Texas high school student Elliston Berry joins'Fox & Friends' to discuss the House's passage of a new bill that criminalizes the sharing of non-consensual intimate images, including content created with artificial intelligence. Welcome to Fox News' Artificial Intelligence newsletter with the latest AI technology advancements. IN TODAY'S NEWSLETTER: - Peek-a-boo, big tech sees you: Expert warns just 20 cloud images can make an AI deepfake video of your child - 5 AI terms you keep hearing and what they actually mean - AI to monitor NYC subway safety as crime concerns rise First Lady Melania Trump, joined by U.S. President Donald Trump, delivers remarks before President Trump signed the TAKE IT DOWN Act into law in the Rose Garden of the White House on May 19, 2025 in Washington, DC. The first lady made the Tools to Address Known Exploitation by Immobilizing Technological Deepfakes on Websites and Networks (TAKE IT DOWN) Act a priority, traveling to Capitol Hill to lobby lawmakers and show her support for the legislation, which addresses non-consensual intimate imagery, or "revenge porn," and artificial intelligence deepfakes posted online and to social media. DEEPFAKE DANGERS: Parents love capturing their kids' big moments, from first steps to birthday candles.

Texas high school student Elliston Berry joins'Fox & Friends' to discuss the House's passage of a new bill that criminalizes the sharing of non-consensual intimate images, including content created with artificial intelligence. Parents love capturing their kids' big moments, from first steps to birthday candles. But a new study out of the U.K. shows many of those treasured images may be scanned, analyzed and turned into data by cloud storage services, and nearly half of parents don't even realize it. A survey of 2,019 U.K. parents, conducted by Perspectus Global and commissioned by Swiss privacy tech company Proton, found that 48% of parents were unaware providers like Google Photos, Apple iCloud, Amazon Photos and Dropbox can access and analyze the photos they upload. First lady Melania Trump, joined by President Donald Trump, delivers remarks before President Trump signed the Take it Down Act into law in the Rose Garden of the White House May 19, 2025, in Washington, D.C. (Chip Somodevilla/Getty Images) These companies use artificial intelligence to sort images into albums, recognize faces and locations and suggest memories.

Accurate forecasts of distributed solar generation are necessary to reduce negative impacts resulting from the increased uptake of distributed solar photovoltaic (PV) systems. However, the high variability of solar generation over short time intervals (seconds to minutes) caused by cloud movement makes this forecasting task difficult. To address this, using cloud images, which capture the second-to-second changes in cloud cover affecting solar generation, has shown promise. Recently, deep neural networks with "attention" that focus on important regions of an image have been applied with success in many computer vision applications. However, their use for forecasting cloud movement has not yet been extensively explored. In this work, we propose an attention-based convolutional long short-term memory network to forecast cloud movement and apply an existing self-attention-based method previously proposed for video prediction to forecast cloud movement. We investigate and discuss the impact of cloud forecasts from attention-based methods towards forecasting distributed solar generation, compared to cloud forecasts from non-attention-based methods. We further provide insights into the different solar forecast performances that can be achieved for high and low altitude clouds. We find that for clouds at high altitudes, the cloud predictions obtained using attention-based methods result in solar forecast skill score improvements of 5.86% or more compared to non-attention-based methods.

There are hundreds of blog posts on machine learning and deep learning projects, and I've learned a lot from the ones that I've read. I wanted to add to this body of knowledge by discussing a deep learning side project that I worked on recently. I've shared the project code in a Github repo. Cloud detection in satellite images is an important classification problem. It's used heavily in the field of Remote Sensing, because clouds obscure the land underneath, and too many cloudy images in a dataset make it harder for a model to learn meaningful patterns.