All that is under the Sun
In 1974, the legendary physicist Richard Feynman stood before the graduating class at Caltech and told them a story about the South Sea islanders. During World War II, these islanders had watched magnificent flying machines descend from the sky, bringing with them endless supplies of cargo. When the war ended and the planes stopped coming, …
Imagine you are a detective at the scene of a very peculiar crime. The victim? A once-vibrant houseplant, now droopy and sad. Your suspects? A cast of environmental characters: the amount of sunlight it received, the quantity of water it was given, the type of soil it’s planted in, and the music played in the …
Imagine you’ve built an AI model to sort fruit. After training, you test it on 100 pieces of fruit (80 apples and 20 oranges). The model correctly identifies 85 of them. Is it a good model? Your first instinct might be to say it’s “85% accurate,” and therefore pretty good. But what if I told …
The Art of Choosing One From Many: Categorical Cross-Entropy and the AI’s Grand Decision Imagine you’re a librarian training a new assistant. This isn’t just any library; it has 50,000 different sections. You hand the assistant a book and ask, “Where does this go?” The assistant, being new, doesn’t just point to one section. Instead, …
Imagine you’re trying to teach a machine the difference between a cat and a dog. You show it a picture of a cat, and it says, with 90% confidence, “That’s a cat.” Great! Now you show it a dog, and it says, with 60% confidence, “That’s a cat.” Not so great. The second guess is …
Imagine you’re teaching an archery student. They loose an arrow, and it thuds into the target, a little off-center. You wouldn’t just say, “You missed.” To be a good teacher, you need to know how much they missed by. Was it a near-perfect shot, just a hair’s breadth from the bullseye? Or did it land …