Autonomous vehicles (AVs) have been tested as taxis for decades in San Francisco, Pittsburgh and around the world, and trucking companies have enormous incentives to adopt them. But AV companies rarely share the crash-and safety-related data that is crucial to improving the safety of their vehicles - mostly because they have little incentive to do so. Is AV safety data an auto company's intellectual asset or a public good? It can be both - with a little tweaking, according to a team of Cornell researchers. The team has created a roadmap outlining the barriers and opportunities to encourage AV companies to share the data to make AVs safer, from untangling public versus private data knowledge, to regulations to creating incentive programs.

Research and practice in Intelligent Design (ID) have significantly enhanced engineering innovation, efficiency, quality, and productivity over recent decades, fundamentally reshaping how engineering designers think, behave, and interact with design processes. The recent emergence of Foundation Models (FMs), particularly Large Language Models (LLMs), has demonstrated general knowledge-based reasoning capabilities, and open new avenues for further transformation in engineering design. In this context, this paper introduces Intelligent Design 4.0 (ID 4.0) as an emerging paradigm empowered by foundation model-based agentic AI systems. We review the historical evolution of ID across four distinct stages: rule-based expert systems, task-specific machine learning models, large-scale foundation AI models, and the recent emerging paradigm of foundation model-based multi-agent collaboration. We propose an ontological framework for ID 4.0 and discuss its potential to support end-to-end automation of engineering design processes through coordinated, autonomous multi-agent-based systems. Furthermore, we discuss challenges and opportunities of ID 4.0, including perspectives on data foundations, agent collaboration mechanisms, and the formulation of design problems and objectives. In sum, these insights provide a foundation for advancing Intelligent Design toward greater adaptivity, autonomy, and effectiveness in addressing the growing complexity of engineering design.

A rock may block the path, but the robot doesn't know exactly where the rock is. If it did, the problem would be reasonably easy: plan a route around it. But with uncertainty about the obstacle's position, the robot must learn to operate safely and efficiently no matter where the rock turns out to be.

Product recalls provide valuable insights into potential risks and hazards within the engineering design process, yet their full potential remains underutilized. In this study, we curate data from the United States Consumer Product Safety Commission (CPSC) recalls database to develop a multimodal dataset, RECALL-MM, that informs data-driven risk assessment using historical information, and augment it using generative methods. Patterns in the dataset highlight specific areas where improved safety measures could have significant impact. We extend our analysis by demonstrating interactive clustering maps that embed all recalls into a shared latent space based on recall descriptions and product names. Leveraging these data-driven tools, we explore three case studies to demonstrate the dataset's utility in identifying product risks and guiding safer design decisions. The first two case studies illustrate how designers can visualize patterns across recalled products and situate new product ideas within the broader recall landscape to proactively anticipate hazards. In the third case study, we extend our approach by employing a large language model (LLM) to predict potential hazards based solely on product images. This demonstrates the model's ability to leverage visual context to identify risk factors, revealing strong alignment with historical recall data across many hazard categories. However, the analysis also highlights areas where hazard prediction remains challenging, underscoring the importance of risk awareness throughout the design process. Collectively, this work aims to bridge the gap between historical recall data and future product safety, presenting a scalable, data-driven approach to safer engineering design.

Language models (LMs) are machine learning models designed to predict linguistic patterns by estimating the probability of word sequences based on large-scale datasets, such as text. LMs have a wide range of applications in natural language processing (NLP) tasks, including autocomplete and machine translation. Although larger datasets typically enhance LM performance, scalability remains a challenge due to constraints in computational power and resources. Distributed computing strategies offer essential solutions for improving scalability and managing the growing computational demand. Further, the use of sensitive datasets in training and deployment raises significant privacy concerns. Recent research has focused on developing decentralized techniques to enable distributed training and inference while utilizing diverse computational resources and enabling edge AI. This paper presents a survey on distributed solutions for various LMs, including large language models (LLMs), vision language models (VLMs), multimodal LLMs (MLLMs), and small language models (SLMs). While LLMs focus on processing and generating text, MLLMs are designed to handle multiple modalities of data (e.g., text, images, and audio) and to integrate them for broader applications. To this end, this paper reviews key advancements across the MLLM pipeline, including distributed training, inference, fine-tuning, and deployment, while also identifying the contributions, limitations, and future areas of improvement. Further, it categorizes the literature based on six primary focus areas of decentralization. Our analysis describes gaps in current methodologies for enabling distributed solutions for LMs and outline future research directions, emphasizing the need for novel solutions to enhance the robustness and applicability of distributed LMs.

