May 17, 2020

Alibaba to run trial of facial recognition payment at KFC in China

alibaba. ai
Artificial intelligence
voice recognition
face recognition
Callum Rivett
1 min
Alibaba's Alipay is trialing a facial recognition payment system in KFC in China
Alibaba Group's online payment platform Alipay has announced that it is trailing facial recognition paymentsin a KFC concept restaurant in China.

The n...

Alibaba Group's online payment platform Alipay has announced that it is trailing facial recognition payments in a KFC concept restaurant in China.

The new, healthy-food restaurant is called KPRO and will be situated in Hangzhou, with 'Smile to Pay' services implemented in the design.

Alipay users will be able to authenticate their payment by a combination of facial scanning and inputting their mobile phone number into the service, meaning there is no need for credit or debit cards.

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Ant Financial - the most valuable fintech company in the world - is an affiliate of Alibaba and said that the facial recognition technology has "reached a level of accuracy of security that should be users at ease."

"Smile to Pay provides convenience for users like never before, but we have always placed security at the core of this technology," commented Jidong Chen, Ant Financial’s director of biometric identification technology.

Using state-of-the-art 3D cameras, a multistep process scans the facial features of the payee and uses a "live-ness detection algorithm" to block photos or videos.

Originally unveiled in 2015 by Jack Ma, Smile to Pay then demoed again in January 2017 at the Consumer Electronics Show.

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Jun 11, 2021

Google AI Designs Next-Gen Chips In Under 6 Hours

Google
AI
Manufacturing
semiconductor
3 min
Google AI’s deep reinforcement learning algorithms can optimise chip floor plans exponentially faster than their human counterparts

In a Google-Nature paper published on Wednesday, the company announced that AI will be able to design chips in less than six hours. Humans currently take months to design and layout the intricate chip wiring. Although the tech giant has been working in silence on the technology for years, this is the first time that AI-optimised chips have hit the mainstream—and that the company will sell the result as a commercial product. 

 

“Our method has been used in production to design the next generation of Google TPU (tensor processing unit chips)”, the paper’s authors, Azalea Mirhoseini and Anna Goldie wrote. The TPU v4 chips are the fastest Google system ever launched. “If you’re trying to train a large AI/ML system, and you’re using Google’s TensorFlow, this will be a big deal”, said Jack Gold, President and Principal Analyst at J.Gold Associates

 

Training the Algorithm 

In a process called reinforcement learning, Google engineers used a set of 10,000 chip floor plans to train the AI. Each example chip was assigned a score of sorts based on its efficiency and power usage, which the algorithm then used to distinguish between “good” and “bad” layouts. The more layouts it examines, the better it can generate versions of its own. 

 

Designing floor plans, or the optimal layouts for a chip’s sub-systems, takes intense human effort. Yet floorplanning is similar to an elaborate game. It has rules, patterns, and logic. In fact, just like chess or Go, it’s the ideal task for machine learning. Machines, after all, don’t follow the same constraints or in-built conditions that humans do; they follow logic, not preconception of what a chip should look like. And this has allowed AI to optimise the latest chips in a way we never could. 

 

As a result, AI-generated layouts look quite different to what a human would design. Instead of being neat and ordered, they look slightly more haphazard. Blurred photos of the carefully guarded chip designs show a slightly more chaotic wiring layout—but no one is questioning its efficiency. In fact, Google is starting to evaluate how it could use AI in architecture exploration and other cognitively intense tasks. 

 

Major Implications for the Semiconductor Sector 

Part of what’s impressive about Google’s breakthrough is that it could throw Moore’s Law, the axion that the number of transistors on a chip doubles every five years, out the window. The physical difficulty of squeezing more CPUs, GPUs, and memory on tiny silicon die will still exist, but AI optimisation may help speed up chip performance.

 

Any chance that AI can help speed up current chip production is welcome news. Though the U.S. Senate recently passed a US$52bn bill to supercharge domestic semiconductor supply chains, its largest tech firms remain far behind. According to Holger Mueller, principal analyst at Constellation Research, “the faster and cheaper AI will win in business and government, including with the military”. 

 

All in all, AI chip optimisation could allow Google to pull ahead of its competitors such as AWS and Microsoft. And if we can speed up workflows, design better chips, and use humans to solve more complex, fluid, wicked problems, that’s a win—for the tech world and for society. 

 

 

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