12-Weeks antifragile skills AI Learning
A Journey for a self-learning project to get a better grip on AI, develop skills and prepare future
Here's our living briefing document, capturing the essence and strategic reasoning behind our "Antifragile AI Skills" curriculum journey, designed to seamlessly onboard any new team member.
We embarked on this journey six months and three weeks ago, initiated by Romain Peter, with the ambitious goal of building a 12-week AI "antifragile skills" curriculum. Our design principle is clear: daily 30-minute reading and 30-minute practice sessions, complemented by detailed 2-hour lectures for each day. We believe this blend fosters both theoretical depth and practical application, crucial for genuine skill mastery.
**Week 1: From Philosophy of Mind to Computational Thinking**
We began our exploration by grounding AI in its philosophical roots, understanding that a strong theoretical foundation is key to navigating the rapidly evolving AI landscape.
* **Day 1: Foundations of Machine Intelligence.** Our focus was the Turing Test and its profound philosophical implications. We aimed to dissect intelligence criteria through analytical dialogue, comparing diverse AI responses. Our decision to dedicate a full 2-hour lecture to this topic stemmed from its foundational importance. We segmented the lecture to facilitate focused learning, integrate practical exercises exploring the Turing Test's mechanics, and establish clear recap questions. Crucially, we committed to explicitly linking Turing's original ideas to contemporary AI, especially Large Language Models (LLMs) and current AI ethics. This connection is vital for new team members to grasp the historical lineage of modern AI. Given Romain's deep background in Philosophy of Mathematics, we identified Gödel's Incompleteness Theorems and their relation to modern AI capabilities, particularly LLMs, as a critical area for exploration. Our deep study documents for this day provided a comprehensive overview of Turing's 1950 paper, emphasizing his "imitation game" and "learning machines" as revolutionary concepts that foreshadowed modern machine learning. We also meticulously detailed Turing's nuanced stance on Gödel's incompleteness theorem, where he acknowledged machine limitations but argued they don't negate the possibility of artificial intelligence or invalidate the imitation game. To broaden our philosophical lens, we included a summary of Douglas Hofstadter's "Gödel, Escher, Bach," noting its early influence on AI by critiquing symbolic AI and championing emergent intelligence.
* **Day 2: Computational Theory of Mind.** Our attention shifted to functionalism and the computer model of consciousness, with the practical goal of mapping our own decision-making processes in computational terms. The 2-hour lecture provided a robust framework for understanding the mind as a computational system. We structured it to cover historical roots (formal logic, the Turing Machine, cognitive psychology), core tenets (mental representations, computational processes, the Language of Thought hypothesis, Modularity of Mind), and supporting arguments (productivity, systematicity, cognitive science's success, intentionality). We proactively addressed criticisms, including the symbol grounding problem, the frame problem, the problem of consciousness, and embodied/situated cognition, recognizing that understanding limitations is as crucial as understanding capabilities. We integrated reflection questions throughout the lecture to foster deeper engagement and provided curated resources for further study, ensuring comprehensive learning.
* **Day 3: Representation & Symbol Grounding.** This day tackled the profound symbol grounding problem in AI, with the practice involving prompt engineering to test meaning representation. Our 2-hour lecture, "Bridging the Gap Between Symbols and Meaning," was meticulously designed. The morning session laid the groundwork for "The Symbol Grounding Problem," defining symbols and meaning, introducing Stevan Harnad's pivotal challenge, and differentiating between bottom-up and top-down processing. We then explored diverse "Representation in AI Systems," including vector representations, embedding spaces (like Word2Vec and GloVe), semantic networks, and the architecture of meaning (compositionality, distributed representations, context sensitivity). The interactive discussion section included crucial case studies on GPT models, visual-language models, and the challenging representation of concrete versus abstract concepts, coupled with probing questions about AI understanding versus mere processing. The afternoon session, "Practical Applications," delved into the specifics of implementation, covering detailed representation techniques (Word Embeddings, Contextual Representations, Multi-modal Grounding, Knowledge Graphs) and common challenges (ambiguity, context-dependency, abstract concepts, cross-modal alignment). We then moved into "Experimental Design," outlining how we test AI symbol grounding, including symbol manipulation tests, meaning preservation experiments, context switching challenges, and abstraction level analysis, complete with rigorous evaluation metrics. We also created a dedicated document, "The Grounding Problem in AI and its Relevance to Recent Advancements (Especially in 2024)," which elaborated on Harnad's symbol grounding problem and Mollo and Millière's vector grounding problem. We identified five types of grounding: sensorimotor, communicative, epistemic, relational, and referential. We specifically highlighted the increased focus on "grounding" in 2024, particularly in mitigating AI hallucinations through Retrieval-Augmented Generation (RAG) methods being developed by Google and Microsoft, underscoring the real-world impact of this theoretical challenge. The document also emphasized the critical importance of ground-truth data, the application of grounded AI in diverse fields, and the evolving public perception. We outlined key research directions like neuro-symbolic AI, embodied AI, cognitive architectures, and Explainable AI (XAI), while acknowledging persistent challenges such as data bias, contextual understanding, and dynamic environments.
**New Subjourney: Databases**
In parallel with our core curriculum, we recognized a fundamental gap: the necessity of database knowledge for a comprehensive understanding of AI. Consequently, we initiated a new subjourney specifically for "Databases," starting from scratch. Our clear goal is to master database theory fundamentals and gain a practical understanding sufficient for tackling small projects and, crucially, for grasping the technological and AI advancements that inherently require database knowledge. This strategic decision ensures our team members are not only proficient in AI concepts but also equipped with the underlying data management skills that power modern AI systems. For Day 1 of this subjourney, Romain detailed a 30-minute theoretical module on fundamental data types (Integer, Floating-Point, Text, Boolean, Date/Time, NULL) and structures (arrays, lists, and especially tables as the cornerstone of relational databases). The 30-minute practical module involved setting up SQLite and learning essential dot-commands. We practiced creating and populating a `users` table using `CREATE TABLE` and `INSERT INTO` statements, followed by simple data retrieval with `SELECT` commands. This day concluded with an assessment and considerations for visual learning and common pitfalls. Romain then provided a detailed explanation of the `SELECT` command, covering its basic syntax, filtering with `WHERE` and logical operators, sorting with `ORDER BY`, and limiting results with `LIMIT`. We also explored aggregate functions (`COUNT`, `SUM`, `AVG`, `MIN`, `MAX`), grouping with `GROUP BY`, and filtering groups with `HAVING`, as well as handling `NULL` values. This explanation also briefly touched upon modern SQL features like Window Functions and Common Table Expressions (CTEs), including practical exercises and a visual flowchart of the `SELECT` query's order of operations. Finally, Romain presented SQL's syntax essentials, emphasizing its logical, language-like, and declarative nature, which is crucial for AI applications in data preprocessing, querying training datasets, and efficient data management. We covered core CRUD operations and typical `SELECT` clause structure, logical operators, and common aggregate functions. Romain highlighted common SQL patterns relevant to AI/ML, such as data sampling, feature extraction, and data cleaning queries. He also shared "gotchas" and best practices, including caution with SQL injection and using aliases. Looking beyond SQL, Romain noted the future benefits of exploring Object-Relational Mapping (ORM) tools, like Django's ORM, a favorite of Mike's, as a bridge between SQL databases and object-oriented code, underscoring that mastering SQL fundamentals will greatly enhance our ability to leverage ORMs and Python's data libraries effectively, as "SQL's logical thinking patterns translate well." Mike is also building a Python library for rapid code mutation that Romain will be able to use.By Romain Peter