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1、 itif.org How Innovative Is China in AI?HODAN OMAAR|AUGUST 26,2024 Chinas relentless drive and strategic investments in AI suggest it is only a matter of time before it catches upif not surpassesthe United States early lead.KEY TAKEAWAYS China is the global leader in AI research publications and is

2、neck and neck with the United States on generative AI.However,Chinas research publications have less impact than U.S.ones,with fewer citations and less private-sector involvement.Tsinghua University in Beijing is the breeding ground for Chinas leading AI start-ups,including four of the countrys“AI t

3、igers”Zhipu AI,Baichuan AI,Moonshot AI,and MiniMaxwhich were all founded by faculty and alumni.Chinese large language models are closing the performance gap with U.S.models,with some Chinese models outperforming their U.S.counterparts in bilingual benchmarks.China has less private AI investment than

4、 the United States,but foreign investment in Chinas generative AI sector is growing,with Saudi Arabias Aramco leading the way.State-directed capital funds and financial aid are proving effective at supporting high-potential firms in regions of China the private sector typically underinvests in.China

5、s open-source LLM ecosystem is rapidly advancing,with models such as Alibabas Qwen 1.5 and Zhipu AIs ChatGLM3 outperforming some U.S.counterparts and gaining recognition for their impressive capabilities.U.S.policymakers should not prioritize containing China,but rather craft and fund a comprehensiv

6、e national AI strategy that addresses the twin goals of increased AI development and increased AI adoption to stay ahead.INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|AUGUST 2024 PAGE 2 CONTENTS Key Takeaways.1 Introduction.3 Background and Methodology.4 Chinas AI Industry and Market.4 How China Supp

7、orts Its AI Innovation Ecosystem.5 The Chinese Government Provides Financial Support to Domestic AI Firms.5 Top Chinese Start-Ups are Pushing the Boundaries of AI.6 Chinas Frontier AI Models Are Closing the Performance Gap With the United States.8 Assessing Chinese Innovation Inputs for AI.10 AI Res

8、earch.10 AI Patents.11 AI Talent.14 AI Infrastructure.15 Data.15 Compute.16 Company Case Studies.17 Zhipu AI.17 Moonshot AI.19 Chinas Government Policies Supporting the AI Sector.20 Chinas Strategy for AI Innovation.20 Chinas Industrial AI Strategy.22 Chinas Strategy for AI Resources.22 What Should

9、America Do?.23 Endnotes.26 INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|AUGUST 2024 PAGE 3 INTRODUCTION Artificial intelligence(AI)refers to the simulation of human intelligence in machines,enabling them to perform tasks such as visual perception,speech recognition,decision-making,and language trans

10、lation.The field has advanced significantly in the past decade,and researchers have pushed the boundaries of AI technology with large language models(LLMs)that perform a number of tasks as well as humans.1 The United States has been at the forefront of AI innovation,benefiting from leading research

11、universities,a robust technology sector and,until now,a supportive regulatory environment.However,China has emerged as a formidable competitor over the past decade.2 And the narrative that China is merely a copier is false and outdated.Chinas strong academic institutions and innovative research,part

12、icularly from Tsinghua University,has produced the majority of Chinas top AI start-ups,including the current top four generative AI start-upsZhipu AI,Baichuan AI,Moonshot AI,and MiniMaxall founded by its faculty and alumni.China now produces more AI research than the United States,and it is rapidly

13、closing the performance gap with U.S.LLMs,especially in bilingual benchmarks.While China leads in research volume,the United States excels in translating cutting-edge research into real-world products,driven by its private-sector firms.This is evidenced by the United States producing more notable ma

14、chine learning and foundation models,with 61 notable machine learning models compared with Chinas 15 in 2023,and 109 U.S.foundation models compared with 20 from China.Chinas financial landscape further underscores its rapid catch-up.Although the United States dwarfs China in private AI investment,fo

15、reign investment is beginning to trickle into Chinas generative AI sector.The venture capital arm of Saudi Arabias Aramco recently backed Zhipu AI in a$400 million deal,highlighting confidence in Chinas AI capabilities.The Chinese government is also stepping in to fill funding gaps,supporting domest

16、ic AI companies with state-directed capital and financial aids,proving particularly effective in supporting domestic AI companies in regions typically overlooked by the private sector.Despite extensive U.S.efforts to restrict Chinas access to advanced technology through export controls,these measure

17、s have had limited success.In fact,these measures have helped spur China to advance its homegrown ecosystem.Chinese companies are circumventing restrictions by training in public clouds wherever they can and innovating with developing on-premises private clouds such as Huaweis“AI-in-a-box”products w

18、herever they cant.The reality is the United States efforts to hold back Chinas AI progress are unlikely to succeed.China is advancing rapidly in AI research and application,challenging the United States dominance in this critical field.Despite hurdles,Chinas robust academic foundations,innovative me

19、thodologies,and increasing foreign investment are propelling it toward becoming a leading AI powerhouse.The question is not whether the United States can contain China in AI,but whether it can keep ahead.To implement the goal of increasing AI development,U.S.policymakers should do the following:1.St

20、imulate private investment in AI R&D.2.Revitalize the federal funding process for AI.INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|AUGUST 2024 PAGE 4 3.Avoid policies that undermine U.S.AI leadership and bolster Chinese competitors.4.Develop a national data strategy to dramatically expand the availab

21、ility of data for training AI models.To implement the goal of increasing AI adoption,U.S.policymakers should do the following:5.Create a national AI roadmap for adoption.6.Prioritize the rapid adoption of AI in the federal government.7.Support digital transformation.8.Incentivize AI workforce traini

22、ng investment.BACKGROUND AND METHODOLOGY The Smith Richardson Foundation provided support to the Information Technology and Innovation Foundation(ITIF)to assess how innovative Chinese industries are.As part of this research,ITIF is focusing on particular sectors,including AI.To be sure,its difficult

23、 to assess the innovation capabilities of any countrys industries,but it is especially difficult for Chinese industries.In part,this is because,under President Xi Jinping,China discloses much less information to the world than it used to,especially about its industrial and technological capabilities

24、.Notwithstanding this,ITIF relied on three methods to assess Chinese innovation in AI.First,we conducted an in-depth case study evaluation of two leading AI companies.Second,we conducted interviews and held a focus group roundtable with global experts on the Chinese AI industry,allowing participants

25、 to speak anonymously unless they asked to be named in the report.And third,we assessed global data on AI innovation,including scientific articles,patents,talent,and access to infrastructure.CHINAS AI INDUSTRY AND MARKET Nations must have healthy AI ecosystems in order to develop innovative AI techn

26、ologies and firms.For example,nations must have sufficient venture capital and private equity funding to connect inventors with the money,expertise,and contacts necessary to develop and sell their products or services.Looking at venture capital and private equity funding can therefore be an indicato

27、r of innovation capacity.In addition,the number of firms indicates the health of a nations ecosystem.Table 1:Overview of AI companies,investments,and investment value 201420243 Metrics(Absolute Number or Value)China(Mainland)United States Number of companies 1,944 9,500 Number of incoming investment

28、s 8,194 59,534 Estimated total value of incoming investments(millions)$85,650$605,416 When comparing the investment scene between China and the United States,the scale and scope in the United States is considerably larger(see table 1).With 9,500 AI companies,the United States hosts nearly five times

29、 as many companies as China in absolute numbers.It is INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|AUGUST 2024 PAGE 5 important to clarify that in this report,we use data from the Emerging Technology Observatory,a database which relies on proprietary data about the number of firms in the AI category

30、 group on Crunchbase.Different reports may use varying definitions of what constitutes an AI firm,leading to discrepancies in existing literature.The Crunchbase AI category includes many different types of AI firms,including those conducting AI research,those developing AI-related products,and those

31、 applying AI to significant societal needs.Some studies use broader definitions of AI firms which results in counting more businesses as AI firms.It also seems to have far more funding flowing into these businesses.AI venture capital(VC)and private equity funding can be concentrated in a few large d

32、eals,which is why it is important not only to measure the level of funding in dollar amounts,but to also track the overall number of funding deals.In the United States,there have been almost 60,000 incoming investments in the past decade,dwarfing the almost 8,200 in China.And in terms of value,the d

33、ifference is significant.Estimated investment values in the United States amount to around$605 billion,far outpacing Chinas$86 billion.Data on VC investments in AI by country from the Organization for Economic Cooperation and Development(OECD)support the finding that Chinese firms have less access t

34、o private capital.In 2023,the sum of VC investments in China was just shy of$20 billion,whereas in the United States it was approximately$55 billion.4 The Chinese government is trying to fill the gap with state-backed financial support.How China Supports Its AI Innovation Ecosystem There were three

35、key insights about how China supports original innovation capabilities from the experts we interviewed.First,the Chinese government provides financial support to nurture high-potential firms,especially in regions that might otherwise be overlooked,fostering a broader base for innovation.Second,there

36、 is an emerging set of Chinese AI start-ups,supported by the prestigious Tsinghua University,that are not replicating existing technologies but pushing the boundaries of AI innovation,particularly in generative AI.And finally,China is able to produce competitive,and sometimes superior,AI models comp

