Intelligent Cities Index China 2020
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An initiative of Business School Intelligent Cities Index China 2020 Assessing the artificial intelligence capabilities of Chinese cities An introduction to the six city clusters leading the way on AI Authors: Kai Riemer, Professor of Information Technology and Organisation Sandra Peter, Director Sydney Business Insights Huon Curtis, Senior Research Analyst Kishi Pan, China Analyst Layout and visual design: Nicolette Axiak Contact: [email protected] This report is a publication of Sydney Business Insights at the University of Sydney Business School. Download this report as pdf here: http://sbi.sydney.edu.au/intelligent-cities-index-china/ Full citation: Riemer, Kai; Peter, Sandra; Curtis, Huon; Pan, Kishi (2019) “Intelligent Cities Index China: Assessing the artificial intelligence capabilities of Chinese Cities” Sydney Business Insights, http://sbi.sydney.edu.au/intelligent-cities-index-china/ Cover image: Photo by Minhao Shen on Unsplash, Photo by Yuanbin Du on Unsplash Intelligent Cities Index China Contents Executive overview 5 Background: Artificial intelligence 6 Background: Intelligent cities 12 Study overview 16 Scoring model 18 The Intelligent Cities Index China (ICI-CN) 21 Intelligent City clusters 28 China’s economic regions 29 Intelligent Capital 30 East Coast Challengers 32 Rising Centre 35 Giants of the South 36 Industrial Northeast 38 Developing West 40 References 42 Authors and glossary 43 Page 3 Photo by Hiurich Granja on Unsplash Intelligent Cities Index China Page 4 Photo by Zhang Kaiyv on Unsplash Intelligent Cities Index China Executive overview What is the Intelligent Cities Key insights Index China (ICI-CN)? The Intelligent Cities Index China (ICI-CN) The Intelligent Cities Index China provides a reveals six main clusters of leading cities in ranking of Chinese cities according to their AI. These clusters correspond with the activity in the emerging field of Artificial Economic Regions of China (see page 14). Intelligence. The Top 6 Intelligent cities are all located in It provides a resource for decision-makers the East Coast Region. Each of the other and stakeholders who want to gain a regional three economic regions comprises at least and geographic overview of AI activity in one Top 10 intelligent city. China. Beijing is the Intelligent Capital, a leader in The top cities which make up the Index are research and enterprise activity. portrayed in detail later in the report (see The East Coast Challengers Shanghai, pages 30-41). Nanjing and Hangzhou are allrounders with The Index is the result of meta-research. All strengths across all categories of the ICI. scores and rankings have been developed The Giants of the South Coast, Shenzhen from existing studies and publicly available and Guangzhou, are leading centres of data on AI in China with a focus on cities, government AI engagement and have strong provinces and regions (see page 21). enterprise sectors. A number of reports and rankings on AI in The Rising Centre comprises Wuhan, an up Chinese cities have been published in recent and coming city with strengths in AI years. This study collates their insights and research and enabling infrastructure. consolidates them in one Index. The Industrial Northeast, Harbin and The Index is based on 10 indicators and Shenyang are known for strong AI research. presents the sum of four individual rankings, The Developing West comprises Xi’An and comprising 1) enterprise activity, 2) research Chengdu, two cities with developing proficiency, 3) infrastructure readiness, and 4) research strengths and favourable policy government engagement (see pages 22-25). conditions for attracting AI talent. Page 5 Photo by Scarbor Siu on Unsplash Intelligent Cities Index China Background: artificial intelligence Artificial Intelligence (AI) has At the same time AI stokes fears of counterbalanced with the creation of new farming and agriculture, AI is being tasks in which workers have a comparative deployed to improve crop yield. generated immense hype automation, wide-spread job losses and ‘the robots coming for us’, with even advantage over the new technology.⁷ Given In order to unpack what makes AI a with unrealistic promises and younger, well-qualified employees at risk that AI is not a single technology it is clear powerful new tool, and to distinguish dystopian warnings of job loss through automation from AI, that its effect will be felt unevenly across realistic promises from unwarranted hype, according to a recent Axios study.