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Where it meets a metal ball? Is the metal ball going to end up there? We think it’s a great idea,” says Steve Lea, the head of engineering at Casio. He wants to use the prototype that’s about 10cm by 4cm, with holes to provide access to its parts and its metal ball. He will demonstrate in London tomorrow.

A number of other companies, including one by former Apple designer Richard Schenk and another by a group of designers from Zodiac Software, have already developed prototypes of machine-learning chips. These devices, called artificial brains, use mathematical logic to solve a number of problems. One of the most notable achievements is Alex in the Valley, a brain-like computer that was shown off at the London Olympics and sold for £2,500 last year. It was one of three that went on to win a £25,000 cash prize.

Lea says Casio is confident that his machine will become a “key product” for the next few years or so. For the next few years, he wants to work on improving the machine’s accuracy – in particular, learning how to recognise patterns. To do that, it needs to learn how to recognise numbers, so it can play roulette with them.

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Casio’s machine isn’t alone in its quest to get machines that can do a lot smarter. IBM, among others, is designing chips to make its supercomputers better at the physical problems they are expected to solve. The computer chip is one of the biggest businesses in the world and makes up a third of the economy in developed countries. The chips are also a large chunk of the chips made and sold every year in the market for high-end smartphones and other consumer electronics.

But Casio is a different kind of chip, developed specifically for roulette machines; they are only about the size of a pencil eraser and weigh just 100g when closed, which makes them easy to transport. And they are programmed with a language called neural networks that, in essence, can learn to play roulette and figure out what cards it has to put on the board.

“We are not thinking about an intelligent person,” says Lea. “We are only thinking about machines that learn. There will be some people, for example mathematicians, that say we can’t do that. And there’ll be people, mathematicians, that say no, but then there’ll be other people that say yes.” So far, the proof of what

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