Ai Magazine最新文献

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.

According to an industry survey, many people miss opportunities to apply for government subsidy programs because they do not know how to apply. People also need to search manually and check whether these programs are suitable for them. To address this issue, our study developed a new generative recommender system with both users' information and government subsidy documents. Within our recommender system framework, we modify the existing Residual Quantization Variational Auto-Encoder (RQ-VAE) model to capture deep and abstract information from subsidy documents. Using semantic IDs generated for approximately 185,610 user click-stream histories and 240,000 documents, we train our recommender system to predict the semantic IDs of the next subsidy policy documents in which a user might be interested. In 2024, we successfully deployed our generative recommender system in Wello, a Korean Gov-Tech startup. In collaboration with the Korean government, our generative recommender system helped enhance program effectiveness by saving $7.8 million in unused funds and achieved $27.4 million in advertising efficiency gains. Also, Wello observed a 68% improvement in Click-Through-Ratio (CTR), increasing from 41.4% in the third quarter of 2024 to 69.6% in the fourth quarter of 2024. We thus anticipate that our generative recommender system will have a significant impact on both individuals and the government.

Enterprise AI Assistants are increasingly deployed in domains where accuracy is paramount, making each erroneous output a potentially significant incident. This paper presents a comprehensive framework for monitoring, benchmarking, and continuously improving such complex, multi-component systems under active development by multiple teams. Our approach encompasses three key elements: (1) a hierarchical “severity” framework for incident detection that identifies and categorizes errors while attributing component-specific error rates, facilitating targeted improvements; (2) a scalable and principled methodology for benchmark construction, evaluation, and deployment, designed to accommodate multiple development teams, mitigate overfitting risks, and assess the downstream impact of system modifications; and (3) a continual improvement strategy leveraging multidimensional evaluation, enabling the identification and implementation of diverse enhancement opportunities. By adopting this holistic framework, organizations can systematically enhance the reliability and performance of their AI Assistants, ensuring their efficacy in critical enterprise environments. We conclude by discussing how this multifaceted approach opens avenues for various classes of enhancements, including human-AI collaborative evaluation, paving the way for more robust and trustworthy AI systems.

This year's innovative applications of AI special issue features AI systems deployed in real-world settings, from enterprise platforms to public services, demonstrating both technical rigor and measurable benefits for organizations and society. The eight selected articles span enterprise reliability, cybersecurity, aerospace, education, healthcare logistics, government services, and scalable AI strategy. Collectively, these works illustrate how AI is progressing from research prototypes to systems that organizations now rely on for critical decisions, offering lessons learned for both researchers and practitioners.

Finetuning serves as the critical adaptation mechanism for multimodal large language models, bridging their pretrained knowledge with specialized downstream task requirements. This paper reviews recent finetuning advances across three key dimensions: (1) efficiency-oriented methods that reduce resource costs; (2) capability-specific techniques enhancing specialized multimodal skills; and (3) task-unifying approaches that bridge understanding and generation. We demonstrate how these directions transform multimodal large language models from versatile foundations into adaptive, human-aligned systems, providing researchers with a structured roadmap for developing next-generation multimodal AI.

Ensuring that AI systems do what we, as humans, actually want them to do is one of the biggest open research challenges in AI alignment and safety. My research seeks to directly address this challenge by enabling AI systems to interact with humans to learn aligned and robust behaviors. The way robots and other AI systems behave is often the result of optimizing a reward function. However, manually designing good reward functions is highly challenging and error-prone, even for domain experts. Although reward functions are often difficult to manually specify, human feedback in the form of demonstrations or preferences is often much easier to obtain but can be difficult to interpret due to ambiguity and noise. Thus, it is critical that AI systems take into account epistemic uncertainty over the human's true intent. As part of the AAAI New Faculty Highlight Program, I will give an overview of my research progress along the following fundamental research areas: (1) efficiently quantifying uncertainty over human intent, (2) directly optimizing behavior to be robust to uncertainty over human intent, and (3) actively querying for additional human input to reduce uncertainty over human intent.

The future of artificial intelligence demands a paradigm shift toward multisensory perception—to systems that can digest ongoing multisensory observations, that can discover structure in unlabeled raw sensory data, and that can intelligently fuse useful information from different sensory modalities for decision-making. While we humans naturally perceive the world by looking, listening, touching, smelling, and tasting, traditional forms of machine intelligence mostly focus on a single sensory modality, particularly vision. Therefore, my research, which I refer to as multisensory machine intelligence, seeks to bridge this gap by empowering machines to emulate and enhance human capabilities in seeing, hearing, and feeling, ultimately enabling them to comprehensively perceive, understand, and interact with multisensory world.