Some limitations should be obvious. This algorithm won’t identify a species it wasn’t trained on or any subpopulations of species that differ too much from the example. The quality of the training data matters a lot, too. If we only use photos of chickadees in pine trees, the model could include pine needles in its definition of chickadee-ness.

Without a lot of extra work, we may not know how the model arrives at its answers. The internal mechanisms are pretty much a black box most of the time.

The upside is real, though. Machine learning algorithms often outperform our best human-crafted algorithms, at least in terms of computational efficiency, if not also accuracy. They just have to be used properly, or the limitations will show.

Cloud computing

For weather forecast models, the process isn’t too different from our bird identification example, but the models are trained on two sets of weather data obtained a short time apart.

Because they aren’t solving lots of physics equations in every location, these models run far more quickly than traditional weather models.

A number of companies, including Google, Nvidia, Huawei, and Microsoft, have developed initial models—sometimes in collaboration with independent academics—that could compare favorably to the forecast models we currently use. Once we began to understand where the models excel and struggle, some of the major weather forecast centers started developing their own.

The European Centre for Medium-Range Weather Forecasts (ECMWF) put its first machine-learning-based model into service in February 2025, running it alongside its long-standing Integrated Forecasting System (IFS) model.

The AIFS model is trained using a reanalysis—a dataset built by taking all available weather observations and filling out a physically consistent picture where we don’t have measurements. This critical tool greatly simplifies the machine learning task of predicting the next global snapshot (six hours ahead) based on previous snapshots.