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.

The Israeli spyware maker NSO Group has been on the US Department of Commerce "blacklist" since 2021 over its business of selling targeted hacking tools. But a WIRED investigation has found that the company now appears to be working to stage a comeback in Trump's America, hiring a lobbying firm with the ties to the administration to make its case. As the White House continues its massive gutting of the United States federal government, remote and hybrid workers have been forced back to the office in a poorly coordinated effort that has left critical employees without necessary resources--even reliable Wi-Fi. And Elon Musk's so-called Department of Government Efficiency (DOGE) held a "hackathon" in Washington, DC, this week to work on developing a "mega API" that could act as a bridge between software systems for accessing and sharing IRS data more easily. Meanwhile, new research this week indicates that misconfigured sexual fantasy-focused AI chatbots are leaking users' chats on the open internet--revealing explicit prompts and conversations that in some cases include descriptions of child sexual abuse.

Russia's territorial gains in Ukraine are slowing down dramatically, two analyses have found, continuing a pattern from 2024 at a time when both nations are trying to project strength in the face of United States-mediated negotiations aimed at ending the war. Britain's Ministry of Defence last week estimated that Russian forces seized 143sq km (55sq miles) of Ukrainian land in March, compared with 196sq km (76sq miles) in February and 326sq km (126sq miles) in January. The Institute for the Study of War, a Washington, DC-based think tank, spotted the same trend, estimating Russian gains at 203sq km (78sq miles) in March, 354sq km (137sq miles) in February and 427sq km (165sq miles) in January. These estimates are based on satellite imagery and geolocated open-source photography rather than claims by either side. Should this trend continue, Russian forces could come to a standstill by early summer, roughly coinciding with US President Donald Trump's self-imposed early deadline for achieving a ceasefire.

Understanding how urban environments are perceived in terms of safety is crucial for urban planning and policymaking. Traditional methods like surveys are limited by high cost, required time, and scalability issues. To overcome these challenges, this study introduces Large Multimodal Models (LMMs), specifically Llava 1.6 7B, as a novel approach to assess safety perceptions of urban spaces using street-view images. In addition, the research investigated how this task is affected by different socio-demographic perspectives, simulated by the model through Persona-based prompts. Without additional fine-tuning, the model achieved an average F1-score of 59.21% in classifying urban scenarios as safe or unsafe, identifying three key drivers of perceived unsafety: isolation, physical decay, and urban infrastructural challenges. Moreover, incorporating Persona-based prompts revealed significant variations in safety perceptions across the socio-demographic groups of age, gender, and nationality. Elder and female Personas consistently perceive higher levels of unsafety than younger or male Personas. Similarly, nationality-specific differences were evident in the proportion of unsafe classifications ranging from 19.71% in Singapore to 40.15% in Botswana. Notably, the model's default configuration aligned most closely with a middle-aged, male Persona. These findings highlight the potential of LMMs as a scalable and cost-effective alternative to traditional methods for urban safety perceptions. While the sensitivity of these models to socio-demographic factors underscores the need for thoughtful deployment, their ability to provide nuanced perspectives makes them a promising tool for AI-driven urban planning.

Decision tree optimization is fundamental to interpretable machine learning. The most popular approach is to greedily search for the best feature at every decision point, which is fast but provably suboptimal. Recent approaches find the global optimum using branch and bound with dynamic programming, showing substantial improvements in accuracy and sparsity at great cost to scalability. An ideal solution would have the accuracy of an optimal method and the scalability of a greedy method. We introduce a family of algorithms called SPLIT (SParse Lookahead for Interpretable Trees) that moves us significantly forward in achieving this ideal balance. We demonstrate that not all sub-problems need to be solved to optimality to find high quality trees; greediness suffices near the leaves. Since each depth adds an exponential number of possible trees, this change makes our algorithms orders of magnitude faster than existing optimal methods, with negligible loss in performance. We extend this algorithm to allow scalable computation of sets of near-optimal trees (i.e., the Rashomon set).

