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By Isaac Sacolick, Global CIO and Managing Director, Greenwich Associates
This article is by Featured Blogger Isaac Sacolick from his blog Social, Agile, and Transformation.
I went to graduate school and got my Masters in Electrical Engineering, but that's not what I studied. The University of Arizona had a strong program in optical sciences, medical imaging, information theory, and something called "machine learning" and I opted to take classes and ultimately complete a thesis on these topics. I remember learning the math and computing of neural networks, the computer vision algorithms behind facial recognition, and the underlying mathematics of mpeg encoding.
And I remember spending countless hours in a lab testing algorithms on a Unix workstation. Would a reinforcement learning algorithm work better than a two layer neural network? Should a genetic algorithm work better, or am I programming it incorrectly? Should I apply a fuzzy controller, perform an operation in the Fourier space, focus on heuristics or prove out the underlying mathematics?
Most of what I remember is waiting for that workstation to spit out a result. There wasn't a supercomputer that I had access or a cloud environment where I could ramp up and run several experiments in parallel. In the end, artificial intelligence back then was a lot of experimentation between what data sets to test, what algorithms to apply, what parameters to configure, and how to best program them to get better performance.
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