Exploring expanding expertise: artificial intelligence as an existential threat and the role of prestigious commentators, 2014–2018

The post-2010 technological resurgence in artificial intelligence (AI) was followed by a series of public warnings by prestigious intellectuals, scientists, and entrepreneurs presenting AI as an existential threat. While their expertise in other fields is undeniable, their knowledge of AI remains questionable. With expert studies as a theoretical point of departure, the empirical data collected narrate the events which systematically shaped this view between 2014 and 2018. This chronology captures the interplay between such statements by ‘expanding experts’ in the press and traces their impact on governmental policy documents in the EU, UK, and US, while highlighting the overall absence of experts in these debates. The conclusions recommend the inclusion of AI experts in relevant policymaking schemes and the further exploration of the networks between such prestigious individuals, the purposes and origins of emerging future/risk studies institutions, and their impact on AI R&D in the long run via empirical means.     

The impact of artificial intelligence on economic growth and welfare

Focusing on the self-accumulation ability and the nonrival characteristic of artificial intelligence (AI), this paper develops a three-sector endogenous growth model and investigates the impact of the development of AI along the transitional dynamics path and the balanced growth path. The development of AI can increase economic growth along the transitional dynamics path, and can increase household short-run utility if an increase in the accumulation of AI is due to the rising productivity in the goods or AI sector, but can be detrimental to household short-run utility if an increase in the accumulation of AI is because firms use more AI to replace human labor. In addition, the development of AI is not necessarily beneficial to household welfare in the long run. The main results are unaffected when considering the case where AI can improve the accumulation of human capital, the traditional research and development model, and different kinds of physical capital.     

人工智能技术对碳排放的影响

随着新一轮科技革命和产业变革的深入推进，人工智能技术在应对气候变化方面扮演重要角色，并赋能“碳达峰、碳中和”目标的实现。利用2006—2019年中国内地省级面板数据，考察人工智能技术对碳排放的影响。研究发现：人工智能技术与碳排放之间呈倒U型关系，即当人工智能技术达到一定阈值后，其碳减排效应逐渐凸显；人工智能技术主要通过提高能源利用效率实现碳减排；在东部和西部地区，人工智能技术与碳排放之间存在显著倒U型关系，而在中部地区，人工智能技术对碳排放发挥持续促进作用。因此，在推进“双碳”目标过程中，需要以绿色低碳发展为目标开发人工智能技术，正确引导人工智能在碳减排领域的研发应用，针对不同区域实际制定差异化发展政策。     

全球人工智能发展现状、挑战及对中国的建议

当前,人工智能(AI)已经成为各国必争的科技创新高地。政府、学术机构、企业都高度重视。基础研究方面,AI论文、期刊、学术会议数量都逐年增加;技术创新方面,算法进一步成熟,走进产业化,AI投资和专利产出大幅增长。围绕AI的国际竞争愈发激烈,许多国家都在加强布局AI研发和应用布局。本文结合近年来技术创新态势和主要智库、媒体观点,对当前人工智能发展形势、应用前景、主要挑战及国际竞争等进行了分析,并结合我国国情提出了促进人工智能创新发展的建议。     

全球人工智能人才发展态势浅析

培养和吸引人工智能人才对于发展人工智能是至关重要的。当前,我国的人工智能人才存在若干问题,如缺少顶尖人才、人才结构不均衡以及研究人员外流情况严重等。为弄清我国人工智能人才发展的现状及不足,为人工智能人才的发展提供对策,本文以人工智能人才为研究对象,梳理全球人工智能人才的总体情况,在分析我国人工智能人才存在的不足和问题的基础上,借鉴国外发展人工智能的成功经验,对我国人工智能人才发展提出了建议。     

Artificial intelligence: neither Utopian nor apocalyptic impacts soon

ABSTRACT

After a number of AI-winters, AI is back with a boom. There are concerns that it will disrupt society. The immediate concern is whether labor can win a ‘race against the robots’ and the longer-term concern is whether an artificial general intelligence (super-intelligence) can be controlled. This paper describes the nature and context of these concerns, reviews the current state of the empirical and theoretical literature in economics on the impact of AI on jobs and inequality, and discusses the challenge of AI arms races. It is concluded that despite the media hype neither massive jobs losses nor a ‘Singularity’ is imminent. In part, this is because current AI, based on deep learning, is expensive and difficult for most businesses to adopt, not only displaces but in fact also create jobs, and may not be the route to a super-intelligence. Thus AI is unlikely to have either Utopian or apocalyptic impacts soon. Considering Amara's Law, one should however be wary not to underestimate the long-run impacts of AI.     

