AI Logic by Zhi SystemsA four-unit course on how artificial intelligence actually reasons — from inference and entailment to notation, meta-logic, and models. 32 micro-lectures with an AI tutor, adaptive practice, and AI-graded assessments. No math or coding background required. Sign in with Google to start the course.

Unit 1 — Fundamental Concepts: inference, entailment vs. pattern activation, the ampliative thesis, knowledge in an AI system.

Unit 2 — Notational Conventions: defeasible inference, non-monotonic reasoning, semantic networks and embedding space.

Unit 3 — Meta-Logical Principles: soundness and completeness reconceived, defeasibility, the meta-logic of learning.

Unit 4 — Models: neural networks as models, embedding spaces, parsimony, symbolic and learned reasoning.