주제탐구세미나2: 언어, AI 그리고 로봇
Language, AI, and Robots – The Future of Humanity Through the Lens of Philosophy and Media
991.102(005)
The advent of artificial intelligence and robots that comprehend human language serves as a mirror of humanity, opening a new dimension of self‑understanding. This course invites students to look inward through that lens and, building on these insights, contemplate how the future may unfold in the era after AI and robots. Instead of probing technical intricacies, we collectively view and analyze films, animation, literature, and other media, exploring the themes of language, identity, power, and mutual communication they embody—while connecting them to philosophical frameworks. Drawing on key concepts from thinkers such as Carl Jung, Jacques Lacan, Friedrich Nietzsche, Thomas Hobbes, and Claude Lévi‑Strauss, the course weaves in the latest research on AI‑ and robot‑focused works. Through discussion, presentations, and team projects, participants are encouraged to develop and articulate their own informed perspectives.
- Location: Bld 220, Room 201
- Lecture: Wednesday 14:00 - 16:50
Instructor
Teaching Assistants
References
- Kim, et al. “E2Map: Experience-and-Emotion Map for Self-Reflective Robot Navigation with Language Models.” IEEE International Conf. on Robotics and Automation (ICRA), 2025.
Grading (S/U)
- Attendance: 40%
- Assignment: 30%
- Project PT: 30%
Lecture Schedule
| Week | Date | Lecture | ||
|---|---|---|---|---|
| 1 | 9/2 | Course opening and basic concepts - Blade Runner 1982 | Robot architecture basics | Primitive physical architecture of Intelligence and Language |
| 2 | 9/9 | The First Language | Saussurean Linguistics | Token - Signifier Revisited through LLMs |
| 3 | 9/16 | Special session – Vibe Coding & Evaluation Metric – Practice | ||
| 4 | 9/23 | Project ideation pitch (PT) | ||
| 5 | 9/30 | Abstraction | Implementation of Abstraction | Prediction in time |
| 6 | 10/7 | Distance in time | Map of meaning | Understanding Temporal Causality |
| 7 | 10/14 | Mid-term exam | ||
| 8 | 10/21 | Sequence learning | Seq-to-Seq learning | Lab session – 1: LLM basic practice (Local LM & api) TA Baek & Lee |
| 9 | 10/28 | Translation | Neural Machine Translation - 1 S2S, Attention | Lab session – 2: RAG & Langchain TA Lee |
| 10 | 11/4 | Translation Evaluation, How Neural Net Learned Meaning | Self-Attention and Transformer | Lab session – 3: Agentic AI(Harness) TA Lee |
| 11 | 11/11 | Image-Language Understanding Vision-Language-Action Model Evaluation of Generation and Babel tower dilemma | Lab session – 4: Agentic AI(MCP) & Project Notice TA Lee | |
| 12 | 11/18 | Reinforcement learning | Policy gradient | ChatGPT - Human Feedback & Emotion |
| 13 | 11/25 | Information-Theoretic Model of Trust, Cooperation, and Antagonism | Anatomy of motivation | Team work |
| 14 | 12/2 | Final PT | ||
| 15 | 12/9 | Final report, grading, feedback | ||