Understanding the limits of deep learning

Google replaced Google Translate's architecture with neural networks, and now machine translation is also closing in on human performance. At the recent AI By The Bay conference, Francois Chollet emphasized that deep learning is simply more powerful pattern recognition than previous statistical and machine learning methods. "The most important problem for AI today is abstraction and reasoning," explains Chollet, an AI researcher at Google and famed inventor of widely used deep learning library Keras. While neural networks achieve statistically impressive results across large sample sizes, they are "individually unreliable" and often make mistakes humans would never make, such as classifying a toothbrush as a baseball bat.