37、ared with those from the United States,highlighting its capacity for high-level,original innovation.The Chinese Government Provides Financial Support to Domestic AI Firms Several experts we interviewed noted that the Chinese government financially supports the development of domestic AI companies.Ac

38、cording to experts,two of the most important mechanisms government uses are government guidance funds and subsidies.Government guidance funds are essentially state-directed capital funds.These funds are raised from both the public and private sectors and for projects that align with government objec

39、tives.5 According to 2024 research published in the National Bureau of Economic Research,between 2000 to 2023,Chinese government VC funds invested in 9,623 unique firms in the AI space through more than 20,000 transactions,totaling$184 billion.6 The definition this research uses for AI firms include

40、s firms working on big data,image processing,facial recognition,natural language processing,machine learning,deep learning,neural networks,robotics,automation,computer vision,data science,and cognitive computing.7 Government guidance funds are used across a wide variety of strategic industries and b

41、usiness activities,but their impact has been found to be underwhelming.These funds have tended to INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|AUGUST 2024 PAGE 6 raise less money than anticipated and have failed to deploy investments in many cases,and there have been too many with overlapping priori

42、ties leading to inefficiencies.8 But evaluating the funds created specifically for AI firms surfaces three important advantages:First,government VC funds are more spatially distributed than private funds,which are mainly based in Chinas wealthy coastal regions.9 Because Chinese government VC funds a

43、re spread more evenly across the country,including less-developed inland areas,the government is helping support high-potential firms in regions private VCs might overlook due to mobility restrictions and information gaps.Second,government VC funds invest in firms that initially show weaker performa

44、nce indicators,but these firms end up growing faster than those funded by private VCs.Finally,government VC funds often invest in AI firms before private VC funds,and these government investments attract subsequent private VC investments.This pattern is especially strong when the government directly

45、 invests in firms with initially weaker performance indicators,suggesting that government choices are seen as valuable signals by private investors.The Chinese government also supports its AI sector through subsidies.For example,Beijing city authorities have introduced subsidies for firms buying dom

46、estically produced AI chips to boost Chinas semiconductor industry and lessen dependency on foreign technology.10 The initiative will provide financial support to companies based on a percentage of their investment in domestically controlled graphics processing unit(GPU)chips.In addition,at least 16

47、 local governments,including the largest,Shanghai,are providing companies with vouchers to access subsidized processing power from large state-operated data centers that consolidate limited supplies of advanced chips.11 Top Chinese Start-Ups are Pushing the Boundaries of AI Five generative AI start-

48、upsZhipu AI,Baichuan AI,Moonshot AI,Minimax,and 01.AIare among Chinas AI“unicorns,”meaning they are valued at over$1 billion and are at the heart of the generative AI ecosystem in the country.The press has dubbed them the“new AI Tigers of China,”as they represent a new wave of AI companies focused o

49、n LLMs,like their Western counterparts such as OpenAI and Anthropic.12 This is also to contrast them with what they call the older technology“dragons,”such as SenseTime,Megvii,CloudWalk Technology,and Yitu Technology that focused mostly on facial and image recognition technologies.13 One expert we i

50、nterviewed said that while many of the new Chinese generative AI companies are trying to replicate OpenAIs success with ChatGPT,there is no clear leader or breakthrough application that has emerged as the most popular or most widely used in Chinaand some analysts report that customers struggle to de

51、termine which companys AI solutions are the best for their particular needs.Others,however,argue that Zhipu AI,whose models perform consistently,is the Chinese frontrunner in the race to challenge OpenAI(see more on Zhipu AI in the company case studies section).Interestingly,the influence of these C

52、hinese start-ups is so significant that even top Western institutions are starting to follow their lead.A recent controversy involving Stanford University revealed that its AI model,Llama 3-V,was suspiciously similar to MiniCPM-Llama3-V 2.5,a model developed by Tsinghua Universitys Natural Language

53、Processing Lab and the Chinese start-up ModelBest.Liu Zhiyuan,cofounder of ModelBest,highlighted this shift,acknowledging the“significant”gap between Chinas generative AI models and top-tier Western projects such as INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|AUGUST 2024 PAGE 7 GPT-4,but also notin

54、g that China has rapidly gone“from a nobody more than a decade ago to a key driver of AI technology innovation.”14 Tsinghua University in Beijing is at the root of much of Chinas entrepreneurship in AI.The elite university has been a critical breeding ground for many of Chinas most successful AI sta

55、rt-ups(see figure 1),supporting them with talent,research resources,and funding through the universitys investment vehicles.Zhipu AI was directly incubated within its research labs,while Baichuan AI,Moonshot AI,and MiniMax were all founded by Tsinghua faculty or alumni.Zhipu has also invested in sev

56、eral other start-ups connected to Tsinghua,including ModelBest,Shengshu,and Lingxin AI,indicating a continuous cycle of innovation and growth within Tsinghuas own AI ecosystem.Figure 1:The AI start-up scene connected to Tsinghua University15 INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|AUGUST 2024 P

57、AGE 8 Chinas Frontier AI Models Are Closing the Performance Gap With the United States In the past two years,China has seen a rapid increase in large-scale AI models from leading industry and academic labs.Companies must seek government approval before introducing AI chatbots built on LLMs into the

58、market,and as of March 2024,Chinas Cyberspace Administration had approved at least 117 generative AI products.16 According to data provider IT Juzi,at least 262 start-ups are competing to bring out generative AI products in China.17 Chinas large-scale AI models fall into two broad categories.In the

59、first category are general-purpose AI platforms used across various sectors,including both closed-source and open-source models.In the second category are industry-specific models tailored to vertical sectors,such as finance,biopharmaceuticals,and remote sensing.An example in the latter group is Mat

60、hGPT from the TAL Education Group,a smart-learning solutions provider in China.The model is trained on extensive data on mathematics learning and can power different EdTech applications by providing dialogue-based problem-solving.18 While China has many companies developing powerful AI models,U.S.mo

61、dels currently outperform them.One expert pointed to performance of U.S.and Chinese models on a comprehensive Chinese LLM benchmark called SuperCLUE.SuperCLUE was developed by a team of researchers,from both Chinese and international labs,to assess model capabilities on Chinese-language understandin

62、g,including semantic understanding and extraction,generating code,logic and reasoning,calculations,roleplaying,and safety.19 From the latest results in 2024,three findings emerge:1.The gap between the leading models from U.S.industry leaders and those developed by Chinas foremost tech giants and sta

63、rt-ups is quickly closing.Figure 2 shows the overall scores for U.S.and Chinese models on the SuperCLUE benchmark in April 2024 and June 2024.In April,the top three models were all different generations of OpenAIs GPT model series,while the fourth model was Claude,which was developed by U.S.-based s

64、tart-up Anthropic.Then came several Chinese LLMs.The gap between the best Chinese model(Baichuans Baichuan3)and best U.S.model(OpenAIs GPT-4 Turbo)in April was considered significant,but it was smaller than it was last year.20 However,just two months later in June,Chinese companies had risen up the

65、leaderboard and dethroned several U.S.models from their top spots.OpenAIs GPT-4 still leads the pack,but Alibabas LLM series Qwen 1.5 ties for second with Claude while Chinese AI start-up DeepSeek newly enters the leaderboard and ties for third with Chinese start-up Zhipu AIs GLM-4 model and Chinese

66、 AI company SenseTimes Sensechat model.2.The makeup of players developing Chinas top models are a mix of start-ups and tech giants.Models from large tech giants include Baidus Erniebot,Alibabas Tongyi Qianwen,and Tencents Hunyuan,while models from the start-up scene are being led by Zhipu AI,Baichua

67、n AI,Moonshot AI,and MiniMax.3.Chinas open-source LLM ecosystem is gaining significant traction.Alibabas LLM series Qwen 1.5 showed impressive capabilities across various sizes,but particularly its largest model with 72 billion parameters.Some iterations of Chinas open-source models also beat their

68、U.S.counterparts,such as Zhipu AIs ChatGLM3 and Baichuans Baichuan2,which outperformed Googles Gemma and Metas Llama 2 series.21 Additionally,Hugging Face,which evaluates open-source models,ranks several iterations of Chinese AI start-up 01.AIs open-source model(called the Yi series)highly for commo

69、n sense reasoning,math,coding,and reading ability.22 INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|AUGUST 2024 PAGE 9 Figure 2:Scores of Chinese and U.S.LLMs on Chinese-language understanding based on SuperCLUE benchmark23 April 2024 June 2024 Model Score Model Score GPT-4-Turbo-0125 79 GPT-4o 81 GPT

70、-4-Turbo-0409 77 Claude-3.5-Sonnet-200K 77 GPT-4 75 Qwen2-72B-Instruct 77 Claude3-Opus 74 DeepSeek-V2 76 Baichuan3 73 GLM-4-0520 76 GLM-4 73 SenseChat5.0 76 通義千問2.1 72 AndesGPT 75 騰訊Hunyuan-pro 72 GPT-4-Turbo-0409 75 文心一言4.0 72 GPT-4 73 Moonshot(Kimi)70 Baichuan4 72 從容大模型V1.5 70 Doubao-pro-32K-0615

