The rapid evolution of Large Language Model (LLM) agents has highlighted critical challenges in cross-vendor service discovery, interoperability, and communication. Existing protocols like model context protocol and agent-to-agent protocol have made significant strides in standardizing interoperability between agents and tools, as well as communication among multi-agents. However, there remains a lack of standardized protocols and solutions for service discovery across different agent and tool vendors. In this paper, we propose AgentDNS, a root domain naming and service discovery system designed to enable LLM agents to autonomously discover, resolve, and securely invoke third-party agent and tool services across organizational and technological boundaries. Inspired by the principles of the traditional DNS, AgentDNS introduces a structured mechanism for service registration, semantic service discovery, secure invocation, and unified billing. We detail the architecture, core functionalities, and use cases of AgentDNS, demonstrating its potential to streamline multi-agent collaboration in real-world scenarios. The source code will be published on https://github.com/agentdns.

System identification involves constructing mathematical models of dynamic systems using input-output data, enabling analysis and prediction of system behaviour in both time and frequency domains. This approach can model the entire system or capture specific dynamics within it. For meaningful analysis, it is essential for the model to accurately reflect the underlying system's behaviour. This paper introduces NonSysId, an open-sourced MATLAB software package designed for nonlinear system identification, specifically focusing on NARMAX models. The software incorporates an advanced term selection methodology that prioritises on simulation (free-run) accuracy while preserving model parsimony. A key feature is the integration of iterative Orthogonal Forward Regression (iOFR) with Predicted Residual Sum of Squares (PRESS) statistic-based term selection, facilitating robust model generalisation without the need for a separate validation dataset. Furthermore, techniques for reducing computational overheads are implemented. These features make NonSysId particularly suitable for real-time applications such as structural health monitoring, fault diagnosis, and biomedical signal processing, where it is a challenge to capture the signals under consistent conditions, resulting in limited or no validation data.

The global semiconductor market is expected to grow 13.1% in 2024 to a record $588.36 billion, following a slump this year, thanks to growing demand for chips used for artificial intelligence, according to a forecast by an industry organization. The World Semiconductor Trade Statistics, an organization formed by major chip manufacturers, revised its growth forecast higher for the next year from the previous growth estimate made in June of 11.8%. If realized, the market size in terms of billings will exceed the previous record of $574.08 billion in 2022. In 2023, the market is expected to decrease 9.4% to $520.13 billion due to weaker demand for memory chips. The optimistic outlook comes as the industry has started to see signs of recovery in demand driven by widespread use of generative AI following the launch of ChatGPT, an AI chatbot developed by U.S.-based OpenAI, and improving sales of PCs and smartphones.

Servizi Elaborazioni Dati SpA is a public company owned by Municipality of L Aquila, it supplies the institution with network services and software applications for distributing services to citizens. The future policy of the company is to enlarge the offer of its services to nearby communities that are unable to set up and maintain their own network and software structures. This paper presents thus a possible architecture model to support small municipalities in supplying public services to citizens, with the aid of SED Spa. Through second level platforms based on Blockchain networks and Multi-agents Systems running on smart contracts, the system will focus on Waste Tax (Ta.Ri) management system in the Fascicolo del Cittadino environment.

I talked to her about the significance of the role in The Peripheral, where she plays a trans person in the future. The show is based on a novel by William Gibson, who coined the term cyberspace, and it was produced by Westworld creators Lisa Joy and Jonathan Nolan. It's a complicated story that moves around in time and explores whether the digital world is real or not. And the show is different from the book, as it uses Gibson's story as a jumping off point for ideas about our future. And that gives Billings some interesting leeway to play Lowbeer as a trans person in the show.

Emerging tools such as artificial intelligence and natural language processing are being used in the insurance sector, but costs remain high and there are questions about bias being introduced into machine learning, according to a speaker at the Public Risk Management Association's annual meeting Monday. "Everything is smart these days," said Brian Billings, vice president of predictive analytics in Ballwin, Missouri, for Midwest Employers Casualty Co., part of W.R Berkeley Corp., and such devices as cell phones and televisions now collect data from their users. "All of that technology is being driven by the use of data." Machine learning, including artificial intelligence and natural language processing, takes the data being collected and tries to predict some kind of outcome, Mr. Billings said, such as a numerical value or, in the case of the insurance sector, a claims scenario. With natural language processing, a model is trained to read text, Mr. Billings said.

Artificial intelligence, machine learning, and business intelligence are being widely used to boost the success and capabilities of various organizations. Even telecom industries utilize AI to eradicate network issues, poor data analysis, high costs, and a crowded marketplace. As a telecommunication company running certain operations remotely, you will need specific data and software to help you manage your work. A software that is perfect for remote businesses is coAmplifi. It is an excellent option as it allows you to boost productivity and monitor your employees right from the comfort of your home.

Today, nearly every role in every industry is facing some level of disruption. We are tasked with achieving more with less – less time, less resources, less information, and often less support. No industry has experienced this more than healthcare. Healthcare workers, from clinicians to administrative coordinators, are feeling the pressure to simultaneously improve patient outcomes and experience, reduce operating costs and increase billings. Though it's not front and center, one major component within any healthcare system that greatly impacts all three of these goals--and where administrative and clinical workflows are intertwined--is revenue cycle management.

The legal industry has been undergoing a technological revolution in the past decade, and few technologies have been having a more significant impact than artificial intelligence. Lawyers everywhere are curious to know how artificial intelligence will affect the practice of law. Nick Whitehouse, GM of the Onit AI Center of Excellence, recently sat down with Jared Correia, host of Above the Law's Non-Eventcast podcast (available on Apple and Spotify), to discuss how AI impacts the legal world. Spoiler alert: it's not Terminator time just yet. The conversation started with an icebreaker about the latest Pixar movie, Lightyear, which proved to be an ideal segue into the topic of AI.

Artificial Intelligence (AI) is the latest technology development gaining significant traction in various areas of the healthcare industry, including diagnostics, patient outreach, and revenue cycle management (RCM) activities. Because revenue cycle management functions require substantial time, financial, and personnel resources, this area is particularly suited to benefit from the adaption of AI. In fact, payers and providers spend $496 billion on billing and insurance-related (BIR) costs each year. Manual and redundant tasks like coding, billing, collections, and denials become instantly simplified with appropriate artificial intelligence. AI imitates human intelligence through algorithms that identify patterns and plan for future outcomes, unlike machine learning or other robotic processes that only focus on accuracy.