Michael Segal in 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. Matthias Kaschube studies that wiring by building models that describe experimentally observed brain activity. Kaschube and his colleagues at the Frankfurt Institute for Advanced Studies have found a host of features that stand in stark contrast to the circuits that engineers build: spontaneous activity and correlation, dynamic context generation, unreliable transmission, and straight-up noise. These seem to be fundamental features of what some call the universe’s most complex object—the brain.
What’s the biggest difference between a computer circuit and a brain circuit?
Our computers are digital devices. They operate with binary units that can be on or off, while neurons are analog devices. Their output is binary—a neuron fires in a given moment or not—but their input can be graded, and their activity depends on many factors. Also, the computing systems that we build, like computers, are deterministic. You provide a certain input and you get a certain output. When you provide the same input again and again, you get the same output. This is very different in the brain. In the brain, even if you choose the exact same stimulus, the response varies from trial to trial.
More here.