Grounding for Artificial Intelligence
This addresses the foundational issue of how AI systems can understand and interact with the real world, which is crucial for researchers and developers aiming to improve intelligence in AI, but it is incremental as it builds on prior high-level grounding studies.
The paper tackles the problem of grounding in artificial intelligence, which connects language and knowledge to real-world representations, by systematically studying fine-grained grounding levels, with the result that it identifies a gap in existing work and emphasizes its necessity for advancing large language models and achieving Artificial General Intelligence.
A core function of intelligence is grounding, which is the process of connecting the natural language and abstract knowledge to the internal representation of the real world in an intelligent being, e.g., a human. Human cognition is grounded in our sensorimotor experiences in the external world and subjective feelings in our internal world. We use languages to communicate with each other and the languages are grounded on our shared sensorimotor experiences and feelings. Without this shard grounding, it is impossible for us to understand each other because all natural languages are highly abstract and are only able to describe a tiny portion of what has happened or is happening in the real world. Although grounding at high or abstract levels has been studied in different fields and applications, to our knowledge, limited systematic work at fine-grained levels has been done. With the rapid progress of large language models (LLMs), it is imperative that we have a sound understanding of grounding in order to move to the next level of intelligence. It is also believed that grounding is necessary for Artificial General Intelligence (AGI). This paper makes an attempt to systematically study this problem.