71、72 MiniMax-abab6.1 70 Moonshot(Kimi)72 山海大模型 70 山海大模型4.0 72 訊飛星火V3.5 69 360gpt2-pro 72 Llama-3-70B-Instruct 69 MiniMax-abab6.5 71 階躍星辰step-1-32K 69 通義千問2.5 71 qwen-1.5-72B-chat 68 文心一言4.0 69 云雀大模型 67 Llama-3-70B-Instruct 69 360gpt-pro 67 Gemini-1.5-Pro 68 GPT3.5-Turbo-0125 67 階躍星辰step-1-32K 68 Gemin

72、i-Pro 64 訊飛星火V4.0 67 qwen-1.5-14B-chat 64 GPT-3.5-Turbo-0125 64 Llama-3-8B-Instruct 57 Yi-1.5-34B-Chat-16K 63 XVERSE-13B-L 56 qwen2-7B-instruct 62 qwen-1.5-7B-Chat 55 XVERSE-65B-2-32K 60 Llama-3-70B-Instruct 54 qwen1.5-32B-chat 57 Baichuan2-13B-Chat-v2 53 Llama-3-8B-Instruct 54 ChatGLM3-6B 51 Baichu

73、an2-13B-Chat-v2 53 Gemma-7B 44 Yi-1.5-6B-Chat 53 Chinese-Alpaca2-13B 44 Phi-3-mini-128K-Instruct 40 Llama2-13B-Chat 41 Gemma-7B 38 Llama2-7B-Chat 40 qwen2-1.5B-instruct 38 Llama-2-13B-chat 35 INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|AUGUST 2024 PAGE 10 ASSESSING CHINESE INNOVATION INPUTS FOR AI

74、Several metrics reveal important insights about the current state of Chinese innovation capabilities.In this report,we examine four categories of metricsresearch,patents,talent,and infrastructureto gauge the overall health and potential of the AI ecosystem in China.AI Research For years,the dominant

75、 narrative in AI research has been that while Chinese institutions generated the greatest quantity of AI papers,the quality of those papers lagged behind,and much of Chinas research involved applying fundamental advances made by researchers in the United States,Europe,and elsewhere.Indeed,China has

76、long pulled ahead in the sheer volume of AI research,with the five largest global producers of AI research articles being Chinese institutions.24 Despite this quantity,the quality,often measured by citations,has traditionally been higher in the United States,where both academic institutions and priv

77、ate-sector firms are producing cutting-edge research that translates more effectively into practical applications.25 The dynamic for the emerging field of generative AI is different.Currently,China and the United States both publish at similar levels,with China having around 12,450 generative AI pub

78、lications as of 2023 and the United States having 12,030.26 China has a smaller group of prolific institutions at its publication helm,as shown by the fact that half of the top 10 publishers in the world are Chinese organizations(see figure 3).However,despite being comparable in terms of the number

79、of publications,China and the United States differ significantly in terms of citations.On the top 10 list of top-cited generative AI research,China only claims one spot.The United States,by comparison,claims half of the leaderboard,including four of the top five(alongside Canada).Figure 3:Leading or

80、ganizations in AI generative research in 202327 All Research Top-cited Research 1 Chinese Academy of Sciences Alphabet 2 Tsinghua University University of California,Berkeley 3 Stanford University Universit de Montral 4 Alphabet Stanford University 5 Shanghai Jiao Tong University Meta 6 Massachusett

81、s Institute of Technology DeepMind 7 Zhejiang University Seoul National University 8 Harvard University Massachusetts Institute of Technology 9 Carnegie Mellon University Chinese Academy of Sciences 10 University of Oxford Imperial College London INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|AUGUST 2

82、024 PAGE 11 Interestingly,all the top 10 institutions in AI research from China are academic institutions,whereas the institutions producing top-tier AI research from the United States are a mix of academic organizations and private-sector firms.In fact,the wider top 20 leaderboard given in a 2024 r

83、eport from the World Intellectual Property Organization(WIPO)shows that all the companies that feature on the top 20 leaderboard are U.S.-owned(Alphabet,Meta,DeepMind,NVIDIA,OpenAI,Microsoft,Twitter,and Indico Research).28 All other entrants on the publications and citations lists from all countries

84、,including China,are universities or research institutions.This distinction is significant because it shows that the active involvement of U.S.private-sector firms in AI research is a major factor in maintaining the United States leadership.This strong presence of industry-driven research ensures th

85、at the United States remains at the forefront of AI innovation,not only producing high-quality research but also rapidly converting it into impactful technologies and applications.Indeed,the United States produces more notable machine learning models and foundation models than does China.In 2023,the

86、 United States had 61 notable machine learning models while China had only 15.29 Most of the worlds foundation models have originated from the United States(109),compared with those from China(20).30 AI Patents Intellectual property rights have long been recognized as fostering innovation.The idea i

87、s that those who combine the spark of imagination with the grit and determination to see their vision become reality in books,technology,medicines,designs,sculpture,services,and more deserve the opportunity to reap the benefits of their innovationand that those rewards incentivize more creative outp

88、ut.Patents can therefore indicate the ability of a firm or nation to innovate.China has a higher number of AI patents,but the United States surpasses in patent quality.The number of global AI patents has increased sharply in recent years.From 2021 to 2022,AI patent grants worldwide increased by almo

89、st 63 percent,and since 2010,the number of granted AI patents has grown more than 30-fold.The United States and China dominate the filings for AI patents,followed by Japan.These three offices account for 78 percent of total patent filings according to a 2019 report from WIPO.31 That said,China is fa

90、r and away the leader in number of AI patents(though it lags behind in patent quality,as discussed later);China has been the largest originator of granted AI patents since 2013,and in 2022,Chinese organizations filed approximately four times as many AI patents as their U.S.counterparts and the Chine

91、se patent office granted almost three times more than the U.S.patent office did.(See figure 4.)In fact,a 2024 report from WIPO on generative AI finds that since 2017,China has published more patents in the field each year than all other countries combined.32 INFORMATION TECHNOLOGY&INNOVATION FOUNDAT

92、ION|AUGUST 2024 PAGE 12 Figure 4:Number of AI patents in 202233 This is important because the sheer volume of patent applications from China,irrespective of their quality,poses a challenge for U.S.innovators.This influx creates an extensive pool of“prior art,”which refers to the global repository of

93、 existing scientific and technical knowledge patent examiners use to assess whether an invention is novel.34 American inventors have to demonstrate that their innovations are not already covered in any prior publications,including those from Chinese-language patents filed both domestically and inter

94、nationally,which gets increasingly hard to do as the pool of prior art increases.One reason IP experts believe might explain Chinas lead is its advantage in data quantity.Since AI-related innovations are enabled by data,it is believed that organizations that generate the most AI-related patents are

95、often the ones that have access to more data.35 As we explore later in this report,China does indeed have an advantage in data quantity,but having more data is not the only requirement for true innovation.Innovation also depends on the quality of data,advanced algorithms,robust infrastructure,and sk

96、illed talent.However,for the purpose of generating patents,a large volume of data might indeed be sufficient because patents often focus on novel approaches and applications,which can be derived from extensive datasets.The explanation of“more data,more patents”might be particularly true for generati

97、ve AI,which tends to be particularly data heavy.Generative patents currently represent 6 percent of all AI patents globally,but this is an uptick from the 4.2 percent share they held in 2017.36 China currently leads the field,far outpacing United States more than 6 to 1.37 Another reason some expert

98、s posit that China leads in AI patents is because China has a broader scope of what AI innovations are eligible to be patented.In 2017,Chinas examination guidelines revised the definition of“patent eligible”in a way that some saw as a conscious effort to expand the scope of patent eligibility.38 How

99、ever,other legal experts find that Chinas revisions align its 80,46015,11035,31012,080020,00040,00060,00080,000100,000120,000ChinaUnited StatesGrantedNot grantedINFORMATION TECHNOLOGY&INNOVATION FOUNDATION|AUGUST 2024 PAGE 13 approach to patenting AI more closely with Europes framework,which is more

100、 stringent and consistent than the United States.39 What is also clear from the data in figure 5 is that patent offices in both countries accept only a fraction of the patent filings they receive.This is part of a widening discrepancy:Before 2015,more AI patents across the world were granted than we

101、re not granted,but since then,the opposite has been true,and the gap has been getting wider.40 China also dominates the top 20 patent owners in generative AI,accounting for 13 of them(figure 5)and four of the top five(the fifth is IBM).Corporations dominate the entire list,but there are three resear

102、ch organizations in the top 20 and they are all Chinese(the Chinese Academy of Sciences,Tsinghua University,and Zhejiang University).The most prolific of these research universities,the Chinese Academy of Sciences,ranks fourth overall.Figure 5:Top patent owners in generative AI,as of 202341 05001,00

103、01,5002,0002,500Tencent Holdings(China)Ping An Insurance Group(China)Baidu(China)Chinese Academy of Sciences(China)IBM(US)Alibaba Group(China)Samsung Electronics(Republic of Korea)Alphabet(US)ByteDance(China)Microsoft(US)BBK Electronics(China)Netease(China)NTT(Japan)Huawei(China)Tsinghua University(

104、China)Zhejiang University(China)China Mobile(China)State Grid(China)Adobe(US)Sony Group(Japan)INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|AUGUST 2024 PAGE 14 The number of patent filings for a nation can be a problematic metric for innovation capability because patents vary greatly in quality.Many

