{"id":83174,"date":"2023-08-16T19:00:00","date_gmt":"2023-08-16T16:00:00","guid":{"rendered":"https:\/\/forklog.com\/en\/?p=83174"},"modified":"2025-09-12T09:34:59","modified_gmt":"2025-09-12T06:34:59","slug":"distinguishing-wife-from-cat-how-chinese-ai-began","status":"publish","type":"post","link":"https:\/\/forklog.com\/en\/distinguishing-wife-from-cat-how-chinese-ai-began\/","title":{"rendered":"Distinguishing wife from cat: how Chinese AI began"},"content":{"rendered":"

China’s surveillance system \u2014 a bogeyman for the media, a technological revolution, or a grim dystopia that awaits the world? American journalist Jeffrey Kane set out to find out. He went straight to Xinjiang \u2014 a region, on the one hand infamous for its \u201csanatoriums\u201d for Uyghurs, and on the other hand, the Chinese capital of AI research. Following his trip, Kane wrote the book \u201c\u0413\u043e\u0441\u0443\u0434\u0430\u0440\u0441\u0442\u0432\u043e \u0441\u0442\u0440\u043e\u0433\u043e\u0433\u043e \u0440\u0435\u0436\u0438\u043c\u0430<\/a>\u201d, the translation of which was published by Individuum. We publish, with minor abridgments, the chapter \u201cDeep Neural Network.\u201d<\/strong><\/p>\n

In dealing with foreign partners, Chinese state-supported corporations practised what in business is called \u201cforced technology transfer.\u201d To access China\u2019s closed market, foreign companies typically had to strike deals with Chinese partners. One informal requirement was the transfer to Chinese firms of so-called sensitive technologies \u2014 semiconductors, medical equipment and oil-and-gas gear.<\/p>\n

Under World Trade Organization rules, such a demand is illegal; yet American companies, albeit reluctantly, disclosed trade secrets in the hope of gaining access to 1.4 billion potential customers in China.<\/p>\n

As China began collecting data on its citizens \u2014 tracking the use of apps and services like WeChat \u2014 the prospect of leading the expanding and lucrative AI industry attracted many local tech start-ups. Chinese AI researchers, increasingly numerous, watched closely the breakthroughs taking place in the United States, the world\u2019s AI leader. Chinese firms hoped to unlock AI secrets by recruiting talented Chinese developers who studied abroad and worked at Microsoft and Amazon, luring them home with high salaries and calls to patriotism. By the early 2010s, Chinese programmers were closing in on creating a deep neural network \u2014 the holy grail of a surveillance state; a system capable of learning and identifying patterns across millions of images and data points.<\/p>\n

For many years AI researchers relied on the so\u2011called rule\u2011based programming approach. They wired a program into a computer to recognize a cat, telling it: \u201cLook for a circle with two triangles on top.\u201d This approach made sense because computers lacked the processing power for more. Yet it also constrained AI: not all images of cats are a perfect circle with two triangles on top, and not all circles with triangles on top are cats.<\/p>\n

\"Distinguishing
Data: Dream\/Kandinsky 2.2.<\/figcaption><\/figure>\n

More modern technology \u2014 deep neural networks \u2014 offered a number of advantages. Operators no longer needed to perform monotonous, tedious work of manually categorising images and data, or then write rules for the AI system. Instead, software learned to connect disparate data, scanning vast amounts of information, and then learning from it. Subsequently the program could refine the algorithm to solve the task for which it was created. The fewer operators controlled and constrained the software, the more AI applications emerged for companies. Deep neural networks learned to operate unmanned vehicles, assist doctors in diagnosing, and flag credit-card fraud.<\/p>\n

