Vein-pattern recognition is the latest technology driving China’s AI, robotics revolution

“Because we programme machines, they will react the way we do. The best AI is that which behaves closest to the human way,” Chen says.

Technology will lead to the loss of many jobs, either because of an increase in productivity or because machines take the place of humans [altogether].

“Fast forward 100 years; now we have machines and the [population] has increased several fold. But unemployment is not higher than it was then. And what has happened to the efficiency of the global population? It may surprise you, but it has actually decreased.”If we look forward another century, we will see machines taking over many of the tasks humans do, so humans can concentrate on other things.

“Technology will lead to the loss of many jobs, either because of an increase in productivity or because machines take the place of humans [altogether],” Wu says during an interview in Shanghai.

“We thought human intelligence could never be copied, much less replaced. We are still very far from that because AI is not yet able to reason, but we need to understand that human intelligence is not irreplaceable. Machines will eventually make decisions, and do so better than us.”

Chen has put a time frame on developments: “The computational ability of a machine increases exponentially every year, and machines are getting smaller and smaller. A human brain has around 30 billion neurons. In about two years a chip will surpass this capability. In China, we have already reached the equivalent of 8 billion neurons.”

“The future is made up of human consciousness, which can’t be replicated because it’s unique to each individual, and the computational ability of machines,” says Chen.But will that superintelligence be free of human bias?

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The untold story of Stripe, the secretive $20bn startup driving Apple, Amazon and Facebook

It’s where they began laying the foundations for Stripe, their payments company valued at $20 billion when it last raised money in September 2018 and which handles payments for Amazon,, Lyft, Deliveroo, Shopify, Salesforce and Facebook.

The fintech ‘revolution’ that followed wasn’t much of an uprising but more of a spot of portfolio diversifying by some banks that laid down the payment rails any eager startup had to ride on.

Stripe processed payments through its own servers, allowing payers and vendors to connect with minimum fuss.

Adyen – which describes itself on the site as “a payments-technology company that provides a single global platform to accept payments anywhere in the world. Businesses can process payments across online, mobile and in-store with over 250 payment methods and 187 currencies” – has 23 fans, is a favourite for 11 developers and has some 39 upvotes as of July 2018.

Braintree – which says it “Replaces traditional payment gateways and merchant accounts. From one touch payments, to mobile SDKs and international sales, we provide everything you need to start accepting payments today” – does a little better with 130 fans, 29 favourites and 87 up votes.

Where PayPal injects itself into the checkout process, Stripe operates like a white-label merchant account, processing payments, checking for fraud and taking a small percentage: 1.4 per cent plus 20 pence for European cards and 2.9 per cent plus 20 pence for all others.

“The idea driving 402 was that it’s obvious that support for payments should be a first-class concept on the web and it’s obvious that there should be a lot of direct commerce taking place on the web,” says John “In fact, what emerged is a single dominant business model which is advertising. That leads to a lot of centralisation, because you get the highest cost per clicks and with the largest platforms. A big part of what we’re trying to do with Stripe is continually make it easy for new business to start, and for new businesses to succeed. Having commerce and direct payment succeed on the internet is a very important component of that. It’s the final piece in the Dream Machine.”

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Watch just a few self-driving cars stop traffic jams

Now, scientists have shown that a few self-driving cars can prevent such jams-and in some cases double the average speed of surrounding vehicles.

In others, one or several lanes of traffic merged, or the cars traversed a Manhattan-like city grid with traffic lights at each crossing.

The team looked at various ratios of self-driving cars mixed with regular cars that simulated typical human driving.

In the Manhattan scenario, the AI-controlled traffic lights instead of cars.

In the merge scenarios, replacing 10% of the regular cars with self-driving cars also increased overall traffic flow, in some cases doubling the average car speed.

The self-driving cars sped up traffic in part by keeping a buffer between themselves and the cars in front of them, forcing them to brake less often.

Giving the algorithm control over traffic lights in a Manhattan-style traffic grid increased the number of cars passing through by 7%. The tested algorithms leave plenty of room for improvement, says study author Eugene Vinitsky, an AI researcher at the University of California, Berkeley.

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