105、patents issued by the Chinese Patent Office are of relatively poor quality and therefore patent counts from China cannot be compared easily against patents issued by the U.S.Patent and Trademark Office(USPTO)or from European patent offices.42 Indeed,just 4 percent of AI patents first filed in China

106、were also filed in another jurisdiction,compared with 32 percent of patents first filed at USPTOwhich is an indicator of the significantly higher quality of U.S.patents.43 As a result,it is useful to examine Patent Cooperation Treaty(PCT)patent applications and highly cited patent families,which are

107、 patents filed for the same invention in numerous jurisdictions.A 2019 report from the Center for Data Innovation finds that per one million workers,the United States(11 PCT applications)far outpaced China(1).44 However,while patent quality may be lower in China,some evidence suggests China is addre

108、ssing issues in this area.45 AI Talent Talent is essential for a nations innovation capability because it provides the necessary expertise,creativity,and problem-solving skills to develop new technologies and drive economic growth.A nation that invests in attracting,nurturing,and retaining AI talent

109、 is better positioned to achieve long-term economic growth and maintain a competitive edge in the global economy.There is a robust debate over what type of talent is most important to enhancing national or institutional AI capabilities.While some argue that countries should prioritize cultivating a

110、large workforce of relatively lower-skilled AI engineers,others contend that its more important to prioritize developing and attracting elite researchers.China is leading in the production of talent and retaining more of its own than it used to,but it lags behind in upskilling existing workers.When

111、it comes to producing top-tier AI researchers,China is a leading country.This can be seen in the percentage of Chinese researchers whose papers were published at the Conference on Neural Information Processing Systems,or NeurIPS,as it is known.NeurIPS focuses on advances in neural networks,which hav

112、e anchored recent developments in generative AI,and it is considered one of the most selective and prestigious AI conferences,making the cohort it accepts a useful proxy for elite AI research talent.Analysis from MacroPolo,a think tank run by the Paulson Institute,shows that in 2022,China produced 4

113、7 percent of the worlds top-tier AI researchers(“top-tier”is defined as the top fifth of all AI researchers),compared with 29 percent in 2019.46 For the most elite AI researchers(defined as the top 2 percent of all AI researchers),26 percent come from China compared with 28 percent from the United S

114、tates.This is a significant jump from 2019,when China was only producing 10 percent of elite AI researchers compared with the United States producing 35 percent.More of Chinas top AI talent ends up in the United States than anywhere else.The United States has long benefitted from large numbers of Ch

115、inese talent moving to U.S.universities to complete doctoral degrees,and most of them stay and help power competitiveness of the U.S.AI ecosystem.But while the United States remains the leading destination for elite Chinese talent,several countries including Australia,Canada,France,and the United Ki

116、ngdom have introduced new visas in the last few years to entice foreign talent to create Silicon Valley-like tech hubs in their own countriesand they seem to be working.A recent report from Tsinghua University,Chinas leading institution overall and second most-leading institution for AI research,sho

117、ws the INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|AUGUST 2024 PAGE 15 number of Tsinghua graduates who chose to study in the United States of America has dropped from 11 percent in 2018 to 3 percent in 2021.47 Some attribute this decline to the COVID-19 pandemic;however,the proportion of Tsinghua

118、graduates studying in the United Kingdom has not declined at all,and the number of those choosing to study in Singapore has risen.The United States outmoded visa laws,as well as recent concerns about Chinese researchers and international competition for AI talent from other countries,are contributin

119、g to international AI scientists and engineers looking outside the United States for education and employment.While there is a prevailing impression that China is facing a brain drain,according to an October 2023 New York Times article,data suggests that a growing number of high-quality Chinese rese

120、archers are choosing to stay in China.48 That is to say,while the sheer number of Chinese researchers emigrating might be growing,the percentage of the best and brightest who are leaving its shores might actually be declining.MacroPolos Global AI Tracker found that in 2022,28 percent of top AI resea

121、rchers were working in China,up from only 11 percent in 2019.49 This trend could indicate that China is successfully creating attractive opportunities and environments for its top talent,counterbalancing the outward migration and fostering domestic innovation.AI Infrastructure Data AI systems often

122、rely on vast quantities of data for training.Large datasets help AI systems develop highly accurate models to perform tasks ranging from navigating without a map to identifying faces to answering Google search queries.Moreover,machine learning techniques allow AI systems to recognize subtle patterns

123、 in large datasets that are difficult or impossible for humans to perceive.This is one reason why many AI systems perform certain tasks better than human experts,such as identifying the signs of lung cancer in commutated tomography scans.However,determining whether one country has a data edge in AI

124、over another is not as simple as measuring total data output.The role of data in AI is much like the role of labor in the economy;China may have a large working population,but the structure,quality,and adaptability of that workforce are just as important to driving economic growth.50 Similarly,data

125、isnt just a matter of having more of it;its about the quality of the data,how well it is curated,and how accessible it is.Many metrics find that China leads in sheer quantity of data but lags behind in quality and diversity.High-quality data refers to both correct and consistent data and also data t

126、hat is appropriately formatted for its intended use.51 China has introduced policies to promote data standards in certain sectors,but significant amounts of data remain unreadable by computers,lowering the quality and usability of data for analysis.According to the findings of the National Data Reso

127、urces Survey Report 2023,which was the first nationwide data resource survey by Chinese authorities,while Chinese companies produced 22 percent more data than the previous year,they digitized and stored only about 3 percent more.52 Where China leapfrogs its competitors is in providing its firms and

128、researchers with access to data.This is particularly true for data from public spaces,which the Chinese government collects primarily through an extensive network of surveillance,security,and traffic cameras.A common way in which private Chinese AI firms gain access to this valuable government data

129、is by providing services to the state,such as optimizing traffic management through smart-city INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|AUGUST 2024 PAGE 16 projects.These services not only improve the efficiency and effectiveness of government operations but also provide Chinese AI firms with va

130、luable,large datasets.A 2023 paper study published in The Review of Economic Studies examined nearly 1,900 government contracts for facial recognition technology(FRT)in China and finds that companies with access to high-quality government data through these contracts produced significantly more comm

131、ercial AI software products than those without such access,indicating that government data access has materially contributed to the rise of Chinas facial recognition AI firms.53 And in FRT,Chinese firms are certainly leaders.According to the Face Recognition Vendor Test(FRVT)by the U.S.National Inst

132、itute of Technology,which evaluates the performance of facial recognition algorithms from various vendors,Chinese firms produce a quarter of the top 25 algorithms,3 of which crack the top 10 list.54 Compute Access to advanced chips is critical in researching and developing advanced AI systems,which

133、often demand extensive computing power.Unfortunately for China,it depends entirely on the United States and U.S.allies for access to these chips.A recent study of 20 Chinese LLMs finds 17 models were built using chips produced by the U.S-based firm NVIDIA and only 3 models built with Chinese-made ch

134、ips.55 Since October 2022,the United States has imposed a series of export control measures to limit Chinas access to its chips,but there is disagreement on how effective these policies are.While some experts we spoke to noted that the controls seem to be undercutting Chinese AI developments in the

135、short term,others noted that these measures are unlikely to hold back Chinas progress in AI.China already has an existing stock of chips and is quickly pivoting to develop its own supply that is not reliant on U.S.companies.56 An article published in Caijing,an independent magazine based in Beijing

136、that covers societal,political,and economic issues,reported that in the wake of the United States export ban,demand for these chips spiked,and tens of thousands of resellers are active in the Pearl River Delta.57 According to some resellers,the market price of certain NVIDIA chips doubled within a w

137、eek.58 Moreover,Chinese AI companies are circumventing U.S.export controls by accessing high-end U.S.chips through cloud providers.Indeed,despite sanctions,companies such as state-backed voice recognition company iFlytek allegedly rent access to NVIDIAs A100 chips,highlighting loopholes in enforceme

138、nt.59 Even if the U.S.government closes gaps in export control enforcement,China is working on being self-sufficient in compute capacity and reducing reliance on the United States.For one,China has prioritized creating a domestic semiconductor industry.The expertise,capital,and scale needed to devel

139、op a new semiconductor design,or build a new semiconductor fab,is extremely high and increasingand Chinese firms have weaknesses in almost all the subsectors needed for chip design and manufacture,especially photolithography,metrology,and inspection.60 Still,one of Chinas major microchip foundries,t

140、he Semiconductor Manufacturing International Corporation(SMIC)is working to produce microprocessors using less-advanced equipment,demonstrating Chinas commitment to advancing its semiconductor capabilities despite challenges;however,experts are doubtful this process can ever be commercially competit

141、ive.61 And Huaweis Ascend 910B offers a competitive and cost-effective alternative for AI applications,but it is not as efficient as NVIDIAs A100.For a country with Chinas resources and political will,it could still gain market share in AI-related semiconductors by focusing on developing better chip

142、s for certain tasks or application INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|AUGUST 2024 PAGE 17 areas.Outside the market for the types of chips that are needed to best train AI models,there is also a newer,smaller,and dynamic market for the types of chips that are best at applying AI models.For

143、instance,while NVIDIA leads the markets for GPUs,which are specialized chips designed to train and run AI models,there are field programmable gate arrays(FPGAs),which are mostly used to apply trained AI algorithms to new data inputs.FPGAs are different from other AI chips because their architecture