Until 2012, the idea that a deep neural network could influence the market was regarded as nonsense. No matter how hard Microsoft Research Asia and new start\u2011ups tried, their efforts bore little fruit. In 2012, AI developers in China and Silicon Valley told me that creating a neural network would be a golden opportunity for Microsoft. In May 2011 Microsoft acquired Skype, the popular worldwide calling and video conferencing service, in what was then the industry\u2019s largest deal. If Skype or Microsoft Windows could recognise voice and faces, it would be a breakthrough. It would lay the groundwork for real\u2011time translation features and cybersecurity systems based on facial recognition.<\/p>\n

In 2011, in Beijing, I met a group of young Chinese researchers who worked themselves to the bone trying to solve a whole set of thorny questions. The main ones were: \u201cHow can a computer system learn to \u2018see\u2019 and \u2018perceive\u2019 a human? How can it hear and recognise a person\u2019s voice? Can AI learn to talk?\u201d<\/p>\n

\n

\u201cNow is the right moment,\u201d one of them told me after work at dinner. \u201cThe Internet and social media can serve as data sources for AI. We can gather data on clicks, purchases and people\u2019s preferences.\u201d<\/p>\n<\/blockquote>\n

He said that in 2005 fewer than 10% of China\u2019s population were online, but they quickly became the world\u2019s most active users of social networks, mobile apps and mobile payments. In 2011 almost 40% of the population, about 513 million people, had an Internet connection of their own. All these users left information about their purchases and online actions that could be used to teach neural networks to solve a wide range of tasks, including monitoring users.<\/p>\n

In the same year, two junior researchers working with the world\u2011renowned AI researcher Geoffrey Hinton<\/a>, a professor of computer science at the University of Toronto and a Google employee, made a breakthrough in hardware. The researchers realised they could use graphics processors (GPUs) \u2014 devices that accelerate graphics in computer games \u2014 to speed up the processing of data by deep neural networks. AI developers could use GPU\u2011oriented methods of rendering shapes and images on screen to train neural networks to search for patterns.<\/p>\n

Previously, building a neural network was prohibitively expensive. But the cost of the key equipment on which the software runs fell thanks to GPUs. For many years they grew cheaper even as memory and computing power increased.<\/p>\n

With improvements in hardware and the growth of data sets, the moment arrived for a deep neural network that could process this data.<\/p>\n

By trial and error, the Microsoft team under Dr. Sun Jiyan found a solution: increase the number of \u201clayers\u201d in the neural network, allowing the AI to continually update its knowledge as information passes through. The layers of a neural network resemble clusters of neurons that receive data, process it, and then pass it to later layers for further processing \u2014 thus AI knows more<\/a> about the subject under analysis.<\/p>\n

In theory, more layers meant better thinking; in practice, it proved harder. One problem was that signals vanished after passing each layer, hindering Microsoft researchers from training the system.<\/p>\n

In 2012, the system learned to recognise images with eight neural layers. By 2014, with thirty. Increasing the number of layers, the researchers achieved a breakthrough in the computer\u2019s ability to recognise objects in videos and images. \u201cWe did not even believe that this single idea could be so important,\u201d said Dr. Sun.<\/p>\n

China\u2019s tech ecosystem began to attract venture capitalists, who turned their attention away from the traditional finance and tech hubs in Silicon Valley and New York. They sought to act quickly in two sectors where there was immense potential for a surveillance ecosystem: facial recognition and speech recognition.<\/p>\n

The first major investment went to facial recognition technology.<\/p>\n

In 2013, Sinovation Ventures, a Kai-Fu Lee\u2013founded AI-focused venture firm, backed the developing facial-recognition platform Megvii (MegaVision). The amount was not disclosed. Then SenseTime (Megvii\u2019s Hong Kong\u2013based rival, founded in 2014) released the first algorithm capable of identifying people under certain conditions with accuracy\u2014surpassing human eye performance\u2014and claimed to have outperformed Facebook; this marked a milestone in the AI industry.<\/p>\n

According to Yan Fan, head of SenseTime\u2019s development department and a former Microsoft employee, public-safety applications proved to be a profitable market.<\/p>\n

