The systematic assessment of AI systems is increasingly vital as these technologies enter high-stakes domains. To address this, the EU's Artificial Intelligence Act introduces AI Regulatory Sandboxes (AIRS): supervised environments where AI systems can be tested under the oversight of Competent Authorities (CAs), balancing innovation with compliance, particularly for startups and SMEs. Yet significant challenges remain: assessment methods are fragmented, tests lack standardisation, and feedback loops between developers and regulators are weak. To bridge these gaps, we propose the Sandbox Configurator, a modular open-source framework that enables users to select domain-relevant tests from a shared library and generate customised sandbox environments with integrated dashboards. Its plug-in architecture aims to support both open and proprietary modules, fostering a shared ecosystem of interoperable AI assessment services. The framework aims to address multiple stakeholders: CAs gain structured workflows for applying legal obligations; technical experts can integrate robust evaluation methods; and AI providers access a transparent pathway to compliance. By promoting cross-border collaboration and standardisation, the Sandbox Configurator's goal is to support a scalable and innovation-friendly European infrastructure for trustworthy AI governance.

The fact that there's three chip types in AI laptops nowadays: Snapdragon X Series processors, Ryzen AI chips, and Intel x86 chips, even in laptops made by the same OEMs, has left many scratching their heads. Naturally questions like: Which one is the best? Have become a mantra for consumers overwhelmed by the different options out there. At the HP Imagine AI event in New York City, I asked Guayente Sanmartin, SVP and division president of commercial systems and display solutions, how HP planned on managing that complex environment for consumers. To target the different technologies at consumer personas -- personalizing the experience for each user.

Summary: The Transcarent Analytics and Reporting team empowers the company to make data-driven decisions and provides critical business insights that help us execute in a world-class fashion. We are seeking a talented and motivated technical expert to accelerate our efforts to drive trust, adoption, and democratization of insights externally to our clients and members as well as internally to our stakeholders and leaders. The Analytics Data Engineer will work closely with our Data Engineering, Product, and Solution stakeholders to deliver well-defined, transformed, tested, documented, and code-reviewed analytical datasets that would be single source of truth for reliable, and consistent results. The ideal candidate will have broad skills in database design, be comfortable dealing with large and complex data sets, have experience building self-service dashboards, and applying analytical rigor to inform business decisions. They will work very efficiently to deliver the right solutions and continuously think of how to best automate and expand the delivery of the analytical solutions.

Synthetic media technologies are rapidly advancing, making it easier to generate nonveridical media that look and sound increasingly realistic. So-called "deepfakes" (owing to their reliance on deep learning) often present a person saying or doing something they have not said or done. The proliferation of deepfakesa creates a new challenge to the trustworthiness of visual experience, and has already created negative consequences such as nonconsensual pornography,11 political disinformation,19 and financial fraud.3 Deepfakes can harm viewers by deceiving or intimidating, harm subjects by causing reputational damage, and harm society by undermining societal values such as trust in institutions.7 What can be done to mitigate these harms?

The government's highest priority investment in artificial intelligence needs to be its AI workforce. It is not adopting AI as quickly as the private sector, and potentially as quickly as our adversaries. Most government teams developing AI solutions we have met face high barriers when they begin a project. They include limited access to data sets, constrained system authorities, and less computing power than they need. As a result, projects are slower and more expensive than they might be, delaying the fielding of systems that can decrease costs, increase capabilities, and help improve national security.

While artificial intelligence (AI) and other automation technologies might lead to enormous progress in healthcare, they may also have undesired consequences for people working in the field. In this interdisciplinary study, we capture empirical evidence of not only what healthcare work could be automated, but also what should be automated. We quantitatively investigate these research questions by utilizing probabilistic machine learning models trained on thousands of ratings, provided by both healthcare practitioners and automation experts. Based on our findings, we present an analytical tool (Automatability-Desirability Matrix) to support policymakers and organizational leaders in developing practical strategies on how to harness the positive power of automation technologies, while accompanying change and empowering stakeholders in a participatory fashion.

The term "Industry 4.0" refers to the transformation of the manufacturing sector by digital technologies, such as the internet of things (IoT), artificial intelligence (AI), machine learning, robotics, 3-D printing, visualization, virtual/augmented reality and analytics. Automated management of assembly lines, inventories and even downtime for preventive maintenance has improved flexibility, throughput and productivity. The use of robotics in complex processes has helped enhance reliability and quality while reducing occupational risks. This transformation has thus far largely been limited to shop floors. Yet, there are three pillars that support the business of manufacturing companies: sales, production and service. Unless sales and customer service are also transformed, manufacturing companies will not fully benefit from their adoption of Industry 4.0.

Having the right skills are worth their weight in gold, especially when operating within the modern-day market that is known for its agility and operational precision. Perhaps, this is one of the main reasons why the industry is always on the lookout for professionals with dynamic skills, the ones who can reap higher customer satisfaction. But these prized skills, as is the case, are rarely available in the market. This makes the ones available an even more precious commodity. The challenge that every growing business faces today is to deliver an exceptional customer experience and at the same time utilize the skills of its human resources to its full capacity.