Ai Magazine最新文献

{"title":"Multisensory machine intelligence","authors":"Ruohan Gao","doi":"10.1002/aaai.70026","DOIUrl":"https://doi.org/10.1002/aaai.70026","url":null,"abstract":"<p>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.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"46 3","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.70026","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144897785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semi-Markovian planning to coordinate aerial and maritime medical evacuation platforms","authors":"Mahdi Al-Husseini,&nbsp;Kyle H. Wray,&nbsp;Mykel J. Kochenderfer","doi":"10.1002/aaai.70023","DOIUrl":"https://doi.org/10.1002/aaai.70023","url":null,"abstract":"<p>The transfer of patients between two aircraft using an underway watercraft increases medical evacuation reach and flexibility in maritime environments. The selection of any one of multiple underway watercraft for patient exchange is complicated by participating aircraft utilization histories and participating watercraft positions and velocities. The selection problem is modeled as a semi-Markov decision process with an action space, including both fixed land and moving watercraft exchange points. Monte Carlo tree search with root parallelization is used to select optimal exchange points and determine aircraft dispatch times. Model parameters are varied in simulation to identify representative scenarios where watercraft exchange points reduce incident response times. We find that an optimal policy with watercraft exchange points outperforms an optimal policy without watercraft exchange points and a greedy policy by 35% and 40%, respectively. In partnership with the United States Army, we deploy for the first time the watercraft exchange point by executing a mock patient transfer with a manikin between two HH-60M medical evacuation helicopters and an underway Army Logistic Support Vessel south of the Hawaiian island of Oahu. Both helicopters were dispatched in accordance with our optimized decision strategy.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"46 3","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.70023","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144897786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reclaiming authorship in the age of generative AI: From panic to possibility","authors":"Mohsen Askari","doi":"10.1002/aaai.70022","DOIUrl":"https://doi.org/10.1002/aaai.70022","url":null,"abstract":"<p>The advent of generative AI, particularly large language models like ChatGPT, has precipitated a seismic shift in academia. Far from a gradual evolution, its sudden emergence has jolted educational institutions, leaving many academics grappling with a perceived encroachment upon their intellectual domain. This upheaval has sparked intense debates, with concerns ranging from the erosion of academic integrity to the devaluation of scholarly labor. This essay contends that such apprehensions, while understandable, may overlook the transformative potential of AI as a collaborative tool. Drawing parallels to historical disruptions—such as the advent of photography challenging traditional art forms—we explore how AI can augment human creativity rather than supplant it. By examining the dynamics of authorship, originality, and accountability, we argue for a redefinition of these concepts in the context of AI-assisted work. Emphasizing the importance of human oversight in guiding AI outputs, we advocate for a framework that recognizes the symbiotic relationship between human intellect and machine efficiency. Such a perspective not only preserves the essence of academic rigor but also embraces the democratization of knowledge production. Ultimately, this essay calls for a balanced approach that mitigates risks while harnessing the innovative capacities of generative AI in academia.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"46 3","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.70022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144897777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feeling heard: Can AI really understand human's feeling?","authors":"Nuke F. Hatta","doi":"10.1002/aaai.70017","DOIUrl":"https://doi.org/10.1002/aaai.70017","url":null,"abstract":"","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"46 3","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144888541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tiered copyrightability for generative artificial intelligence: An empirical analysis of China and the United States judicial practices","authors":"Zichun Xu,&nbsp;Zhilang Xu","doi":"10.1002/aaai.70018","DOIUrl":"https://doi.org/10.1002/aaai.70018","url":null,"abstract":"<p>The rapid advancement of generative artificial intelligence (AI) poses significant challenges to traditional copyright frameworks, intensifying debates over the copyrightability of AI-generated outputs. By comparing judicial practices in China and the United States, it has been observed that the United States maintains a conservative stance of adhering to substantive control, while China demonstrates an inclusive approach through the criterion of creative contribution. Building upon this, this article transcends the traditional binary judgment model and constructs a tiered copyright determination model. Based on the level of human control and contribution in the AI generation process, it introduces dimensions such as technological controllability and density of human intent, classifying generative AI into three tiers: strong protection, weak protection, and non-protection. Regarding the copyrightability of content generated by generative AI, this article argues that the issue should be addressed within the framework of copyright law itself. When human participation is involved and the substantial contribution of the direct user is reflected in the AI-generated content, meeting the requirements for copyrightable works under copyright law, corresponding protective measures should be granted.