\n

\u201cThere is high, competitive demand driven by \u2018smart city\u2019 systems and video surveillance,\u201d \u2014 he told Forbes Asia.<\/p>\n<\/blockquote>\n

But facial-recognition software needed the most advanced semiconductors. Where would they come from?<\/p>\n

SenseTime and other AI companies in China turned to American firms for semiconductors. It turned out that their US counterparts were interested in Chinese software for mobile apps and policing systems. American telecoms operator Qualcomm struck a deal with Megvii: in exchange for semiconductors, Qualcomm gained the right to use Megvii\u2019s AI software in its devices.<\/p>\n

\u201cIn China there is explosive demand,\u201d noted Li Xu, co\u2011founder and CEO of SenseTime, at a tech conference in June 2016 during a joint appearance with Jeff Herbold, Nvidia\u2019s Vice President for Ventures Development.<\/p>\n

Seven to eight years after its founding in 1993, Nvidia became a leading GPU maker. It was now poised to reap the profits of the coming AI boom.<\/p>\n

Shortly after, Nvidia began striking high\u2011profile deals with Chinese facial-recognition firms. Using GPUs produced by Nvidia and its main rival Intel, world\u2011class supercomputers were built at the Cloud Computing Center in Urumqi, opened in 2016, used for surveillance. In one day these computers processed more surveillance footage than a person would in a year.<\/p>\n

\n

\u201cIn China I see cameras on every streetlight,\u201d Herbold observed. \u201cIt seems that almost everything is monitored. But the problem is the video ends up in a control room where some guy waits for something to happen. Isn\u2019t all this supposed to be automated?\u201d<\/p>\n<\/blockquote>\n

Li Xu acknowledged the Chinese government\u2019s interest in public safety, as well as the fact that \u201cthe existing surveillance system was severely hampered by the lack of an intelligent control mechanism, particularly in processing video.\u201d<\/p>\n

He proposed an alternative path.<\/p>\n

Li Xu knew that Nvidia\u2019s chip technology, borrowed from similar graphics-processing approaches, played a \u201cfundamental\u201d role in his work, and that to sustain facial recognition Nvidia deploys 14,000 such chips across servers in Asia.<\/p>\n

\u201cI feel we are in for a long collaboration,\u201d Nvidia\u2019s Herbold told him at a business conference. He may not have intended it, but his words sounded ominous. By 2015 all the components of the surveillance ecosystem had fallen into place: software learned to recognise faces, scan text messages and emails, and identify patterns in written language and people\u2019s interactions.<\/p>\n

Now investors began pouring money into the next key element: software capable of understanding and processing human voice.<\/p>\n

In the late 1990s, young, promising researcher Liu Qingfeng left an internship at Microsoft Research Asia and devoted himself to his own startup iFlyTek, aiming to develop advanced voice-recognition technology.<\/p>\n

\n

\u201cI told him he was a talented young researcher, but China was far behind the American giants of the speech-recognition industry, such as Nuance, and there would be fewer consumers of this technology in China,\u201d Kai-fu Lee wrote. \u201cCredit to Liu: he ignored my advice and threw himself into iFlyTek.\u201d<\/p>\n<\/blockquote>\n

In 2010 iFlyTek established a laboratory in Xinjiang devoted to developing speech-recognition technology for translating Uyghur into Mandarin. Soon that technology would be used for surveillance and monitoring of Uyghur populations. By 2016 iFlyTek was supplying Kashgar with some 25 systems of \u201cvoice fingerprints\u201d that created unique vocal signatures aiding identification and tracking.<\/p>\n

