- To accelerate progress and tackle global challenges much more effectively, we need a new way to do science.
- Artificial Intelligence will “close the loop” in scientific discovery.
- Human efforts are essential to the whole — we need more so-called knowledge -sharing, collaborative “communities of discovery” and they must be diverse in nature.
COVID-19 has been a gut punch. Our response? Largely frantic, like deer caught in the headlights. Researchers are racing to find a vaccine, as we pause in lockdown mode. But the process of drug discovery is lengthy and expensive, just like the process of discovering and designing any material crucial to fighting existential problems.
But these problems are piling up: pandemics, climate change, antibiotic resistance, food security, cyber-challenges, shared-economic prosperity and so on. We urgently need to change our traditional approach to science.
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For centuries, we’ve done science in a linear way: an issue prompts a hypothesis, followed by a model and a test. If the result is a failure, the process starts again, and iterations may take years. And it’s got us far; it’s how we’ve developed better plastics, more efficient solar panels and lighter-but-stronger composites for modern aircraft.
But the world is changing rapidly; in order to tackle today’s global challenges with the speed and effectiveness they demand, we need a new way to do science.
Science is an inherently creative process; scientists are constantly expanding their imagination to explore new designs of drugs and chemicals. But the human brain has its limits. After all, there are more possible designs of a molecule than there are atoms in the universe. No human can sift through all of them to come up with the best option.
The good news is we do have the ingredients to give science — or our brains’ limits — a boost: cutting-edge computing technology and talent. The real challenge is to apply them strategically, in both public and private sectors.
Helping science determine a new path
The world is witnessing a revolution in computing. Artificial Intelligence (AI) is enhancing traditional computing and could soon boost the emerging quantum ones: the very machines that could allow us to solve some of the world’s greatest problems. They can be accessed from anywhere on the planet through a hybrid cloud.
More and more companies and labs are now using AI, whose deep neural networks are able to extract scientific knowledge at scale from all the literature published on a specific topic.
Say a scientist needs to create a new catalyst for better artificial fertilizers. Instead of blindly trying to determine the catalyst’s chemical structure, AI would first sift through a multitude of patents, academic papers and other publications to see what had already been done on this topic.
Next, AI would automatically generate hypotheses based on the data it found, to expand the search for new molecular designs. Based on the most promising hypothesis, high-performance computers and quantum computers would simulate a new molecule.
Digital work done, the simulation would be confirmed or refuted during increasingly autonomous lab tests. Finally, AI would assess the result, identify anomalies and extract new knowledge. New questions would surface and the loop would continue.
To shift the paradigm of scientific discovery, we need to enable AI, hybrid cloud, and — eventually — quantum computing to converge. We also need a second ingredient – new types of scientific collaborations or ‘communities of discovery’ — to be added to the mix.
What would we gain? An accelerated scientific method, fit for catalysing major transformations in science, and with unprecedented speed and automation. We could design new materials faster than ever before, impacting all aspects of our lives — from healthcare to manufacturing, to agriculture and beyond.
For the first time, closing the loop in scientific discovery seems a very real and imminent possibility. When it does happen, we will have achieved the dream of scientific advancement being a self-propelled and never-ending process.
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The need for new communities of discovery
But it’s not just technology that that will drive this new level of discovery; people will too. The world is teeming with the talent and creativity of millions of scientists spread across academia and industry, who shouldn’t be tackling the numerous global crises they face independently. Indeed, no single company or university lab can overcome a pandemic on its own.
National and international private-public collaborations share knowledge, data and the latest technology, speeding up the process of discovery. Our need for more of them has never been greater.
They also need to be diverse. In science, problems can be big and complex, or small and more focused. For instance, CERN (the European Organization for Nuclear Research) requires a deeply coordinated community with scientists from 42 countries to run some two-million experiments every day across about 170 labs – and that’s just for the science coming from Large Hadron Collider.
And yet, science is becoming more open, with researchers from private and public sectors increasingly sharing papers, experiments, data, results and resources.
One successful example of such a smaller, new community of discovery is the COVID-19 High-Performance Computing Consortium. A collaboration of 87 partners from academia, industry and national labs, it has been granting researchers from around the world who are fighting the current pandemic access to supercomputers.
Industry partners are often rivals, but not in the current coronavirus vaccine endeavour. Every member of the Consortium is united by a common goal: to accelerate our search for a new treatment or vaccine against COVID-19. The benefits of collaboration are greater speed and accuracy; a freer exchange of ideas and data; and full access to cutting-edge technology. In sum, it supercharges innovation and hopefully means the pandemic will be halted faster than otherwise.
But material design isn’t the limit.
With continuing evolution as an AI-accelerated approach that builds on data, advanced compute in hybrid cloud, progress in quantum computing and growing communities of discovery, the upgraded, self-propelled continuous scientific method should greatly impact multiple aspects of our lives. And with all the global crises of today and tomorrow, the need for it has never been greater.