AI is Taking On ever-larger challenges
The amazing feat of developing Covid-19 vaccines has demonstrated science at its best. As we praised the exemplary work of our health workers on March 20, 2022 my neighbors asked “Why isn’t AI assisted?” A fair question.
Machine learning techniques have helped in specific areas and aid in the preparation for a future pandemic. However, the reality is that this test came too early for AI to fully demonstrate its potential.
However, eight months later, in the middle of the pandemic, AI was able to solve a more than 50 years-old major biology challenge that was the protein-structure prediction issue. Life scientists declared this breakthrough “the remarkable and significant advancement in the field of life science that shows the capabilities in AI.”
From this time, AI-powered structure prediction has revolutionized biology. From speeding up research into the development of new enzymes that eat plastic to increasing our understanding of how cells function as well as aiding biologists in finding ways to solve a myriad of issues that could be beneficial to the entire world.
AI has also made advances in different areas of science, such as particle physics, astronomy, medical imaging, organic chemistry conservation, and fusion. Innovations like these will keep coming. However, we are also at the edge of a fundamental shift.
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In 2023, we’ll be witnessing artificial intelligence be recognized as an essential daily instrument for scientists across all disciplines and domains. Similar to how thousands of workers depend on their email and word processors, scientists will soon depend on machine-learning models and AI systems the same way.
In the case of AI-powered protein structure prediction, what used to cost biologists thousands of dollars , or many years of grueling research is now as easy as the Google search. We’re sure that this will be extended to other areas. In the field of genomics, AI will enable scientists to discover a deeper understanding of diseases and to develop ways to treat these diseases.
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As we create more generalized systems which learn the fundamental principles that govern complicated issues, we’ll begin to be able to see AI’s impact extending beyond traditional disciplines. Researchers investigating all sorts of problems will use it as a tool to augment human intelligence–optimizing processes, automating procedures, informing new theories, and providing a better understanding of uncertainty.
The drought that has hit Europe and the floods across South Asia, and extreme weather across the globe over the last few years have demonstrated how urgent is the climate crisis that we face. We need to embrace sustainability in consumption, and a more ambitious approach to policy-making, but we must not depend on it all by ourselves.
AI as well as machine-learning are also beginning to aid in the development of more accurate models to predict the changes in the climate. The new meteorological models, such as Nowcasting can help us make better decisions and plan on the individual, national and global scales.
Digital twins, which are real-time virtual representations of physical systems in the real world — could help us gain more insight into changing climate conditions, costs of inaction, as well as the potential impact of policies and technological options.
Machine learning and AI can offer the rapid technological progress needed to tackle the complex and vast problems humanity and science are facing. When they are released the scientific breakthroughs are awe-inspiring and attract the attention of people, but frequently cause misplaced expectations.
It is crucial that, when not being successful it isn’t a reason to reduce our expectations. We must also keep in mind that these tools are instruments, and the rewards are realized when researchers, scientists, and engineers utilize these tools in their daily tasks. We’ve seen this transformation already in the field of biology. In 2023, we’ll witness AI finally make its mark in the toolbox of every scientist. I am eager for the results they come up with.
