Spring 2024 MW 4:30pm-5:50pm, April 1st – June 5th
Location: Gates
B12
Edward
Chang (echang@cs.stanford.edu)
Adjunct Professor, Computer Science, Stanford
University
CA:
Matthew
Jin (mjin73@stanford.edu)
Office Hours: MW after class to 6:15pm at Gates B12 or
104.
Announcements
· (5/29) Final
lecture deck posted. Please sign up your
final presentation time.
· (5/21) Recorded
lectures of 5/20 and 5/22 posted.
· (5/15) Recorded
“Cancer Diagnosis and Treatment” lectures by Dr. Ko posted online.
· (5/1) Syllabus
refined, adding Llama-3 fine-tuning.
· (4/30) Guest
lecture slides of Dr. Duvvuri’s posted.
· (4/10) HW#2 posted
on Canvas, due in class on 4/15.
· (4/3/2024) HW#1
posted on Canvas, due by 4:30pm 4/10.
· (3/25/2024)
Textbook is online
at Kindle store.
· (3/07/2024)
Both SocraSynth and RAFEL are cutting edge research of LLMs. Reading materials are available on the sites,
and a course reader will be available by the start of the quarter.
· (1/29/2024) Course project can be on any subjects, e.g.,
psychology, laws, business, literature, political science, healthcare, computer
science, etc.
About
The 2024 edition of this course will
delve into Generative AI topics, encompassing innovations like SocraSynth,
GPT-4, ChatGPT, Gemini, and models of consciousness. In recent years,
artificial intelligence, particularly through deep learning, attention
mechanisms, and foundation models, has revolutionized technology. AI surpasses
human capabilities in various tasks, including computer vision and natural
language processing. Yet, we encounter challenges similar to
those from the initial AI boom five decades ago. This course will critically
analyze these challenges (such as issues with generalization, biases,
hallucinations, and reasoning) in prevalent AI algorithms like CNNs,
transformers, generative AI, and LLMs, including GPT and Gemini.
To overcome these hurdles, the course
will cover topics like transfer learning for data scarcity, knowledge-guided
multimodal learning for data diversity, and modeling of emotions, behaviors,
and ethics, along with multi-LLM collaborative dialogue.
Teaching will blend lectures with
project sessions. Guest speakers from academia and industry will share insights
on AI's niche applications, like in diagnosing and treating cancer or
depression. Students, from disciplines like CS, Business, Law, Medicine, and
Data Science, will undertake term projects that might include literature
reviews, idea development, and practical implementation. They are encouraged to
craft a project that complements their graduate research, fostering a deep,
integrative learning experience.
Note: The following
prerequisite for taking this course have been waved because of the ease-of-use
of ChatGPT.
Level: Senior/graduate of any majors.
Perquisite: Introductory course in
AI, Statistics, or Machine Learning.
Assignments
· No exams.
· Three assignments, using Gemini, ChatGPT and GPT-4 to
complete.
· Term project: A group project of two to three using LLMs, RAFEL,
and SocraSynth to address practical problems.
Textbook (required)
·
SocraSynth,
Edward Y. Chang, March 2024
Grading
· Assignments 30%
· Class participation 10%
· Literature survey and project proposal 20%
· Project implementation and demo 40%
Syllabus
Date |
Description |
Course Materials |
Notes |
Week #1 4/1/2023 |
Course
Aims and Syllabus ChatGPT
Intro [slides]
|
Intro to AI, GAI and why you should or should not
take this course. |
E. Chang |
4/3/2023 |
LLMs (Large
Language Models) Part 1 of 5 LLM
Insights and Aphorisms [slides] |
Textbook Chapter 2 and Appendix X2 |
E. Chang Assignment #1, posted on Canvas (due by 4:30pm on
4/10) |
Week #2 4/8/2023 |
P4
Medicine, Part 1 of 3: Intro
to P4 Medicine [link] |
[1] Schwartz, W. B., R. S. Patil, and P. Szolovits. "AI in Medicine: where do we
stand." New England Journal of Medicine 316 (1987): 685-688.