144、can be modified by programmers after fabrication.There is also a group of AI chips called“application-specific integrated circuits”(ASICs),which can be used for either training or inference tasks.ASICs have hardware that is customized for a specific algorithm and typically provide more efficiency th

145、an do FPGAs,but because they are so narrow in their application,they grow obsolete more quickly as new AI algorithms are created.In the long term,there are many areas of AI chip development for competitors to thrive,especially those that make more energy-efficient chips,as the use of electricity is

146、proving to be a huge cost for companies training and running AI models.62 As table 2 shows,there are Chinese firms in the market for all these chip architectures.Table 2:Notable supplier companies for different AI logic chips AI Chip Architecture U.S.Companies Chinese Companies GPUs NVIDIA,AMD Hygon

147、 Information Technology,Jingjia Micro,Enflame Technology FPGAs Achronix,Intel,Xlininx,Lattice Baidu,DeePhi ASICs Amazon,Google,Cerebras,Tesla HiSilicon,Cambricon,Horizon Robotics,Intellifusion That said,Chinese firms currently have a negligible market share in AI chip design,except for ASICs,which c

148、an be easier to design than other types of chips because they are designed for specific applications rather than are more general-purpose chips,making them more feasible for many companies to produce.However,they are not widely commercialized due to small market sizes and high development costs.More

149、 promising is Huaweis innovation with“AI-in-a-box”solutions,which combine its AI chips with industry-specific software and pre-trained models,making it easier and cheaper for businesses to use AI without needing advanced chips such as NVIDIAs.63 This allows companies to implement AI solutions quickl

150、y and securely on their own premises.COMPANY CASE STUDIES This section provides case study analyses of two leading Chinese generative AI companies.Zhipu AI Zhipu AI is Chinas most prominent generative AI start-up,the countrys biggest AI start-up by number of employees with 800 staff,and one of its h

151、ighest valued AI start-ups,with a valuation of$3 billion as of May 2024.64 The company was founded by professors Tang Jie and Li Juanzi at Tsinghua Universitys Department of Computer Science and Technology.Jie and Juanzi were INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|AUGUST 2024 PAGE 18 participa

152、nts in the universitys Knowledge Engineering Group(KEG),a research lab that focuses on knowledge engineering theory,methods,and applications in network environments.Zhipu AI began investing in and developing LLMs in 2020,ahead of many other companies.In a paper its researchers published in April 202

153、2 titled“GLM:General Language Model Pretraining with Autoregressive Blank Infilling,”Zhipu AI introduced its language model GLM,and made it clear the company was trying to solve a different challenge than existing language models were at the time,such as Googles BERT and OpenAIs GPT.BERT,GPT,and Zhi

154、pu AIs GLM are all based on the transformer model architecture,which was introduced in a seminal 2017 paper“Attention is All You Need”by Google scientists,but Zhipu AI integrates innovative features into GLM to make a unique model that is highly accurate for both Chinese and English tasks.To see wha

155、t makes GLM unique,consider the following comparison:BERT(Bidirectional Encoder Representations from Transformers)uses a method known as masked language modeling,which essentially hides certain words in the input text and trains the model to guess these hidden words using the context from both direc

156、tions(before and after the hidden words).This helps BERT understand the meaning of words in context.GPT(Generative Pre-trained Transformer)uses an autoregressive model in which each token is predicted based on the tokens that preceded it.This is a unidirectional approach because the model is trained

157、 to predict the next word in a sentence by looking only at the words that come before it.GPTs strength is in generating coherent and contextually appropriate text over longer passages,leveraging its training to predict the next word given all the previous words.GLM(General Language Model)combines fe

158、atures of both of these approaches by hiding parts of the text and then predicting the missing parts in sequence.This method helps the model understand the context from both directions,like BERT,and generate text like GPT.GLM also uses advanced techniques to handle both Chinese and English languages

159、 effectively,making it very versatile and powerful for a range of language tasks.Much like OpenAI created ChatGPT as a specialized application of the GPT model,Zhipu AI has created ChatGLM as an application based on its GLM architecture,designed to excel in bilingual tasks in Chinese and English.But

160、 ChatGLM is not Zhipu AIs only offering;the company has a whole host of products including WebGLM,designed for web search and retrieval capabilities;VisualGLM,designed to understand and generate both text and images;CogVLM,a multimodal understanding model that was designed to be effective for tasks

161、that require interpreting visual data;and code generation model CodeGeeX2-6B.Zhipu AI is aiming for breakthrough advancements rather than incremental improvements.The companys CEO,Zhang Peng,has been reported to have said the companys slogan is to strive for artificial general intelligence(AGI),whic

162、h is also the mission of OpenAI and Google DeepMind.65 However,Zhang has been clear that Zhipu is on the road to AGI in its own way,and not simply following its competitors.He said in an interview:Today,I feel that Zhipu AI is undergoing a qualitative change from quantitative change,especially in te

163、rms of the emergence of large models.Standing at this juncture,whether looking forward or backward,I feel that our team is quite INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|AUGUST 2024 PAGE 19 fortunate.The market has given us,hard workers,many opportunities and chances.Although the road of indepen

164、dent innovation and research and development will be difficult,we are still persevering.In addition,the development of artificial intelligence will focus more on our general artificial intelligence(AGI),achieving super cognitive intelligence beyond human level,realizing AIs self-explanation,self-ass

165、essment,self-supervision.At the same time,to ensure that the models performance aligns with human values and safety standards,a super alignment technology is also under development with the goal of achieving machine automatic alignment with human intelligence and human values to enable model self-re

166、flection and control.As a company specializing in large models.66 For many,Zhipu AI might become OpenAIs rival,and foreign investors who do not want Silicon Valley to dominate in AI are throwing their weight and funding behind Zhipu AI in support.Saudi Arabias Prosperity7,which is part of the state-

167、owned oil group Aramcos venture capital arm,became the first foreign investor in Zhipu AI in 2024,participating in a roughly$400 million investment round.67 Moonshot AI Moonshot AI was founded in March 2023.The company is focused on the development of LLMs but is unique in that it is working on long

168、-text processing,a specialization that is crucial for enabling detailed and thorough interactions,such as LLMs that can address extensive customer service questions.This capability is becoming a significant competitive advantage in the LLM market,as the capacity to understand and maintain context ov

169、er extended texts can substantially improve performance.68 Moonshot AIs unique selling point is heavily influenced by the technical background of its three founders.One of the cofounders is Yang Zhilin,a former student of one of Zhipu AIs founders at Tsinghua University.Zhilin completed his Ph.D.at

170、Carnegie Mellon University in 2019 and did internships at Meta and Google Brain in 2017 and 2018,respectively,and it is during this period when he wrote and published two of his most widely cited papers titled“Transformer-XL:Attentive Language Models Beyond a Fixed-Length Context”and“XLNet:Generaliz

171、ed Autoregressive Pretraining for Language Understanding.”The first paper introduced a method to extend the context length in transformer models,which essentially enables models to remember more information from earlier in a given text,helping them to understand and generate longer,more coherent pie

172、ces of text.The second paper introduced a method to help models understand more complex relationships in data,and Zhilin was able to demonstrate better performance than models such as BERT on many language tasks by using this method.The other two cofounders are Xinyu Zhou and Yuxin Wu.Zhou had previ

173、ously worked at Hulu,Tencent,and Megvii and conducted research in deploying deep neural networks on hardware with limited computational resources,while Wu worked at Google Brain on foundation models and at Meta AI Research on computer vision.Together,Zhilin,Zhou,and Wu combined their expertise to cr

174、eate the concept at the core of Moonshot AIs business:lossless long-context.“Lossless long-context”refers to techniques that allow these models to process long sequences of text without losing important information or context.Traditional transformer models,which are the backbone of many language mod

175、els,INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|AUGUST 2024 PAGE 20 typically have a fixed-length context window,meaning they can only consider a certain number of tokens(words or characters)at a time,which limits their ability to understand and generate coherent long-form text.Moonshot AIs chatbot

176、 Kimi,which was launched in October 2023,had a 200,000 context-window in Chinese,a capacity that allegedly far exceeded its nearest competitors.Other companies were hot on Moonshots heels,with Chinese start-up Baichuan announcing a model that could process up to 350,000 Chinese characters at once sh

177、ortly after Kimi came out,but Moonshot continues to innovate.It claimed in March 2024 that its chatbot could handle up to 2 million Chinese characters in a single prompt,a tenfold increase in just six months.69 By comparison,OpenAIs GPT-4Turbo and Googles Gemini 1.5 Pro support a 128,000 token conte

178、xt window,while Anthropics Claude 3 family offers a 200,000 context window,with only some select use cases allowing up to 1 million tokens.CHINAS GOVERNMENT POLICIES SUPPORTING THE AI SECTOR Chinas Strategy for AI Innovation The annual sessions of Chinas top legislature and political advisory bodyth

179、e National Peoples Congress(NPC)and the Chinese Peoples Political Consultative Conference(CPPCC)took place in Beijing between March 4 and March 11,2024.Known as the“Two Sessions,”these meetings gathered thousands of representatives from diverse sectors of society to participate in the deliberation o

