Cognitive Abstraction

Cognitive abstraction involves identifying and representing higher-level concepts from lower-level data, aiming to create more efficient and generalizable models of complex systems. Current research focuses on developing algorithms and model architectures, such as contrastive learning, model-based reinforcement learning, and large language models (LLMs), to achieve this abstraction across diverse domains, including reinforcement learning, robotics, and program synthesis. This work is significant because effective abstraction is crucial for building robust and adaptable AI systems capable of handling complex, real-world problems, and for gaining a deeper understanding of how humans and machines process information. The development of benchmarks like the Abstraction and Reasoning Corpus (ARC) further facilitates the evaluation and advancement of abstraction capabilities in AI.