According to a recent report, artificial intelligence (AI) and big data have enormous potential to contribute to ‘Thailand 4.0’. The core aspect of which emphasises developing new S-curve industries, which includes investing in digital, robotics, and the regional medical hub.
Coupled with better analytics capabilities more real-world information is available for policy- and decision-makers than ever before.
Today, the digital economy – with extensive use of AI and Big Data – is growing at a pace that far exceeds the global economy.
The world has witnessed some phenomena in this area.
First, current AI systems have the capacity to process enormous amounts of data, compute at incredible speeds and manage and analyse extremely complex data.
These systems have been able to generate and capture data that are in larger volumes and wider variety than ever before – terabytes (1 TB is equal to 1,000,000 MB) of data are being generated every 60 seconds globally.
Second, the costs of IT infrastructure have been on the decline. Thirty years ago, it would have cost more than US$560 to store 1 GB of data; now it costs less than 1 US cent.
Third, data processing has been much faster and with better analytics capability than ever before.
Using AI in the public arena
It is common knowledge that AI is being extensively used by the private sector for commercial and profit-making purposes like forecasting demand, predicting churns, suggesting advertisements on social media platforms, recommending products to potential buyers.
However, untapped potential lies in the ability of AI and big data to enhance human development and to address many development and social challenges.
Some of which include analysing vast quantities of healthcare data, leading to scientific breakthroughs; helping to create AI-powered climate modelling can help predict climate-related disasters, and even driving more balanced hiring practices and spotlight gender inequality.
While AI and big data have been known to help drive exponential innovation, their use is currently very limited. As terabytes of data are being generated every minute, only 1% of this data is being used or analysed. Public sector use of big data analytics and AI is the lowest.
Thus, one of the world’s most reputed international financial institutions has been supporting the use of AI and big data to achieve its goals of decreasing poverty and increasing shared prosperity.
It recently launched the AI for Development initiative and an Artificial Intelligence Lab. Boosting capacity is key in achieving these goals.
In this regard, the institution organized a five-day skill-building program focusing on big data, AI, and decision science in health and nutrition in Bangkok, featuring the participation of the Bank’s various country teams as well as government and academic partners from Bangladesh, India, Indonesia, Lao PDR, Myanmar, and more.
Besides elaborating on the landscape for utilization of AI and decision science in health and nutrition, this training workshop also aimed to build the skills sets of decision makers by training them on various tools that utilize these technologies and aid in decision-making processes.
Participants got to practice on real-world scenarios, many of which were from their own regions or countries and learned how to utilize tools such as Optima HIV, Health Service Prioritization tool, as well as broader big data techniques, in addressing challenges facing decision making in public health today.
The workshop was successful in creating awareness of the need for analytics to improve decision and delivery choices in health and development, and building capacity on analytical optimization tools that can answer pertinent policy and implementation questions for sectors.
The team in Thailand, comprising of both government officials and staff from the bank, specifically aim to apply their newfound knowledge in improving the efficiency of resource allocations to support the national roadmap for ending the AIDS epidemic as a public health threat in Thailand by 2030 – specifically this is to further reduce annual new HIV infections from 6,500 to less than 1,000, cut AIDS-related deaths from almost 13,000 to under 4,000 and reduced HIV-related discrimination in health-care settings by 90%.