The recent developments in the research area of large language models (LLMs) and generative AI are steering the field towards more interactive and dynamic forms of knowledge exchange and learning. A significant trend is the shift from static information retrieval to engaging directly with expert models, which not only answers queries but also enhances the learning experience through interaction. This paradigm shift is underpinned by theoretical advancements that aim to understand and formalize the emergence of language and symbol systems through collective predictive coding, offering a unified framework for the development of sophisticated AI systems. Furthermore, the integration of LLMs into educational frameworks is being reimagined, with a focus on developing interactional intelligence as a new skill set that leverages the capabilities of GenAI for enhanced learning experiences.

Noteworthy Papers

• Musings About the Future of Search: A Return to the Past?: Proposes a future where search evolves into direct engagement with expert models, moving beyond static content consumption.
• Generative Emergent Communication: Large Language Model is a Collective World Model: Introduces a unifying theoretical framework for understanding emergent communication and the role of LLMs as collective world models.
• Interactionalism: Re-Designing Higher Learning for the Large Language Agent Era: Presents Interactionalism as a blueprint for integrating GenAI into learning practices, emphasizing the development of interactional intelligence.