12- Week AI "antifragile skills" curriculum

12-Week AI "Antifragile Skills" Curriculum Designed for daily sessions of 30 minutes reading and 30 minutes Practice in Socra. When generating each Day, you will also generate a detailed plan for a 2

### 12-Week AI "Antifragile Skills" Curriculum This curriculum is designed for daily sessions of 30 minutes reading and 30 minutes practice in Socra. For each day, we'll also generate a detailed plan for a 2-hour lecture on the same subject. #### Week 1: From Philosophy of Mind to Computational Thinking * [x] Day 1: Foundations of Machine Intelligence * **Theory:** The Turing Test and its philosophical implications * **Practice:** Analytical dialogue comparing different AI responses to explore intelligence criteria * **Detailed Lecture Plan:** * **Morning Session: The Turing Test** * Core Concepts: Historical context (1950), key components of the imitation game, modern interpretations, major critiques and defenses, evolution to contemporary AI evaluation. * Key Questions: What makes a machine "intelligent"? How do we measure it? Is behavior-based testing sufficient? What are its limitations? * **Afternoon Session: Philosophical Implications** * Deep Dive Topics: Operational vs. genuine intelligence, role of consciousness, behavioral vs. cognitive approaches, Chinese Room argument connection, modern LLM capabilities vs. Turing's vision. * Critical Analysis Framework: Behavioral evidence (observable vs. hidden), internal mechanisms (pattern matching vs. understanding, role of training data), ethical considerations (deception, authenticity, responsibility, agency). * [x] Day 2: Computational Theory of Mind * **Theory:** Functionalism and the computer model of consciousness * **Practice:** Map your own decision-making process in computational terms * [x] Day 3: Representation & Symbol Grounding * **Theory:** The symbol grounding problem in AI * **Practice:** Experiment with prompt engineering to test meaning representation * [x] Day 4: Language & Thought * **Theory:** Searle's Chinese Room and modern language models * **Practice:** Design experiments to probe AI understanding vs. processing * [x] Day 5: Emergence & Complexity * **Theory:** Emergent properties in AI systems * **Practice:** Observe and document emergent behaviors in AI interactions #### Week 2: Logic Systems & AI Reasoning * [x] Day 1: Formal Logic & AI * **Theory:** Propositional logic in AI reasoning * **Practice:** Transform logical arguments into AI prompts * **Detailed Lecture Plan:** * **Theory Session (30 minutes):** * Foundations of Propositional Logic: Basic logical operators (AND, OR, NOT, IF-THEN), truth tables, logical evaluation, equivalence, implications. * AI Implementation of Logic: How LLMs process logical statements, logical reasoning in natural language, common logical patterns in AI responses. * Practical Applications: Decision trees in AI, rule-based systems vs. neural approaches, combining symbolic logic with modern AI. * **Practice Session (30 minutes):** * Logical Translation: Convert natural language to logical propositions, test AI handling of logical operators, compare formal vs. informal logic. * Prompt Engineering with Logic: Design prompts using explicit logical structures, test complex logical chains, analyze AI's logical consistency. * Error Detection: Identify logical fallacies in AI responses, test boundary cases, document logical limitations. * **2-Hour Lecture Plan:** * **Hour 1: Theoretical Foundations** * Introduction to propositional logic (20 min) * Logic in computer science history (20 min) * Modern AI approaches to logic (20 min) * **Hour 2: Applied Logic in AI** * Practical demonstrations (30 min) * Interactive exercises (20 min) * Discussion and Q&A (10 min) * [ ] Day 2: Inductive vs. Deductive Reasoning * **Theory:** How AI systems handle different types of reasoning * **Practice:** Design prompts that require both reasoning types * [ ] Day 3: Bayesian Thinking * **Theory:** Probability in AI decision-making * **Practice:** Solve problems using Bayesian reasoning with AI assistance * [ ] Day 4: Fallacies & AI Limitations * **Theory:** Common AI reasoning failures * **Practice:** Identify and document AI logical limitations * [ ] Day 5: Integration & Review * **Theory:** Synthesize week's concepts * **Practice:** Create a concept map linking philosophical concepts to AI capabilities #### Week 3: Neural Networks & Cognitive Architecture * [ ] Day 1: Basic Neural Architecture * **Theory:** From biological neurons to artificial neural networks * **Practice:** Visualize neural network decision-making paths * [ ] Day 2: Pattern Recognition * **Theory:** How neural networks identify patterns vs. human cognition * **Practice:** Design experiments to test pattern recognition capabilities * [ ] Day 3: Learning & Memory in AI * **Theory:** Different types of machine learning approaches * **Practice:** Compare your learning process with AI learning patterns * [ ] Day 4: Attention Mechanisms * **Theory:** How AI models focus and prioritize information * **Practice:** Experiment with attention-directing prompts * [ ] Day 5: Knowledge Representation * **Theory:** How AI systems store and retrieve information * **Practice:** Map your knowledge structure vs. AI knowledge base #### Week 4: Information Theory & Communication * [ ] Day 1: Information Theory Basics * **Theory:** Shannon's information theory and its AI applications * **Practice:** Measure information content in different prompts * [ ] Day 2: Encoding & Compression * **Theory:** How AI systems encode and compress information * **Practice:** Experiment with different ways to represent the same information * [ ] Day 3: Noise & Signal * **Theory:** Managing uncertainty in AI systems * **Practice:** Design prompts that test signal extraction * [ ] Day 4: Channel Capacity * **Theory:** Limits of information processing in AI * **Practice:** Explore context window limitations and solutions * [ ] Day 5: Integration & Synthesis * **Theory:** Connecting information theory to AI capabilities * **Practice:** Design an optimal information flow for AI collaboration #### Week 5: Probability & Statistical Foundations * [ ] Day 1: Probability Fundamentals * **Theory:** Basic probability concepts in AI decision-making * **Practice:** Analyze AI confidence scores and predictions * [ ] Day 2: Statistical Distributions * **Theory:** Normal distribution and its role in AI * **Practice:** Experiment with probability-based prompts * [ ] Day 3: Correlation & Causation * **Theory:** How AI systems interpret relationships * **Practice:** Design prompts to test causal reasoning * [ ] Day 4: Sampling & Bias * **Theory:** Understanding AI training biases * **Practice:** Identify and mitigate bias in AI responses * [ ] Day 5: Statistical Inference * **Theory:** How AI systems make predictions * **Practice:** Create predictive prompts and analyze results #### Week 6: Advanced AI Concepts * [ ] Day 1: Large Language Models * **Theory:** Architecture and capabilities of LLMs * **Practice:** Compare different models' responses * [ ] Day 2: Transformer Architecture * **Theory:** How transformers process information * **Practice:** Optimize prompts for transformer models * [ ] Day 3: AI Alignment * **Theory:** Value alignment and AI safety * **Practice:** Design safety-conscious prompts * [ ] Day 4: Multi-modal AI * **Theory:** How AI processes different types of data * **Practice:** Experiment with multi-modal prompting * [ ] Day 5: Future Directions * **Theory:** Emerging trends in AI development * **Practice:** Design experiments for testing AI capabilities #### Week 7: Advanced Prompt Engineering & System Design * [ ] Day 1: Chain-of-Thought Prompting * **Theory:** Breaking down complex reasoning tasks * **Practice:** Design multi-step reasoning prompts * [ ] Day 2: Role-Based Prompting * **Theory:** Context and persona in AI interactions * **Practice:** Experiment with different AI roles and behaviors * [ ] Day 3: Meta-Prompting * **Theory:** Self-reflective and recursive prompts * **Practice:** Create prompts that improve other prompts * [ ] Day 4: Few-Shot Learning * **Theory:** Teaching AI through examples * **Practice:** Design effective few-shot prompts * [ ] Day 5: System Prompts * **Theory:** Creating robust AI instruction sets * **Practice:** Build and test system prompts #### Week 8: AI Problem-Solving & Integration * [ ] Day 1: Problem Decomposition * **Theory:** Breaking complex problems into AI-solvable parts * **Practice:** Decompose a