\u201cAll these companies were coming to Xinjiang before my eyes,\u201d recalls Irfan<\/span>. \u201cI saw their hardware, their software.\u201d Dozens of Uyghurs who fled Xinjiang after 2014 recalled spotting these companies\u2019 logos on equipment. The presence of these firms in Xinjiang is reflected in government tenders circulating on the internet, in official corporate reports, human-rights assessments, American sanctions documents, and in reports in Chinese state media. \u201cBut many did not find this dangerous. The mood was: \u2018We are simply fighting crime\u2019,\u201d Irfan notes.<\/p>\n

\"Distinguishing
Data: Dream.<\/figcaption><\/figure>\n

From 2010 to 2015, Huawei \u2014 the national tech symbol of China \u2014 finally entered the Xinjiang market, developing cloud services in collaboration with local police. Huawei (roughly translated: \u00abChina Holds Out Hope\u00bb<\/span>) was founded by former military engineer Ren Zhengfei with startup capital of $3,000. In the 1980s the company began developing telephone switches \u2014 copying foreign models. As one of the early advocates of a government\u2011driven acceleration of technology, Huawei became known in China and abroad for its surveillance hardware and networking equipment, and expanded its footprint in the smartphone market.<\/p>\n

Ren Zhengfei, described by former colleagues as a partly mysterious figure who spoke in parables about streams and mountain summits, harboured grand plans for global expansion. These could be realised only if Western democracies could be persuaded that Huawei was not tied to the Chinese Communist Party and would not use its technology for spying. At the same time Huawei\u2019s leadership sought to sell networking equipment to Xinjiang authorities, viewing \u201cpublic safety\u201d as a profitable business.<\/p>\n

\n

\u201cIn 2015 we were at a team-building event,\u201d William Plummer, a former American diplomat who served as Huawei\u2019s vice president for external relations in Washington, told me. \u201cSomeone showed a slide with the heading: \u2018What is Huawei about?\u2019 The first point read: \u2018For internal Huawei \u2014 a Chinese company supporting the Chinese Communist Party.\u2019 Then came: \u2018For abroad \u2014 an independent company following internationally recognised corporate practice.\u2019\u201d<\/p>\n<\/blockquote>\n

Essentially they meant that China\u2019s rules must be followed at home, while foreign countries\u2019 rules apply abroad. But to include this in a presentation… even this slide was compromising.<\/p>\n

By 2015, the final element of the surveillance ecosystem was in reach: cheaper video-surveillance camera technology \u2014 inexpensive enough to spread on an industrial scale. Into Xinjiang came Hikvision, the world\u2019s largest supplier of surveillance cameras. It supplied millions of cameras that allowed authorities to monitor the population. The cameras were so advanced that they could identify people from fifteen kilometres away and used AI software from iFlyTek, SenseTime and others to analyse faces and voices.<\/p>\n

\u201cSkynet,\u201d the radical and all\u2011encompassing state surveillance system, conceived a decade earlier, could now become a reality.<\/p>\n

\"Distinguishing
Data: Dream\/Kandinsky 2.2.<\/figcaption><\/figure>\n

All this technological leap, in a sense a sinister synthesis of elements that culminated in a state built on AI, did not go unnoticed. By 2010, the United States had begun to grow wary \u2014 the world\u2019s leading antagonist to China on the international stage.<\/p>\n

American policymakers suspected Huawei and its partners were a cover for the People’s Liberation Army to exploit backdoors in hardware and software for cyber\u2011espionage purposes.<\/p>\n

\u201cWith a high degree of confidence, we can state that the growing role of multinational companies and foreign persons in the information technology and services supply chains in the United States creates a threat of continuous covert sabotage,\u201d read documents from the U.S. National Security Agency (NSA). Those records were disclosed by whistleblower Edward Snowden in 2010.<\/p>\n

Moreover, Snowden\u2019s leaks showed that the NSA tracked twenty Chinese hacking groups attempting to breach U.S. government networks, as well as Google and other company systems. The NSA also hoped to reach the undersea cables laid by Huawei to monitor communications of targets it considered high\u2011priority, in Cuba, Iran, Afghanistan and Pakistan.<\/p>\n