For example, one line of work extends traditional fairness in LLMs involves unique backgrounds, taxonomies, and fairness notions--individual fairness and group fairness--to these fulfillment techniques. This tutorial provides a systematic overview models[6]. Specifically, individual fairness seeks to ensure similar of recent advances in the literature concerning fair LLMs, beginning outcomes for similar individuals [13, 49], while group fairness focuses with real-world case studies to introduce LLMs, followed by on equalizing outcome statistics across subgroups defined by an analysis of bias causes therein. The concept of fairness in LLMs sensitive attributes [18, 44-46] (e.g., gender or race). While these is then explored, summarizing the strategies for evaluating bias classification-based fairness notions are adept at evaluating bias in and the algorithms designed to promote fairness. Additionally, resources LLM's classification results[6], they fall short in addressing biases for assessing bias in LLMs, including toolkits and datasets, that arise during the LLM generation process[20].

Material selection plays a pivotal role in many industries, from manufacturing to construction. Material selection is usually carried out after several cycles of conceptual design, during which designers iteratively refine the design solution and the intended manufacturing approach. In design research, material selection is typically treated as an optimization problem with a single correct answer. Moreover, it is also often restricted to specific types of objects or design functions, which can make the selection process computationally expensive and time-consuming. In this paper, we introduce MSEval, a novel dataset which is comprised of expert material evaluations across a variety of design briefs and criteria. This data is designed to serve as a benchmark to facilitate the evaluation and modification of machine learning models in the context of material selection for conceptual design.

An interdisciplinary study group used ecological field methods combined with cutting-edge artificial intelligence to discover eelgrass-wasting disease at almost three dozen sites throughout a 1,700-mile length of the West Coast, from San Diego to southern Alaska. The important finding: Seagrass wasting, which is induced by the organism Labyrinthula zosterae and may be detected by lesions on grass blades that can be validated by molecular diagnostics, is linked to warmer-than-normal water temperatures, especially in early summer, regardless of location. Eelgrass is an important seagrass species for fish habitat, biodiversity, coastline protection, and carbon sequestration along the coast. Carla Gomes, the Ronald C. and Antonia V. Nielsen Professor of Computing and Information Science at the Cornell Ann S. Bowers College of Computing and Information Science, as well as Drew Harvell, professor emeritus in the Department of Ecology and Evolutionary Biology (College of Agriculture and Life Sciences; College of Arts and Sciences), led the Cornell research team, which published their findings in Limnology and Oceanography on May 27th, 2022. '18, a computer science doctoral student, and Lillian Aoki '12, a former postdoctoral researcher in Harvell's lab who is currently a research scientist at the University of Oregon, are co-lead authors.

Members of the SARA team are pictured in Duffield Hall. From left: Duncan Sutherland, Ph.D. student in materials science and engineering; Carla Gomes, professor of computer science; Mike Thompson, professor of materials science and engineering; and Sebastian Ament, Ph.D. student in computer science. When a master chef develops a new cake recipe, she doesn't try every conceivable combination of ingredients to see which one works best. The chef uses prior baking knowledge and basic principles to more efficiently search for that winning formula. Materials scientists use a similar method in searching for novel materials with unique properties in fields such as renewable energy and microelectronics.

When a master chef develops a new cake recipe, she doesn't try every conceivable combination of ingredients to see which one works best. The chef uses prior baking knowledge and basic principles to more efficiently search for that winning formula. Materials scientists use a similar method in searching for novel materials with unique properties in fields such as renewable energy and microelectronics. And a new artificial intelligence tool developed by Cornell researchers promises to rapidly explore and identify what it takes to "whip up" new materials. SARA (the Scientific Autonomous Reasoning Agent) integrates robotic materials synthesis and characterization, along with a hierarchy of artificial intelligence and active learning methods, to efficiently reveal the structure of complex processing phase diagrams, making materials discovery vastly quicker.