The AI Breakthrough Will Require Researchers Burying Their Hatchets

At their core, neural networks are complex mathematical functions composed of thousands of variables. During the "training" phase, the network ingests numerous labeled examples and tunes its variables based on the common patterns it finds among each class of examples. Afterward, when you run a new piece of data through the network, it can classify the data based on its statistical similarity to examples the network has previously seen. Neural networks are especially efficient at tasks such as image classification, voice recognition, and natural language processing, areas where rule-based AI has historically struggled.