Press release Artificial intelligence Here’s one definition of science: it’s essentially an iterative process of building models with ever-greater explanatory power. A model is just an approximation or simplification of how we think the world works. In the past, these models could be very simple, as simple in fact as a mathematical formula. But over time, they have evolved and scientists have built increasingly sophisticated simulations of the world as new data has become available. A computer model of the Earth’s climate can show us temperatures will rise as we continue to release greenhouse gases into the atmosphere. Models can also predict how infectious disease will spread in a population, for example. Computer models can be rejected if experimental evidence does not support them. So there’s a kind of arms race to keep models competitive as new data appears. And the revolution occurring in the field of artificial intelligence (AI) could make these vital tools even better. Take weather and climate forecasting. The numerical models used to predict weather are large, complex and demanding in terms of the amount of computing power needed to run them. They are also unable to learn from past weather patterns. However, methods based around artificial intelligence, including a subset of AI known as machine learning, have shown huge potential to improve on what we currently have. Machine learning involves creating algorithms (sets of mathematical rules to perform particular tasks) that can learn from data and apply these lessons to unseen data. But until recently, weather models that incorporated machine learning techniques weren’t considered suitable for what’s called ensemble forecasting, a set of forecasts that shows the range of possible future weather conditions. Nor were they useful for weather and climate simulations over the longer term, as opposed to near-term forecasts. However, a recent […]