The Evolution of Intelligence Models: From LLMs to EGMs to AUI

As the field of artificial intelligence evolves, the limitations of current systems—especially Large Language Models (LLMs)—have become increasingly apparent. This paper outlines a probable three-phase evolution of intelligent systems, culminating in Artificial Universal Intelligence (AUI). Each phase is characterized by different mechanisms, strengths, limitations, and strategic implications.

Phase 1: Large Language Models (LLMs) – Present Day

- Definition: Transformer-based models trained on massive corpora using backpropagation.

- Strengths: Fluent language generation, wide adoption, versatile tools.

- Limitations: Brittle under noise, poor generalization, hallucinations.

- Use Cases: Writing aids, coding assistants, customer support.

- Dominant Actors: OpenAI, Google DeepMind, Anthropic, Meta.

- Transition Trigger: Demand for robustness in low-data, high-stakes environments.

Phase 2: Evolved Generalizing Models (EGMs) – Near-Term Future

- Definition: Models with evolved internal structures designed under generalization pressure.

- Mechanism: Evolved rather than trained weights; optional hybridization with backprop.

- Strengths: High generalization, robust to noise, interpretable.

- Limitations: Slower to evolve, boutique-specific applications.

- Use Cases: Finance (e.g., L7A), medicine, military decision systems.

- Dominant Actors: Initially boutique; future interest from major players.

- Transition Trigger: Infrastructure for evolutionary modeling and hybrid systems.

Phase 3: Artificial Universal Intelligence (AUI) – Long-Term Horizon

- Definition: Fully evolved systems capable of prediction across arbitrary environments.

- Core Principle: Intelligence = the ability to predict the future.

- Strengths: Cross-domain generalization, emergent planning, noise resilience.

- Limitations: High computational demands, complex verification, ethical challenges.

- Use Cases: Autonomous science, synthetic policy agents, interplanetary systems.

- Dominant Actors: Global research coalitions, national labs, post-LLM companies.

- Transition Trigger: Evolution-at-scale infrastructure, multi-environment testing.

Model Evolution Summary

┌────────────────────────────────────┐
│ Artificial Universal Intelligence │
│ (AUI) – Fully evolved cognition │
└────────────────────────────────────┘
▲
│
┌────────────────────────────────────┐
│ Evolved Generalizing Models (EGMs) │
│ Boutique, domain-specific, robust │
└────────────────────────────────────┘
▲
│
┌────────────────────────────────────┐
│ Large Language Models (LLMs) │
│ Fluent but brittle transformers │
└────────────────────────────────────┘