Ai Magazine最新文献

Artificial intelligence (AI) is transforming industries worldwide, and the e-commerce sector is at the forefront of leveraging its capabilities to drive innovation and efficiency. The paper explores the integration of artificial intelligence in e-commerce, focusing on the ethical and regulatory implications introduced by the EU AI Act. This legislative framework aims to ensure the responsible deployment of AI by classifying AI systems into risk categories and imposing compliance requirements. It also underscores both the opportunities and challenges that AI presents to businesses, particularly in enhancing consumer experiences through automation and data-driven decision-making processes. The paper provides a comprehensive review of the AI landscape in Europe, analyzing the impact of the EU AI Act, particularly on small and medium-sized enterprises and startups. Through a mixed-methods approach, the study investigates how regulatory compliance may influence business innovation, market competitiveness, and consumer trust. The recommendations proposed aim to develop a trustworthy AI ecosystem that could stimulate long-term growth and enhance the global positioning of small European businesses.

Agentic AI shifts stacks from request-response to plan-execute. Systems no longer just answer; they act—planning tasks, calling tools, keeping memory, and changing external state. That shift moves privacy from policy docs into the runtime. This opinion piece argues that we do not need a new privacy theory for agents; we need enforceable, observable controls that render existing rights as product behavior. Anchoring on GDPR—with portable touchpoints to CPRA, LGPD, and PDPA, we propose a developer-first toolkit: optional, bounded, user-visible memory; a purpose-aware egress gate that enforces minimization and transfer rules; proportional safeguards that scale with stakes; and traces that tell a coherent story across components and suppliers. We show how the EU AI Act's risk management, logging, and oversight can scaffold these controls and enable evidence reuse. The result is an agentic runtime that keeps people in control and teams audit-ready by design.

The long-standing goal of creating a comprehensive, multi-purpose knowledge resource, reminiscent of the 1984 Cyc project, still persists in AI. Despite the success of knowledge resources like WordNet, ConceptNet, Wolfram|Alpha and other commercial knowledge graphs, verifiable, general-purpose, widely available sources of knowledge remain a critical deficiency in AI infrastructure. Large language models struggle due to knowledge gaps; robotic planning lacks necessary world knowledge; and the detection of factually false information relies heavily on human expertise. What kind of knowledge resource is most needed in AI today? How can modern technology shape its development and evaluation? A recent AAAI workshop gathered over 50 researchers to explore these questions. This paper synthesizes our findings and outlines a community-driven vision for a new knowledge infrastructure. In addition to leveraging contemporary advances in knowledge representation and reasoning, one promising idea is to build an open engineering framework to exploit knowledge modules effectively within the context of practical applications. Such a framework should include sets of conventions and social structures that are adopted by contributors.

As cyber threats become increasingly automated and sophisticated, novel solutions must be introduced to improve defense of enterprise networks. Deep reinforcement learning (DRL) has demonstrated potential in mitigating these advanced threats. Single DRL agents have proven utility toward execution of autonomous cyber defense. Despite the success of employing single DRL agents, this approach presents significant limitations, especially regarding scalability within large enterprise networks. An attractive alternative to the single-agent approach is the use of multi-agent reinforcement learning (MARL). However, developing MARL agents is costly with few options for examining MARL cyber defense techniques against adversarial agents. This paper presents a MARL network security environment, the fourth iteration of the cyber autonomy gym for experimentation (CAGE) challenges. This challenge was specifically designed to test the efficacy of MARL algorithms in an enterprise network. Our work aims to evaluate the potential of MARL as a robust and scalable solution for autonomous network defense.

Model-based planning and execution systems offer a principled approach to building flexible autonomous robots that can perform diverse tasks by automatically combining a host of basic skills. This idea is almost as old as modern robotics. Yet, while diverse general-purpose reasoning architectures have been proposed since, general-purpose software platforms that support the construction of planner-based controllers and their integration with modern robotic platforms have emerged only recently, starting with the influential ROSPlan system. Since then, a growing number of domain-independent model-based platforms for robot task-level control have emerged. In this paper, we consider the diverse design choices and issues existing platforms attempt to address, the different solutions proposed so far, and suggest avenues for future development. We also briefly discuss the elephant in the room: foundation models.

