Diffusion of AI(Artificial Intelligence) and Change of Skill

Diffusion of AI(Artificial Intelligence) and Change of Skill. Researcher in Charge: Gyuhee Hwang / Senior Research Fellow Summary Analysis of data from the United States Patent and Trademark Office (USPTO) and the Occupational Information Network (O*NET) shows that higher-order cognition and management skills have become increasingly important with the spread of intelligent information technologies in recent years. In this context, an expert survey conducted on human resource cultivation indicates that vocational education and training (VET) should focus on developing the cognitive ability and that communication skills should be enhanced to promote cooperation and facilitate collaborative rather than individual action. In addition, considering that the diffusion of intelligent information technologies will continue to eliminate existing jobs while creating new business opportunities, start-up education needs to be strengthened to cope with these changes. Keywords: intelligent information technology, changing skills, human resource cultivation, vocational education and training (VET). I.. Research Background and Purpose. ■ Research Background • Since IBM’s Watson supercomputer beat its human competition hands down on the U.S. quiz show Jeopardy!, it has been put to work in an increasingly wide range of fields including finance and healthcare. Meanwhile, Google is now testing self-driving cars on public roads. Against this backdrop, intelligent information technologies are drawing international attention. • The relentless rise of artificial intelligence (AI) and the Fourth Industrial Revolution were raised as key issues at the 2016 World Economic Forum (WEF) in Davos. With regard to the employment landscape, possible exacerbation of job losses and inequality were the focus of discussion, with enterprise-initiated retraining and rethinking of education systems being suggested as countermeasures to cope with these looming hardships. ■ Research Purpose • This paper aims to analyze how skill demand shifts are being driven by the growth of intelligent information technologies while also discussing the significance and the urgent need for various countermeasures.. II. Research Overview I.. Diffusion of Intelligent Information Technologies.

■ Diffusion of Intelligent Information Technologies • Since the 1940s, when the emergence of computers sparked discussions on the possibility of “mechanical brains,” two boom-and-bust cycles have taken place and the third artificial intelligence (AI) boom is now underway. - IBM’s Watson project and Google’s AlphaGo are the main driving forces behind this third AI boom sparked by deep learning technology which achieved a breakthrough in image recognition in 2012. - Thanks to “deep learning,” or the ability to learn features and use this knowledge to perform tasks, it is expected that technology will be able to replace white-collar jobs once traditionally perceived as the domain of humans and safe from machines. • Among the patents issued by the United States Patent and Trademark Office (USPTO) during the period 1991-2015, those filed under the 706 Class (data processing: artificial intelligence) of the United States Patent Classification (USPC) system showed an exponential increase globally from 194 in 1991 to 1,139 in 2012, and then to 4,929 in 2015. [Figure] USPTO: Trends in Intelligent Information Technology-Related Patents. II. Skill Demand Shifts Caused by the Spread of Intelligent Information Technology ■ Analysis Materials • Data from the U.S. Occupational Information Network (O*NET) was analyzed to identify changes in job functions brought about by technological change. ■ Analysis Method • Step 1: Analyze skill shifts within the same job category - Regarding technology-driven changes in job functions since the 2000s, skill indices for the years 2016 and 2002 were compared focusing on 628 jobs listed on O*NET. • Step 2: Analyze changes in significance and level of skill according to the skill level of each job category - After being classified into five groups according to the skill level required by each job category, changes in skill within each group were examined. The results show changing.

