Art Masterpieces to Color

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Any image is fed into the network as an array of pixel values. In the first layer, the network will learn the lowest order features, typically the presence or absence of edges. Then the second layer may put these edges, and their orientations and locations, into a higher order combination to correspond to parts of the objects. The network keeps “feeding forward” so the subsequent layers build the representation of the objects in the image to a higher-order, more abstract level: a color boundary to the edge of an eye to an eye.

We referred to statistics, risk and fair-treatment policies or laws preventing discrimination. We can use machine learning to create a lot of new knowledge from a relatively small amount of prior knowledge. This makes these algorithms very powerful engines of discovery. But it can also result in propagation of any bias when they are scaled up and used across many problems, computers or social networks. So what new sources of unfairness should we be worried about with the diffusion of machine learning algorithms?

The dilemma is in privacy and security and in creating an experience that’s trusted. Too personal can be profoundly uncomfortable. Designing truly human-centric artificial intelligence is harder than it looks and, outside of science fiction, hasn’t been achieved very often. In summary, many things that are being revolutionized by artificial intelligence are unchanged. They still function according to the rules that are already in place. But pay particular attention to the new places the rules need to be applied – the way data is collected and used, what proportion of false or incorrect results algorithms generate and who sees those results, the attitude of people to receiving automatically generated decisions, the way customers react to accurate personalization.

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