Artificial intelligence and hybrid cloud are changing how businesses operate but to get the most from this transformation requires a change in outlook. Andy Stanford Clark CTO UKI believes that business leaders need to think like scientists to create the discovery driven enterprise.
About the author
Andy Stanford-Clark is the Chief Technology Officer for IBM in UK and Ireland.
The COVID-19 pandemic has created an unprecedented global scientific response that the media has largely illustrated with the archetypal lab coat and laboratory image, the time-honored visual shorthand for scientific development. Unfortunately, this portrayal is not fully illustrative of today’s modern methods and procedures. Goggles, test tubes and Bunsen burners may be more visually appealing than artificial intelligence (AI) and hybrid cloud, but the latest scientific breakthroughs owe as much to raw processing power as pipettes and beakers.
Out in the wider world things are very similar; established ways of working, including the traditional method of scientific discovery, are being supplanted thanks to breakthroughs in AI, hybrid cloud, automation, and in the near future quantum computing. The timing could not be better. A wide array of pressing societal and commercial challenges need knowledge to develop at a dramatically faster pace if they are to be satisfactorily resolved. From government policy to enterprises, modern scientific methods can now a central plank of everyday decision making.
At its heart, the scientific method (question, research, hypothesize, experiment, observe, conclude, replicate) remains unchanged. However, these steps can now be accomplished at a rate that was previously unimaginable, which is bringing unparalleled levels of speed, automation and scale to the process of discovery.
One example of the accelerated discovery workflow could help address is food security – by solving the decades-long nitrogen fixation problem.
Certain bacteria on the roots of plants fix nitrogen naturally – that’s nature’s clever way to make its own fertilizers to feed plants that feed us. Researchers have been trying to engineer a catalyst to rival bacteria since the 1960s, in a bid to address the limited supply of naturally fixed nitrogen and tackle the looming global food crisis.
We are not there yet. But the discovery of new materials, in this case catalysts, could help. Currently, nitrogen for fertilizations is produced using the Haber-Bosch process that relies on a very energy-intensive iron-based catalyst. Some 10 MWh of energy is needed to produce one ton of ammonia, roughly equivalent to the energy contained in one ton of coal. The process accounts for two percent of global carbon emissions.
One solution could be creating new materials to facilitate the reaction between nitrogen and hydrogen. This could be achieved by using fuel cells – devices that convert the chemical energy of a fuel into electricity. It’s like a reverse battery – instead of storing energy, it uses energy from renewable sources to combine nitrogen from the atmosphere and hydrogen from water to produce ammonia.
AI and quantum computing could help us find new catalytic molecules to lower the amount of energy needed to sustain this process. First, in the Deep Search step of the Accelerated Discovery flow, AI would sift through the existing knowledge about catalysts. Then, in the Intelligent Simulation step, a quantum computer could precisely simulate different molecules and their behavior, further augmenting our knowledge. Then researchers would use the resulting data to construct Generative Models and determine possible configurations of the new molecules. And finally, the candidate materials would be tested in AI-driven chemical labs and screened to check for effectiveness. This experimental data would also be crucial to improve the predictive capabilities of the models, with the objective of finding the correct catalyst for nitrogen fixation.
Aside from the accelerated scientific discovery, there is another dramatic change: these tools are not just reserved for technologists and engineers. The scientific method is also helping shape the world outside the laboratory walls.
There is an increasing number of applications where the scientific method is used in ways that have nothing to do with natural sciences. Governments can benefit from taking a similar approach to policy making. For example, to the question of “what interventions increase educational outcomes at the lowest cost?” The answer can be determined through randomized controlled trials. Similarly, to the question about pricing medicines, hypotheses drive randomized controlled trials to test the hypotheses and ultimately guide policies and actions.
Our findings and their implications are spelt out in a new IBM white paper, ‘The Science & Technology Outlook 2021’. It provides detailed examples of why business leaders should be thinking like scientists when it comes to innovation and what it means to be a discovery-driven enterprise.
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