NSA infiltrated the Huawei headquarters, monitored communications of its top executives, and carried out an operation code\u2011named Shotgiant<\/span>, aimed at identifying links between Huawei and the People\u2019s Liberation Army. The NSA then attempted to use Huawei technologies sold to other countries and organisations to penetrate Huawei servers and telephone networks and wage cyberattacks on those countries. All this was achieved through expansive hacking capabilities, and backdoors created in collaboration with American telecom companies that allowed mass surveillance of foreign nationals to overcome standard technological barriers.<\/p>\n

Representatives of the United States and China talked of a cold war, notwithstanding their trade and tech ties. In 2012, a Congressional committee released the results of a yearlong investigation, stating it had obtained documents from former Huawei employees suggesting the company provided its services to China\u2019s cyber\u2011warfare arm.<\/p>\n

The U.S. authorities began focusing on Ren Zhengfei\u2019s daughter, Meng Wanzhou, widely known as \u201cCathy.\u201d As Huawei\u2019s socialite, Cathy hosted business events that included Q&As with Alan Greenspan<\/span> and other guests. The FBI and the Department of Homeland Security tracked Cathy\u2019s business activity and Huawei. They suspected Cathy supervised a front company in Iran called Skycom, which violated U.S. sanctions by doing business with Iranian telecommunications companies.<\/p>\n

\n

\u201cWe provided the U.S. government with information about Huawei in Iran, about Skycom, and that it was an independent company, even though she sat on the board for two years \u2014 we supplied these assurances because that is what we were told, and it seemed better that way. But it was all a lie. Skycom employees in Iran walked around with Huawei business cards,\u201d Plummer later recalled.<\/p>\n<\/blockquote>\n

Plummer said that in 2013 he was contacted by Huawei\u2019s senior management. Homeland Security agents subjected Meng to extra screening, delaying her before boarding a flight at John F. Kennedy Airport as she returned from one of her glamorous business events.<\/p>\n

\u201cThey held her computer, tablet and both phones for four hours,\u201d Plummer recounted. \u201cThe authorities copied everything.\u201d Meng was released. Huawei\u2019s leadership prepared for a looming legal battle, shutting the Skycom office in Tehran and distancing themselves from Skycom.<\/p>\n

But while the United States smeared Huawei and China as enabling backdoors for state\u2011backed hacking, NSA was caught installing its own backdoors in American network products supplied to China.<\/p>\n

Der Spiegel, the German newspaper, obtained a fifty\u2011page catalog created by NSA\u2019s Advanced Network Technology Division, which monitored the most secure networks. The NSA gained access to shipments of American Cisco network equipment destined for China and secretly installed surveillance devices. Cisco later said it did not know that its own government had hacked it.<\/p>\n

Another NSA product, HALLUXWATER, turned out to be a backdoor implant. It hacked Huawei firewalls, enabling the NSA to implant malware and control device memory.<\/p>\n

\u201cThere is nothing unexpected about this kind of controlling behaviour by the United States,\u201d Ren Zhengfei told reporters in London. \u201cBut now it has been proven.\u201d<\/p>\n

With geopolitics in play, China\u2019s government opened its cards. The country holds about 93% of the world\u2019s rare earth reserves used in batteries and displays in iPhones and televisions, including lithium and cobalt. In September 2010, a Chinese fishing trawler collided with two Japanese coast guard vessels near the disputed Senkaku Islands. Japan arrested the trawler captain for allegedly violating its fishing rights and control of the region \u2014 an area China also claims.<\/p>\n

China struck back by blocking rare earth exports to Japan, putting at risk production of the highly popular Toyota Prius, which relies on rare earths for its engine. A little over two weeks later, Japan freed the crew members without charging them.<\/p>\n

\u201cChina and Japan are meaningful neighbours with important responsibilities in the international community,\u201d Japanese Prime Minister Naoto Kan said in New York at the United Nations, attempting to calm nerves.<\/p>\n

