AI-agility, with which an organization can be quickly adapted to its business priorities, is desired even for the development and operations of generative AI (GenAI) applications. Especially in this paper, we discuss so-called GenAI Model-agility, which we define as the readiness to be flexibly adapted to base foundation models as diverse as the model providers and versions. First, for handling issues specific to generative AI, we first define a methodology of GenAI application development and operations, as GenAIOps, to identify the problem of application quality degradation caused by changes to the underlying foundation models. We study prompt tuning technologies, which look promising to address this problem, and discuss their effectiveness and limitations through case studies using existing tools.

End-user development (EUD) represents a key step towards making robotics accessible for experts and nonexperts alike. Within academia, researchers investigate novel ways that EUD tools can capture, represent, visualize, analyze, and test developer intent. At the same time, industry researchers increasingly build and ship programming tools that enable customers to interact with their robots. However, despite this growing interest, the role of EUD within HRI is not well defined. EUD struggles to situate itself within a growing array of alternative approaches to application development, such as robot learning and teleoperation. EUD further struggles due to the wide range of individuals who can be considered end users, such as independent third-party application developers, consumers, hobbyists, or even employees of the robot manufacturer. Key questions remain such as how EUD is justified over alternate approaches to application development, which contexts EUD is most suited for, who the target users of an EUD system are, and where interaction between a human and a robot takes place, amongst many other questions. We seek to address these challenges and questions by organizing the first End-User Development for Human-Robot Interaction (EUD4HRI) workshop at the 2024 International Conference of Human-Robot Interaction. The workshop will bring together researchers with a wide range of expertise across academia and industry, spanning perspectives from multiple subfields of robotics, with the primary goal being a consensus of perspectives about the role that EUD must play within human-robot interaction.

The upcoming year seems to be the time security and technology professionals think artificial intelligence and machine learning will have mass application for security and detection. But just as the industry embraces the technology's potential, bad actors will look to capitalize on the new capabilities that could be unlocked for deception techniques such as deepfakes and disinformation. The economy and how it might affect security budgets weighed heavily on the minds of those who submitted predictions this year, and technology was no exception as some predict new tech and services will be driven by budget-conscious decisions in mind. We're seeing AI and powerful data capabilities redefine the security models and capabilities for companies. Security practitioners and the industry as a whole will have much better tools and much faster information at their disposal, and they should be able to isolate security risks with much greater precision.

Artificial Intelligence (AI) technology is gradually taking the world in its arms. The capabilities and applications of this intelligent technology are incredible and can completely revolutionize the world. Nowadays, enterprises of all sizes are adopting AI technology. AI can optimize work efficiency, automate repetitive tasks, and also assist companies in providing virtual customer support services. AI-powered mobile applications are also one of the best innovations of technology that help businesses reach targeted audiences globally and generate sustainable revenues.

"They are turning to providers for consulting and outsourcing services that deliver business value." The 2022 ISG Provider Lens Next-Gen ADM Services report for the U.K. finds that the country's application development and management (ADM) market is growing faster than its overall technology sector. Major companies, including automakers, have begun to reorganize product development around applications. Because talent is scarce and building large application development systems requires advanced expertise and tools, many enterprises are partnering with service providers in this area. "Companies in the U.K. are realizing that applications will play a significant role in the future of their business," said Anna Medkouri, partner and Technology Modernization Solutions lead for ISG EMEA.

Join us on November 9 to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers at the Low-Code/No-Code Summit. On my 12th birthday I got my first computer: an Amiga 500. And at 17, I founded my first company, making software that helped photographers serve their customers. As I reflect on my decades of coding, I'm reminded that low-code technology started with tools enabling users to build custom reports and applications with very little coding. When I started coding, low-code was somewhat analogous to the position artificial intelligence holds today: exciting, much hyped and poorly understood.

RALEIGH, NC, Oct 12, 2022 – EDJX, the pioneer in decentralized global serverless edge computing, today announced that it has formed a strategic partnership with Zeblok Computational to integrate the capabilities of the EDJX Platform with the Zeblok Ai-MicroCloud, a cloud-native, turnkey ML ops platform that enables businesses to deploy artificial intelligence (AI) applications easily and efficiently to thousands of edge locations at scale. The partnership will provide Zeblok customers access to the EDJX Platform capabilities utilizing EDJX compute, network, and storage resources. Zeblok's Ai-MicroCloud solves the problem of scaling at the edge, making it easy to deploy AI inferences to edge locations. Together the offering is a digital foundation for enterprises, cloud service providers, managed service providers, OEMs and ISVs to execute their AI strategies from Cloud-to-Edge for diverse use cases such as Smart Retail, Industry 4.0, Smart Cities, Smart Transportation and Logistics, and more. EDJX provides a decentralized Operating System EdjOS or the EDJX Platform that makes it possible for developers to build IoT, AI, and M2M applications and have the requisite computations executed as close as possible to the sources of data.

Artificial Intelligence (AI) has gained widespread popularity in recent years, with its application flooding every business sector. AI has gained astonishingly great acceptance in the world of portable technology, by making various functions available at your fingertips. The fast speed of AI development, and accomplishments in automation, automated vehicles, the capacity to beat people at mind games, and computerized user support mean that AI is a progressive technology that will receive extraordinary rewards over the long haul. The ongoing AI environment comprises robotics, machine learning (ML), and artificial neural networks (ANNs) that are improving the semantic, numerical, and legitimate thinking skills of any framework using AI. The two main variables driving the quick reception of AI are top caliber, versatile learning models, and the need to process a lot of information at reasonable costs.

Summary: Autonomous driving is a hot topic that has gained immense popularity -- but is it an actual concept. If yes, what are the probabilities that it brings along as it sounds oxymoron? Let's understand the challenges of autonomous driving and how agile technology can play an important role in bringing this concept to reality. Autonomous driving is a new concept, and everyone is excited to know about driverless vehicles. The technology is undergoing testing in several areas; the picture is not clear yet. Autonomous vehicles rely on sensors, machine learning systems, artificial intelligence technology, processors, and complex algorithms.

Anyone who has been following the news on AI in 2022 knows of the high rate of AI project failures. Somewhere between 60-80% of AI projects are failing according to different news sources, analysts, experts, and pundits. However, hidden among all that doom and gloom are the organizations who are succeeding. What are those 20% of organizations doing that are setting themselves apart from the failures, leading their projects to success? Surprisingly, it has nothing to do with the people they hire or the technology or products they use.