Multiplicity is not science fiction. A combination of machine learning, the wisdom of crowds, and cloud computing already underlies tasks Americans perform every day: searching for documents, filtering spam emails, translating between languages, finding news and movies, navigating maps, and organizing photos and videos.
Consider Google’s search engine. It runs on a set of algorithms with input from a large number of human users who share valuable feedback every time they click on or skip over a link. The same is true for spam filters. Every time someone marks an email as spam or overrides a filter, it helps fine-tune the system for determining what is relevant.
Multiplicity allows Amazon to recommend books, Netflix to suggest movies, and Facebook to organize newsfeed posts. Millions of people show their preferences by clicking, and that data is used to build and maintain statistical models that predict what users want. The key is clustering people and products, which allows the algorithm to make recommendations under the assumption that similar people have similar tastes. A continuing stream of human interaction ensures that the system evolves as new items are introduced and as tastes change.
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