Industrial research is like trekking across shifting sand dunes — technologies rise, evolve, and disappear faster than any single skill set can keep pace with. Unlike academic research, which allows sustained focus in an area, industrial research is governed by practical forces: the cost of adoption, commercial strategy, and the pressure to deliver real-world impact. Over three decades at the IBM Watson Research Center, I watched wave after wave of innovation reshape the landscape beneath my feet. Each breakthrough opened new possibilities; each disruption forced a reassessment of skills, assumptions, and priorities. That volatility is simultaneously exhilarating and unforgiving. This article traces my career through those shifting conditions — what they taught me about technical innovation, organizational dynamics, and personal reinvention. From that journey, I have distilled eight lessons about adapting and growing in an environment where change is not the exception. It is the only reliable constant.
Low Earth orbit (LEO) satellite constellations are becoming orbital edge computing platforms that can sense, process, and learn near data sources. Federated Learning (FL) is promising in this setting because it supports repeated model adaptation without forcing every raw observation to be downlinked, centralized, or redistributed. Yet orbital FL is not terrestrial FL placed in space: it must operate over shell- and plane-structured constellations, sparse ground-station access, time-varying inter-satellite links, heterogeneous onboard hardware, limited energy and thermal budgets, and delayed or noisy labels. This article connects representative architectures, mission use cases, and open challenges for GS-centric, GS-assisted, and fully on-orbit FL.
Large Language Models (LLMs) are reshaping interaction on the Web, enabling users to express goals in natural language rather than navigating predefined application flows. However, conversational interfaces lack structure and affordances for direct manipulation. On the other hand, GUIs struggle to accommodate fluid, intention-driven interaction. This article discusses design trade-offs that can assist this Web transition toward agent-mediated, goal-driven paradigms and models, and proposes a unifying conceptual framework clarifying how agents can interpret user goals and assemble Web interfaces. This contribution outlines a shift toward goal-driven computing on the Web, with implications for design, interoperability, and agentic architectures.
The Internet of Things (IoT) is rapidly evolving toward increasingly intelligent, autonomous, and interconnected ecosystems, creating new challenges in security, reliability, and trustworthiness. This special issue brings together recent advances in securing next-generation IoT systems, with a focus on intelligent digital twin technologies and lightweight real-time anomaly detection frameworks. The selected articles demonstrate how artificial intelligence, edge computing, virtualization, and adaptive system design can be leveraged to improve the security and resilience of modern IoT environments. Collectively, these contributions highlight emerging directions for building scalable, robust, and deployment-orientated IoT security solutions and provide insights into the future evolution of secure and trustworthy IoT infrastructures.