180、f state affairs and the national agenda for the coming year.70 One of the key events during the Two Sessions was the delivery of the Government Work Report,which highlights recent progress and sets new policy goals for the future.The Government Work Report makes clear that one of Chinas top prioriti

181、es for the year ahead is to pursue digital transformation by integrating AI across all sectors of the economy as part of the newly announced“AI+”initiative.71 The Government Work Report for 2024 outlines this priority as follows:We will actively develop the digital industry,transform traditional ind

182、ustries with digital technologies,and fully integrate digital technology into the real economy.We will step up R&D and application of big data and AI,launch an AI Plus initiative,and build digital industry clusters with international competitiveness.Exactly what the AI+initiative will entail is stil

183、l an open question,as the government has not yet provided these specifics.However,during the Two Sessions,political advisors submitted proposals and offered suggestions.These proposals play a crucial role in contributing to the countrys development,as previous sessions show that submitted content wi

184、ll be taken into careful consideration by relevant authorities.72 Thus,looking into representatives suggestions is helpful to understand future steps China might take in specific fields after the Two Sessions.There were four main categories for AI proposals:model development,data sharing,skills deve

185、lopment,and safety.First,some participants called for China to focus on building cutting-edge AI models to catch up with foreign competitors such as OpenAI and replicate its success with ChatGPT.Zhou Hongyi,CEO of 360 Group,presented a two-pronged proposal for AI model development.73 On the first IN

186、FORMATION TECHNOLOGY&INNOVATION FOUNDATION|AUGUST 2024 PAGE 21 track,Zhou encouraged collaborations between large tech companies and key research institutions,similar to the partnership between Microsoft and OpenAI,where those working on technological breakthroughs in AI pair up with a resource-rich

187、 company.On the second track,Zhou called for a long-term open-source AI project to establish multiple national-level,open-source AI models and an open innovation ecosystem.Echoing the desire to create national AI models,Liu Qingfeng,chairman of iFlytek,proposed during the Two Sessions that China sho

188、uld build a national AI development plan to build a domestic AI ecosystem of large AI models.74 Second,some representatives proposed improving data sharing.Liu also called for the open sharing of high-quality data,noting that China should open and share high-quality training data from various sector

189、s at the national level to develop its AI industry.Cao Fei,CFO of Weibo,presented a proposal to promote the development of an AI data trading market to make it easier for Chinese firms to buy and sell the high-quality data necessary to develop leading AI models while protecting the rights of data su

190、bjects.She also called for accelerating and supporting the implementation of new rules governing cross-border data flows to promote cooperation on international projects.Third,some participants proposed ideas for training AI talent.Lei Jun,CEO of Xiaomi,pointed out that the fast development of AI te

191、chnologies creates massive needs for AI talent in all fields,and strengthening AI training would be a pivotal factor in facilitating continuous industrial advancement.Thus,Lei suggested including AI classes in the K-12 syllabus and opening more AI-relevant majors in universities.He also encouraged c

192、ompanies to collaborate with universities to help future workers adjust to the fast-changing AI landscape.Zhou Yuan,CEO of Zhihu,also offered suggestions for training AI talent,calling for better aligning vocational training options for skilled professionals with companies needs and suggesting colle

193、ges for technical workers collaborate with companies to set their courses accordingly.Zhang Yunquan,a researcher at the Chinese Academy of Sciences,recognized the resource gap in AI education,noting that high-quality courses,experienced professors,and advanced technologies are gathered in top univer

194、sities and cities.He suggested building an educational platform for sharing resources and content about teaching AI that lead to more educational opportunities,including in rural areas.Like other representatives,he encouraged collaboration between companies and universities to develop AI knowledge f

195、or real-world scenarios.Finally,some representatives focused on AI safety and oversight.Zhou Hongyi,CEO of 360 Group,pointed out that companies and local governments should pay attention to possible AI risks.Qi Xiangdong,CEO of QAX,suggested more focus on innovation around“AI+Safety”and promoting“AI

196、+Safety”products in all industries.Zhang Yi,a partner at the law firm King&Wood Mallesons,advocated for moving forward with a new AI law as soon as possible that would classify AI algorithms by risk level and apply different regulatory measures for different levels.Zhou Yuan,CEO of Zhihu,proposed su

197、pervising and reviewing the data collection sources,processing methods,and compliance of large AI models,as well as conducting social impact and risk assessment to identify and address potential problems models in a timely manner.The AI+initiative is part of a history of prior efforts aimed at digit

198、al transformation of the Chinese economy.For example,the Government Work Report in 2015 proposed“Internet+,”and in 2019,proposed“Smart+.”The“AI+”initiated announced this year reflects the global interest in AI and Chinas focus on diligently putting AI technologies into practice.Chinas attention in I

199、NFORMATION TECHNOLOGY&INNOVATION FOUNDATION|AUGUST 2024 PAGE 22 the past was primarily on developing the technology,and while it still seeks to lead the industry,it has signaled that 2024 will be“The Year of AI Application.”Chinas Industrial AI Strategy President Xi first introduced the term“new qua

200、lity productive forces”in September 2023,and it has become a popular idea in China since then.The term describes Chinas new industrial strategy of driving economic growth through innovation in frontier technologies.It is the next evolution of the“Made in China 2025”strategy that focuses on reorienti

201、ng Chinas economic growth by upgrading its domestic manufacturing capabilities to produce higher quality goods more efficiently.The Made in China 2025 initiative has seen some success,with China now producing many of the technologies it set out to produce back in 2015,such as electric vehicles,photo

202、voltaics,and wind turbines.For example,more than 40 percent of the exhibitors at the 135th Canton Fair in April 2024previously a place to source labor-intensive manufactured productspresented intelligent and green products(e.g.,smart mobility,industrial automation,intelligent manufacturing),showcasi

203、ng the nations progress.75 Chinas Premier Li Qiang has stated that AI is an important engine for the new productive forces.76 In order to cultivate AI and other emerging technologies as new quality productive forces,the National Development and Reform Commission,a government agency responsible for r

204、estructuring Chinas economic systems and formulating policies for social development,is focusing on forming a new industrial ecosystem.First,to obtain a high-quality technical and skilled workforce for future development,the commission has called for collaboration between universities and companies,

205、establishing open talent policies to recruit international professionals,and creating regional innovation hubs for technology-focused innovation and entrepreneurship.Second,it has urged all companies to work with universities on technological breakthroughs and to apply advanced technologies in produ

206、ction processes.Finally,the commission has suggested planning out the development of“future industries”industries driven by cutting-edge technologies and currently in incubation.The goal is for China to gain market share in advanced industries to become a global innovation leader.One expert we spoke

207、 to noted that Chinas strategic approach prioritizes specialized,industrial AI applications over more broad-based,versatile uses like LLMs.This strategy focuses on embedding AI in sectors such as manufacturing and raw materials to enhance efficiency and profitability,rather than developing AI techno

208、logies for general,consumer-facing purposes,in a bid to more seriously drive economic efficiency and growth.Chinas Strategy for AI Resources The Chinese government is increasing access to computing and data resources to support its domestic innovation ecosystem.For one,it plans“to create a nationall

209、y unified computing system”to ensure sufficient access to computing resources by continuing its“Eastern Data,Western Computing”project.77 This initiative aims to optimize the distribution of data centers in China by connecting businesses in the east of the country that have high data processing dema

210、nds with data centers in the west,where land and energy is much cheaper.These investments in digital infrastructure will ensure Chinese businesses can access the computing resources they need for AI adoption.INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|AUGUST 2024 PAGE 23 The Chinese government has

211、also recognized the importance of data as a key element for using AI.One of its goals for 2024 is to“improve basic data systems and vigorously promote the development,openness,distribution,and utilization of data.”78 Indeed,China has already shown significant progress in this area.Last year,the gove

212、rnment established the National Data Administration,a new agency responsible for creating data infrastructure and promoting data utilization across the economy.In December 2023,the National Data Administration launched a three-year campaign to“promote the high-level application of data,ensure the qu

213、ality of data supply,improve the environment of data circulation.”79 In the coming year,China plans to pursue a“Data Multiply”(“Data”)initiative to“unleash the multiplier effect of data.”80 This initiative will include conducting a national data resources survey,reforming management of public data r

214、esources,and establishing new mechanisms for using enterprise data.China is also working on specific types of data that support sector-specific AI innovation;for instance,the state has several policies that promote the countrys biodata holdings.These include policies such as the 14th Five-Year Plan

215、for National Informatization,which requires hospitals to digitize health records;the National Health Commissions Healthy China 2030 initiative,which promotes the standardization of data between health services and institutions;and the Biosecurity Law,which emphasizes the states sovereignty over coll

216、ecting,managing,and sharing certain biodata.81 Chinese companies benefit significantly from these policies because they gain extensive access to biodata for developing AI technologies,while similar companies in other counties must partner with specific hospitals or research institutions to access ce

217、rtain types of biodata.WHAT SHOULD AMERICA DO?The United States is the global leader in developing AI,but it faces fierce and increasing competition from China.Focusing on stymying Chinas growth will not impede Chinas progress in the long run.Instead,the United States should be focusing on maintaini

218、ng,if not expanding,its lead.It is critical for Congress and the White House to craft and fund a comprehensive national AI strategy that addresses the twin goals of increased AI development and increased AI adoption.82 To implement the goal of increasing AI development,U.S.policymakers should do the

