As modern software systems grow in complexity and scale, their ability to autonomously detect, diagnose, and recover from failures becomes increasingly vital. Drawing inspiration from biological healing--where the human body detects damage, signals the brain, and activates targeted recovery--this paper explores the concept of self-healing software driven by artificial intelligence. We propose a novel framework that mimics this biological model: system observability tools serve as sensory inputs, AI models function as the cognitive core for diagnosis and repair, and healing agents apply targeted code and test modifications. By combining log analysis, static code inspection, and AI-driven generation of patches or test updates, our approach aims to reduce downtime, accelerate debugging, and enhance software resilience. We evaluate the effectiveness of this model through case studies and simulations, comparing it against traditional manual debugging and recovery workflows. This work paves the way toward intelligent, adaptive, and self-reliant software systems capable of continuous healing, akin to living organisms.

Unsupervised graph-level anomaly detection (UGLAD) is a critical and challenging task across various domains, such as social network analysis, anti-cancer drug discovery, and toxic molecule identification. However, existing methods often struggle to capture the long-range dependencies efficiently and neglect the spectral information. Recently, selective State Space Models (SSMs), particularly Mamba, have demonstrated remarkable advantages in capturing long-range dependencies with linear complexity and a selection mechanism. Motivated by their success across various domains, we propose GLADMamba, a novel framework that adapts the selective state space model into UGLAD field. We design View-Fused Mamba (VFM) with a Mamba-Transformer-style architecture to efficiently fuse information from different views with a selective state mechanism. We also design Spectrum-Guided Mamba (SGM) with a Mamba-Transformer-style architecture to leverage the Rayleigh quotient to guide the embedding refining process. GLADMamba can dynamically focus on anomaly-related information while discarding irrelevant information for anomaly detection. To the best of our knowledge, this is the first work to introduce Mamba and explicit spectral information to UGLAD. Extensive experiments on 12 real-world datasets demonstrate that GLADMamba outperforms existing state-of-the-art methods, achieving superior performance in UGLAD. The code is available at https://github.com/Yali-F/GLADMamba.

Masks created with photos from social media that can penetrate a system protected by face ID. They sound like the stuff of science fiction, but these techniques are already available to criminals preying on everyday consumers. The proliferation of scam technology has alarmed regulators, police and people at the highest levels of the financial industry. Artificial intelligence in particular is being used to "turbocharge" fraud, U.S. Federal Trade Commission Chair Lina Khan warned in June, calling for increased vigilance from law enforcement. Even before AI broke loose and became available to anyone with an internet connection, the world was struggling to contain an explosion in financial fraud.

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Imagine if you could walk into a supermarket, grab whatever you need, and just walk out. Your account would automatically be charged and an itemized bill will be sent your way. Amazon Go stores are living examples of such a utopian dream. You can learn more about Amazon Go from the video below. And that reality is being powered by artificial intelligence.

The 48th IT Press Tour had the opportunity to meet with the management team at CloudFabrix. This is the team's fourth startup, with the previous three being sold to Cisco. They are doing a lot of interesting things automating operations. Multi-cloud challenges continue, as 73% of enterprises use two or more clouds. This is projected to be 81% by 2024.

The leading television stations serving the Birmingham television market began broadcasting with NextGen TV, a revolutionary new digital broadcast technology. Today's launch includes WABM (ABC) and WDBB (ABC and CW), WIAT (CBS), WBRC (Fox), WVTM-TV (NBC), WTTO (CW), and WSES (Heroes and Icons). Based on the same fundamental technology as the Internet and digital apps, NextGen TV can support a wide range of features that are currently in development. In addition to providing a new, improved way for broadcasters to reach viewers with advanced emergency alerts, NextGen TV features stunning video with brilliant color, sharper images and deeper contrast to create a more life-like experience. NextGen TV adds a new dimension to TV viewing, with vibrant video and new Voice dialogue enhancement that brings voices to the foreground.

One of the biggest names in racing is going even bigger. Performance automaker Lotus launched its first SUV, the Eletre, earlier this week. The fully electric vehicle sacrifices little in terms of speed and outperforms when it comes to technology. It features an immersive digital cockpit, lengthy battery range of up to 370 miles and autonomous-driving capabilities powered by the NVIDIA DRIVE Orin system-on-a-chip. The Eletre's autonomous-driving system is designed for more than easier commutes.

Near-real-time data on avian-solar interactions will help the energy industry understand risks and opportunities for wildlife at solar energy plants. How does an array of solar panels change a habitat? The question is complex--and increasingly important, as solar energy plants proliferate across the United States. The industry and researchers, however, currently don't have a lot of answers. Researchers at the Department of Energy's (DOE) Argonne National Laboratory are developing technology that can help.

Before AI came into use, translation was a job that was time-consuming, well-paid, and required a high level of education. Thanks to AI, translation software makes translating a common service that is instant, free, and convenient. In this article, we will explore what machine translation is, how AI improves the industry, and why AI-powered software cannot replace human translators. Machine Translation uses AI-powered software to automatically translate the language in the source material to another language, without any interventions from human agents. In 1970, the first machine translation software was developed.