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"46 3","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.70018","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144888542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Against AI welfare: Care practices should prioritize living beings over AI","authors":"John Dorsch,&nbsp;Mariel K. Goddu,&nbsp;Kathryn Nave,&nbsp;Tillmann Vierkant,&nbsp;Mark Coeckelbergh,&nbsp;Paula Gürtler,&nbsp;Petr Urban,&nbsp;Friderike Spang,&nbsp;Maximilian Moll","doi":"10.1002/aaai.70016","DOIUrl":"https://doi.org/10.1002/aaai.70016","url":null,"abstract":"<p>In this Comment, we critique the growing “AI welfare” movement and propose a novel guideline, the Precarity Guideline, to determine care entitlement. In contrast to approaches that emphasize potential for suffering, the Precarity Guideline is grounded in empirically identifiable features. The severity of ongoing humanitarian crises, biodiversity loss, and climate change provides additional reasons to prioritize the needs of living beings over machine learning algorithms as candidates for care.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"46 3","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.70016","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144767453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Attracting artificial intelligence talent in the time of generative AI","authors":"Michael Wollowski","doi":"10.1002/aaai.70014","DOIUrl":"https://doi.org/10.1002/aaai.70014","url":null,"abstract":"<p>Public statements by leading AI researchers and recognizable people in the computer world are suggesting that AI may soon replace many jobs, including software engineers. Some even state that soon, AI will be smarter than us. We believe such statements are unhelpful when it comes to attracting talent to our field. We document several such statements. We believe that the future need for AI talent is tremendous and that we should take extreme efforts to attract students to our field. We present a sample of the expected opportunities and needs. Some of these opportunities may be attractive to students who in the past may not have considered AI as a career option. We argue that even with the anticipated automation of AI work, there nevertheless will be a prodigious need for talent to develop good AI. We summarize work that argues that AI is going to be a fundamental skill and as such should be introduced to learners across many age groups and many backgrounds. We suggest that a well-reasoned statement of the anticipated needs be developed by experts in our field and communicated to future talent. We suggest that, as part of this message, pathways forward toward developing AI talent across a wide range of backgrounds be developed and communicated.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"46 2","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.70014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI literacy as a core component of AI education","authors":"Sri Yash Tadimalla,&nbsp;Mary Lou Maher","doi":"10.1002/aaai.70007","DOIUrl":"https://doi.org/10.1002/aaai.70007","url":null,"abstract":"<p>As generative artificial intelligence (AI) becomes increasingly integrated into society and education, more institutions are implementing AI usage policies and offering introductory AI courses. These courses, however, should not replicate the technical focus typically found in introductory computer science (CS) courses like CS1 and CS2. In this paper, we use an adjustable, interdisciplinary socio-technical AI literacy framework to design and present an introductory AI literacy course. We present a refined version of this framework informed by the teaching of a 1-credit general education AI literacy course (primarily for freshmen and first-year students from various majors), a 3-credit course for CS majors at all levels, and a summer camp for high school students. Drawing from these teaching experiences and the evolving research landscape, we propose an introductory AI literacy course design framework structured around four cross-cutting pillars. These pillars encompass (1) understanding the scope and technical dimensions of AI technologies, (2) learning how to interact with (generative) AI technologies, (3) applying principles of critical, ethical, and responsible AI usage, and (4) analyzing implications of AI on society. We posit that achieving AI literacy is essential for all students, those pursuing AI-related careers, and those following other educational or professional paths. This introductory course, positioned at the beginning of a program, creates a foundation for ongoing and advanced AI education. The course design approach is presented as a series of modules and subtopics under each pillar. We emphasize the importance of thoughtful instructional design, including pedagogy, expected learning outcomes, and assessment strategies. This approach not only integrates social and technical learning but also democratizes AI education across diverse student populations and equips all learners with the socio-technical, multidisciplinary perspectives necessary to navigate and shape the ethical future of AI.