The surprising importance of spontaneous order and noise to how we think.
By Michael Segal | NAUTILUS
One of the core challenges of modern AI can be demonstrated with a rotating yellow school bus. When viewed head-on on a country road, a deep-learning neural network confidently and correctly identifies the bus. When it is laid on its side across the road, though, the algorithm believes—again, with high confidence—that it’s a snowplow. Seen from underneath and at an angle, it is definitely a garbage truck.
The problem is one of context. When a new image is sufficiently different from the set of training images, deep learning visual recognition stumbles, even if the difference comes down to a simple rotation or obstruction. And context generation, in turn, seems to depend on a rather remarkable set of wiring and signal generation features—at least, it does in the human brain.