Only T3-AI can reach human-level intelligence: A variety argument

TL;DR

This paper argues that achieving human-level intelligence requires a T3-structured adaptive system, which involves a hierarchical organization of policies, surpassing traditional T2-based AI models.

Contribution

It introduces the necessity of T3-organization in AI, demonstrating that only a T3-agent can handle the complexity of real-life human behavior.

Findings

T3-structure is essential for human-level AI

Hierarchical policies enable complex adaptive behavior

Traditional T2-agents are insufficient for real-life complexity

Abstract

The recently introduced theory of practopoiesis offers an account on how adaptive intelligent systems are organized. According to that theory biological agents adapt at three levels of organization and this structure applies also to our brains. This is referred to as tri-traversal theory of the organization of mind or for short, a T3-structure. To implement a similar T3-organization in an artificially intelligent agent, it is necessary to have multiple policies, as usually used as a concept in the theory of reinforcement learning. These policies have to form a hierarchy. We define adaptive practopoietic systems in terms of hierarchy of policies and calculate whether the total variety of behavior required by real-life conditions of an adult human can be satisfactorily accounted for by a traditional approach to artificial intelligence based on T2-agents, or whether a T3-agent is needed instead. We conclude that the complexity of real life can be dealt with appropriately only by a T3-agent.