Software automation has long been a central goal of software engineering, striving for software development that proceeds without human intervention. Recent efforts have leveraged Artificial Intelligence (AI) to advance software automation with notable progress. However, current AI functions primarily as assistants to human developers, leaving software development still dependent on explicit human intervention. This raises a fundamental question: Can AI move beyond its role as an assistant to become a core component of software, thereby enabling genuine software automation? To investigate this vision, we introduce AI-Driven Self-Evolving Software, a new form of software that evolves continuously through direct interaction with users. We demonstrate the feasibility of this idea with a lightweight prototype built on a multi-agent architecture that autonomously interprets user requirements, generates and validates code, and integrates new functionalities. Case studies across multiple representative scenarios show that the prototype can reliably construct and reuse functionality, providing early evidence that such software systems can scale to more sophisticated applications and pave the way toward truly automated software development. We make code and cases in this work publicly available at https://anonymous.4open.science/r/live-software.

This paper provides a summary of the 281 sorties that were flown by the 10 different models of small unmanned aerial systems (sUAS) at Hurricane Ian, and the failures made in the field. These 281 sorties, supporting 44 missions, represents the largest use of sUAS in a disaster to date (previously Hurricane Florence with 260 sorties). The sUAS operations at Hurricane Ian differ slightly from prior operations as they included the first documented uses of drones performing interior search for victims, and the first use of a VTOL fixed wing aircraft during a large scale disaster. However, there are substantive similarities to prior drone operations. Most notably, rotorcraft continue to perform the vast majority of flights, wireless data transmission capacity continues to be a limitation, and the lack of centralized control for unmanned and manned aerial systems continues to cause operational friction. This work continues by documenting the failures, both human and technological made in the field and concludes with a discussion summarizing potential areas for further work to improve sUAS response to large scale disasters.

Data collected at Hurricane Ian (2022) quantifies the demands that small uncrewed aerial systems (UAS), or drones, place on the network communication infrastructure and identifies gaps in the field. Drones have been increasingly used since Hurricane Katrina (2005) for disaster response, however getting the data from the drone to the appropriate decision makers throughout incident command in a timely fashion has been problematic. These delays have persisted even as countries such as the USA have made significant investments in wireless infrastructure, rapidly deployable nodes, and an increase in commercial satellite solutions. Hurricane Ian serves as a case study of the mismatch between communications needs and capabilities. In the first four days of the response, nine drone teams flew 34 missions under the direction of the State of Florida FL-UAS1, generating 636GB of data. The teams had access to six different wireless communications networks but had to resort to physically transferring data to the nearest intact emergency operations center in order to make the data available to the relevant agencies. The analysis of the mismatch contributes a model of the drone data-to-decision workflow in a disaster and quantifies wireless network communication requirements throughout the workflow in five factors. Four of the factors-availability, bandwidth, burstiness, and spatial distribution-were previously identified from analyses of Hurricanes Harvey (2017) and Michael (2018). This work adds upload rate as a fifth attribute. The analysis is expected to improve drone design and edge computing schemes as well as inform wireless communication research and development.

A career at Lilium is for those who want to do something extraordinary. We take pride in pushing the boundaries of engineering, technology and customer experience. As part of a team, you will tackle challenges and deliver something that has never been done before. By joining Lilium you will have the opportunity to work with a world-class entrepreneurial team of more than 800 people who are as passionate about changing the world as you are. You will always act with safety and integrity in mind and embody our core behaviors of efficient and positive collaboration, ownership of time, continuous improvements and, ultimately delivering results.

We currently have a vacancy for a Data Manager fluent in English, to offer his/her services as an expert who will work remotely. In the context of the first assignment, the successful candidate will be integrated in the team of the company that will closely cooperate with a major client's IT team on site. If you are seeking a career in an exciting and dynamic company, where you will offer your services as part of a team of a major European Institution, operating in an international, multilingual and multicultural environment where you can expect real chances to make a difference, please send us your detailed CV in English, quoting reference (14589/01/2023). We offer a competitive remuneration (either on contract basis or remuneration with full benefits package), based on qualifications and experience. All applications will be treated as confidential.

AI is fast becoming embedded in industries, economies and lives, making decisions, recommendations and predictions. These trends mean it's business-critical to understand how AI-enabled systems arrive at specific outputs. It's not enough for an AI algorithm to generate the right result--knowing "the reason why" is now a business fundamental. The process has to be transparent, trustworthy and compliant--far removed from the opaque "black-box" concept that has characterized some AI advances in recent times. At the same time, these advances should not be stifled.

Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. Digital transformation has fundamentally changed how businesses interact with their partners, supply chains, and customers. It has also exponentially increased the amount of data generated and stored by organizations. Modern enterprises generally have hundreds of terabytes, if not petabytes, of data, much of which is unstructured. This type of data can make up 80 to 90% of an enterprise's entire data footprint, and because it is unstructured, it is largely ignored.

The Biospecimen Solutions Data Manager is responsible for collecting data from specimen collection trials, specimen supply sites including lab partners, academic medical centers and other collaborators of the biospecimen business unit. This position ensures data collected is accurate, data is organized per business needs, solves operational problems, and prepares specimen inventory summaries and other reports to support growth. Any data provided as a part of this application will be stored in accordance with our Privacy Policy. Precision Medicine Group is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, age, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status or other characteristics protected by law.

Autonomous vehicles are on the rise to combat the country's motor vehicle fatalities. This article by Red Hat's Pete Brey takes a dive on how machine learning, artificial intelligence, and deep learning work together to achieve this goal. Houston, we have a problem. So does Los Angeles, Atlanta, New York, D.C, Boston, and all cities, towns, and counties throughout the United States. That problem is motor vehicle fatalities.

The lack of a sound power distribution maintenance program can result in a catastrophic power failure that impacts system uptime or factory production. The initial power failure may be due to an unavoidable disaster, but an inability to quickly recover from such an occurrence is typically due to a data center manager's unwillingness to view maintenance as an investment rather than an expense. The primary goal of a preventative maintenance plan for power distribution infrastructure is to minimize the impact that an equipment malfunction or power outage will have on business operations or service. This infrastructure includes but isn't limited to the following equipment: Since their inception, data centers seem to have been in a near constant state of evolution. As we approach the end of another decade, the next evolution cycle is nearing, and with it, traditional data centers must grapple with emerging technologies, environmental concerns, and ever-increasing costs.