Discover how AI's fluid ontologies mirror human cognition, potentially revolutionizing our approach to knowledge in the digital age.
This fluid approach mirrors human cognition, offering new perspectives on AI's role in enhancing our thinking.As I fall farther down the large language model rabbit hole, I'm becoming more interested in how LLMs may be redefining not just what we know but also how we know it. These models—trained on vast amounts of text and capable of generating coherent, context-rich responses—are changing the game for how we organize and relate to information.
Consider the concept of"heart disease." A traditional ontology might place it neatly within a medical framework: It's a type of disease, it has certain symptoms, and it requires specific treatments. An LLM, however, doesn't rely on that rigid structure. Instead, it has seen the term"heart disease" used in countless different contexts—medical research, patient stories, news articles, etc.
This brings us to a deeper question: Are LLMs offering us a new way to think about thinking itself? Their fluid, context-driven structure seems to reflect something inherently human—a departure from rigid, rule-based systems of knowledge toward something more iterative and flexible. This aligns with the broader trend I've discussed in past work: AI as a partner in human cognition, enhancing our ability to think and reason by offering new frameworks and perspectives.
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