Most AI advances and applications are based on a type of algorithm known as machine learning that finds and reapplies patterns in data.
Deep learning, a powerful subset of machine learning, uses neural networks to find and amplify even the smallest patterns.
Neural networks are layers of simple computational nodes that work together to analyze data, kind of like neurons in the human brain.
Using one neural network is really great for learning patterns; using two is really great for creating them.
Welcome to the magical, terrifying world of generative adversarial networks, or GANs.
Their secret lies in the way two neural networks work together-or rather, against each other.
The first network, known as the generator, must produce artificial outputs, like handwriting, videos, or voices, by looking at the training examples and trying to mimic them.
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