How long until human-level AI? Results from an expert assessment

The development of human-level AI has been a core goal of the AI field since its inception, though at present it occupies only a fraction of the field's efforts. To help understand the viability of this goal, this article presents an assessment of expert opinions regarding human-level AI research conducted at AGI-09, a conference for this AI specialty. We found that various experts strongly disagree with each other on certain matters, such as timing and ordering of key milestones. However, we did find that most experts expect human-level AI to be reached within upcoming decades, and all experts give at least some chance that some milestones will be reached within this time. Furthermore, a majority of experts surveyed favor an integrative approach to human-level AI rather than an approach centered on a particular technique. Finally, experts are skeptical about the impact of massive research funding, especially if it is concentrated in relatively few approaches. These results suggest that the possibility of achieving human-level AI in the near term should be given serious consideration.  

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.  

风景园林研究中的人工智能方法综述

人工智能的出现为风景园林研究智能化、数字化提供 了机遇与挑战。首先介绍了人工智能的相关概念、发展，以及 在风景园林研究中的作用，并在前人分类的基础上，根据人工 智能的属性和应用，将风景园林研究中常用的人工智能方法分 为人工生命类、智能随机优化类和机器学习类；然后针对每类 中的典型算法系统论述了其原理、发展、特征，以及在风景园 林分析、设计和评估等领域的具体应用与存在的问题；进而指 出建立混合智能系统的必要性与未来发展前景；最后探讨了风 景园林研究中人工智能方法的局限性，指出风景园林智能化的 发展趋势。  

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)  

Big Data Algorithmic Trading Systems Based on Investors’ Mood

Traditional automated trading systems use rules and filters based on Chartism to send orders to the market, aiming to beat the market and obtain positive returns in bullish or bearish contexts. However, these systems do not consider the investors’ mood that many studies have demonstrated its effects over the evolution of financial markets. The authors describe 2 "big data" algorithmic trading systems over Ibex 35 future. These systems send orders to the market to open long or short positions, based on an artificial intelligence model that uses investors’ mood. To measure the investors' mood, the authors use semantic analysis algorithms that qualify as good, bad, or neutral any communication related to Ibex 35 made on social media (Twitter) or news media. After 1.5 years of research, conclusions are: First, the authors observe positive returns, demonstrating that investors’ mood has predictive capacity on the evolution of the Ibex 35. Second, these systems have beaten the Ibex 35 index, showing the imperfect efficiency of the financial markets. Third, big data algorithmic trading systems numbers are better in Sharpe ratio, success rate, and profit factor than traditional trading systems on the Ibex 35, listed in the Trading Motion platform.  

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。  

科技项目资助对论文产出绩效的作用效率测度研究

以人工智能领域论文和学者为研究对象，探讨科技项目资助对论文产出绩效的作用效率。运用负二项回归分析不同国家、领域资助种类、强度对论文引用和载文期刊水平的影响，结果发现，各项资助指标均可提升论文质量，但在不同地域和研究方向上的作用效果有所差异。运用倾向得分分层和线性回归，从发文、被引量和h指数角度测度资助对学者产出绩效提升的有效性，结果发现，资助能够显著提升学者产出力和影响力。通过Tobit回归测度资助项目数量和金额对不同年龄群体学者发文、被引量与变化率等指标的影响，结果发现，资助项目数量增加可整体提升学者产出绩效，资助金额增加更能扩大产出力和影响力的动态提升幅度。据此，提出资助改进策略和评审机制优化等相关建议。  

基于专利挖掘的感知人工智能技术融合趋势分析

对于新兴产业而言，把握产业技术融合现状对于引导产业发展、加强技术创新具有非常重要的意义。人工智能技术与其它技术融合是促进产业创新发展的主要内在动力，因此对技术融合趋势进行分析有利于提前、准确把握该产业技术发展方向。聚焦感知人工智能领域，率先采用专利分析方法对10 685项专利数据进行挖掘，结合ISI技术分类体系，基于技术共现次数及关联度识别感知人工智能融合核心技术，并从核心技术融合的分散度及专利增长量等方面考察感知人工智能技术融合情况。研究发现，目前感知人工智能技术融合正处在成长期向成熟期过渡的阶段，并很快进入技术融合衰退期，建议政府及投资者积极布局现有感知人工智能主导技术，以期在即将到来的技术融合成熟期掌握主动权。同时，应大力推广“感知人工智能+”模式，提升该领域技术融合动力，延缓融合衰退期的到来。  

新一轮产业革命背景下突破性技术创新与中国产业转型升级

突破性技术创新是推动产业转型升级的重要驱动力，具有显著的技术颠覆性、非连续性和产业关联带动作用等特征。新一轮产业革命背景下，突破性技术创新不断涌现，为后发经济体实现产业赶超提供了机会窗口。通过对突破性技术创新内涵及特征进行理论溯源，提炼出突破性技术创新对产业转型升级的作用机理和路径；以人工智能技术应用为例，分析了突破性技术创新对我国三次产业升级的影响；提出了加快突破性技术创新发展、促进我国产业转型升级的对策思路，即强化基础性创新研究，加大对行业共性突破性技术创新的支持，发挥新一代信息技术、产业融合以及金融的作用。