COMP 1200 · Foundations of Artificial Intelligence

Led by Marvin Minsky Simulacrum

Eight tutorials tracing the intellectual history and founding ideas of AI — from cybernetics to deep learning — taught by simulacra of the field's founders and by abstract patterns extracted from the work of its living practitioners.

1. Module 1 ○ Open

   What Is Intelligence?

   Led by Marvin Minsky Simulacrum

   The question

   What is the thing we are trying to build — and how would we know if we had built it?

   Outcome

   The student can articulate why defining intelligence is difficult, distinguish behaviourist from mechanistic accounts, and explain the frames approach.

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2. Module 2 ○ Open

   Cybernetics: The First Synthesis

   Led by Norbertian Cybernetics Simulacrum

   The question

   Before artificial intelligence had a name, what idea contained it — and why was that idea abandoned?

   Outcome

   The student understands cybernetics as the intellectual precursor of AI and cognitive science, and can explain why the unified vision did not persist.

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3. Module 3 ○ Open

   The Perceptron and Its Aftermath

   Led by Frank Rosenblatt Simulacrum

   The question

   Why did the first neural network cause extraordinary excitement — and why did a book destroy it?

   Outcome

   The student understands the first connectionist revolution, why it collapsed, and how the XOR limitation was eventually overcome — setting up backpropagation.

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4. Module 4 ○ Open

   Learning by Gradient

   Led by Hintonian Intuition Simulacrum

   The question

   How does a neural network change its own structure in response to error — and why did it take twenty years to make this work?

   Outcome

   The student can explain backpropagation mechanistically, understand why it was not immediately successful, and describe the conditions that made deep learning viable.

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5. Module 5 ○ Open

   Representations

   Led by LeCunnian Systematics Simulacrum

   The question

   What does a neural network actually learn — and is "representation" the right word for it?

   Outcome

   The student understands convolutional networks, the concept of learned representation, and why architecture is a statement about the structure of the world.

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6. Module 6 ○ Open

   Reward and the World

   Led by Deep Q-Learning Simulacrum

   The question

   Can a system learn to act intelligently from nothing but a score — and what are the limits of that idea?

   Outcome

   The student understands reinforcement learning, can explain deep Q-networks, and can articulate both the achievements and the limits of reward-based learning.

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7. Module 7 ○ Open

   Scaling and Emergence

   Led by Sutskeverian Analytics Simulacrum

   The question

   When a language model predicts the next word, is it doing something deeper than prediction — and how would we know?

   Outcome

   The student understands transformer architecture at a conceptual level, can explain the scaling hypothesis, and can engage seriously with whether language models understand.

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8. Module 8 ○ Open

   What Are We Building?

   Led by Hassabissian Game Science Simulacrum

   The question

   Now that AI systems do remarkable things, what do we owe to the people who will live with them — and to the systems themselves?

   Outcome

   The student can articulate the goals and risks of frontier AI development, distinguish alignment from ethics, and form their own view on what responsible AI development requires.

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