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"46 2","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.70007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Systematically incorporating equity into design thinking for AI education","authors":"Christelle Scharff,&nbsp;Andreea Cotoranu,&nbsp;Yves Wautelet,&nbsp;James Brusseau","doi":"10.1002/aaai.70008","DOIUrl":"https://doi.org/10.1002/aaai.70008","url":null,"abstract":"<p>AI-powered systems increasingly influence critical aspects of daily life, yet these systems often embed and reinforce biases, disproportionately disadvantaging marginalized communities. Addressing these challenges requires a fundamental shift in how we teach the development of these systems, ensuring that future professionals develop not only technical expertise but also are equipped with the skills needed for ethical AI design. This paper adopts a design science research (DSR) approach to develop the equity-aware design thinking for AI (EquiThink4AI) framework, a dual-component model that systematically embeds equity principles into AI education. EquiThink4AI's first component extends design thinking (DT) by incorporating principles from EquityXDesign (EXD) and liberatory design (LD), ensuring that equity concerns are proactively addressed throughout AI system development. The second component enhances the framework with pedagogical strategies, including problem-based learning (PBL), experiential learning, and interdisciplinary collaboration, fostering student engagement, real-world problem-solving, and ethical reasoning. EquityThink4AI provides educators and students with a structured methodology for teaching and applying equity-centered AI development. This study is explorative in nature, yet it presents concrete strategies for integrating EquiThink4AI into AI curricula, bridging the gap between design, AI ethics, and educational practices.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"46 2","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.70008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Guest Editorial: Introduction to special issue of AI magazine on AI literacy and AI education","authors":"Mary Lou Maher,&nbsp;David S. Touretzky,&nbsp;Michael Wollowski","doi":"10.1002/aaai.70006","DOIUrl":"https://doi.org/10.1002/aaai.70006","url":null,"abstract":"&lt;p&gt;Artificial intelligence has evolved into a readily accessible tool, increasing its impact on our daily lives, society, and the economy. This accessibility necessitates a critical reassessment of existing AI educational programs and curricula. There is a pressing need to develop strategies that increase the capacity for AI education, establish diverse pathways for individuals to enter and contribute to AI, and foster a greater awareness of the multifaceted implications of AI-driven technologies. The proliferation of AI has triggered concerns about potential consequences, including the over-reliance on AI leading to worker deskilling, the automation of various job functions, and the resulting uncertainty surrounding the future of work.&lt;/p&gt;&lt;p&gt;It is important to note that this special issue specifically focuses on AI education, not the application of AI in education, often referred to as “AI edtech,” where AI tools are utilized to facilitate teaching and learning across various subjects. While the latter is a rapidly evolving and significant domain, it constitutes a separate area of inquiry from the topics addressed here.&lt;/p&gt;&lt;p&gt;Several key factors contribute to scalable AI education in K-12. Central to success is comprehensive and ongoing teacher professional development (PD), which should include initial intensive workshops, sustained support during the school year, co-teaching with experienced instructors, and time for teachers to plan and adapt lessons. Developing teacher leaders who can provide PD and mentorship is essential for long-term sustainability and wider dissemination. A co-design process that actively involves teachers in curriculum development ensures the materials are relevant and adaptable to diverse learners and classroom environments.&lt;/p&gt;&lt;p&gt;Research-practitioner partnerships (RPPs) leverage expertise from both universities and educational settings, bridging content knowledge with practical realities. A diverse and inclusive approach, considering varying student demographics and learning needs, is critical for broad accessibility. Effective curriculum design that aligns with established AI frameworks, incorporates active learning strategies, and focuses on fundamental understandings is crucial. Ongoing implementation support, online resources, community building, flexibility, adaptability, and continuous evaluation and improvement all contribute to a robust and scalable AI education program in K-12.&lt;/p&gt;&lt;p&gt;Touretzky et al. address AI education in middle school (grades 6–8) in the “AI for Georgia” project (AI4GA.org). Capacity building in K-12 AI education requires extensive teacher support, as most K-12 teachers, including computing teachers, start with little or no AI knowledge. The paper describes a teacher professional development program that begins with instruction in the basics of AI and also brings in teachers as co-designers to help shape the curriculum and tailor it to their classrooms. With continued support, some of the","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"46 2","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.70006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144635094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}