If AI is the future, Hong Kong is still stuck in the dark ages while mainland China has powered ahead
Paul Yip says the lack of funding and manpower, bureaucratic barriers and the government’s failure to effectively collect and share data inhibit the development of artificial intelligence in Hong Kong
Having attended a symposium on artificial intelligence on the mainland, I am convinced Hong Kong lags behind in the use of AI despite having three universities in the top 100 of the Times Higher Education rankings. The application of AI on the mainland has been phenomenal, from taxi-hailing apps to designing complex business blockchains. Big data has had a far-reaching impact in the business, health, leisure and security sectors, although concerns about its use persist.
Developments on the mainland make me feel like someone from an underdeveloped city. On reflection, there are a number of factors that have made Hong Kong less competitive.
First, the mainland has much more funding and manpower resources available for research on AI. The government, university strategic development funds and the private sector are all willing to invest in start-ups as they don’t want to miss out on the latest developments. At the symposium, Peking University said it has set up more than 20 research centres on the use of AI in different disciplines.
The Hong Kong government has been trying hard to catch up but its scale of support still has much room for improvement. The University Grants Committee, which oversees all the government-funded institutions, recently spent much time ensuring that UGC funds are not used on non-UGC activities at the universities. Much energy has been expended in working out guidelines and procedures, creating additional workloads for all concerned.
However, many non-UGC-funded activities, which could include AI activities, at our universities contribute to research and teaching. How should these benefits be accounted for? The UGC’s rule can be a barrier to innovative research, including AI research.
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Moreover, on the mainland, enthusiasm for staying ahead of the AI curve has attracted many local and overseas research students, including from South Asian countries. Hong Kong has not been very receptive to foreign talent to make up for the shortfall of our human capital in these areas.
Our unwelcoming attitude has put Hong Kong at a disadvantage. Moreover, census figures show local talent has been moving to other countries, diminishing our pool of human capital.
Second, AI needs access to lots of data and some of the most useful data is held by the government. The mainland government has been proactive in this regard. The recent development of a national health care data network is one example. It will ensure data is linked and used efficiently, cutting costs and improving heath care provision, especially in rural areas which are usually not well served.
In Hong Kong, researchers still have to fill in a paper application requesting data and wait for weeks, if not months, for it. Sometimes, the data a researcher receives is not exactly what is needed.
Indeed, privacy appears to be less of a concern on the mainland, and should be better protected. However, Hong Kong would do well to review existing policies to facilitate data acquisition without compromising privacy.
On the other hand, sometimes, the government fails to collect the necessary information to improve implementation of its policies. For example, my team has tried in vain to get hold of data on how older adults are using the HK$2 transport subsidy. While the government, which spends HK$1.2 billion annually on the scheme, has not collected the relevant data, private transport operators are using this data for commercial purposes.
Identifying and solving social problems effectively and efficiently requires large amounts of data. Moreover, the government has committed to improve its services using big data.
To sustain growth, Hong Kong will have to up its game with innovation and passion. AI is the future, and we must grapple with it. The world will be a better place if we can use data appropriately.
Paul Yip is chair professor in the Department of Social work and Social Administration at the University of Hong Kong