Article
Self-Taught AI Shows Similarities to How the Brain Works
Quanta Magazine
- Self-supervised learning allows a neural network to figure out for itself what matters. The process might be what makes our own brains so successful.
- to “train” an artificial neural network to correctly distinguish a tabby from a tiger. The strategy has been both spectacularly successful and woefully deficient.
- For example, a neural network might use the presence of grass to recognize a photo of a cow, because cows are typically photographed in fields.
- “We are raising a generation of algorithms that are like undergrads [who] didn’t come to class the whole semester and then the night before the final, they’re cramming,” said Alexei Efros, a computer scientist at the University of California, Berkeley. “They don’t really learn the material, but they do well on the test.”
- Animals — including humans — don’t use labeled data sets to learn. For the most part, they explore the environment on their own, and in doing so, they gain a rich and robust understanding of the world.
- These “self-supervised learning” algorithms have proved enormously successful at modeling human language and, more recently, image recognition.
- Brain models inspired by artificial neural networks came of age about 10 years ago, around the same time that a neural network named AlexNet revolutionized the task of classifying unknown images.
- If a neural network fails to classify an image correctly, the learning algorithm updates the weights of the connections between the neurons to make that misclassification less likely in the next round of training.
- A leading artificial neural network correctly classified the original image as a Model T, but considered the modified image a leopard. It had fixated on the texture and had no understanding of the shape of a car (or a leopard, for that matter).