Empowering Machine Learning and Large Language Models with Domain and Commonsense Knowledge (AAAI-MAKE 2024)

AAAI-MAKE 2024 brings together a diverse community of researchers, practitioners, and industry professionals from the fields of machine learning, knowledge engineering, and large language models (LLMs) to explore the synergy between these domains. The symposium aims to address the critical challenges of trustworthiness, interpretability, and the integration of commonsense reasoning in AI systems. Through a series of keynotes, paper presentations and discussions of the 2.5-day symposium, participants will delve into novel solutions empowering machine learning and large language models with domain and commonsense knowledge.

AAAI-MAKE 2024 • AAAI Spring Symposium Series

AAAI-MAKE 2024 is part of the AAAI Spring Symposium Series, an annual set of meetings run in parallel held at Stanford University, Palo Alto, California, USA.

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 25-27, 2024 @ Stanford University, California, USA

Topics and Format

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

• Trustworthy and knowledge-based explainable AI
• Evaluation of Commonsense Knowledge and Reasoning in Large Language Models
• Commonsense Knowledge in Foundation and Large Language Models
• Interpretability and Safety of Large Language Models with Commonsense Knowledge
• Human-Centered AI, Dialogue Systems, and Conversational AI
• Hybrid and Neurosymbolic AI
• Combination of Knowledge Engineering and Machine Learning
• Integration of Commonsense Knowledge Sources with LLMs and Downstream Tasks

Since AAAI-MAKE 2024 is a dedicated symposium on empowering ML/LLMs with domain/commonsense knowledge, the contributions should address hybrid approaches. The symposium involves presentations of accepted papers, keynotes, (panel) discussions, and plenary sessions.

Keynotes

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

• Ron Brachman is the former director of the Jacobs Technion-Cornell Institute and professor of computer science at Cornell University. He previously served as Director of DARPA’s IPTO (among other things, creating the program that led to Siri) and has been President of AAAI. His recent research focuses on how AI systems can demonstrate common sense in a human-like way. He has been advocating for examining common sense from a broader perspective, considering it in the context of a full cognitive system. He is the co-author of an important textbook on knowledge representation and reasoning and of a 2023 book about AI and common sense.
• Edward A. Feigenbaum, Professor Emeritus of Computer Science at Stanford University, is renowned as the “father of expert systems”. His pioneering contributions have been recognized with the prestigious ACM Turing Award in 1994, for his work in the design and construction of large-scale artificial intelligence systems. Feigenbaum’s career is distinguished by numerous honors, including being elected Fellow of the AAAI and ACM, reflecting his significant impact on AI technology and its commercial applications.
• Noah Goodman is professor of Psychology and Computer Science at Stanford University, and research scientist at Google DeepMind. He studies intelligence in humans and machines, merging behavioral experiments with formal methods from deep learning, statistics, and logic.
• Alison Gopnik is a professor of the Graduate School in the Department of Psychology at the University of California, Berkeley, affiliate professor of philosophy and member of the Berkeley AI Research group. Her research focuses on how children make sense of the world around them, given limited evidence. She was one of the first cognitive scientists to study “theory of mind” and winner of the 2024 David Rumelhart Prize in Cognitive Science.
• Henry Lieberman is a research scientist at the Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology. His research focuses on story understanding, generation, and using commonsense knowledge and analogical reasoning in cognitive systems.
• James McClelland is the Lucie Stern professor in the Social Sciences at Stanford University and director of the Stanford Center for Mind, Brain, Computation, and Technology. He is renowned for his work on mathematical cognition and Parallel Distributed Processing, applying connectionist models (or neural networks) to explain cognitive phenomena.

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

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, National Taiwan University, Taipei, Taiwan

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

Xiang Lorraine Li, University of Pittsburgh, PA, USA

Thomas Schmid, Martin Luther University Halle-Wittenberg, Germany

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

Reinhard Stolle, Fraunhofer IKS, München, Germany

Frank van Harmelen, VU University, Amsterdam, Netherlands

In memoriam of Douglas B. Lenat (1950–2023), our esteemed colleague, friend, co-initiator of AAAI-MAKE, co-organizer of multiple AAAI symposiums and AAAI Fellow.