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IAAI 2027: Annual Conference on Innovative Applications of Artificial Intelligence

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投稿締切日:
2026-07-27 締切済み
通知日:
開催日:
2027-02-16
開催地:
Montreal, Quebec, Canada
開催回数:
ICORE: C   閲覧: 15272   フォロー: 2   参加: 0
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会伴インデックス (CP-I)

61.0 / 100
全 5,764 件中 第 593 位 · 上位 11%

人工知能・機械学習 分野 752 件中 第 49 位

学術的評価 (35%)
58
投稿の選択性 (20%) データなし — 中立の基準値 50 点として算入 —
開催回数 (20%)
99
コミュニティの注目度 (10%)
26
公開情報の充実度 (15%)
55

使用した入力: 収録ランク：ICORE C · 確認できる開催回数：39 · 会伴でフォローしている研究者：2 人 · 過去 24 か月にこのページを開いた研究者：7 人

公開情報で不足しているもの: 過去の採択率 (+4.5) · 最優秀論文の記録 (+2.3)
主催者は会議を認証申請したうえで、このページから直接追加できます。スコアは毎晩再計算されます。このスコアを上げるには

信頼度 80% — スコアのうち、中立の基準値ではなく実際に観測されたデータに基づく割合。 このスコアの算出方法 · ランキングを見る · アルゴリズム版 1.2 · 算出日 2026-11-03

論文募集

IAAI 2027 (Annual Conference on Innovative Applications of Artificial Intelligence) is a ICORE C conference held in Montreal, Quebec, Canada on 2027-02-16. The paper submission deadline is 2026-07-27.

The Thirty-Ninth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-27) is a premier venue for novel studies of AI deployments in real-world applications. IAAI-27 will feature technical papers, best practices, invited talks, and panel discussions that explore challenges, methodologies, and lessons learned from the practical use of AI. Submissions should focus on applied AI; purely theoretical work and algorithmic descriptions are more suited for AAAI-27. IAAI-27 seeks novel contributions in the following areas: (1) Deployed Applications showcasing the novel use of AI with measurable benefits, best practices, and lessons learned; (2) Emerging Applications of AI on trajectory for full deployment; (3) Tools and Methodologies that accelerate safer AI development and deployment and analysis of factors contributing to failures, incidents and their mitigation. Tracks and Topics 1. Deployed Highly Innovative Applications of AI​ Papers submitted to this track must describe deployed applications that demonstrate measurable benefits through innovative use of AI technology. An application is considered deployed once it is in production and used by end-users, with meaningful data collected on its performance. Submissions may focus on either standalone applications or components of larger systems. Papers will be evaluated based on the quality of the problem description, AI approach, innovation in AI use, measurable improvements, deployment details, and lessons learned during development, deployment, and maintenance. In-depth algorithm descriptions are better suited for AAAI. Each accepted paper in this track will receive the IAAI ‘Deployed Application’ Certificate Award. Page Limit: 8 pages, Format: AAAI style and formatting guidelines. *No page limit for references and appendices. 2. Emerging Applications of AI The Emerging Applications track focuses on novel applications of AI methods to real-world problems that are not yet fully deployed but include early deployment or pilot-stage results. Submissions must demonstrate a clear path toward full-scale deployment, addressing emerging engineering or sociotechnical challenges and their practical relevance. Papers will be evaluated on problem significance, innovation, AI methodology, technical quality, and clarity, with a clear path toward deployment. Submissions to this track have greater flexibility in how the papers are reviewed, but the quality threshold for acceptance is heightened with increasing distance from deployment. Papers without an articulated path to deployment may be rejected without review of the paper substance. Page Limit: 6 pages, Format: AAAI style and formatting guidelines. *No page limit for references and appendices. 3. Tools and Methodologies for Moving Faster and Safer Within this track, we solicit papers describing deployed tools, practices, policies, and methods improving applied AI innovation and deployment of AI systems. Areas of interest include, but are not limited to: Incident Analysis: Analysis of development and deployment processes and artifacts, focusing on real-world harm resulting from one or more AI incidents. Incident Trend Analysis: Analyze trends in AI incidents to identify unresolved challenges in the deployment of AI systems. Incident Best Practices: Detail best practices in use for producing and deploying systems to prevent or mitigate AI incidents. Deployed Process Organization: Tools in use that help manage and assure the development, evaluation, or deployment of AI systems. Deployed Data Hygiene and Data Quality Tools: Tools in use that are designed to address challenges in processing raw data in AI systems. Deployed Meta-Optimization: Tools in use for enhancing AI systems through algorithm configurators, algorithm portfolios, and hyperparameter optimization. Deployed novel computational models: Tools in use to exploit new computational hardware, such as neuromorphic processors, quantum computers, and other application-specific AI chips and systems.
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