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Fang’s A-Z of China AI

By far the greatest danger of Artificial Intelligence is that people conclude too early that they understand it,” warned Eliezer Yudkowsky, a leading theorist in the science of AI, nearly fifteen years ago.

Advances since then haven’t always made understanding the sector any easier, as far as WiC is concerned. Fortunately, Helen Fang – Head of Industrials Research, Asia Pacific at HSBC – has done an excellent job of breaking down what’s going on in China’s AI industry in a piece of research titled Artificial Intelligence: Mapping out the value chain published earlier this year.

Fang’s analysis splits the industry into five layers. The foundation block is the AI computing chips that power the algorithms. These chips are housed in a second layer of infrastructure, made up of cloud computing networks and data centres. Next is the machine learning frameworks that train the data in the AI algorithms, which underpin the software models that create AI-enabled solutions for customers. Together, they support the applications of AI-powered technology that are sold into the marketplace, such as traffic management systems in smart cities or the anti-collision capabilities in self-driving vehicles.

WiC spoke to Fang for background on the headline items in her research piece, as well as what they signal for the prospects for China’s best AI firms.

In chipmaking, at least, Chinese companies are still gearing up

It’s commonly reported that Chinese firms are behind their international peers in chipmaking for smartphones and more sophisticated electronics. The situation is similar for the design of the different kinds of high-performance chips that support applications of artificial intelligence.

“AI companies need to train different data sets into something that works as an application” Fang explains. “First they need to collect the graphical information and transform it into digital data. That relies on GPUs, or graphics processing units, the production of which is mainly dominated by American companies like Nvidia.”

After transforming the graphs into digital information, the AI providers need to train the data so that it recognises patterns. The training chips that power this process are another area where Chinese firms are yet to establish a strong position. Once the data has been trained, it starts to make inferences from the rules within the predictive models. And in these kinds of inference chips the Chinese are more advanced, Fang says.

Another Chinese company is also developing its own chips for its AI software models by tailoring them to its intelligent video analysis, which speeds up the inference process.

“In general terms what we are seeing is that Chinese companies are progressing in inference chips but they are behind in the other kinds of chips, perhaps by as much as a few generations,” Fang acknowledges.

“Investors often ask how long it will take for them to catch up but it’s not an easy question to answer. The ‘generations’ are best understood as the sizes of the modules in the chips – in semiconductors, it’s a case of the smaller the chip, the better the performance.”

Companies are active across different parts of the value chain

Businesses are positioning themselves in different areas of the industry.

Some companies are concentrating on chipmaking with a focus on ASIC (‘application specific’) formats that are expected to replace GPUs as the standard over the longer term.

Others are choosing to concentrate more on the processes that support machine learning, while model producers are working on the software deployed to drive the applications for end users.


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