From Understanding AI to Judging How to Use AI
AI Foundations 1: Concepts and Tools (AI Foundations 1: Concepts and Tools) Begins with a fundamental question: What problems can artificial intelligence solve, and how does it learn from data? After understanding the relationships among data, models, training, and inference, one can then grasp the concepts of services and algorithms, and determine whether a given task is suitable for AI application.
This path then brings technology back to its real-world context: where data originates, how privacy is affected, how AI is applied across industries, and how users assess benefits, risks, and responsibilities. The main text, video transcripts, lecture notes, and exercises together form a coherent reading pathway, structured according to knowledge interrelations and organized under the same directory.
Recommended Reading Approach
Start with Artificial Intelligence and Machine Learning and Evolutionary Trajectory to establish foundational concepts, then proceed to Services and Algorithms Retain the full extended text for model customization; additional topics such as prompt engineering, RAG, fine-tuning, tool calling, embeddings, and MCP can be referenced as needed.
Then read Data and Privacy and Industry Applications using the Opportunity Discovery exercises to contextualize problems within your own work. Subsequently, explore Responsible AI and the Four Ethical Principles, first learning to evaluate usage conditions before delving into generative AI.
The generative section sequentially explains capabilities and first experiments, model types and implementation, content creation across media, and personal, interpersonal, social, and environmental impacts. Finally, use the daily work analysis and comprehensive operational solution exercises to connect tools, workflows, evaluation metrics, and accountability.
How the Main Text, Transcripts, and Comprehensive Lecture Notes Work Together
Each page first presents the core knowledge content, followed by the separate English transcript of the corresponding video. Even if concepts are repeated, different examples and explanations offer valuable learning value; the directory allows direct navigation to desired sections. Only one complete translation is retained per file, with a separate link to the chapter for reference.
The two comprehensive lecture notes expand on concepts, prompts, memory, infrastructure, and business opportunities in line with their actual content. Where lecture notes exist but no corresponding spoken transcript is available, no additional commentary is inferred from slides. Custom diagrams are provided to aid understanding; independent videos offer on-demand playback windows and external links. Pages also support device-based English text-to-speech reading.
Exercises are placed adjacent to the main text: Opportunity Discovery, Ethical Reflection, Tool Suitability, Impact Study, and Comprehensive Operational Plan, each designed to apply concepts to specific real-world scenarios. Exercises do not imply that the reader has already verified these concepts.
Extended Materials and Coverage Boundaries
This outline includes 19 primary reading pieces. Three longer reference reports (AI and Skill Development, Economic Potential of Generative AI, and Impact Study of Online Freelance Platforms) are tracked separately for full translation and public usage conditions. These reports are not considered full report texts in the context of individual unit introductions. Unavailable original reading links are not substituted with similar content; any editorially added public references are clearly noted.
Upon completion, you may continue to AI Foundations 2: Business Processes to connect individual capabilities into actionable, verifiable workflows.