Ai MagazinePub Date : 2026-08-13DOI: 10.1002/aaai.70072
Eun Ji Park
{"title":"The missing middle in AI native music production: Compression, editorial musicianship, and governance in the generative stack","authors":"Eun Ji Park","doi":"10.1002/aaai.70072","DOIUrl":"https://doi.org/10.1002/aaai.70072","url":null,"abstract":"<p>This article examines the structural reconfiguration of contemporary music-making within AI-mediated production environments. From a historical perspective of music technology, it compares algorithmic composition, electronic instruments, and Digital audio workstations (DAWs) to argue that the present transition represents not a rupture but a compression of musical practice. As generation, transformation, evaluation, and distribution converge within an AI-native stack, the intermediate layer of musical work is increasingly absorbed into system-level processes. Judgments that once appeared as externally visible artifacts, such as sketches, edit histories, chord charts, and revision processes, are now executed within integrated computational environments. The so-called “missing middle” therefore signals not disappearance but a relocation of musical decision making. Prompt-based creation is reframed as a shift from writing to direction, and as iteration becomes inexpensive, revision moves from stepwise rewriting toward regeneration-centered cycles. In response, this article proposes the reconstruction of a “constructive middle” suited to AI-native production, redefining controllability and revisability as core design parameters of AI-mediated creative systems. In doing so, it articulates the design and governance conditions under which controllable and accountable generative music systems can sustain creative depth within accelerated production infrastructures. For AI leaders and decision-makers, this framework identifies four priorities beyond output quality and speed: inspectable intermediate states, constrained local editing, portable provenance, and workforce capabilities for directing and auditing generative systems.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"47 3","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.70072","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148719247","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}
Ai MagazinePub Date : 2026-08-13DOI: 10.1002/aaai.70067
Zhao Li, Zixin Lin, Donghui Ding, Haitao Xu
{"title":"LiBrain: LLM-powered Li-ion battery diagnostics with time-series-aware retrieval-augmented framework for E-bikes","authors":"Zhao Li, Zixin Lin, Donghui Ding, Haitao Xu","doi":"10.1002/aaai.70067","DOIUrl":"https://doi.org/10.1002/aaai.70067","url":null,"abstract":"<p>The rapid proliferation of smart-city ecosystems has significantly amplified the demand for Li-ion batteries, which now serve as the primary energy source for sustainable transportation systems such as e-bikes. Ensuring battery safety and optimal performance is crucial, yet challenging due to complex intrinsic dynamics and extrinsic operating conditions. This paper presents LiBrain, an innovative LLM-powered, time-series-aware retrieval-augmented framework designed to simultaneously address both safety and performance challenges through three synergistic components: (1) a distributed IoT-enabled edge network for continuous real-time battery monitoring and data acquisition, (2) a pretrained deep multi-task diagnostic engine capable of comprehensive battery performance forecasting, and (3) a knowledge-base augmentation module that transforms technical diagnostics into clear, actionable guidance tailored for e-bike users. Functioning as an intelligent battery management assistant, LiBrain effectively bridges the gap between expert-level real-time analytics and practical, user-friendly instructions. Extensive validation across a real-world operational e-bike battery-swap network demonstrates LiBrain's exceptional capabilities, achieving a 95% adoption rate in hazardous alarm detection and 92% in battery-status prediction. In real application, LiBrain has processed over 500 million battery events, managed almost 10 million inquiries and 1 million alarms annually, and identified 10% of on-site batteries daily for proactive replacement, thereby maintaining operational safety and reliability.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"47 3","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.70067","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148719251","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":"Multimodal AI teacher: Integrating edge computing and reasoning models for enhanced student error analysis","authors":"Tianlong Xu, Yi-Fan Zhang, Zhendong Chu, Qingsong Wen","doi":"10.1002/aaai.70053","DOIUrl":"https://doi.org/10.1002/aaai.70053","url":null,"abstract":"<p>Students frequently make mistakes while solving mathematical problems, and traditional error correction methods are both time-consuming and labor-intensive. This paper introduces an innovative Virtual AI Teacher system (VATE) designed to autonomously analyze and correct student Errors. Leveraging advanced large language models (LLMs), the system utilizes student drafts as a primary source for error analysis, thereby enhancing the understanding of the student's learning process. It incorporates sophisticated prompt engineering and maintains an error pool to reduce computational overhead. The AI-driven system also features a real-time dialogue component for efficient student interaction. Our approach demonstrates significant advantages over traditional and machine learning-based error correction methods, including reduced educational costs, high scalability, and superior generalizability. The system has been deployed on the Squirrel AI learning platform for elementary mathematics education, where it achieves 78.3 accuracy in error analysis and shows a marked improvement in student learning efficiency. Satisfaction surveys indicate a strong positive reception, highlighting the system's potential to transform educational practices.</p>","PeriodicalId":7854,"journal":{"name":"Ai Magazine","volume":"47 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aaai.70053","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147566270","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}