real-world problem * [ ] Day 2: Workflow Design * **Theory:** Creating efficient AI-human workflows * **Practice:** Build a multi-step problem-solving system * [ ] Day 3: Error Handling * **Theory:** Managing AI limitations and failures * **Practice:** Design robust error-handling strategies * [ ] Day 4: Output Optimization * **Theory:** Fine-tuning AI responses * **Practice:** Improve output quality through prompt iteration * [ ] Day 5: Knowledge Integration * **Theory:** Synthesizing 8 weeks of learning * **Practice:** Create your own AI interaction framework #### Week 9: Applied AI Systems & Tools * [ ] Day 1: AI Development Platforms * **Theory:** Overview of major AI platforms and APIs * **Practice:** Compare platform capabilities and use cases * [ ] Day 2: AI Agents & Autonomy * **Theory:** Autonomous AI systems and their architecture * **Practice:** Design an autonomous agent system * [ ] Day 3: AI Tools Integration * **Theory:** Combining multiple AI tools effectively * **Practice:** Build a multi-tool workflow * [ ] Day 4: Custom AI Solutions * **Theory:** Tailoring AI systems to specific needs * **Practice:** Design a custom AI solution for a real problem * [ ] Day 5: AI Performance Optimization * **Theory:** Measuring and improving AI system performance * **Practice:** Optimize an AI workflow #### Week 10: Advanced Applications & Future-Proofing * [ ] Day 1: AI in Research * **Theory:** AI-assisted research methodologies * **Practice:** Design an AI-enhanced research workflow * [ ] Day 2: AI Content Creation * **Theory:** Advanced content generation strategies * **Practice:** Develop sophisticated content creation systems * [ ] Day 3: AI Decision Support * **Theory:** Building reliable AI advisory systems * **Practice:** Create a decision-support framework * [ ] Day 4: Emerging AI Technologies * **Theory:** Next-generation AI capabilities * **Practice:** Experiment with cutting-edge features * [ ] Day 5: Personal AI Strategy * **Theory:** Developing your AI mastery roadmap * **Practice:** Create your long-term AI development plan #### Week 11: AI Ethics & Responsible Development * [ ] Day 1: Ethical Frameworks * **Theory:** Major ethical approaches to AI development * **Practice:** Develop ethical guidelines for AI use * [ ] Day 2: Privacy & Security * **Theory:** Data protection in AI systems * **Practice:** Design privacy-preserving prompts * [ ] Day 3: Transparency & Explainability * **Theory:** Making AI decisions interpretable * **Practice:** Create explainable AI workflows * [ ] Day 4: Bias & Fairness * **Theory:** Advanced bias detection and mitigation * **Practice:** Audit AI systems for fairness * [ ] Day 5: Social Impact * **Theory:** AI's societal implications * **Practice:** Design socially responsible AI applications #### Week 12: Mastery Integration & Future Development * [ ] Day 1: Personal AI Framework * **Theory:** Building your AI interaction philosophy * **Practice:** Document your AI principles * [ ] Day 2: Advanced Problem-Solving * **Theory:** Complex system design patterns * **Practice:** Solve advanced AI challenges * [ ] Day 3: Innovation & Creativity * **Theory:** AI-enhanced creative processes * **Practice:** Design novel AI applications * [ ] Day 4: Knowledge Transfer * **Theory:** Teaching and sharing AI expertise * **Practice:** Create educational AI content * [ ] Day 5: Future Planning * **Theory:** Staying ahead in AI development * **Practice:** Create your advanced learning roadmap ### Reflection Prompts At the end of each week, take a moment to reflect on your learning journey: * What unexpected insights did you gain? * What challenges did you encounter, and how did you address them? * How can you apply these insights to your future learning? ### Visual Mapping Consider integrating visual mapping exercises for complex concepts and clear transition markers between topics to enhance your understanding and retention. Color-coding different types of activities may also help in organizing your learning process. ### Collaborative Exercises To enrich your learning experience, think about collaborating with others to test your concepts and share insights. Exploring specific LLM tools related to each topic could provide practical enhancement to your curriculum.

By Romain Peter