The AAAI 2025 Bridge on “Explainable AI, Energy and Critical Infrastructure Systems” was held at the Pennsylvania Convention Centre, Philadelphia, Pennsylvania, USA, on February 25, 2025. The bridge gathered researchers and practitioners, bringing together innovation research across explainable AI, energy and critical infrastructure systems so they can enhance each other. The Bridge featured five keynote presentations by experts, one tutorial, poster presentations by authors who contributed their research findings, and three breakout sessions to discuss new challenges arising at the intersection of these exciting disciplines.

We present a comprehensive enterprise AI strategy developed within the AI Center of Excellence at Fidelity Investments, emphasizing the strategic integration of open-source AI frameworks into scalable, modular, and reproducible enterprise-grade solutions. Our approach is structured around five key pillars: learning from offline data, learning from online feedback, intelligent decision-making, automated assistants, and responsible AI practices. Through a suite of 12 open-source libraries, we demonstrate how modular and interoperable tools can collectively enhance scalability, fairness, and explainability in real-world AI deployments. We further illustrate the impact of this strategy through three enterprise case studies. Finally, we distill a set of best deployment practices to guide organizations in implementing modular, open-source AI strategies at scale.

This paper extends our previously published work on the virtual AI teacher (VATE) system, presented at IAAI-25. VATE is designed to autonomously analyze and correct student errors in mathematical problem-solving using advanced large language models (LLMs). By incorporating student draft images as a primary input for reasoning, the system provides fine-grained error cause analysis and supports real-time, multi-round AI—student dialogues. In this extended version, we introduce a new snap-to-solve module for handling low-reasoning tasks using edge-deployed LLMs, enabling faster and partially offline interaction. We also include expanded benchmarking experiments, including human expert evaluations and ablation studies, to assess model performance and learning outcomes. Deployed on the Squirrel AI platform, VATE demonstrates high accuracy (78.3%) in error analysis and improves student learning efficiency, with strong user satisfaction. These results suggest that VATE is a scalable, cost-effective solution with the potential to transform educational practices.

The National Vulnerability Database (NVD) publishes over a thousand new vulnerabilities monthly, with a projected 25 percent increase in 2024, highlighting the crucial need for rapid vulnerability identification to mitigate cybersecurity attacks and save costs and resources. In this work, we propose using large language models (LLMs) to learn vulnerability evaluation from historical assessments of medical device vulnerabilities in a single manufacturer's portfolio. We highlight the effectiveness and challenges of using LLMs for automatic vulnerability evaluation and introduce a method to enrich historical data with cybersecurity ontologies, enabling the system to understand new vulnerabilities without retraining the LLM. Our LLM system integrates with the in-house application—Cybersecurity Management System (CSMS)—to help Siemens Healthineers (SHS) product cybersecurity experts efficiently assess the vulnerabilities in our products. Also, we present a comprehensive set of experiments that helps showcase the properties of the LLM and dataset, the various guardrails we have implemented to safeguard the system in production, and the guidelines for efficient integration of LLMs into the cybersecurity tool.

Infusing artificial intelligence algorithms into production aerospace systems can be challenging due to costs, timelines, and a risk-averse industry. We introduce the Onboard Artificial Intelligence Research (OnAIR) platform, an open-source software pipeline and cognitive architecture tool that enables full life cycle AI research for on-board intelligent systems. We begin with a description and user walk-through of the OnAIR tool. Next, we describe four use cases of OnAIR for both research and deployed onboard applications, detailing their use of OnAIR and the benefits it provided to the development and function of each respective scenario. Lastly, we describe two upcoming planned deployments which will leverage OnAIR for crucial mission outcomes. We conclude with remarks on future work and goals for the forward progression of OnAIR as a tool to enable a larger AI and aerospace research community.