Artificial General Intelligence

This course explores theories and methodologies for developing AI systems capable of human-level general problem-solving abilities. Students will study various approaches including large models, program synthesis, meta-learning, representation learning, and neuro-symbolic methods, while examining current research trends in improving abstraction and reasoning capabilities. The course includes practical projects where students design and implement general-purpose AI systems.

  • Time: Mon/Wed 9:00am-10:30am (Fall 2026)
  • Venue: TED-Hall
  • Codes: AI5102
  • Prerequisites: Students are expected to have solid understanding on machine learning and deep learning (AI5213, AI5214).

Notice

  • We will start our first lecture at 9/2 (Wed).
  • Access the AI5102 Ed Discussion forum. If you haven’t already been added to the class, use this invitation link.
    • Use your full name in the format “Firstname Lastname” with the first letter of each name capitalized (e.g., Sundong Kim).
    • Don’t forget to use your valid GIST email address.
  • Submit your assignments at the AI5102 Gradescope. Entry code is 4DBD25. Make sure to enter your correct student ID.
    • Use the same name format as for Ed Discussion: “Firstname Lastname” with the first letter of each name capitalized. (e.g., Sundong Kim).
    • Make sure to enter your correct student ID and your valid GIST email address.

Syllabus

We don’t have official textbook, but we will cover papers relevant to the topic. Main readings will be based on recent research papers and technical reports in the below syllabus.

Week Description Readings Homeworks
8/31      
9/2 Opening (Introduction)   Sign-up Survey
9/7 Opening (Refining problems)    
9/9 Invited Talk (Gabriel Lima; on AI Governance)  

Staffs

Communication: The course schedule and all resources will be posted on this course website. All class discussions, announcements and other communication will take place via Ed Discussion. Use the public comments if your question is relevant to the course material. We aim to respond to questions within 2 business days, often sooner. If you need to contact the course staff, please make a private question on Ed instead of sending a personal email. You’re free to visit TAs during office hours.

Sundong Kim

Sundong Kim

Instructor

QnA: After class (Lecture room) or Ed discussion
Office: AI Building (S7), Room 204

Byungchang Kim

Byungchang Kim

TA

QnA: After class (Lecture room) or Ed discussion
Office: RISE Bldg #205

Mohammad Saadati

Mohammad Saadati

TA

QnA: After class (Lecture room) or Ed discussion
Office: AI Building (S7), 1F Metaverse Studio

Byungrae Cha

Byungrae Cha

Staff

QnA: After class (Lecture room) or Ed discussion
Office: Kumho Hall (C4), 3F

Homeworks

Submit your assignments at the AI6105 Gradescope.

Gradings

You will earn A if (but not only if) your score is at least \(80\times(1-ε_1)\%\) of the total score, B if your score is at least \(60\times(1-ε_2)\%\), C if your score is at least \(40\times(1-ε_3)\%\), for some \(ε_i ≥ 0\) to be determined later. All participants in the course are evaluated equally, regardless of the course codes.

  • Homeworks (100%)

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