Today marks the one year anniversary since the launch of ChatGPT. While the underlying large language models powering chatbots like ChatGPT, Claude or Pi demonstrate impressive linguistic abilities, they lack the reasoning and common sense that comes from a deeper understanding of the world.
These models are trained on vast amounts of text data, allowing them to recognize patterns and generate human-like text. However, they have no inherent concept of objects, causality, or the typical relationships between things in the world. As a result, their responses may sometimes defy logic or physical realities.
Researchers at OpenAI, Meta, Google and other AI labs are working on a solution: integrating a "world model" that equips the language model with knowledge about how the world works. Think of adding a world model to a large language model as making the large language model go to primary school - it can simulate and emulate the world around it based on scientific principles and observational data.
By linking this model of the world with the pattern recognition power of a language model, an AI system could gain more contextual, causal, and factual grounding for its linguistic outputs. It could provide more logical, consistent responses while retaining its ability to handle open-ended topics and tasks. There are still massive challenges in developing such an integrated system, but this presents a promising path toward less brittle, more broadly intelligent AI.