Coastal landscapes are constantly being reshaped by natural forces, and as climate change causes more frequent storms and sea ...

Oversimplifies trends and ignores real-world disruptions. Can’t predict economic downturns, competitor actions and shifts in customer behavior on its own. Ignores randomness; every forecast will have ...

Paying invoices sounds simple enough. A vendor creates an invoice and sends a bill, your team approves it, and the money goes out. In practice, though, invoice payments are where a lot of finance ...

A team of scientists from around the world has created the first system that can predict when and where extremely powerful solar storms, called superflares, are most likely to happen. These storms can ...

Kalshi says it's more than just betting and that it offers high-quality forecasts. Now, a research paper from a group of Federal Reserve economists is backing that up. The researchers found that ...

Amid the myriad discussions about AI – from the astounding amount of money being spent by vendors and enterprises and the debate about actual ROI those businesses are getting to the technology’s ...

Traditional long-term forecasting models are no longer sufficient as electrification, DER growth, EV adoption, extreme weather events and new large loads introduce unprecedented complexity. The future ...

People are now betting on everything. Prediction markets are amplifying those signals. The timing of the U.S. government shutdown. The likelihood of Taylor Swift canceling a tour date. The exact day ...

In the U.S., the utility-scale sector has the greatest share of the U.S. solar market, with reports indicating that 70% of all new solar capacity added in 2023 was at the utility-scale level. Not only ...

A new study by Shanghai Jiao Tong University and SII Generative AI Research Lab (GAIR) shows that training large language models (LLMs) for complex, autonomous tasks does not require massive datasets.

A team of researchers have developed a domain adaption framework capable of transferring knowledge from solar power plants with abundant data to plants that need to be trained without labelled data.