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MIT AI forecasts extreme weather without historical data

MIT engineers have built an AI tool that forecasts extreme weather without training on historical disaster data. Kai Chang, a mechanical engineering graduate student, and Professor Themis Sapsis developed the tool. It produces maps of events that have not appeared in a region’s historical record but remain statistically-possible. Each map also carries estimates of the […] The post MIT AI forecasts extreme weather without historical data appeared first on AI News.

MIT AI forecasting extreme weather
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MIT

Welcome to the spiderverse, a world measured through webs

Counting the creatures in the world around us is critical for a raft of conservation efforts. It helps scientists gauge biodiversity, track migration, and spot invasive species. That census-taking, though, often requires humans to tabulate what they see, trap, or otherwise sense—a potentially laborious, costly process that can still leave gaps. But developments over the…

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Alibaba's US$10 Billion Share Placement: The Contradictory Signals of a 10% Stock Drop and 3x Oversubscription

Alibaba's US$10.2 billion share placement for AI infrastructure drew 3x oversubscription yet sent its stock down 10%, revealing a market divide between shareholders bearing dilution costs and new investors betting on AI returns. The article examines why Alibaba chose external financing despite ample cash reserves and what signals will determine whether the money is well spent.

Alibaba AI Infrastructure Cloud Computing
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