In the 50s a sophisticated theory of how to look for things was developed. Rooted in operations research, it was largely motivated by the loss of an H-bomb and a nuclear submarine in the middle of the ocean

Closely related to concepts in reinforcement learning

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How are Kernel Smoothing in statistics, Data-Adaptive Filters in image processing, and Attention in Machine Learning related?

My goal is not to argue who should get credit for what, but to show a progression of closely related ideas over time and across neighboring fields.

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cloud computing allows startups to outsource computing to Google

quantum computing allows Google to outsource (outverse?) computing to parallel universes

'There's no sense in being precise when you don't even know what you're talking about.' -- John von Neumann

"Dress for the job you want, not the job you have"

Me on my way to work:

trying to use dynamically typed languages again