trends in the skills demanded depending on education and career level. • Step 3: Derive principal skills demanded by every job category - After normalizing index scores (mean = 0, standard deviation = 1), principal component analysis (PCA) was applied to reduce the dimensionality of 35 skills from the total job categories and derive a list of principal skills. ■ Analysis Result • Skill shifts within the same job categories: Increased significance of higher-order cognitive ability and interpersonal communication skills - After the 35 skill indices were grouped into three categories: cognitive, social, and physical skills (Howell & Wolff, 1991), the changes in skill level demanded by the 682 job categories described in the indices for both 2002 and 2016 were examined. The findings show an increase in the significance of skills based on higher-order cognitive ability including interpersonal communication, persuasion, negotiation, and management. • Changes in significance and level of skill within each job category grouped by skill level - Changes in skills classified into five stages according to the skill level of individual job categories are examined. As a whole, the significance of competencies based on higherorder cognitive skills increased. In particular, among the low-skill job types (job zone 1), the significance of skills that require higher-order cognitive abilities such as complex problem-solving, persuasion, and writing increased sharply. • Comparison of dimensionality-reduced skills by principal component analysis (PCA) - Compared to 2002, the share of existing higher-order cognition and management skills increased while skills related to machine equipment, analysis, and design decreased in 2016. <Table> Principal Skills and Rates of Explanation (%) in 2002 and 2016 2002. 2016. 34%. Higher-order cognition and resource management. Higher-order cognition. 21%. Mathematical science problem-solving. Machine maintenance. 20%. Machine equipment and maintenance. Mathematical science information. 15%. 14%. Social services. Social services. 13%. 7% 4%. Machine analysis and design Machine operation and installation. equipment. 33% and. Material and financial resource management Machine operation and installation. 25%. 10% 3%. III. Cultivating Human Resources and the Spread of Intelligent Information Technologies ■ Method of Survey and Analysis • Survey items: Itemized cross comparison a) Regarding AI at present - Significance - Urgency. b) Human resource policy in response to the spread of AI - Significance. a.1) Mitigating conflict between big data collection and information protection a.2) Promoting discovery of business opportunities using AI a.3) Expanding government R&D investment in AI development b.1) Expanding investment in cultivating core talent b.2) Enhancing basic programming education in secondary.

- Urgency. c) Job training policy in response to the spread of AI - Significance - Urgency. education b.3) Promoting improvement in cognitive and social skills in education and training c.1) Start-up education to promote AI-based start-ups c.2) Education and training to enhance cognitive and social skills in low-skill job categories c.3) Field-based training for reskilling. • -. Total respondents: 38 General researchers (primarily economists): 10 Researchers in fields closely related to AI (experts on intelligent information technology): 10 - Vocational education and training experts (employees of junior colleges and training institutes): 6 - Government policy professionals (public servants and associations): 12 ■ Analysis Result • The relative significance and urgency of improving government policies and systems related to AI at present - The significance and urgency of “mitigating the conflict between big data collection and information protection” were the highest. - The significance and urgency of “expanding government R&D investment in AI development” are slightly higher than that of “promoting discovery of business opportunities using AI.” • The relative significance and urgency of workforce policy in response to the advancement and diffusion of AI within the next five to ten years - The significance and urgency of “expanding investment in cultivating core talent” are much higher than that of “promoting improvement in cognitive and social skills in education and training” or “enhancing basic programming education in secondary education.” - The significance and urgency of “promoting improvement in social skills” and that of “enhancing basic programming education in secondary education” are similar. • The relative significance and urgency of education and training policy in relation to the advancement and diffusion of AI technology within the next five to ten years - The significance and urgency of “start-up education to promote AI-based start-ups” are the highest. - The significance and urgency of “field-based training for reskilling”are slightly higher than that of “education and training to enhance cognitive and social skills in low-skill job categories.”. III. Policy Suggestions •. •. With a view to consistently responding to and devising preemptive strategies against accelerating technological change including AI, vocational education and training should focus on cultivating cognitive ability. At the same time, communication skills should also be reinforced to promote cooperation and facilitate collective rather than individual action. Adaptive actions should be taken as the spread of intelligent information technologies is expected to continuously replace not only technical skills but also core routine professional tasks. Education to promote AI-based start-ups should be enhanced in order to prepare for a.

-. -. situation where the diffusion of intelligent information technologies is continuously eliminating existing jobs and creating new business opportunities. Along with the advancement of new technologies, start-up activities are being pursued aggressively based on the sharing of ideas and technology as seen in maker-centered space-providing services and on/offline communities. As start-up by makers themselves is valued, it is suggested that education for makers or start-up education for makers be included in the curriculum..