Tombone Explains Smart Software Approaches
March 23, 2015
Baffled by smart software, machine learning, and related buzz words. “Deep Learning vs Machine Learning vs Pattern Recognition” does a good job differentiating each of these disciplines. The write up is approachable and lamentably does not include the math jargon that crops up in textbooks and learned journals. Here’s an example of the information available in the article. This passage comes very close to revealing the secret sauce for Autonomy’s DRE and IDOL “inventions”:
Sometime in the early 90s people started realizing that a more powerful way to build pattern recognition algorithms is to replace an expert (who probably knows way too much about pixels) with data (which can be mined from cheap laborers). So you collect a bunch of face images and non-face images, choose an algorithm, and wait for the computations to finish. This is the spirit of machine learning. “Machine Learning” emphasizes that the computer program (or machine) must do some work after it is given data. The Learning step is made explicit. And believe me, waiting 1 day for your computations to finish scales better than inviting your academic colleagues to your home institution to design some classification rules by hand.
The article is worth a read. Attention, failed middle school teachers, mid tier consultants, and litigious search experts—the article may assist you when you convince clients you are an expert in smart software and great inventions like Watson, which IDC admires.
Stephen E Arnold, March 24, 2015
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