As we grapple with the implications of the digital revolution and a rapidly changing natural environment, AI may hold the key to unraveling some of the complexity that has exceeded our comprehension. But with the means of research firmly in industry hands, policymakers will need to ensure that new tools provide public goods.
LONDON – We have just witnessed the start of a paradigm shift in earth science. A paper published in Nature in July showed that a neural network (artificial intelligence) predicted weather better than the European Center for Medium-Range Weather Forecasts, which has the world’s most advanced forecasting system. Then, in November, Google’s DeepMind announced that its weather-forecasting AI had produced even stronger predictions.
The traditional approach to weather forecasting is to use observations taken at a point in time as initial conditions for equations based on physical principles. By contrast, an AI will ingest data collected over long periods of time and then “learn” the dynamics that traditional equations must describe explicitly. Both the traditional and the AI-based method rely on supercomputers, but the AI has no need for formally developed theories.
Weather forecasting determines when and where planes fly, which routes ships take, and helps manage all manner of civilian and military risks that come with a variable environment. It matters. While these are still relatively early days for AI applications in this field, and much still needs to be worked out, as in other sectors, AI-driven forecasting may displace skilled labor, since neural networks don’t require knowledge of dynamical meteorology (the authors of the Nature paper are engineers with no such background). But the implications hardly stop there.
LONDON – We have just witnessed the start of a paradigm shift in earth science. A paper published in Nature in July showed that a neural network (artificial intelligence) predicted weather better than the European Center for Medium-Range Weather Forecasts, which has the world’s most advanced forecasting system. Then, in November, Google’s DeepMind announced that its weather-forecasting AI had produced even stronger predictions.
The traditional approach to weather forecasting is to use observations taken at a point in time as initial conditions for equations based on physical principles. By contrast, an AI will ingest data collected over long periods of time and then “learn” the dynamics that traditional equations must describe explicitly. Both the traditional and the AI-based method rely on supercomputers, but the AI has no need for formally developed theories.
Weather forecasting determines when and where planes fly, which routes ships take, and helps manage all manner of civilian and military risks that come with a variable environment. It matters. While these are still relatively early days for AI applications in this field, and much still needs to be worked out, as in other sectors, AI-driven forecasting may displace skilled labor, since neural networks don’t require knowledge of dynamical meteorology (the authors of the Nature paper are engineers with no such background). But the implications hardly stop there.