We study the propagation of outliers in cyclic causal graphs with linear structural equations, tracing them back to one or several "root cause" nodes. We show that it is possible to identify a short list of potential root causes provided that the perturbation is sufficiently strong and propagates according to the same structural equations as in the normal mode. This shortlist consists of the true root causes together with those of its parents lying on a cycle with the root cause. Notably, our method does not require prior knowledge of the causal graph.

Recent advancements in tool-augmented large language models have enabled them to interact with external tools, enhancing their ability to perform complex user tasks. However, existing approaches overlook the role of personalisation in guiding tool use. This work investigates how user preferences can be effectively integrated into goal-oriented dialogue agents. Through extensive analysis, we identify key weaknesses in the ability of LLMs to personalise tool use. To this end, we introduce TAPS, a novel solution that enhances personalised tool use by leveraging a structured tagging tool and an uncertainty-based tool detector. TAPS significantly improves the ability of LLMs to incorporate user preferences, achieving the new state-of-the-art for open source models on the NLSI task.

Dr. Mehmet Oz, the new administrator for the Centers for Medicare and Medicaid Services (CMS), spent much of his first all-staff meeting on Monday promoting the use of artificial intelligence at the agency and praising Robert F. Kennedy Jr.'s "Make America Healthy Again Initiative," sources tell WIRED. During the meeting, Oz discussed possibly prioritizing AI avatars over frontline health care workers. Oz claimed that if a patient went to a doctor for a diabetes diagnosis, it would be 100 per hour, while an appointment with an AI avatar would cost considerably less, at just 2 an hour. Oz also claimed that patients have rated the care they've received from an AI avatar as equal to or better than a human doctor. Because of technologies like machine learning and AI, Oz claimed, it is now possible to scale "good ideas" in an affordable and fast way.

As the use of facial recognition technology is expanding in different domains, ensuring its responsible use is gaining more importance. This paper conducts a comprehensive literature review of existing studies on facial recognition technology from the perspective of privacy, which is one of the key Responsible AI principles. Cloud providers, such as Microsoft, AWS, and Google, are at the forefront of delivering facial-related technology services, but their approaches to responsible use of these technologies vary significantly. This paper compares how these cloud giants implement the privacy principle into their facial recognition and detection services. By analysing their approaches, it identifies both common practices and notable differences. The results of this research will be valuable for developers and businesses by providing them insights into best practices of three major companies for integration responsible AI, particularly privacy, into their cloud-based facial recognition technologies.

Modern applications are increasingly built as vast, intricate, distributed systems. These systems comprise various software modules, often developed by different teams using different programming languages and deployed across hundreds to thousands of machines, sometimes spanning multiple data centers. Given the ir scale and complexity, these applications are often designed to tolerate failures and performance issues through inbuilt failure recovery techniques (e.g., hardware or software redundancy) or extern al methods (e.g., health check - based restarts). Computer systems experience frequent failures despite every effort: performance degradations and violations of reliability and K ey Performance Indicators (K PI s) are inevitable. These failures, depending on their nature, can lead to catastrophic incidents impacting critical systems and customers. Swift and accurate root cause identification is thus essential to avert significant incidents impacting both service quality and end users. In this complex landscape, observability platforms that provide deep insights into system behavior and help identify performance bottlenecks are not just helpful -- they are essential for maintaining reliability, ensuring optimal performance, and quickly resolving issues in production. The ability to reason a bout these systems in real - time is critical to ensuring the scalability and stability of modern services. To aid in these investigations, observability platforms that collect various telemetry data t o inform about application behavior and its underlying infrastructure are getting popular .

RStudio on Amazon SageMaker is the industry's first fully managed RStudio Workbench integrated development environment (IDE) in the cloud. You can quickly launch the familiar RStudio IDE and dial up and down the underlying compute resources without interrupting your work, making it easy to build machine learning (ML) and analytics solutions in R at scale. In conjunction with tools like RStudio on SageMaker, users are analyzing, transforming, and preparing large amounts of data as part of the data science and ML workflow. Data scientists and data engineers use Apache Spark, Hive, and Presto running on Amazon EMR for large-scale data processing. Using RStudio on SageMaker and Amazon EMR together, you can continue to use the RStudio IDE for analysis and development, while using Amazon EMR managed clusters for larger data processing.

Amazon SageMaker Studio can help you build, train, debug, deploy, and monitor your models and manage your machine learning (ML) workflows. Amazon SageMaker Pipelines enables you to build a secure, scalable, and flexible MLOps platform within Studio. In this post, we explain how to run PySpark processing jobs within a pipeline. This enables anyone that wants to train a model using Pipelines to also preprocess training data, postprocess inference data, or evaluate models using PySpark. This capability is especially relevant when you need to process large-scale data.

Alibaba Cloud, Alibaba Group's digital technology and intelligence backbone, today unveiled its latest large language model, Tongyi Qianwen. The new AI model will be integrated into Alibaba's various business units in the near future to improve user experience. The company's customers and developers will have access to the model to cost-effectively build custom AI functions. Alibaba Cloud also announced lower-cost options for key cloud products, including Elastic Compute Service (ECS) and Object Storage Service (OSS), by introducing new ECS instances, OSS Reserved Capacity (OSS-RC), and OSS Anywhere Reserved Capacity ( OSS-RC). The move will make computing more accessible and affordable for companies looking to unlock new opportunities in China's new AI era.

Gary Fowler is a serial AI entrepreneur with numerous startups and an IPO. He is CEO and cofounder of GSDVS.com and Yva.ai. If you've checked any social media platforms, forums or publishers recently, you've likely seen how the entire media world has been inundated with ChatGPT reviews, explanations and use cases. The generative AI technology from OpenAI is taking the world by storm--and there is no stopping it. Now, you might ask me, "Gary, AI has been in the news for almost a decade now.

Spry Squared is a Minority and Woman Owned Small Business headquartered in Denver, Colorado with offices across the United States of America. We are an experienced federal government and commercial service provider with security cleared personnel working on various projects across the USA and the globe. Spry Squared provides organizations with Best in Class Enterprise Solutions, Managed IT Services, Cybersecurity Solutions, IT Professional Services, Recruiting Services, Project/Program Management and technology products. We are your strategic partner and value-added reseller, solving complex business challenges by leveraging technology solutions that reduce costs, optimize productivity and minimize risk. The analyst will support the development and maintenance of reference data bases in support of critical kill-chain scenarios.