In the last three weeks, we laid down the basics of AI. To recap:
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Most AI advances and applications are based on a type of algorithm known as machine learning that finds and reapplies patterns in data.
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Deep learning, a powerful subset of machine learning, uses neural networks to find and amplify even the smallest patterns.
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Neural networks are layers of simple computational nodes that work together to analyze data, kind of like neurons in the human brain.
Now we get to the fun part. 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.
GANs are having a bit of a cultural moment. They are responsible for the first piece of AI-generated artwork sold at Christie’s, as well as the category of fake digital images known as “deepfakes.”
Their secret lies in the way two neural networks work together—or rather, against each other. You start by feeding both neural networks a whole lot of training data and give each one a separate task. 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. The second, known as the discriminator, then determines whether the outputs are real by comparing each one with the same training examples.
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