The effect of artificial intelligence on China’s labor market

ABSTRACT

Automation and artificial intelligence technology have played a pivotal role in today’s economic and social development. They represent a labor-substituted technological progress, featuring more and more jobs to be replaced by AI. Based on the adoption rate calculated in our paper and theoretical substitution probability estimated by existing studies, our research estimates the actual substitution probability by AI for various occupations in China. By using this actual substitution probability on occupation level, we also explore the substitution effects on labor force with different characteristics and find that AI has larger substitution impacts on labors of female, old age, low education and low income. We also predict the number of employed people that would be replaced by AI in each industry, and the results show that China will have 278 million labors (201 ~ 333 million under different adoption rates) replaced by AI by 2049, representing 35.8% of the current employment in China.

Abbreviation: Artificial intelligence (AI), internet of things (IoT), Council of Economic Advisers (CEA)     

The UK technology foresight programme: An assessment of expert estimates

Since the early 1990s, ‘Technology Foresight’ exercises with special emphasis on the use of Delphi surveys have played an important role in science and technology (S + T) policy across Europe in an effort to focus resource allocation. Yet, none of the estimates made in the European Delphi surveys have been formally assessed in retrospect, while this process has been incorporated into the Japanese surveys since 1996. Taking the UK Technology Foresight Programme, this research sets out to assess the estimates of three of the fifteen panel Delphi surveys. Whilst on average 2/3 of Delphi statements were predicted to be realised by 2004, it will be shown that only a fraction of these statements had been realised by 2006. Based on the evidence collected from the published panel reports, the ‘Hindsight on Foresight’ survey conducted by OST in 1995 and interviews with panel members, it will be argued that the overwhelming majority of estimates were overly optimistic. While optimism and strategic gaming of experts is the most convincing explanation for these results, process factors were also explored, including the quality of expert panels used, the Delphi statements and the respondents of the Delphi questionnaire. It is argued that at least the issue of short-range optimism and strategic gaming of experts should be addressed in future Delphi exercises, as decision makers relying on expert advice cannot deal with this issue alone.     

人工智能技术发展对“发明人”角色的挑战与应对

目前,人工智能已逐步从创造工具向自主创造转化,对“发明人”角色提出了挑战。人工智能能否成为“发明人”关系到创造物的权利归属、专利审查程序、专利许可等制度修改。利用文献研究、历史研究、逻辑推理等方法从不同维度分析：人工智能将具有“发明”的意识能力,未来成为法律意义的“人”也不存在理论障碍。但是,人工智能成为“发明人”,不仅需要全社会形成共识,且要有充分的理由修改专利法。从专利制度看,人工智能成为“发明人”不符合专利法制定的目标,具体实施也存在障碍,目前亟待解决人工智能创造物的权利归属问题。应对挑战,应坚持专利法中“发明人”的基本规定,但可以要求申请人履行说明人工智能承担的义务。     

专利转化特征精准识别与预测——以人工智能芯片为例

探讨前沿科技领域专利转化特征并对其进行精准识别与预测，对于我国破解“卡脖子”技术难题及实现科技自立自强具有重要意义。选取人工智能芯片专利领域，采用机器学习算法测度最优转化预测方案，分析全球范围内主要国家或地区专利成功转化影响因素，从企业/高校、国内/国际等不同层面总结专利成功转化的主要特征。结果发现：随机森林算法预测效果较好，人工智能芯片领域专利转化概率服从对数曲线分布，影响高校/企业、国内/国外专利转化特征的因素有所不同。最后，提出高校/科研机构应注重高价值专利维持和团队合作、企业应提升专利技术质量和撰写质量等政策建议。