Artificial intelligence feeds on data, and data is piling up from increasingly cheap sensors and surging Internet use: videos, images, text; time series data, machine data; structured, unstructured and semi-structured data. And while AI is currently confined to narrow problems in discreet domains, the ambition of machine-learning researchers globally is to write algorithms that can cross domains, transferring learning from one kind of data to another.

When that eventually happens, AI systems will no longer focus on their little data hill, but crawl along the entire mountain range.

The first place this is likely to happen is on data that has been annotated by humans. While there are many streams of AI, the one that has carved the deepest ravine is called supervised learning. Just as humans learn in school by being told repeatedly the correct answer to a question, supervised learning systems are given thousands, even millions of examples of what they are being trained to recognize until they can spot those things in data they have never seen before.

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