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Category: robotics/AI – Page 18

Microsoft has developed an artificial intelligence (AI) model that beats current forecasting methods in tracking air quality, weather patterns, and climate-addled tropical storms, according to findings published Wednesday.

Dubbed Aurora, the new system—which has not been commercialized—generated 10-day weather forecasts and predicted hurricane trajectories more accurately and faster than traditional forecasting, and at lower costs, researchers reported in the journal Nature.

“For the first time, an AI system can outperform all operational centers for hurricane forecasting,” said senior author Paris Perdikaris, an associate professor of mechanical engineering at the University of Pennsylvania.

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Alex de Vries-Gao, a PhD candidate at VU Amsterdam Institute for Environmental Studies, has published an opinion piece about the results of a simple study he conducted involving the possible amount of electricity used by AI companies to generate answers to user queries. In his paper published in the journal Joule, he describes how he calculated past and current global electricity usage by AI data centers and how he made estimates regarding the future.

Recently, the International Energy Agency reported that data centers were responsible for up to 1.5% of in 2024—a number that is rising rapidly. Data centers are used for more things than crunching AI queries, as de Vries-Gao notes. They are also used to process and store cloud data, notably as part of bitcoin mining.

Over the past few years, AI makers have acknowledged that running LLMs such as ChatGPT takes a lot of computing power. So much so, that some of them have begun to generate their own electricity to ensure their needs are met. Over the past year, as de Vries-Gao notes, AI makers have become less forthcoming with details regarding energy use. Because of that, he set about making some estimates of his own.

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Our cells rely on microscopic highways and specialized protein vehicles to move everything—from positioning organelles to carting protein instructions to disposing of cellular garbage. These highways (called microtubules) and vehicles (called motor proteins) are indispensable to cellular function and survival.

The dysfunction of motor proteins and their associated proteins can lead to severe neurodevelopmental and neurodegenerative disorders. For example, the dysfunction of Lis1, a partner protein to the motor protein , can lead to the rare fatal birth defect lissencephaly, or “smooth brain,” for which there is no cure. But therapeutics that target and restore dynein or Lis1 function could change those dismal outcomes—and developing those therapeutics depends on thoroughly understanding how dynein and Lis1 interact.

New research from the Salk Institute and UC San Diego captured short movies of Lis1 “turning on” dynein. The movies allowed the team to catalog 16 shapes that the two proteins take as they interact, some of which have never been seen before. These insights will be foundational for designing future therapeutics that restore dynein and Lis1 function, since they shine a light on precise locations where drugs could interact with the proteins.

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Hyperspectral imaging (HSI), or imaging spectroscopy, captures detailed information across the electromagnetic spectrum by acquiring a spectrum for each pixel in an image. This enables precise identification of materials through their spectral signatures.

HSI supports Earth remote sensing applications such as automated classification, abundance mapping, and estimation of physical and biological properties like soil moisture, sediment density, , biomass, leaf area, and pigment content.

Although HSI offers detailed insight into a remote sensing scene, HSI data may not be readily available for an intended application. Recent studies have attempted to combine HSI with traditional red-green-blue (RGB) video acquisition to lower costs and improve performance. However, this fusion technology still faces technical challenges.

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Make sure to watch this next video about Type 1 to Type 4 Civilizations: https://youtu.be/5fTNGvuPTMU.

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Researchers from the University of Innsbruck have unveiled a novel method to prepare quantum operations on a given quantum computer, using a machine learning generative model to find the appropriate sequence of quantum gates to execute a quantum operation.

The study, recently published in Nature Machine Intelligence, marks a significant step forward in realizing the full extent of .

Generative models like diffusion models are one of the most important recent developments in (ML), with models such as Stable Diffusion and DALL·E revolutionizing the field of image generation. These models are able to produce high quality images based on text description.

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