Daily Review: How Cloud-Based AI Manages the World's Largest Rapid Transit System

There are a fairly limited number of companies dominating the cloud world. Their names are known, western, and their performance is continuously compared. However, one of the most interesting and impressive cases is a behemoth from Asia, powering the minds of subway kiosks in Shanghai. What can the western world learn from how AI that lives on Alibaba cloud serves the everyday needs of over 24 million people populating Shanghai?

The Shanghai Metro system is reported to be the world’s largest rapid transit system by route length, totaling 396 miles. It is the second largest by number of stations with 387 stations on 15 lines. It also ranks second in the world by annual ridership with 3.4 billion rides delivered in 2016. The daily ridership record was set at 12.231 million on March 9, 2018. Over 10 million people use the system on an average workday.

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The ticket kiosks at Shanghai’s frenetic subway station have a mind of their own.

Walk up to one and state your destination, and it’ll automatically recommend a route before issuing a ticket. It’ll even check your identification (a necessary step in China) by looking at your face. In the interest of reducing the rush-hour stampede, the system is setup to let you find information and buy tickets without pushing a button or talking to a person.

More impressive still, all this happens successfully in the middle of a crowded, noisy station. Each kiosk has to figure out who is speaking to it; zero in on that person’s voice within the crowd; transcribe the incoming speech; parse its meaning; and compare the person’s face against a massive database of photos – all within a few seconds.

To do it, the kiosks use several cutting-edge machine learning algorithms. The really interesting thing, though, isn’t the algorithms themselves. It’s where they live. All that image processing and speech recognition is served upon demand by a cloud computing system owned by one of China’s most successful companies, the e-commerce giant Alibaba.

Alibaba is already using AI and machine learning to optimize its supply chain, personalize recommendations, and build products like Tmall Genie, a home device similar to the Amazon Echo. China’s two other tech supergiants, Tencent and Baidu, are likewise pouring money into AI research. The government plans to build an AI industry worth around $150 billion by 2030 and has called on the country’s researchers to dominate the field by then.

Read more of the enlightening article by Will Knight, Senior Editor, MIT Review.

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While successfully meeting all the business requirements that had been set out, the PoC evidenced the considerable prerequisites for such a solution to be adopted by the industry – for instance, all account servicers would first need to migrate from batch to real-time liquidity reporting and processing, and bank office applications would need to be upgraded to feed the platform with real-time updates.

The PoC also showed that further progress is needed on the DLT technology itself before it will be ready to support production-grade applications in large-scale, mission-critical global infrastructures. For example, while 528 channels were required in the PoC to ensure Nostro accounts would only be stored on the nodes of their account servicers and owners, to productize the solution, more than 100,000 channels would need to be established and maintained, covering all existing Nostro relationships, presenting significant operational challenges.

Read more.