'America Reports' panelists Meghan Hays and David Avella discuss Democrats' ongoing criticism of DOGE cuts. Tax season is upon us, and while many of you are preparing to file your returns, it's crucial to be aware of the ever-evolving world of tax scams. This year, it's more important than ever to stay informed and on your guard. New research by McAfee, a cybersecurity company, has shed light on how common these scams are and what kind of scams they are, revealing some surprising trends and highlighting the importance of protecting yourself. GET SECURITY ALERTS & EXPERT TECH TIPS – SIGN UP FOR KURT'S THE CYBERGUY REPORT NOW Scam written on tax forms (Kurt "CyberGuy" Knutsson) Before diving into the scams, let's look at how people are handling their taxes these days.

Tehran, Iran – Iran's army and Islamic Revolutionary Guard Corps (IRGC) have been showcasing and testing new defensive and offensive weapons in large-scale military exercises for the past three months. The country is preparing for another tumultuous year amid threats by the United States and Israel to bomb Iranian nuclear facilities, critical energy infrastructure, and military sites. Iran is also promising a third iteration of its major military strikes on Israel, in retaliation for Israeli attacks amid the devastating war on Gaza. The exercises – Eqtedar, Zolfaqar and Great Prophet – have been held across Iran, the Sea of Oman and the northern Indian Ocean. The weapons tested show Iran intends to maintain its defiance of Israel and the West, refusing to negotiate with US President Donald Trump under his "maximum pressure" policy and continuing to advance its nuclear programme.

Animation shows traditional counter-drone technology vs. Epirus' Leonidas system that can take out entire swarms of drones at once. A new high-powered microwave system that can knock swarms of drones out of the sky at once is going to "touch every aspect of warfare," according to Epirus founder, Joe Lonsdale. "It's kind of like a Star Trek shield," Lonsdale, founder of Epirus and a co-founder of fast-rising defense technology company Palantir, explained of its Leonidas counter-drone system. "It's able to turn them off from very far away." "This is going to touch every aspect of warfare over the next decade," said Lonsdale.

The United States Army is employing a prototype generative artificial intelligence tool to identify references to diversity, equity, inclusion, and accessibility (DEIA) for removal from training materials in line with a recent executive order from President Donald Trump. Officials at the Army's Training and Doctrine Command (TRADOC)--the major command responsible for training soldiers, developing leaders, and shaping the service's guidelines, strategies, and concepts--are currently using the AI tool, dubbed CamoGPT, to "review policies, programs, publications, and initiatives for DEIA and report findings," according to an internal memo reviewed by WIRED. The memo followed Trump's signing of a January 27 executive order entitled, "Restoring America's Fighting Force," which directed Defense Secretary Pete Hegseth to eliminate all Pentagon policies seen as promoting what that the commander-in-chief declared "un-American, divisive, discriminatory, radical, extremist, and irrational theories" regarding race and gender, a linguistic dragnet that extends as far as past social media posts from official US military accounts. Chris Robinson confirmed the use of CamoGPT to review DEIA materials. "[TRADOC] will fully execute and implement all directives outlined in the Executive Orders issued by the President. We ensure that these directives are carried out with the utmost professionalism, efficiency, and in alignment with national security objectives," Robinson says.

Veteran Democratic strategist James Carville said that he is so frustrated by Republican support for tariffs that he has contemplated punching his computer in rage. Veteran Democratic Party strategist James Carville said that the Republican response to the consequences of tariffs has him contemplating smashing his own computer in rage. CNN host Wolf Blitzer asked Carville what he makes of President Donald Trump's tariffs on goods from Mexico, Canada and China and the fallout. "I've come to think maybe Donald Trump hates the United States," Carville suggested, arguing that Trump's economic and foreign policy strategies are otherwise nonsensical. "I just can't get it out of my mind that I think this man – there's some possibility - we have to consider the possibility that our president hates our country."

As robotaxis become a more familiar sight on the streets of Los Angeles, Tesla has taken a step that could bring it closer to building its own fleet of self-driving electric vehicles, the California Public Utilities Commission confirmed last week. In November, Tesla applied for a permit that would allow the electric vehicle manufacturing giant to deploy transportation services with company-owned vehicles and human drivers. The permit would be required for Tesla to advance to autonomous cabs. Chief Executive Elon Musk has long made clear his ambitions for a robotaxi service powered by Tesla vehicles, though his company has been criticized by the U.S. government's highway safety agency for making statements that its vehicles can drive themselves. To be sure, the automaker is still a long way off before it can launch a service.

