A typical solution isdirect democracywhich decides based on amajority vote, the so called "wisdom of the crowd."

In practical situations, the clustering result must be stable against points missing in the input data so that we can make trustworthy andconsistentdecisions.

Optimal transport (OT) is a widely used technique in machine learning, graphics, and vision that aligns two distributions or datasets using their relative geometry. In symmetry-rich settings, however, OT alignments based solely on pairwise geometric distances between raw features can ignore the intrinsic coherence structure of the data. We introduce Bis-pectral Optimal Transport, a symmetry-aware extension of discrete OT that compares elements using their representation using the bispectrum, a group Fourier invariant that preserves all signal structure while removing only the variation due to group actions. Empirically, we demonstrate that the transport plans computed with Bispectral OT achieve greater class preservation accuracy than naive feature OT on benchmark datasets transformed with visual symmetries, improving the quality of meaningful correspondences that capture the underlying semantic label structure in the dataset while removing nuisance variation not affecting class or content.

Modern operational landscapes, spanning domains such as retail, healthcare, finance, and software systems, are increasingly characterized by complex interdependencies and massive data streams. In such settings, anomalies rarely arise from a single isolated factor; rather, they emerge as the cumulative effect of multi-hop causal chains. Existing RCA methods typically focus on detecting outliers or isolating single nodes based on correlation or localized attribution. However, these approaches do not provide a complete explanation of why a failure occurred. In other words they do not systematically trace all possible causal pathways from an observed effect back to its initial triggers. The primary motivation for our work is to address this limitation by developing a package that systematically reconstructs the full causal pathway from an observed anomaly back to its root cause. By leveraging the strengths of the DoWhy causal inference library, our method extends existing techniques to not only identify individual anomalous nodes but also trace entire multi-hop causal chains. This end-to-end approach enables practitioners to intervene precisely at the earliest disruption points, thereby reducing the risk of recurring failures and improving overall system reliability.

Current frameworks for training offensive penetration testing agents with deep reinforcement learning struggle to produce agents that perform well in real-world scenarios, due to the reality gap in simulation-based frameworks and the lack of scalability in emulation-based frameworks. Additionally, existing frameworks often use an unrealistic metric that measures the agents' performance on the training data. NASimEmu, a new framework introduced in this paper, addresses these issues by providing both a simulator and an emulator with a shared interface. This approach allows agents to be trained in simulation and deployed in the emulator, thus verifying the realism of the used abstraction. Our framework promotes the development of general agents that can transfer to novel scenarios unseen during their training. For the simulation part, we adopt an existing simulator NASim and enhance its realism. The emulator is implemented with industry-level tools, such as Vagrant, VirtualBox, and Metasploit. Experiments demonstrate that a simulation-trained agent can be deployed in emulation, and we show how to use the framework to train a general agent that transfers into novel, structurally different scenarios. NASimEmu is available as open-source.

Chatbot Development Cost: A few years ago, a chatbot would have resembled a miracle. Whenever you visit a website, you will get a message "Hello! How can I help you?" on the webpage's side corner and you would have felt thrilled after all a robot asking you queries. It was the biggest revolutionary aspect of technology. AI-integrated Chatbots are undoubtedly revolutionizing all industries (Such as IT, Ecommerce, Real Estate, Travel, and many more) around the globe.

Automation involves having a machine perform simple, repetitive operations that follow instructions or workflows set by humans. Automation tasks are very repetitive, predictable tasks. Think of a machine in a factory that makes the same part the same way over and over again. For many people, artificial intelligence (AI) means robots that perform complex human tasks in science fiction movies. Actually, this is partially true.

Intel Corp. is planning a major reduction in headcount, likely numbering in the thousands, to cut costs and cope with a sputtering personal-computer market, according to people with knowledge of the situation. The layoffs will be announced as early as this month, with the company planning to make the move around the same time as its third-quarter earnings report on Oct. 27, said the people, who asked not to be identified because the deliberations are private. The chipmaker had 113,700 employees as of July. Some divisions, including Intel's sales and marketing group, could see cuts affecting about 20% of staff, according to the people. Intel is facing a steep decline in demand for PC processors, its main business, and has struggled to win back market share lost to rivals like Advanced Micro Devices Inc.

No-code artificial intelligence (AI) tool sets out to demystify and democratize AI by providing non-technical users with code-free environments for building AI models. No-code tools use techniques like intuitive interfaces, templates, and drag-and-drop editors to build AI for tasks like image recognition, object detection, data classification, and predictive analytics. Here, we look at some of the no-code products currently available-- from free computer vision tools for home users to enterprise-level platforms. An uncluttered interface offers three categories of project to pick from: image, audio, or pose (body positions). Training data--image files and one-second audio clips--can be uploaded or captured via a Webcam or mic.

Artificial Intelligence can enable an amateur to capture expert images of a damaged -- or undamaged -- ... [ ] vehicle, according to Ravin AI. High-tech companies are bringing Artificial Intelligence to bear on the vehicle-inspection process. That sounds a little dry, but it's potentially a game-changer for anyone with a smartphone who wants to file an insurance claim, pick up or drop off a rent-a-car or a ride-share, turn in a lease vehicle, or someday, make a dealership service appointment, knowing in advance what's wrong with the vehicle. "I call it'proliferation of inspection,' " said Eliron Ekstein, co-founder and CEO of Ravin AI, based in London, with offices in Israel and Austin, Texas. "Traditionally, you have to bring the car to an auto auction, or to a special place at a dealership, and have a specialist look at it. We see a need to inspect vehicles during their use by non-professionals, by ordinary people like you and me," he said in a phone interview.