But as the Cold War intensified, it became apparent that the United States and China differed in their technological strategies.<\/p>\n

China sought to steal American technologies, including trade secrets and intellectual property, to hand them to private Chinese firms seeking to outpace Silicon Valley.<\/p>\n

The United States, in turn, aimed to infiltrate Huawei and other Chinese firms. Their aim wasn\u2019t simply to steal Chinese tech and transfer it to private companies like Amazon and Google, but to collect information about potential links to military structures and threats to U.S. national security by China and its companies.<\/p>\n

\"Distinguishing
Data: Dream\/Kandinsky 2.2.<\/figcaption><\/figure>\n

During a June 2014 reporting trip through Beijing, Shanghai and Shenzhen\u2019s tech hub, I sensed the rise of a self-proclaimed nationalism \u2014 almost tangible \u2014 especially among Chinese youth.<\/p>\n

Talk of a new cold war seemed to trigger a sense of unease, reinforced by state propaganda.<\/p>\n

One local tech executive tried to explain China\u2019s newfound swagger. \u201cYou know Google left China,\u201d he told me with pride. After four years on the Chinese market, Google closed its Chinese search site in 2010 amid a clash over hacks and censorship. \u201cBut that doesn\u2019t matter,\u201d he explained. \u201cWe have our own search engine, Baidu. Now we have our own companies. The world is changing, and I hope Silicon Valley and the NSA won\u2019t dominate forever.\u201d<\/p>\n

\u201cBut don\u2019t you think that if China is to reach the level of Silicon Valley, it will have to open up the Internet,\u201d I asked. \u201cResearchers need to access the information necessary to create quality tech.\u201d \u201cThat doesn\u2019t matter either,\u201d he replied. \u201cIn China our technologies are tied to the future of our country. We do not have the same explicit separation of powers as in the United States. Our only goal is to make China great. We want to be on equal terms with the Americans, so that no one looks down on us again.\u201d<\/p>\n

Translation from English by Dmitry Vinogradov. Published for Jeffrey Kane\u2019s work. <\/em>The State of the Strict Regime. Inside China\u2019s Digital Dystopia<\/em><\/a>. Moscow: Individuum, 2023.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

Chapter from journalist Jeffrey Kane’s book \u201cThe State of the Strict Regime\u201d about how China developed its first AI systems for citizen surveillance.<\/p>\n","protected":false},"author":1,"featured_media":83175,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"select":"1","news_style_id":"1","cryptorium_level":"","_short_excerpt_text":"","creation_source":"","_metatest_mainpost_news_update":false,"footnotes":""},"categories":[1144],"tags":[438,133,2348],"class_list":["post-83174","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-longreads","tag-artificial-intelligence","tag-china","tag-control"],"aioseo_notices":[],"amp_enabled":true,"views":"45","promo_type":"1","layout_type":"1","short_excerpt":"","is_update":"","_links":{"self":[{"href":"https:\/\/forklog.com\/en\/wp-json\/wp\/v2\/posts\/83174","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/forklog.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/forklog.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/forklog.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/forklog.com\/en\/wp-json\/wp\/v2\/comments?post=83174"}],"version-history":[{"count":1,"href":"https:\/\/forklog.com\/en\/wp-json\/wp\/v2\/posts\/83174\/revisions"}],"predecessor-version":[{"id":83176,"href":"https:\/\/forklog.com\/en\/wp-json\/wp\/v2\/posts\/83174\/revisions\/83176"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/forklog.com\/en\/wp-json\/wp\/v2\/media\/83175"}],"wp:attachment":[{"href":"https:\/\/forklog.com\/en\/wp-json\/wp\/v2\/media?parent=83174"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/forklog.com\/en\/wp-json\/wp\/v2\/categories?post=83174"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/forklog.com\/en\/wp-json\/wp\/v2\/tags?post=83174"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}