There is a “timing optimism” that artificial general intelligence will be achieved soon, but some literature has suggested that people have mixed feelings about its overall impact. This study expanded their findings by investigating how Taiwanese university students perceived the overall impact of high-level-machine-intelligence (HLMI) in three areas: a set of 12 human professions, autonomous vehicles, and smart homes. Respondents showed a relatively more positive attitude, with a median answer of “on balance good”, toward HLMI’s development corresponding to those occupations having a higher probability of automation and computerization, and a less positive attitude, with a median of “more or less neutral”, toward professions involving human judgment and social intelligence, and especially creativity, which had a median of “on balance bad”. On the other hand, they presented a highly positive attitude toward the AI application of the smart home, while they demonstrated relatively more reservation toward autonomous vehicles. Gender, area of study, and a computer science background were found as predictors in many cases, whereas traffic benefits, and safety and regulation concerns, among others, were found as the most significant predictors for the overall impact of autonomous vehicles, with comfort and support benefits being the most significant predictor for smart homes. Recommendations for educators, policy makers, and future research were provided.

This study explores the overall picture of how people perceive the importance level and urgency level regarding issues associated with automated vehicles, by sorting out ten issues, developing a questionnaire with 66 measurement items, and investigating how Artificial Intelligence (AI) experts and Computer Science (CS)/Electrical Engineering (EE) majors assessed these issues. The findings suggest that AI experts in Taiwan believed that the top five issues for preparing a society for autonomous vehicles (AVs) should include (1) data privacy and cybersecurity, (2) regulation considerations, (3) infrastructure, (4) governance, and (5) public acceptance. On the other hand, for their student counterparts, the results (1) demonstrate a somewhat different order from the third to the fifth place, (2) show an attention-focused profile on the issue of cybersecurity and data privacy, and (3) indicate that gender and a few wider-impact variables (technology innovation, infrastructure) are significant predictors for the assessment on the importance level of AVs, while some wider-impact variables (technology innovation, governance, economic benefits, infrastructure), which are positively associated, as well as concerns variables (cybersecurity and data privacy, regulations), which are negatively associated, could be predictors for the urgency level of AVs. Suggestions for future research and policymakers are provided.