When Is A.I. Trustworthy? When Is A.I. Useful?
What a map to a Garfield-themed restaurant can teach us about A.I.
When we talk about the weather, we often don’t stop to consider that we’re leaving out a lot of information. If I asked someone how hot it was outside, and they started listing positions and velocities for various air particles, I would walk away in alarm and confusion (or try to learn how they obtained such knowledge). The reality is that we, as humans, have a fairly innate grasp of the distinction between informative and useful. Telling someone it’s “real hot” outside rather than saying it’s 38.94 degrees Celsius is less informative, but also less cumbersome. This act of discarding and summarizing information is the very essence of prediction, and we can define, measure (approximate), and take advantage of this process to improve predictive models and A.I. (and always be correct when you predict the weather).
Boltzmann entropy
“Nothing is more practical than a good theory.”
—Ludwig Boltzmann
If you’re familiar with the concept of entropy, chances are you probably learned one or two systematic definitions of it (for example, thermodynamic entropy, information entropy). Otherwise, you might have been told it’s a measure of “randomness.”…