Scientific machine learning (SciML) integrates machine learning (ML) into scientific workflows to enhance system simulation and analysis, with an emphasis on computational modeling of physical systems. This field emerged from Department of Energy workshops and initiatives starting in 2018, which also identified the need to increase "the scale, rigor, robustness, and reliability of SciML necessary for routine use in science and engineering applications" [5]. The field's subsequent growth through funding initiatives, conference themes, and high-profile publications stems from its ability to unite ML's predictive power with the domain knowledge and mathematical rigor of computational science and engineering (CSE). However, this surge in SciML development has outpaced good practices and reporting standards for building trust [66, 51, 109, 117]. SciML models must demonstrate trustworthiness to be safe and useful [44]. Organizational and computational trust definitions [92, 106] inform our criteria for trustworthy SciML: competence in basic performance, reliability across conditions, transparency about processes and limitations, and alignment with scientific objectives. These criteria span technical attributes (correctness, reliability, safety) and human-centric qualities (comprehensibility, transparency).

Fox News congressional correspondent Aishah Hasnie has more on who will be in attendance and policies President-elect Donald Trump will enact during his first day in office on'Special Report.' President-elect Donald Trump's inauguration guest list will include some of America's most influential billionaires, including Meta CEO Mark Zuckerberg and Amazon founder Jeff Bezos--signaling a sharp political shift among the tech industry's biggest players. Silicon Valley, traditionally a stronghold for left-leaning ideals, has largely embraced Trump following the November election. The incoming president amassed a record-breaking inaugural fund with substantial donations from tech executives. The heads of companies such as Google, OpenAI, Apple, Uber, and Microsoft have also forked over millions to fund inaugural events, including parades and swanky parties.

Fox News correspondent William La Jeunesse joins'Fox News Sunday' to discuss the evolution of AI and the push lawmakers are making to regulate it. FIRST ON FOX: A select group of tech industry titans and venture capitalists will gather in Washington, D.C., this week to welcome the incoming Trump administration and celebrate new opportunities for global innovation in artificial intelligence and entrepreneurship. Presidents and CEOs from companies on the cutting edge of AI tech and their big financial backers, along with personnel from the incoming administration, will attend a dinner on Thursday organized by Outside the Box Ventures, a firm founded last year by journalist-turned-investment banker Katherine Tarbox, along with Laurent Bili, the French ambassador to the U.S. The list of those invited to Thursday's dinner includes "DOGE" chief Elon Musk, Silicon Valley investor and GOP mega-donor Peter Thiel, NVCA chief executive Bobby Franklin, incoming White House AI and crypto czar David Sacks, OpenAI's Sam Altman, investor Joe Lonsdale and Narya co-founder Colin Greenspon. "This gathering represents more than discussion. We hope it symbolizes a new chapter in public-private collaboration to harness technology's transformative power for the nation's future," a source close to the planning told Fox News Digital.

Robust classification of the operational environment of wireless devices is becoming increasingly important for wireless network optimization, particularly in a shared spectrum environment. Distinguishing between indoor and outdoor devices can enhance reliability and improve coexistence with existing, outdoor, incumbents. For instance, the unlicensed but shared 6 GHz band (5.925 - 7.125 GHz) enables sharing by imposing lower transmit power for indoor unlicensed devices and a spectrum coordination requirement for outdoor devices. Further, indoor devices are prohibited from using battery power, external antennas, and weatherization to prevent outdoor operations. As these rules may be circumvented, we propose a robust indoor/outdoor classification method by leveraging the fact that the radio-frequency environment faced by a device are quite different indoors and outdoors. We first collect signal strength data from all cellular and Wi-Fi bands that can be received by a smartphone in various environments (indoor interior, indoor near windows, and outdoors), along with GPS accuracy, and then evaluate three machine learning (ML) methods: deep neural network (DNN), decision tree, and random forest to perform classification into these three categories. Our results indicate that the DNN model performs the best, particularly in minimizing the most important classification error, that of classifying outdoor devices as indoor interior devices.