Artificial General Intelligence
Overview
Artificial General Intelligence (AGI) is the theoretical development of autonomous systems capable of performing any intellectual task a human can, characterized by cross-domain reasoning, transfer learning, and autonomous problem-solving.
Current Paradigms
- large-language-models (LLMs): The dominant current approach, utilizing generative-ai to predict sequences of tokens based on massive linguistic datasets.
- World Models: Architectures designed to understand and simulate the causal and physical properties of reality.
- Self-Supervised Learning: A training methodology where models learn representations from unlabeled data by predicting missing parts of the input.
Emerging Architectures & Research
- vl-jepa (Meta FAIR Lab):
- Lead Researcher: Yann LeCun.
- Shift in Approach: Represents a strategic movement away from purely linguistic generative-ai toward vision-centric intelligence.
- Core Philosophy: Operates on the thesis that “Language is not Intelligence”; argues that linguistic prediction alone is insufficient for true cognitive competence.
- Mechanism: Focuses on predictive modeling within a latent space (Joint-Embedding Predictive Architecture) rather than generative token production.
Backlink: 2026 04 14 New paper for a vision approach to AGI not LLM