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Shan Carter, a researcher at Google Brain, recently visited his daughter’s second-grade class with an unusual payload: an array of psychedelic pictures, filled with indistinct shapes and warped pinwheels of color. He passed them around the class, and was delighted when the students quickly deemed one of the blobs a dog ear. A group of 7-year-olds had just deciphered the inner visions of a neural network.
Carter is among the researchers trying to pierce the “black box” of deep learning. Neural networks have proven tremendously successful at tasks like identifying objects in images, but how they do so remains largely a mystery. Their inner workings are shielded from human eyes, buried in layers of computations, making it hard to diagnose errors or biases. On Wednesday, Carter’s team released a new paper that offers a peek inside, showing how a neural network builds and arranges visual concepts.
Olah - Team - Research - Institute - OpenAI
Since then, Olah, who now runs a team at research institute OpenAI devoted to interpreting AI, has worked to make those types of visualizations more useful. Neural networks are composed of layers of what researchers aptly call neurons, which fire in response to particular aspects of an image. For each level of the network, Carter and Olah grouped together pieces of images that caused roughly the same combination of neurons to fire. Then, as with Deep Dream, the researchers reconstructed an image that would have caused the neurons to fire in the way that they did: at lower levels, that might generate a vague arrangement of pixels; at higher levels, a warped image of a dog snout or a shark fin. They arranged similar groups near each other, calling the resulting map an “activation atlas.”
That lets researchers observe a few things about the network. By toggling between different layers, they can see how the network builds...
(Excerpt) Read more at: WIRED
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