are housed in a funky-looking building overlooking the river. Two miles downstream, in a shared office space near Blackfriars Bridge, lives Arkera, a firm that uses machine-learning technology to sort intelligence from newspapers, websites and other public sources for emerging-market investors. Its location is happenstance. London has the right time zone, between the Americas and Asia. It is a nice place to live. The Thames happens to run through it.
Analysts have used text data to try to predict changes in asset prices for a century or more. In 1933 Alfred Cowles, an economist whose grandfather had founded the, published a pioneering paper in this vein. Cowles sorted stockmarket commentary by William Peter Hamilton, a long-ruling editor of the, into three buckets and attached an action to each .
It is tempting to focus on the black-box elements of all this: the language software that “reads” the source text and the algorithms that use the data to make predictions. But this is like judging a hi-fi system by its speakers. A lot of the important work comes earlier in the process. Arkera, for instance, spends a lot of effort finding all the relevant text and “cleaning” it—stripping it of extraneous junk, such as captions and disclaimers. “A good signal is crucial,” says Mr Gupta.
Do we know what we mean - by the 'I' in AI?
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Breaking: How Skynet Is Steadily Self-Actualizing
Computers are already programmed for trades.
Revolutionising what?
ML is revolutionizing intelligence alright...