Multidimensional Mental Models for AI-Assisted Problem Solving
Thought
Reflection on the limitations of current artificial intelligence in interpreting and solving complex human problems.
Note
AI needs to employ multidimensional mental models to truly understand and assist with complex human problem solving.
Analysis
Artificial Intelligence (AI) systems have advanced remarkably, but their ability to interpret and solve complex human problems remains limited by the models they employ. Problems humans face are often multidimensional, involving not just logical or mathematical challenges, but also ethical considerations, emotional factors, and sociocultural contexts.
The initial idea of giving AI multidimensional mental models relates to Arthur Koestler's concept of bisociation in "The Art of Creation". Bisociation refers to connecting independent matrices of thought to generate creative insights. In the context of AI, it would mean an AI that can integrate discrete mental models of logic, ethics, emotions, and culture to make more nuanced decisions.
This integration would require an AI system to operate in a fashion akin to interdisciplinary thinking in humans, engaging with principles from a variety of domains and considering the implications of its problem-solving across multiple perspectives.
Exploring such a concept would consider assumptions about intelligence—its composition and whether it fundamentally requires multidisciplinary integration to function effectively at a human-like level. We must question if contemporary AI, with its emphasis on pattern recognition and optimization, can incorporate these mental models or if an entirely new architecture is required.
Books
- "The Art of Creation" by Arthur Koestler
- "Society of Mind" by Marvin Minsky explores the idea of intelligence as an aggregate of numerous mechanical processes, which could be interpreted as an argument for multidimensional modeling.
- "Human Frontiers" by Michael Bhaskar, which discusses the future of AI and the integration of human elements into technological advancement.
Papers
- "Reward is enough" by David Silver et al., presents a view on the fundamental drive of AI, but could AI's 'rewards' be tied to multidimensional success criteria?
- "Interdisciplinary Research: Process and Theory" by Allen F. Repko - provides a framework on how interdisciplinary approaches work, which could inform AI model development.