With this at hand, finance leaders must be able to look into and understand the key industry trends for computing power to maximise the potential of technology for the benefit of their organisations.
The report named the following industry and technological trends for computing power growth in Southeast Asia:
The technology perspective
The development of computing power drives artificial intelligence to upgrade to the third generation. The knowledge-driven first-generation AI uses knowledge, algorithms and computing power to build AI. The data-driven second-generation AI adds data as the fourth dimension. And the third generation of AI is the convergence of the previous two and facilitates to secure, trusted, reliable and scalable AI innovation.
The policy perspective
With the release of ChatGPT and the large language model, Asian governments are accelerating the deployment of AI. For example, Hong Kong plans to invest $3.8 billion to develop large language models for healthcare, law and finance.
In addition to investment, governments are working more closely with stakeholders to promote AI governance. An open, collaborative and sustainable governance platform is needed to achieve international consensus.
The industry perspective
Moving from a small model to a large industry model, the main theme of AI development will be accelerating industry intelligence.
A good example is the Thailand Meteorological Department’s exploration of the Huawei Pangu-Weather model, an advanced AI model for weather forecasting. According to a Nature paper, this Pangu model is the first AI prediction model to demonstrate higher precision than the traditional numerical forecast method, allowing a 10,000x improvement in prediction speed and reducing prediction time to just seconds.
Computing infrastructure itself has become one of the major sources of carbon emissions.
As an example, Singapore has more than 60 data centres, accounting for 7% of the country’s power consumption. Therefore, its movements toward green computing are particularly important. Traditionally, the data centre computing architecture focuses on single-node computing power. Driven by the low-carbon trend, the new date centre computing architecture will develop towards computing power diversity, efficient cooling systems and green energy storage devices for low power supply efficiency.
The ecosystem perspective
According to a McKinsey report, in China only, the AI talent gap is expected to reach 4 million by 2030. Behind this number is an urgent requirement to upgrade the current workforce and ecosystem.