219、 following:1.Stimulate private investment in AI R&D.This is crucial to cementing U.S.leadership in AI because the private sector in the United States plays a uniquely important role in conducting AI R&D and keeping the country ahead,unlike in China,where the government plays a bigger direct role in

220、conducting R&D.Congress should double the R&D tax credit and restore first-year expensing.83 2.Revitalize the federal funding process for AI.The current federal funding mechanisms are too slow and rigid to keep up with the rapid pace of AI advancements.Congress should introduce flexible funding mode

221、ls that release money as projects reach specific goals(milestone-based awards)and create flexible funds that can be quickly directed to the most promising AI research(program-agnostic funds).84 This approach ensures that federal investments are agile and responsive to the rapidly evolving AI landsca

222、pe.3.Avoid policies that undermine U.S.AI leadership and bolster Chinese competitors.U.S.policymakers should evaluate the impact of policies that may unintentionally hurt U.S.INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|AUGUST 2024 PAGE 24 competitiveness in AI.For instance,overly stringent export c

223、ontrols may undermine U.S.chip competitiveness and inadvertently bolster Chinas chip industry.85 Similarly,unwarranted antitrust actions could unduly weaken large U.S.tech companies,diminishing their capacity to fund and leverage AI advancements across multiple business lines,and neither the governm

224、ent nor startup ecosystem would be able to adequately fill the resulting gap.4.Develop a national data strategy to dramatically expand the availability of data for training AI models.China recently announced the creation of a new regulatory body to improve its use of data as a strategic economic res

225、ource.As part of ongoing efforts to promote Chinas digital economy,the National Data Administration will work toward building smart cities,digitizing government services,and facilitating data-driven development.Policymakers in the United States should learn from this example and strengthen their com

226、mitment to data-driven innovation and increase commercial access to data.86 To implement the goal of increasing AI adoption,U.S.policymakers should focus on the following:5.Create a national AI roadmap for adoption.Widespread adoption of AI should be one of U.S.policymakers top priorities,especially

227、 in key sectors such as education,transportation,government,and healthcare where,because of existing regulations and government involvement,the public sector will need to work with the private sector to deploy the technology.The administration should develop a national AI roadmap that outlines secto

228、r-specific opportunities and barriers,as well as a detailed strategy for achieving widespread adoption in each of these sectors.6.Prioritize the rapid adoption of AI in the federal government.One of the most important things the federal government can do to spur AI is to be a robust adopter of AI te

229、chnologies,increasing its own productivity,reducing costs,and improving public services.The AI Center of Excellence within the Government Services Administration should identify the most important core processes in which AI can make a difference.Ideally,these would be ones where AI would either lead

230、 to significant improvements in customer service and quality or reductions in cost(to both the government and users of government services).7.Support digital transformation.AI adoption among U.S.firms is low partly because it requires foundational technologies such as Internet of Things-enabled equi

231、pment in manufacturing.Successful AI applications depend on cumulative technological advancements.The federal government should invest in a broad array of digital infrastructure including broadband,cybersecurity,smart cities,and more.8.Incentivize AI workforce training investment.Employers are curre

232、ntly underinvesting in AI workforce training,and without government intervention,the rate of employee training is unlikely to reach the optimal level for both societal and economic benefit.87 Policymakers should introduce a tax credit covering at least 50 percent of training expenditures to strongly

233、 encourage businesses to expand their investment in workforce development and grow the pool of AI talent.INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|AUGUST 2024 PAGE 25 Acknowledgments ITIF wishes to thank the Smith Richardson Foundation for supporting research on the question,“Can China Innovate?”

234、Other reports in this series cover biopharmaceuticals,chemicals,consumer electronics,nuclear power,semiconductors,robotics,and quantum technologies.(Search#ChinaInnovationSeries on itif.org.)The author would also like to thank Robert Atkinson and Daniel Castro for their assistance with this report.A

235、ny errors or omissions are the authors responsibility alone.About the Author Hodan Omaar is a senior policy manager at the Center for Data Innovation.Previously,she worked as a senior consultant on technology and risk management in London and as an economist at a blockchain start-up in Berlin.She ha

236、s an M.A.in Economics and Mathematics from the University of Edinburgh.About ITIF The Information Technology and Innovation Foundation(ITIF)is an independent 501(c)(3)nonprofit,nonpartisan research and educational institute that has been recognized repeatedly as the worlds leading think tank for sci

237、ence and technology policy.Its mission is to formulate,evaluate,and promote policy solutions that accelerate innovation and boost productivity to spur growth,opportunity,and progress.For more information,visit itif.org/about.INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|AUGUST 2024 PAGE 26 ENDNOTES 1

238、.Helen Toner,“What Are Generative AI,Large Language Models,and Foundation Models?”Center for Security and Emerging Technology(CSET),Georgetown University,May 12,2023,https:/cset.georgetown.edu/article/what-are-generative-ai-large-language-models-and-foundation-models/.2.Daniel Castro and Michael McL

239、aughlin,“Who Is Winning the AI Race:China,the EU,or the United States?2021 Update”(Center for Data Innovation,January 2021),https:/datainnovation.org/2021/01/who-is-winning-the-ai-race-china-the-eu-or-the-united-states-2021-update.3.Investment metrics for China,United States within all AI Fields,“Co

240、untry Activity Tracker:Artificial Intelligence,”Emerging Technology Observatory,Center for Security and Emerging Technology(CSET),Georgetown University,accessed August 2,2024,https:/cat.eto.tech/?expanded=Summary-metrics&countries=China+%28mainland%29%2CUnited+States&countryGroups=&dataset=Investmen

241、t.4.“Investments in AI and Data,”OECD.AI Policy Observatory,accessed accessed July 24,2024,https:/oecd.ai/en/data?selectedArea=investments-in-ai-and-data&selectedVisualization=vc-investments-in-ai-by-country.5.Ngor Luong,Zachary Arnold,and Ben Murphy,“Understanding Chinese Government Guidance Funds,

242、”Center for Security and Emerging Technology(CSET),Georgetown University,March 2021,https:/cset.georgetown.edu/publication/understanding-chinese-government-guidance-funds/.6.Martin Beraja et al.,“Government as Venture Capitalists in AI,”National Bureau of Economic Research,July 2024,https:/www.nber.

243、org/system/files/working_papers/w32701/w32701.pdf.7.Ibid.8.Emily Feng,“Chinas state-owned venture capital funds battle to make an impact,”Financial Times,December 24,2018,https:/ et al.,“Government as Venture Capitalists in AI.”10.“Beijing city to subsidise domestic AI chips,targets self-reliance by

244、 2027,”Reuters,April 26,2024,https:/ Huang and Jiyoung Sohn,“For Apples AI Push,China Is a Missing Piece,The Wall Street Journal,June 20,2024,https:/ Jiang,“Chinas 4 new AI Tigers Baichuan,Zhipu AI,Moonshot AI and MiniMax emerge as investor favourites,”South China Morning Post,April 19,2024,https:/

245、Peng,“Stanford University team apologises over claims they copied Chinese project for AI model,”South China Morning Post,June 4 2024,https:/ camp:backbone of Chinas AI start-up scene,”last modified May 1,2024,https:/table.media/en/china/sinolytics-radar/the-tsinghua-camp-the-backbone-of-chinas-ai-st

246、art-up-scene.16.Huang and Sohn,“For Apples AI Push,China Is a Missing Piece.”INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|AUGUST 2024 PAGE 27 17.Eleanor Olcott,“Four start-ups lead Chinas race to match OpenAIs ChatGPT,”Financial Times,May 3,2024,https:/ Pioneers AI in Math Tutoring,”GenAI Gazette,Ja

247、nuary 4,2024,https:/ Xu et al.,“SuperCLUE:A Comprehensive Chinese Large Language Model Benchmark,”July 23,2023,https:/arxiv.org/abs/2307.15020.20.Jeffrey Ding,“ChinAI#264:One Year of Ranking Chinese Large Language Models,”ChinAI Newsletter,https:/ start-ups lead Chinas race to match OpenAIs ChatGPT.

