether that be in computing power or the commoditization of analytics, to actually start to implement

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