² work tasks and the economy in general. it is important to understand what exactly Artificial Intelligence (AI) invokes promises It is obvious that AI, heavily influenced by Without question, AI is likely to change the we mean by AI, and how AI differs from of thinking machines and higher pop culture narratives rooted in science nature and design of a great many jobs, the traditional computing. intelligences capable of solving pernicious fiction, has generated much unhelpful tasks that make up those jobs, and the problems in never seen before ways. It hype and hyperbole. It is then no wonder types of skills needed to perform them. promises unprecedent innovation and that early dystopian predictions are still Adoption of AI has already been seen in Select sectors adopting AI applications economic growth. For example, a recent being heavily cited and publicly reinforced sectors such as finance, manufacturing, McKinsey study suggests that global in media and political discourse,³ even energy, transport, health care and adoption of AI technologies could raise though they have since been discredited. agribusiness. But the pace and extent of global GDP by as much as $13 trillion by One such study predicted that 47 percent adoption of AI is likely to vary significantly 2030, about 1.2 percent additional GDP of all workers in the US have jobs at high across sectors and economies. In financial growth per year.¹ risk of potential automation over the next services, automated financial advice agribusiness healthcare two decades.⁴ In Australia, the CSIRO products and algorithmic trading systems predicted a similar proportion of jobs at are increasingly prevalent. In addition, risk and the Bank of England warned that AI-enabled systems have been developed 80 million US and 15 million UK jobs might for up to 25 years to assist in the detection of fraud by financial firms.⁸ Major TRILLION be lost to automation.⁵ $13 technology firms including Microsoft, IBM, energy manufacturing BY 2030 More balanced analyses from labour Google and Intel have invested in economists have since shown how healthcare startups, and partnered with technological developments are both research institutes and national health $ $ complementary and displacing in different $ services in efforts to build systems that aid ways.⁶ Economic history has shown that medical diagnosis and treatment.⁹ Even in GDP job loss from automation is always finance transport Page 6 Intelligent cities index China Deep Learning (DL) – a new revolutionary, so-called “deep learning DL algorithms are created by ‘training’ network thus ‘learns’ to recognise patterns computing paradigm algorithm” in the now famous image layers of complex networks of numerical in the input data and associate it with recognition competition ImageNet that AI values (so-called neural networks) with a labels we provide it with. The usefulness of was catapulted back into the spotlight. training data set prepared by humans such deep learning is that the network will The field of AI is almost as old as This breakthrough in machine learning that for each training input (e.g. a classify any input of the same kind and give computing itself, with roots in the 1950s. combined with advances in computing particular picture) a pathway through the us an output, even if that input was not Yet, early AI solutions were very different power and new forms of data collection network is generated that arrives at the contained in the training data. It does so by to the current suite of technologies. In the through social media to create the current right output category (e.g. a label interpolating, by filling in the blanks so to early days, computer scientists had set hype. describing what is in the picture). The speak. their minds on teaching the computer all the facts and rules available about the How Deep Learning works world, in order to replicate in the machine Figure 1: Deep learning neural network the same reasoning abilities that make Deep Learning (DL) is very different from human cognition so powerful. However, conventional computing. In conventional Input layer this initiative failed spectacularly when computing, an algorithm is a set of the field encountered what is today known pre-determined steps to achieve a goal. In Hidden layers as ‘the common-sense problem’, the ways other words, we program software with a Output layer in which human knowing is embodied in set of instructions, or rules to achieve a tacit forms of knowledge that cannot be specific task. Deep learning however is expressed in explicit facts and logical radically different. Rather than encoding statements.¹⁰ explicit rules for solving a problem, the idea is to derive patterns from existing The failure in the 1980s of what is now data sets that are then used for commonly known as “Good old-fashioned classification. As such, DL systems can be AI (GOFAI)” led to the long AI winter, immensely useful in fields such as during which research persisted in document classification, image and speech Universities yet without wide-spread recognition, or decision