Principles and Structure of AI: ① How Does AI Work?

We provide an easy understanding of the concepts and principles of generative AI from a humanities perspective, including literature, film, music, and history, while exploring the cultural impacts and life changes AI will bring. We offer practical insights and strategies on how individuals and companies should respond and prepare for the AI era.

Even when using AI, the reason you’re still busy lies not in the tools, but in how the work is arranged. By asking four questions—roles, flow, handoffs, and exceptions—you can turn your work into a single blueprint, separate what can be handled by rules from what requires judgment, and reduce how often you need to call on AI in the first place. Use an Excel file containing 1,000 transactions to lock the standards into the tool and generate reports, break tasks down with sub-agents, and hand off what can’t be done to another AI, receiving only the results and carrying on. Finally, connect a messenger as the entry point and add four safety measures, completing the conductor’s role of calling on AI only where needed rather than handing everything over across eight installments, including the OT.

Understand Transformer-based LLM architectures and GPU utilization strategies, and gain hands-on experience with the actual serving process using vLLM. This course covers the entire practical workflow, from building AI system pipelines to monitoring and multi-GPU utilization, and is designed for intuitive understanding through diagrams and practice without complex formulas.

An AI that understands Korean well, handles long documents, and even carries out real-world tasks—that is exactly the direction Solar Open 2 is aiming for. This class provides an accessible explanation of Upstage’s Solar Open 2 technical report in the order of “Introduction → Design → Training → Evaluation.” Technical terms are explained through everyday analogies and visual materials, enabling even beginners to understand “what,” “how,” and “why” as one coherent flow.

How does the architecture change when an LLM is integrated into an existing backend? This is an introductory system design course that goes beyond simple LLM API calls to help you understand core AI service components such as Model Routers, LLM Gateways, and Vector DBs, and step-by-step design an operational AI service architecture including RAG and AI Agents. It is designed to help those who want to create their own LLM-based AI services quickly master the design workflow.

Diagnose the future of my work and learn how to grow together with AI What we need now is the power to read the future of my work! In the midst of industrial restructuring and technological transformation trends, we'll specifically tell you from a job function perspective what changes are coming to your work now and how you can prepare for them.

You can benchmark manufacturing innovation methods for semiconductors and run machine learning and deep learning with semiconductor data without coding.

LLM and Agent, the fundamental technologies behind AI like ChatGPT. LLM is currently the technology that has come closest to AGI. In this lecture, we will explore LLM and Agent technologies from a layperson's perspective.

Mastering difficult AI with just one single analogy?! A first step into AI principles, understanding through core concepts rather than just terminology.

Did you give up while learning Verilog and EDA tools? A semiconductor professor who designs NPUs himself will show you how to understand everything from CPUs to NPUs by calculating with just 9 Excel files.

When researching AI or conducting a project using it, basic paper implementation is essential. Let's upgrade our practical skills by implementing an actual paper through this lecture!

Do you know the basics but feel overwhelmed when it comes to applying them in practice? Drawing on real-world experience, we make complex LLM architectures easy to understand.

You use ChatGPT, but haven’t you found it difficult to explain why it produces answers like these? “I know terms like RAG, agents, and fine-tuning… but it’s difficult to explain them precisely.” “When I hear LLM-related terminology, I’m at a loss for words.” “Explanations of concepts in AI meetings are always vague.” This course was created specifically for people like you. This is a theory course designed to help you understand LLMs not as ‘tools,’ but as ‘structures.’ Rather than teaching you how to use ChatGPT or Gemini, it gives you a framework for explaining why they work the way they do.

This lecture is designed for job seekers preparing for Evaluation and Analysis roles in Samsung Electronics' DS Division (Memory, S.LSI, Foundry, TSP, AVP) and PE, AE, and Solution PE roles at SK hynix, who are struggling with the differences between business units and job functions. You will learn how to categorize job titles by company based on the actual workflow of Product Evaluation roles and how to translate your major-related experiences into professional terminology suitable for resumes and interviews. We will help you establish an application strategy by connecting experiences in Analog circuit design, Verilog/FPGA, process experiments, and data analysis with key industry terms such as Yield, Wafer Map, Shmoo Plot, and Failure Analysis.

This introductory course for AI engineers is designed to provide a brief hands-on experience of the entire process, from data processing and model development to cloud, MLOps, and ethical considerations. It focuses on building practical skills by helping students understand the process of connecting models to actual services, rather than just creating them. The course includes hands-on exercises and examples so that even those learning AI for the first time can easily follow along.

"We need to do DX/AX." It's always serious in the meeting room, but when you actually get down to the field, don't you feel lost about what to do first and how to do it? You've done a few PoCs, but they never transition to regular operations You bought solutions and equipment, but they're treated as cumbersome tools on-site There's a lot of talk about data and systems, but you can't figure out how to apply them to your current line or process There's no dedicated TFT or team, so you're a practitioner who has to handle both your main job and DX/AX... This course was created specifically for people like you.

Conquer everything about world-changing AI in just 4 lectures! Lecture 1. The True Identity of Artificial Intelligence AI is not magic. It's a digital brain created by analyzing human 'intelligence' like a mathematical formula! We thoroughly dissect the core structure of seemingly smart artificial intelligence. Lecture 2. Algorithm, AI's Creative (?) Thinking Method How does a pondering AI find answers? Experience firsthand the strategies of AI that plays Go, finds routes, and predicts your search results. Lecture 3. AI Hardware, The Engine That Turns Imagination Into Reality! The chip inside your smartphone is the heart of AI. We dissect everything from CPU, GPU, TPU, to even those unknown names. Lecture 4. The Great Clash of Machine Learning and Deep Learning! What on earth does it mean for AI to 'learn'? From classic rule-based AI to the currently hot ChatGPT! We trace the evolution of learning.

A general education lecture for the public to shed the vague fear of artificial intelligence. Through concrete examples, we learn what AI learning truly means. Also, via specific examples, we examine AI's 'brain structure' and how it thinks.

For those who have tried running hand-recognition examples but feel lost when it comes to teaching an AI your own hand gestures and turning them into something useful, I’ll guide you all the way from installation to data collection, training, and completing the web interface using just a webcam, drawing on my experience overseeing development and training vocational instructors.

The era of AI simply making PPTs "for" you is already over. What matters now is not "how well you use AI," but "how you think and design structures alongside AI." This is not a lecture that simply lists Claude and Gemini features. Through a practical PPT production workflow that you can use immediately tomorrow, we help you drastically reduce report writing time and focus on the more important task of "thinking." With a practice-oriented curriculum that can be applied directly to weekly report PPTs, go beyond shortening production time and complete your own AI-based work system. Point 1. AI Collaboration Structure Design: Speed changes when you divide roles - Division of labor: Claude for analysis, reasoning, and drafting; Gemini for visualization and slide generation - Designing a structure to use AI tools as "work partners" rather than simple utilities - Shifting work methods to reduce repetitive tasks and focus on thinking and judgment Point 2. Drastically shorten PPT production time with the R-S-D-P pipeline - Internalizing the 4-step production process: Research → Structure → Draft → Polish - Increasing work speed by standardizing the flow of report and proposal creation - Learning practical PPT production methods that reduce time by more than 70% compared to traditional methods Point 3. Structuring repetitive tasks by building your own AI automation system - Building a personalized AI environment using Claude Projects and Gemini Gems - Designing "your own AI assistant" trained on your company's tone, manner, and templates - Completing a sustainable work system by automating weekly and monthly reports