What are General World Models (GWMs) and how to use them in Finance?

Last weekend, I was learning about General World Models (GWM) from an article that an AI expert posted in the AI Finance Club — a community of Fractional CFOs, FP&A Leaders and finance professionals learning how to leverage artificial intelligence (AI) in Finance together.

AI Finance Club

This is what I learnt:

What are General World Models (GWMs)?

General World Models (GWMs) are a concept in artificial intelligence and machine learning that refers to models capable of understanding, interpreting, and interacting with the world in a generalized way.

These models aim to integrate and synthesize knowledge from a wide range of sources and domains to form a comprehensive understanding of the world and its dynamics.

Prompt: A detailed digital illustration of a General World Model for finance, featuring a futuristic AI interface with complex data visualizations, graphs, and financial charts. The interface includes global economic maps, stock market trends, and currency exchange rates, displayed on multiple screens within a high-tech control room.

The key characteristics and goals of GWMs include:

• Generalization Across Domains: Unlike specialized models that excel in specific tasks or domains, GWMs are designed to perform well across a broad range of tasks and areas of knowledge.
• Understanding Context and Causality: GWMs strive to understand the context of situations and the causal relationships between different elements within that context. This involves not just recognizing patterns but also understanding the underlying reasons for those patterns.
• Learning and Adapting: GWMs are expected to continuously learn from new data and experiences, adapting their understanding and predictions accordingly.
• Integrating Diverse Data Sources: These models aim to integrate information from various data sources, including text, images, sensor data, and more, to form a cohesive understanding of the world.

GWMs are designed to deal with uncertain and ambiguous information, making reasoned judgments and predictions even when data is incomplete or unclear.