![]() Joe brings that same passion to How-To Geek. If something piques his interest, he will dive into it headfirst and try to learn as much as possible. Outside of technology, Joe is an avid DIYer, runner, and food enthusiast. After several years of jailbreaking and heavily modifying an iPod Touch, he moved on to his first smartphone, the HTC DROID Eris. ![]() He got his start in the industry covering Windows Phone on a small blog, and later moved to Phandroid where he covered Android news, reviewed devices, wrote tutorials, created YouTube videos, and hosted a podcast.įrom smartphones to Bluetooth earbuds to Z-Wave switches, Joe is interested in all kinds of technology. He has written thousands of articles, hundreds of tutorials, and dozens of reviews.īefore joining How-To Geek, Joe worked at XDA-Developers as Managing Editor and covered news from the Google ecosystem. Joe loves all things technology and is also an avid DIYer at heart. He has been covering Android and the rest of the Google ecosystem for years, reviewing devices, hosting podcasts, filming videos, and writing tutorials. "By developing new tools and accelerating research, we hope AI can empower the global community to tackle our greatest environmental challenges.Joe Fedewa has been writing about technology for over a decade. But our wider research is not just about anticipating weather - it's about understanding the broader patterns of our climate," Lam wrote. "Pioneering the use of AI in weather forecasting will benefit billions of people in their everyday lives. They can also help scientists see shifts in climate patterns over time and get a clearer view of the bigger picture. Make sure your phone, computer and headset are on the same Wi-Fi network. Instead, AI models could complement other forecast methods and generate faster predictions, the researchers said. Download the Meta Quest mobile app to your phone. Regular forecasts are still needed to verify and set the starting data for any prediction, and as machine learning algorithms produce results they cannot explain, they can be prone to errors or "hallucinations." "By contrast, traditional forecasts had greater variability in where and when landfall would occur, and only locked in on Nova Scotia about six days in advance."ĭespite the model's impressive performance, scientists don't see it supplanting currently used tools anytime soon. "In September, a live version of our publicly available GraphCast model, deployed on the ECMWF website, accurately predicted about nine days in advance that Hurricane Lee would make landfall in Nova Scotia," Rémi Lam, a research engineer at DeepMind, wrote in a statement. You can easily control the projection mode, hit Win+P from the keyboard to open the projection menu. Could we ever pull enough carbon out of the atmosphere to stop climate change? Now select your wireless display and you’ve successfully connected to your TV. How would just 2 degrees of warming change the planet? When did scientists first warn humanity about climate change? GraphCast can also predict extreme weather events, such as heatwaves, cold spells and tropical storms, and when Earth's upper atmospheric layers were removed to leave only the lowest level of the atmosphere, the troposphere, where weather events that impact humans are prominent, the accuracy shot up to more than 99%. Running GraphCast alongside the ECMWF's high-resolution forecast, which uses more conventional physical models to make predictions, the scientists found that GraphCast gave more accurate predictions on more than 90% of the 12,000 data points used. The algorithm established patterns between variables such as air pressure, temperature, wind and humidity that not even the researchers understood.Īfter this training, the model extrapolated forecasts from global weather estimates made in 2018 to make 10-day forecasts in less than a minute. ![]() For the new AI model, researchers trained GraphCast on 38 years' worth of global weather readings up to 2017. The accuracy of these predictions relies on granular details within the models, and they are energy-intensive and expensive to run.īut machine learning weather models can operate more cheaply because they need less computing power and work faster. Forecasting today relies on plugging data into complex physical models and using supercomputers to run simulations.
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