‘AI can strengthen R&D, accelerate innovation and commercialisation’
Everyone is talking about artificial intelligence (AI) these days.
Governments worldwide see AI as an opportunity and a challenge. Industries look forward to using AI to ramp up productivity. The academia is busy developing and applying AI in their teaching and research.
The public, however, is concerned about the negative aspects of AI. Job loss is one of them. The other big worry is how AI can lead to more online scams. Many have lost money through such cybercrimes.
Social media may be inundated with deep fakes using AI. The education business is worried about how AI will lead to more plagiarism and cheating in examinations.
One thing is for sure. AI is here to stay. The world has no choice but to manage it. This entails mitigating the risks, and harvesting the opportunities.
In a world where science and innovation are opening up new opportunities, many see AI’s impact on businesses. Admittedly, AI can strengthen science systems in various ways, enhancing research and development, analysis, and collaboration.
Some key areas where AI can have a significant impact have been flagged. Big data has emerged as a new challenge for the world. Handling and benefiting from such big data require dependable analytics. AI can process and analyse massive datasets much faster than humans, uncovering patterns and correlations that might be missed otherwise.
This capability is crucial in fields like genomics, climate science, and epidemiology. Machine learning models can predict outcomes and identify trends in scientific data, helping scientists make informed decisions and develop new hypotheses.
AI-driven robots and systems can perform repetitive experimental tasks, increasing efficiency and allowing scientists to focus on more complex aspects of their research. AI can automate data entry, curation, and management, reducing human error and freeing up time for researchers. This would enhance research capabilities.
AI can scan and summarise vast amounts of scientific literature, helping researchers stay up to date with the latest developments and find relevant information quickly. AI can also suggest new research directions and hypotheses based on existing data, potentially leading to innovative discoveries.
Much of the world’s problems call for interdisciplinary solutions. AI can facilitate collaboration between scientists from different disciplines and geographical locations through advanced communication tools and platforms.
Language algorithms can help in writing and translating research papers, making scientific knowledge more accessible globally. This would facilitate the open science agenda.
AI can run complex simulations in fields like physics, chemistry, and biology, helping to predict the outcomes of experiments and understand complex systems better. In the field of medicine, AI models can simulate the interaction of drugs with biological systems, speeding up the discovery of new medications for the world.
Using AI, precision medicine and personalised treatment will soon be a reality. AI can analyse genetic data to develop personalised treatment plans for patients, improving the effectiveness of medical treatments.
AI-driven diagnostic tools can help in early detection and diagnosis of diseases, improving patient outcomes. By integrating AI into science systems, researchers can leverage these technologies to push the boundaries of what is possible, leading to faster, more accurate, and more innovative scientific discoveries.
A sticking point in the science systems is poor R&D commercialisation. Choosing research topics that can efficiently link outputs with current, and emerging market needs, is seen as a crucial step.
This is where the process to evaluate R&D proposals can result in AI providing the optimal matching with industry needs. In the manual evaluation that is done currently, achieving the right match with market expectations is a major challenge. Poor market knowledge by academia, as well as low understanding of academic rigour by industry, also contribute to the challenge.
Clearly, there is much that AI can offer to enhance the productivity of science systems. How it can be done should be a subject of more discourse and study among the stakeholders.
Since science is destined to assume a greater role in the global future, it is imperative for nations to further enhance science systems. After all, science is no small investment. AI can help realise better returns.
Professor Datuk Dr Ahmad Ibrahim is an associate fellow at the Ungku Aziz Centre for Development Studies, Universiti Malaya.
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