By count of individuals, dinosaurs are the most successful land vertebrates today.
At least 4 species of dinosaurs survived the K-Pg boundary. One was the ancestor of large flightless birds like ostriches. Another was the ancestor of waterfowl like ducks and geese. Another was the ancestor of land fowl like chickens and turkeys. And another was the ancestor of the 95% of the other species of birds.
Fortunately for their genes, and unfortunately for the individuals, humans find the descendants of two of those dinosaur species very tasty.
There’s a deep contradiction in interpretability research.
A good explanation, a la David Deutsch, is one that is “difficult to vary”. A good explanation can predict the true data, and cannot without difficulty be modified to predict false data.
Deep learning teaches the opposite lesson. Want to modify a good image classifier to claim these six dogs are spoons? Easy, you barely need to perturb the weights. And the bigger the model, the better the model, the less you need to perturb the weights to claim those six dogs are spoons.
Interpretability wants “hingey” explanations: a few select latent variables that, had they been different, would greatly affect the output. Neural nets seem to work for the opposite reason.