If the US Has to Build Data Centers, Here's Where They Should Go A new analysis tries to calculate the coming environmental footprint of AI in the US and finds that the ideal sites for data centers aren't where they're being built. A data center for cryptocurrency mining, cloud services, and AI computing in Stutsman County, North Dakota.Video: halbergman/Getty Images Tech companies have invested so much money in building data centers in recent months, it's actively driving the US economy--and the AI race is showing no signs of slowing down. Meta chief Mark Zuckerberg told President Donald Trump last week that the company would spend $600 billion on US infrastructure--including data centers--by 2028, while OpenAI has committed already to spending $1.4 trillion. An extensive new analysis looks at the environmental footprint of data centers in the US to get a handle on what, exactly, the country might be facing as this buildout continues over the next few years--and where the US should be building data centers to avoid the most harmful environmental impacts. The study, published in the journal Nature Communications on Monday, uses a variety of data, including demand for AI chips and information on state electricity and water scarcity, to project the potential environmental impacts of future data centers through the end of the decade. The study models a number of different possible scenarios on how data centers could affect the US and the planet--and cautions that tech companies' net zero promises aren't likely to hold up against the energy and water needs of the massive facilities they're building.

More'Frankenstein' rabbits are appearing across the US, sparking fears of a wider outbreak. Originally spotted in Colorado, these bizarre rabbits, with tentacle-like growths sprouting from their faces, have now been reported in Minnesota, Nebraska, and South Dakota. The animals are infected with cottontail rabbit papilloma virus (CRPV), also known as Shope papilloma virus, which can be spread through mosquito and tick bites. While humans are unlikely to contract CRPV, Dr Omer Awan of the University of Maryland School of Medicine cautioned that people could still face risks from other diseases carried by ticks or mosquitoes that have fed on infected rabbits. 'You're not going to get CRPV, and you likely won't show symptoms of it,' Dr Awan told the Daily Mail.

Remote sensing (RS) techniques, by enabling non-contact acquisition of extensive ground observations, have become a valuable tool for corn yield prediction. Traditional process-based (PB) models are limited by fixed input features and struggle to incorporate large volumes of RS data. In contrast, machine learning (ML) models are often criticized for being ``black boxes'' with limited interpretability. To address these limitations, we used Knowledge-Guided Machine Learning (KGML), which combined the strengths of both approaches and fully used RS data. However, previous KGML methods overlooked the crucial role of soil moisture in plant growth. To bridge this gap, we proposed the Knowledge-Guided Machine Learning with Soil Moisture (KGML-SM) framework, using soil moisture as an intermediate variable to emphasize its key role in plant development. Additionally, based on the prior knowledge that the model may overestimate under drought conditions, we designed a drought-aware loss function that penalizes predicted yield in drought-affected areas. Our experiments showed that the KGML-SM model outperformed other ML models. Finally, we explored the relationships between drought, soil moisture, and corn yield prediction, assessing the importance of various features and analyzing how soil moisture impacts corn yield predictions across different regions and time periods.

AI-generated "deepfake" materials are flooding the internet, sometimes with dangerous results. In just the last year, AI has been used to make deceiving voice clones of a former US president and spread fake, politically-charged images depicting children in natural disasters. Nonconsensual, AI-generated sexual images and videos, meanwhile, are leaving a trail of trauma impacting everyone from high schoolers to Taylor Swift. Large tech companies like Microsoft and Meta have made some efforts to identify instances of AI manipulation but with only muted success. Now, governments are stepping in to try and stem the tide with something they know quite a bit about: fines.

Most Europeans have a unique idea about what Americans look like in each US state, and artificial intelligence has brought these views to life in lifelike images. Mdjourney, a system that creates images based on users' text prompts, created an image for each of the 50 states based on how those across the Atlantic perceive them. According to AI, Europeans think Alabamians typically have missing teeth, all Utahans are Mormons and Virginians are stuck in the Victorian era. While some images are far-fetched, they offer insight into potential biases and stereotypes in AI technology. According to AI, Europeans perceive Alabamians as being very rugged-looking, with blue eyes and a few teeth missing in their mouths, according to BuzzFeed.

Mobile phones and other electronic gadgets or devices have aided in collecting data without the need for data entry. This paper will specifically focus on Mobile health data. Mobile health data use mobile devices to gather clinical health data and track patient vitals in real-time. Our study is aimed to give decisions for small or big sports teams on whether one athlete good fit or not for a particular game with the compare several machine learning algorithms to predict human behavior and health using the data collected from mobile devices and sensors placed on patients. In this study, we have obtained the dataset from a similar study done on mhealth. The dataset contains vital signs recordings of ten volunteers from different backgrounds. They had to perform several physical activities with a sensor placed on their bodies. Our study used 5 machine learning algorithms (XGBoost, Naive Bayes, Decision Tree, Random Forest, and Logistic Regression) to analyze and predict human health behavior. XGBoost performed better compared to the other machine learning algorithms and achieved 95.2% accuracy, 99.5% in sensitivity, 99.5% in specificity, and 99.66% in F1 score. Our research indicated a promising future in mhealth being used to predict human behavior and further research and exploration need to be done for it to be available for commercial use specifically in the sports industry.

Is it still cool to memorize a lot of stuff? Is there even a reason to memorize anything? Having a lot of information in your head was maybe never cool in the sexy-cool sense, more in the geeky-cool or class-brainiac sense. But people respected the ability to rattle off the names of all the state capitals, or to recite the periodic table. It was like the ability to dunk, or to play the piano by ear--something the average person can't do.

The Virginia-class, nuclear-powered, fast-attack submarine, USS North Dakota (SSN 784), transits the Thames River as it pulls into its homeport on Naval Submarine Base New London in Groton, Conn - file photo. Bringing massive amounts of firepower closer to enemy targets, conducting clandestine "intel" missions in high threat waters and launching undersea attack and surveillance drones are all anticipated missions for the Navy's emerging Block V Virginia-class attack submarines. The boats, nine of which are now surging ahead through a new developmental deal between the Navy and General Dynamics Electric Boat, are reshaping submarine attack strategies and concepts of operations -- as rivals make gains challenging U.S. undersea dominance. Eight of the new 22-billion Block V deal are being engineered with a new 80-foot weapons sections in the boat, enabling the submarine to increase its attack missile capacity from 12 to 40 on-board Tomahawks. "Block V Virginias and Virginia Payload Module are a generational leap in submarine capability for the Navy," Program Executive Officer for Submarines Rear Adm. David Goggins, said in a Navy report.

A newly discovered species of ancient shark has been named after a 1980s arcade game. The shark swam in the rivers of what is now South Dakota, US, about 67 million years ago, living alongside iconic dinosaur species such as T. rex. It's been named Galagadon, after the 1981 Japanese-US game Galaga, because its teeth resemble the spaceships in the game. The specimen is described in the Journal of Paleontology. "It may seem odd today, but about 67 million years ago, what is now South Dakota was covered in forests, swamps and winding rivers," said co-author Terry Gates, from North Carolina State University.