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The artificial intelligence (AI) boom has ignited the demand for data centers. Goldman Sachs predicts a doubling of the power demand to run the data centers in just two years.

Generative AI tools such as ChatGPT, Gemini, and Preplexity require a significant amount of computing power to train large language models so they are able to answer user queries coming from the average person who is doing a search about basketball game statistics to investment bankers who are looking for nuggets of information that will help them invest in high return stocks.

In most emergency departments, beds are a hot commodity. But some patients don’t actually need a bed for their treatment – and accurately identifying those people is a low-lift but effective way to increase efficiency without sacrificing care quality, finds a study recently published in the journal Management Science.

The researchers developed a simple decision tree, informed by mathematical and machine learning models, to help staff decide whether a patient truly needs a bed or can be routed to a “vertical processing pathway” – that is, an area where they can be treated sitting up. Many hospitals already have such an area but don’t necessarily have a systematic way to decide which patients are treated there.

American teenagers start their school day too early.

Medical experts have been clear for years: sleep cycle changes during puberty make it hard for teenagers to learn in the early morning. Later school start times have been linked to better sleep, improved mental health, stronger academic performance and even safer driving.

Most school district leaders know about this problem and care about fixing it. But changing the schedule can feel operationally impossible.