in partnership with ADP
Survey respondents were asked whether they felt robotics and artificial intelligence (AI) would have a constructive or a destructive effect on several market segments. Their responses were ranked from 1 (AI adoption would result in the destruction of jobs and processes) to 5 (AI would bring a significant increase in the industry’s value and efficiency). Across industry sectors, responses were positive, averaging 3.8 on that scale. They were higher in more technology-dependent industries, such as information technology and communications (ITC), logistics, and manufacturing.
Tellingly, respondents were even more inclined to see positive benefits from AI for their own industry: most industry participants ranked the impact of AI on their own sector higher than the average. Respondents from one sector, however, were actually more pessimistic. Those from the financial-service industry, on average, saw AI as a positive influence, yet many also felt that automated processes and machine-based transactions would destroy value in their industry.
Banking industry fears of artificial intelligence’s destructive potential were also revealed when respondents were asked to rate the impact that these technologies would have on Asia’s industrial, policy, and competitive landscape. Overall, survey respondents felt that advances in artificial intelligence would significantly boost Asia’s competitiveness as a manufacturing and service center, benefit government policy makers in their attempts to boost innovation, and increase overall industry growth prospects.
The only industry cohort that didn’t feel as positively as the respondent average was the financial-services industry. On average, industry respondents felt most encouraged about the positive impact AI and automation would have on their own competitiveness, ranking it 4 out of 5. Once again, the optimism from respondents in retailing, ITC, and manufacturing industries was higher than the average—while sentiment from the financial service industry was noticeably lower.
The relative caution and pessimism among financial sector respondents could result from past experiences. The 2007 global financial crisis may not have started in Asia or hit the region’s banks and financial institutions as hard as it did elsewhere, but the fallout, and the lingering regulatory and compliance burden it created, plague Asian banks to this day. Moreover, mini-crises fomented by new technologies constantly punctuate the industry landscape; “flash crashes,” for example, are fast, sharp stock market plunges brought about by the combination of human error and algorithmic trading. Financial-industry respondents may have these events in the back of their minds when considering what artificial intelligence means for their future.
Modest reticence from finance respondents aside, AI has the potential to affect a number of Asia’s development challenges, from food security to public safety, transportation networks, and health care. Lin Yuanqing, the director of the Institute of Deep Learning at Baidu Research (known as Baidu IDL), believes that all this is coming to a head at once: “It is impossible to point to an industry that will be ‘first’ to adopt AI. Public transportation, logistics—nearly every critical infrastructure platform can benefit from it, and they are all interconnected. AI will come to all industries at once, and it will come sooner than we think.”
AI industry executives feel that two linked factors will allow autonomous and intelligent applications to proliferate somewhat simultaneously. One is the growth of big data in Asia, fed by many hundreds of millions of people in densely populated cities who are connected to the mobile Internet. “Data is the most important resource for successful machine-learning development,” says Zhang Yue, a professor and machine-language researcher at the Singapore University of Technology and Design. “And mobile data, large in volume and rich in context, provides AI developers with a large amount of useful data.” The harnessing of big data through analytics gives rise to the second factor driving AI development: companies are increasingly willing not only to use analytics to increase their own business performance, but to borrow and share automated process insights across sectors.
Baidu IDL’s Lin says a successful ecosystem for AI development requires four input factors: “Big data, the continuous production of algorithms, massive computation power, and ‘big applications.’” Lin describes this last factor as the most crucial: applications that attract a large number of users quickly “drive usage, interaction, and data creation to create a positive development loop” for deep neural networks, which are increasing in scale and capability as processing power becomes ever cheaper and more plentiful.