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.
Are you Ready to Experiment with AI and Machine Learning?
Is AI Today Fundamentally Easier?
Lots of choices, lots of talent, lots of time to implement. But the rewards can be significant.