The Evolution of AI, Explained in Stages

Short summary

A beginner-friendly walkthrough of AI's evolution across six technical stages: rule-based systems, classical machine learning, deep learning, large language models, AI agents, and the broader ANI/AGI/ASI framework. Each stage is explained with how it worked, what made it different, and its key limitations. The article is well-structured but covers familiar ground without novel insights or data.

• •AI evolved through six stages: rule-based, ML, deep learning, LLMs, agents, and ANI/AGI/ASI
• •Each stage solved the previous one's limits but introduced new constraints like hallucination and compounding errors in agents
• •We remain firmly in the ANI stage; AGI and ASI are hypothetical

Generated with AI, which can make mistakes.