Making AI Headlines · Page 6
MIT speeds up climate modeling, where seconds can hold thousands of futures
· New York time
MIT researchers have developed a training method intended to produce fast, adaptable AI models for climate prediction. These models, known as climate emulators, can simulate the effects of human activities on future climate conditions and run scenarios in seconds. The speed matters because planning often requires comparing many possible futures rather than relying on a single projection. Researchers can use emulators to explore variations in temperature or sea level, with applications that include planning for power grids, public health and agriculture. Faster methods can make it easier to examine a wider range of scenarios. MIT says its technique streamlines the production of such models. The resulting projections can support decisions about resilience, but the work does not replace the choices communities must make about how to respond. A quick projection still has to be understood, evaluated and applied to real circumstances. There is a useful reversal in the technology's usual sales pitch. Instead of promising to make every task instantaneous, this research makes it faster to consider consequences that unfold over long periods. A model can help researchers compare many combinations of conditions while officials consider what those differences would mean for infrastructure and people. The computer can run through thousands of possible futures while humans do the slower work of agreeing which risks deserve attention. That second task remains stubbornly resistant to a faster processor.
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