248、”23.SuperCLUE website filtered for April 2024 and June 2024,accessed July 20,2024,https:/.Note:The scores in the left-hand column of the comparison table have been rounded to align with the presentation in the right-hand column,where scores are shown without decimal places.While this approach may no

249、t display the full nuance of the original data,the detailed figures were considered during our analysis and informed the findings presented in this report.24.Alison Snyder,“Exclusive:Inside the AI research boom,”Axios,May 3,2024,https:/ Castro and Michael McLaughlin,“Who Is Winning the AI Race:China

250、,the EU,or the United States?2021 Update”(Center for Data Innovation,January 2021),https:/datainnovation.org/2021/01/who-is-winning-the-ai-race-china-the-eu-or-the-united-states-2021-update.26.World Intellectual Property Organization(WIPO)Patent Landscape Report-Generative Artificial Intelligence(Ge

251、neva:WIPO,2024),https:/www.wipo.int/web-publications/patent-landscape-report-generative-artificial-intelligence-genai/en/index.html.27.“The state of global AI research,”Emerging Technology Observatory,Center for Security and Emerging Technology(CSET),Georgetown University,May 2,2024,https:/eto.tech/

252、blog/state-of-global-ai-research.28.WIPO Patent Landscape Report-Generative Artificial Intelligence(Geneva:WIPO,2024).29.“The state of global AI research,”Emerging Technology Observatory.30.Ibid 31.WIPO Technology Trends 2019:Artificial Intelligence(Geneva:WIPO,2019),https:/www.wipo.int/edocs/pubdoc

253、s/en/wipo_pub_1055.pdf.32.WIPO Patent Landscape Report-Generative Artificial Intelligence.33.Nestor Maslej et al.,The AI Index 2024 Annual Report(Institute for Human-Centered AI,Stanford University,April 2024),https:/aiindex.stanford.edu/wp-content/uploads/2024/05/HAI_AI-Index-Report-2024.pdf.34.“Ch

254、apter 12:Intellectual Property,”Final Report(Washington,D.C.:National Security Commission on Artificial Intelligence,2021),https:/reports.nscai.gov/final-report/chapter-12.35.WIPO Technology Trends 2019:Artificial Intelligence.36.WIPO Patent Landscape Report-Generative Artificial Intelligence.37.“Ch

255、ina-based investors lead on global GenAI patents:UN report,”UN News,July 3,2024,https:/news.un.org/en/story/2024/07/1151761.38.WIPO Technology Trends 2019:Artificial Intelligence.INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|AUGUST 2024 PAGE 28 39.Ma Junhao,“Securing AI Patents in China:The Regulator

256、y Framework,”King&Wood Mallesons,June 5,2024,https:/ 41.WIPO Patent Landscape Report-Generative Artificial Intelligence.42.Robert D.Atkinson and Caleb Foote,“Is China Catching Up to the United States in Innovation?”(Information Technology and Innovation Foundation,April 2019),http:/www2.itif.org/201

257、9-china-catching-up-innovation.pdf.43.WIPO Technology Trends 2019:Artificial Intelligence.44.Daniel Castro,Michael McLaughlin,and Eline Chivot,“Who Is Winning the AI Race:China,the EU,or the United States?”(Center for Data Innovation,August 2019),https:/datainnovation.org/2019/08/who-is-winning-the-

258、ai-race-china-the-eu-or-the-united-states.45.Dewey Murdick and Patrick Thomas,“Patents and Artificial Intelligence:A Primer”(Georgetown CSET,September 2020),https:/cset.georgetown.edu/publication/patents-and-artificial-intelligence/.46.“The Global AI Talent Tracker 2.0,”MacroPolo,Paulson Institute,a

259、ccessed June 29,2024,https:/macropolo.org/digital-projects/the-global-ai-talent-tracker/.47.“Chinas Gain,”The Statesman,August 13,2024,https:/ Yuan,“China Is Suffering a Brain Drain.The U.S.Isnt Exploiting It,”The New York Times,October 3,2023,https:/ Global AI Talent Tracker 2.0,”MacroPolo.50.Ibid.

260、51.Ibid.52.“China reports production of 32.85 ZB of data in 2023,”last updated May 25,2024,https:/ Beraja,David Y Yang,and Noam Yuchtman,“Data-intensive Innovation and the State:Evidence from AI Firms in China,”The Review of Economic Studies,Volume 90,Issue 4(July 2023),https:/doi.org/10.1093/restud

261、/rdac056.54.Based on data from“Face Recognition Technology Evaluation(FRTE)1:1 Verification,”NIST website,accessed July 15,2024,https:/pages.nist.gov/frvt/html/frvt11.html.55.Helen Toner,Jenny Xiao,and Jeffrey Ding,“The Illusion of Chinas AI Prowess,”Foreign Affairs,June 2,2023,https:/archive.is/hfg

262、V8.56.Daniel Castro,“U.S.Policymakers Should Reject Kill Switches for AI”(Center for Data Innovation,March 21,2024),https:/datainnovation.org/2024/03/u-s-policymakers-should-reject-kill-switches-for-ai/.57.Xie Lilong,“NVIDIA chip is bad,I cant play anymore”(translated from Chinese),Lianban Financial

263、 News,April 7,2024,https:/www-mycaijing-com.translate.goog/article/detail/518428?source_id=40&_x_tr_sl=auto&_x_tr_tl=en&_x_tr_hl=en-US.58.Ibid.59.Eleanor Olcott,Qianer Liu,and Demetri Sevastopulo,“Chinese AI groups use cloud services to evade US chip export controls,”Financial Times,March 9,2023,htt

264、ps:/ Hunt,Saif M.Khan,and Dahlia Peterson,“Chinas Progress in Semiconductor Manufacturing Equipment:Accelerants and Policy Implications”(Center for Security and Emerging Technology INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|AUGUST 2024 PAGE 29 (CSET),Georgetown University,March 2021),https:/cset.g

265、eorgetown.edu/wp-content/uploads/CSET-Chinas-Progress-in-Semiconductor-Manufacturing-Equipment.pdf.61.Nigel Inkster,Emily S.Weinstein,and John Lee,“Ask the Experts:Is Chinas Semiconductor Strategy Working,”London School of Economics and Political Science,September 1,2022,https:/blogs.lse.ac.uk/cff/2

266、022/09/01/is-chinas-semiconductor-strategy-working/.62.Sharon Goldman,“Can anyone beat Nvidia in AI?Analysts say its the wrong question,”Fortune,July 2,2024,https:/ war:Huawei races to fill void left by Nvidia in China,with home-grown chips becoming popular components in AI boxes,”South China Mornin

267、g Post,May 30,2024,https:/ Zhang,“Saudi Fund Joins$400 Million Financing for China AI Firm Zhipu,”Bloomberg,May 31,2024,https:/ Se and Valeriia Kuka,“Zhipu AI:The Rise of an AI Tiger Reaching for AGI,”Turing Post,May 3,2024,https:/ AI Completes A New Round of Financing,”Pandaily,May 16,2024,https:/

268、Olcott,“Saudi fund invests in China effort to create rival to OpenAI,”Financial Times,May 31,2024,https:/ Se and Valeriia Kuka,“Moonshot AI:The Chinese Unicorn Revolutionizing Long-Context AI,”Turing Post,May 10,2024,https:/ Jiang,“Alibaba-backed Moonshot AI claims breakthrough in expanded Chinese-c

269、haracter prompt for Kimi chatbot,”South China Morning Post,March 20,2024,https:/ Sheng and Zhao Yusha,“Two sessions to to guide Chinas development with new thoughts,targets,”Global Times,March 11,2024,https:/ Castro,“Chinas Annual Parliamentary Meeting Shows National Commitment to Advancing AI”(Cent

270、er for Data Innovation),March 18,2024,https:/datainnovation.org/2024/03/chinas-annual-parliamentary-meeting-shows-national-commitment-to-advancing-ai/.72.“China opens two sessions with resolve to solidify economic recovery,”C,March 5,2024,http:/ Xu and Daniel Castro,“Whats Next After the Two Session

271、s for AI in China”(Center for Data Innovation),March 25,2024,https:/datainnovation.org/2024/03/whats-next-after-the-two-sessions-for-ai-in-china/.74.Ibid 75.Yanzi Xu,“Explaining Chinas Focus on New Quality Productive Forces”(Center for Data Innovation,May 28,2024),https:/datainnovation.org/2024/05/e

272、xplaining-chinas-focus-on-new-quality-productive-forces/.76.“AI is an important engine for new productive forces,Chinas Li says,”Reuters,March 13,2024,https:/ Annual Parliamentary Meeting Shows National Commitment to Advancing AI.”78.Ibid.INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|AUGUST 2024 PAGE

273、 30 79.Xinhua,“China starts census of data resources,”China Daily,February 20,2024,https:/ 81.Caroline Schuerger et al.,“China and Medical AI:Implications of Big Biodata for the Bioeconomy”(CSET,May 2024),https:/cset.georgetown.edu/publication/china-and-medical-ai/.82.Joshua New,“Why the United Stat

274、es Needs a National Artificial Intelligence Strategy and What It Should Look Like”(Center for Data Innovation,December 2018),https:/www2.datainnovation.org/2018-national-ai-strategy.pdf.83.Joe Kennedy and Robert Atkinson,“Why Expanding the R&D Tax Credit Is Key to Successful Corporate Tax Reform”(IT

275、IF,July 2017),https:/www2.itif.org/2017-rd-tax-credit.pdf.84.Nyah Stewart,“Funding for the Future:The Case for Federal R&D Spending,”(Special Competitive Studies Project,January 2024),https:/www.scsp.ai/wp-content/uploads/2024/01/RD-White-Paper-2.pdf.85.Robert Atkinson,“Stronger Semiconductor Export

276、 Controls on China Will Likely Harm Allied Semiconductor Competitiveness,”(ITIF,October 2023),https:/itif.org/publications/2023/10/12/stronger-semiconductor-export-controls-on-china-will-likely-harm-allied-semiconductor-competitiveness.86.Gillian Diebold,“The United States Can Learn from Chinas New

277、National Data Administration”(Center for Data Innovation,April 2023),https:/datainnovation.org/2023/04/the-united-states-can-learn-from-chinas-new-national-data-administration.87.Hodan Omaar,“U.S.AI Policy Report Card,”(Center for Data Innovation,July 2022),https:/datainnovation.org/2022/07/ai-policy-report-card.