AAAI

Founded in 1979, AAAI is an international, nonprofit, scientific society devoted to promote research in, and responsible use of Artificial Intelligence.

[AAAI @RealAAAI]

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[AAAI @RealAAAI]

Are you looking for your next great hire? Up to 3,005 candidates uploaded searchable resumes on the AAAI Career Center. Visit today: https://t.co/Ls6DMKK09L

[AAAI @RealAAAI]

As a reminder, reviewer bidding exists to match papers with reviewers who have relevant expertise, not to enable reciprocal arrangements or coordinated manipulation of the assignment process. AAAI-27 has adopted specific measures, including hard and soft constraints against reciprocal 2-cycles and an auditing protocol for residual cases, and will refer confirmed violations to the AAAI ethics process. Full details: https://t.co/wnp9J7UhN6

[AAAI @RealAAAI]

The Nineteenth International Symposium on Combinatorial Search (SoCS 2026) was from August 14-16, 2026 in Bremerhaven, Germany. Proceedings are available to review here: https://t.co/FdrnzKPy74

[AAAI @RealAAAI]

Metadata is the foundation of trustworthy, reproducible AI. In the article "The Metadata Ecosystem and AI: Enabling FAIR and AI-Ready Data" by Jane Greenberg, Joel Pepper, Xintong Zhao, Richard Marciano, David Breen, and Yuan An, the authors explore how well-structured metadata makes datasets, models, and workflows more FAIR while strengthening AI readiness across the research lifecycle. Read more: https://t.co/wwylBcux4S

[AAAI @RealAAAI]

Metadata is the foundation of trustworthy, reproducible AI. In the article "The Metadata Ecosystem and AI: Enabling FAIR and AI-Ready Data" by Jane Greenberg, Joel Pepper, Xintong Zhao, Richard Marciano, David Breen, and Yuan An, the authors explore how well-structured metadata makes datasets, models, and workflows more FAIR while strengthening AI readiness across the research lifecycle. Read more: https://t.co/ZJY2ubx2do

[AAAI @RealAAAI]

"Knowledge Engineering for Open Science: Building and Deploying Knowledge Bases for Metadata Standards" by Mark A. Musen, Martin J. O'Connor, Josef Hardi, and Marcos Martínez-Romero
explores how knowledge engineering and the CEDAR Workbench help scientific communities create and apply shared metadata standards that make research data more FAIR. Read the article:
https://t.co/ZJY2ubx2do

[AAAI @RealAAAI]

Datasheets for Machine Learning Sensors" by Matthew Stewart, Yuke Zhang, Pete Warden, Yasmine Omri, Shvetank Prakash, Jacob Huckelberry, João H. Santos, Scott Hymel, Brian Y. Brown, Jim MacArthur, Nathan Jeffries, Emanuel Moss, Michael Sloane, Brian Plancher, and Vijay Janapa Reddi, introduces a datasheet framework for machine learning sensors that standardizes how AI-enabled sensing systems are documented. The framework is designed to improve reproducibility, support regulatory compliance, and build greater trust in physical AI systems. Read the article: https://t.co/5mUJpLEkLr

[AAAI @RealAAAI]

"An Actionable Framework for AI-Ready Data"" by Nikita Majithia, Tom Carey-Wilson, Elena Simperl, and Nigel Shadbolt is available in the Special Issue of AI Magazine. In this article, the authors introduce a practical framework for evaluating whether datasets are prepared for AI applications, outlining key criteria and demonstrating how the framework can be applied to real-world examples. Their work provides a roadmap for improving data quality and readiness across the open data ecosystem. Read the article:

https://t.co/9lb2hTHQuf

[AAAI @RealAAAI]

"Data Readiness Pipeline Patterns for Scientific AI at Scale: Insights from Climate, Fusion, Life Sciences, and Materials" by Wesley Brewer, Patrick Widener, Valentine Anantharaj, Feiyi Wang, Tom Beck, Arjun Shankar, and Sarp Oral is available in the current AI Magazine Special Issue. This article explores practical data readiness pipeline patterns that help researchers prepare scientific data for AI at scale, drawing lessons from climate science, fusion energy, life sciences, and materials research. Read the article:

[AAAI @RealAAAI]

"Introduction to Special Issue on FAIR Principles and Machine Learning, AI Readiness and AI Reproducibility" by Lynne Schreiber, Daniel S. Katz, Yuhan Rao, and Christine R. Kirkpatrick is now available. As artificial intelligence continues to evolve, ensuring that research is FAIR (Findable, Accessible, Interoperable, and Reusable) is crucial. This introduction highlights the growing importance of AI readiness, reproducibility, and responsible data practices in advancing trustworthy AI research. Read the article: https://t.co/T56wKDSFO8

[AAAI @RealAAAI]

The wait is over. AI Magazine Special Issue: "FAIR Principles and Machine Learning, AI Readiness, and AI Reproducibility" is now online. The special issue addresses the challenges of making research objects (data, software, and ML models) findable, accessible, interoperable, and reusable in the age of generative AI and large language models. https://t.co/7NPIQiaRUU

[AAAI @RealAAAI]

We have received reports of potential attempts to collude during the reviewer bidding process. The AAAI-27 Organizing Committee takes this matter very seriously and has already implemented a series of measures to detect and prevent such behavior. As the call for papers states, any confirmed instance of bidding collusion may result in penalties, including but not limited to, the desk rejection of the associated submissions.

[California Institute for Machine Consciousness @CIMCAI]

All talks from the @RealAAAI Spring Symposium on Machine Consciousness are now available on Youtube.

Watch the full series of keynotes and panel discussions on defining, measuring, and building conscious architectures.

Full playlist: https://t.co/GPBtrLp17H

@kanair
@alltbl
@zah_bah
@adamsafron
@drmichaellevin
@karinanguyen
@Plinz
@AngieNormandale
@MiTiBennett
@kenmogi
@camhberg
@Lidinwise
@abazuel
@NicoleSHsing
@rgblong
@bscotrousse

[AAAI @RealAAAI]

The Call for Papers for the Thirty-Ninth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-27) is now available. IAAI-27, co-located with AAAI-27, will take place February 18-20, 2027, in Montreal, Canada.

IAAI-27 is a venue for papers describing highly innovative realizations of AI technology.

Proposals should be submitted via the online submission site. Please visit the Call for Papers for full submission requirements including a link to the submission site:

https://t.co/QdCAGz7cMc

[AAAI @RealAAAI]

Looking for top talent? We've got that. Find the best at:

https://t.co/Ls6DMKK09L

[AAAI @RealAAAI]

Time to find your next job? We've got you covered at our Career Center:
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[AAAI @RealAAAI]

AAAI-27 deadlines are here!

The 41st AAAI Conference on Artificial Intelligence will be held in Montréal, Canada, February 16–23, 2027. Key dates for authors:

July 21, 2026 - Abstracts due (11:59 PM AoE)
July 28, 2026 - Full papers due (11:59 PM AoE)
July 31, 2026 - Supplementary material & code due

See the CFP for more details: https://t.co/rchMmafNGR.

[Anton Arkhipov @AntonSArkhipov]

In January, we ran a workshop "Neuro for AI & AI for Neuro: Towards Multi-Modal Natural Intelligence" at @RealAAAI 2026.
The proceedings are now online in PMLR: https://t.co/CZVzhCngGE
Check it out!
Congrats to all the participants.Really appreciate all your contributions.🎉🖥️🧠