The rise of generative artificial intelligence (AI) as a novel frontier that uniquely merges advanced levels of intelligence with revolutionary user experiences is redefining the AI landscape for future cellular networks. In particular, the transition towards 6G systems has introduced a myriad of challenges inherent to their AI-native network design, requiring innovative solutions to enable real-time network orchestration, intelligent decision-making, and adaptive dynamic configurations. Meanwhile, the envisioned user experiences for 6G are growing increasingly complex, exceeding the capabilities offered by vintage wireless technologies and conventional AI solutions to satisfy their advanced demands. With its disruptive impact evident across diverse fields, generative AI possesses immense potential to tackle these challenges, leveraging its exceptional capabilities to manage complex tasks, operate autonomously, and adapt seamlessly to scenarios beyond its training domain. Remarkably, generative AI provides a transformative opportunity for telecom and cellular networks to bridge this defined gap in 6G systems, thereby shifting towards a new era with cutting-edge AI innovations across the different system and user levels.

While state-of-the-art language models achieve impressive results through next-token prediction, they have inherent limitations such as the inability to revise already generated tokens. This has prompted exploration of alternative approaches such as discrete diffusion. However, masked diffusion, which has emerged as a popular choice due to its simplicity and effectiveness, reintroduces this inability to revise words. To overcome this, we generalize masked diffusion and derive the theoretical backbone of a family of general interpolating discrete diffusion (GIDD) processes offering greater flexibility in the design of the noising processes. Leveraging a novel diffusion ELBO, we achieve compute-matched state-of-the-art performance in diffusion language modeling. Exploiting GIDD's flexibility, we explore a hybrid approach combining masking and uniform noise, leading to improved sample quality and unlocking the ability for the model to correct its own mistakes, an area where autoregressive models notoriously have struggled. Our code and models are open-source: https://github.com/dvruette/gidd/

Monitoring the atmospheric dispersion of pollutants is increasingly critical for environmental impact assessments. High-fidelity computational models are often employed to simulate plume dynamics, guiding decision-making and prioritizing resource deployment. However, such models can be prohibitively expensive to simulate, as they require resolving turbulent flows at fine spatial and temporal resolutions. Moreover, there are at least two distinct dynamical regimes of interest in the plume: (i) the initial ejection of the plume where turbulent mixing is generated by the shear-driven Kelvin-Helmholtz instability, and (ii) the ensuing turbulent diffusion and advection which is often modeled by the Gaussian plume model. We address the challenge of modeling the initial plume generation. Specifically, we propose a data-driven framework that identifies a reduced-order analytical model for plume dynamics -- directly from video data. We extract a time series of plume center and edge points from video snapshots and evaluate different regressions based to their extrapolation performance to generate a time series of coefficients that characterize the plume's overall direction and spread. We regress to a sinusoidal model inspired by the Kelvin-Helmholtz instability for the edge points in order to identify the plume's dispersion and vorticity. Overall, this reduced-order modeling framework provides a data-driven and lightweight approach to capture the dominant features of the initial nonlinear point-source plume dynamics, agnostic to plume type and starting only from video. The resulting model is a pre-cursor to standard models such as the Gaussian plume model and has the potential to enable rapid assessment and evaluation of critical environmental hazards, such as methane leaks, chemical spills, and pollutant dispersal from smokestacks.

As artificial intelligence (AI) systems become increasingly complex and autonomous, concerns over transparency and accountability have intensified. The "black box" problem in AI decision-making limits stakeholders' ability to understand, trust, and verify outcomes, particularly in high-stakes sectors such as healthcare, finance, and autonomous systems. Blockchain technology, with its decentralized, immutable, and transparent characteristics, presents a potential solution to enhance AI transparency and auditability. This paper explores the integration of blockchain with AI to improve decision traceability, data provenance, and model accountability. By leveraging blockchain as an immutable record-keeping system, AI decision-making can become more interpretable, fostering trust among users and regulatory compliance. However, challenges such as scalability, integration complexity, and computational overhead must be addressed to fully realize this synergy. This study discusses existing research, proposes a framework for blockchain-enhanced AI transparency, and highlights practical applications, benefits, and limitations. The findings suggest that blockchain could be a foundational technology for ensuring AI systems remain accountable, ethical, and aligned with regulatory standards.