Machine Learning and Knowledge Engineering for Trustworthy Multimodal and Generative AI (AAAI-MAKE 2025)

AAAI-MAKE 2025 aims to bring together a diverse group of researchers and practitioners to explore integrating machine learning (ML) and knowledge engineering (KE) in hybrid, multimodal AI systems. By focusing on the hybrid AI MAKE paradigm, the symposium will examine how combining symbolic reasoning with machine learning across multiple modalities—such as text, speech, image, video, radar, and lidar—can pave the way for trustworthy, generative AI. The symposium will serve as a platform for presenting cutting-edge research and facilitating collaboration between academia and industry, addressing the challenges of creating AI systems that are robust, explainable, and capable of human-like reasoning across diverse applications.

AAAI-MAKE 2025 • AAAI Spring Symposium Series

AAAI-MAKE 2025 is part of the AAAI Spring Symposium Series, an annual set of meetings run in parallel, held at a common site.

The AAAI Spring Symposium Series «[…] is designed to bring colleagues together in an intimate forum while at the same time providing a significant gathering point for the AI community.

The two and one half-day format of the series allows participants to devote considerably more time to feedback and discussion than typical one-day workshops. It is an ideal venue for bringing together new communities in emerging fields.» (aaai.org)

March 31-April 2, 2025 @ San Francisco Airport Marriott Waterfront, California, USA

Topics

Key topics of discussion will include, but are not limited to:

• Machine Learning, Deep Learning, and Neural Networks
• Knowledge Engineering, Representation, and Reasoning
• Multimodal, Trustworthy, Commonsense, and Explainable AI
• Hybrid AI and Neuro-Symbolic AI
• Generative AI and Large Language Models (LLMs)
• Human-Centered AI, Dialogue Systems, and Conversational AI
• Hybrid (Human-Artificial) Intelligence and Human-in-the-Loop AI

AAAI-MAKE serves as a platform to shape the future of hybrid AI by bridging the gap between machine learning and knowledge engineering. It emphasizes the importance of multimodal systems and generative AI in creating robust, reliable, and transparent AI technologies.

Format and Keynotes

The 2.5-day event will follow the traditional AAAI Spring Symposium Series schedule, with keynotes and morning and afternoon sessions for full, position, and short-paper presentations.

We are pleased to announce the following keynote speakers and invited talks:

• Andrei Barbu is a research scientist at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Center for Brains, Minds, and Machines (CBMM). His research focuses on machine learning, particularly how robots can learn and navigate environments more efficiently, and on improving language understanding through visual perception.
• Leilani H. Gilpin is an assistant professor in Computer Science and Engineering at the University of California, Santa Cruz, and a Science & Justice Research Center affiliate. Her research focuses on developing methodologies for designing self-explaining intelligent ma-chines, particularly emphasizing accountability, liability, and explainability in complex AI systems.
• Alessandro Oltramari is the president of the Carnegie Bosch Institute (College of Engineering, Carnegie Mellon University) and a senior research scientist at the Bosch Research and Technology Center in Pittsburgh. His research focuses on neuro-symbolic reasoning, combining knowledge systems with machine learning algorithms to improve decision intelligence and human-machine collaboration, particularly in autonomous systems and AI-driven decision support.
• Pradeep Ravikumar is a professor in the Machine Learning Department, School of Computer Science at Carnegie Mellon University. His recent research interests are in neuro-symbolic AI, combining statistical machine learning, and symbolic and causal learning.

Submission

Organizing Committee

Andreas Martin (primary contact, 📧), FHNW, Olten, Switzerland

Pedro A. Colon-Hernandez, Apple Inc., CA, USA

Maaike de Boer, TNO, The Hague, Netherlands

Hans-Georg Fill, University of Fribourg, Switzerland

Aurona Gerber, University of the Western Cape, Cape Town, South Africa

Knut Hinkelmann, FHNW, Olten, Switzerland

Pascal Hitzler, Kansas State University, Manhattan, KS, USA

Jane Yung-jen Hsu, Chang Gung University, Taoyuan, Taiwan

Yen-Ling Kuo, University of Virginia, VA, USA

Thomas Schmid, Martin Luther University Halle-Wittenberg, Germany

Paulo Shakarian, Arizona State University, Tempe, AZ, USA

Reinhard Stolle, Fraunhofer IKS, München, Germany