[link]. [2] Wu, T. D. "Efficient Diagnosis of
Multiple Disorders Based on a Symptom Clustering Approach." Proceedings
of AAAI, 1990, pp. 357-364. [3] Universal Equivariant Multilayer Perceptrons, Siamak Ravanbakhsh, ICML, 2020. [4] Tricorder (medical IoTs), E. Y. Chang, et al.,
"Artificial Intelligence in XPRIZE DeepQ
Tricorder. " ACM MM Workshop for Personal Health and Health Care, 2017. [5] Szolovits,
P., and S. G. Pauker. "Categorical and
Probabilistic Reasoning in Medical Diagnosis.” Artificial
Intelligence 11(1-2), 1978: 115-144. [6] Patil, R. S., P. Szolovits, and W. B. Schwartz. "Causal
Understanding of Patient Illness in Medical Diagnosis." In Proceedings
of the Seventh International Joint Conference on Artificial Intelligence. |
E. Chang |
4/10/2023 |
P4
Medicine, Part 2 of 3: Principles
of Diagnosis [link] |
[1] Textbook Chapters 3 and 4 [2] Prompting Large Language Models with the
Socratic Method, E. Y. Chang, IEEE CCWC, March 2023. [link] [3] CRIT: Critical Reading Inquisitive Template,
E. Y. Chang. [link] [4] Noora.cs.stanford.edu |
E. Chang Assignment #1 due Assignment #2: TBD |
Week #3 4/15/2023 |
P4 Medicine,
Part 3 of 3: Small
Data Machine Learning [link] LLM,
Part 2 of 5: |
[1] Textbook Chapter 6 [2] Daphne Koller:
Biomedicine and Machine Learning, AI Podcast #93 with Lex Fridman, May 2020 [link] [3] Edward Y. Chang et al.,
"Context-Aware Symptom Checking for Disease Diagnosis Using
Hierarchical Reinforcement Learning." AAAI (2018). [4] Edward Y. Chang et al.,
"REFUEL: Exploring Sparse Features in Deep Reinforcement Learning for
Fast Disease Diagnosis." NeurIPS (2018). |
E. Chang Assignment #2 due Assignment #3 |
4/17/2023 |
LLM,
Part 3 of 5: Critical
Thinking & the Socratic Method |
Textbook Chapter 4 |
E. Chang |
Week #4 4/22/2023 |
Psychiatric
Disorders 1 of 2, symptoms, and treatments [slides][video] |
|
Dr. Vikas Duvvuri, MD/PhD Stanford Assignment #3 due |
4/24/2023 |
Sam Altman talk |
Sam Altman @NVDIA Auditorium |
|
Week #5 4/29/2023 |
Psychiatric
Disorders 2 of 2, symptoms, and treatments [slides][video] |
|
Dr. Vikas Duvvuri, MD/PhD Stanford |
5/1/2023 |
LLM,
Part 4 of 5: History
of NLP: Pre-LLM Era [slides] Term Project
Title/Abstract Presentations, session #1 |
[ Textbook Chapters 5, 6, 7, 8 [1] Textbook Chapter 1 [3] BERT: Pre-training of Deep Bidirectional
Transformers for Language Understanding [link]. [4] Reformer: The Efficient Transformer [link]. [5] Perceiver: General Perception with Iterative
Attention [link]. |
CA E. Chang |
Week #6 5/6/2023 |
Term Project
Title/Abstract Presentations, Session #2 |
[1] Textbook Chapter 1 [3] BERT: Pre-training of Deep Bidirectional
Transformers for Language Understanding [link]. [4] Reformer: The Efficient Transformer [link]. [5] Perceiver: General Perception with Iterative
Attention [link]. |
E. Chang |
5/8/2023 |
LLM Part 5 of 5: History of NLP: Post-LLM Era BERT, GPT, RAFEL, Virtual Assistant and Augmented LM. [slides] |
[1] Textbook Chapter 11 [2] Genie: A Generator
of Natural Language Semantic Parsers for Virtual Assistant Commands, G. Campagna, S. Xu, M. Moradshahi, R. Socher, and
Monica S. Lam [3] Augmented Language Model,
a survey, Meta, 2023 [link]. |
E. Chang |
Week #7 5/13/2023 |
Fine-tuning
LLM Tutorial: Llama 3 |
|
CA: Matthew J. |
5/15/2023 |
Cancer,
Part 1 od 2: Cancer Causes and Diagnosis [video][Slides] |
[1]Principles and methods
of integrative genomic analyses in cancer, Vessela
N. Kristensen, Ole Christian Lingjærde, Hege G. Russnes, Hans Kristian M. Vollan,
et al., Nature Reviews, 2014. [2] Biomarker development in the precision medicine
era: lung cancer as a case study, Ashley J. Vargas, and Curtis C. Harris,
Natural Reviews, 2016. [optional] Artificial intelligence in radiology,
Ahmed Hosny, Chintan Parmar, John Quackenbush,
Lawrence H. Schwartz,and
Hugo J. W. L. Aerts, Nature Reviews, 2018. |
Dr. Melissa Ko, PhD, Cancer Biology, Stanford |
Week #8 5/20/2023 |
Consciousness
& Mind, Part 1 & 2 of 3: Computational Consciousness, COCOMO [video][slides]. |
[[1] Textbook Chapter 10 [3] COCOMO: Computational Consciousness Modeling,
E.Y. Chang, 2023 [link]. |
E. Chang |
5/22/2023 |
Cancer,
Part 2 of 2: Cancer Treatment and How AI May Help [video][Slides] |
[1] Mass cytometry: blessed with the curse of dimensionality,
Evan W Newell & Yang Cheng, Nature Immunology, June 2016. [2] Next-Generation Machine Learning for Biological
Networks, Diogo M. Camacho, Katherine M. Collins,
Rani K. Powers, James C. Costello, and James J. Collins, Leading Edge
Review, June 2018. [optional] Personalized Cancer Models for Target,
Discovery and Precision Medicine, Carla Grandori1 and Christopher J. Kemp,
Trends in Cancer, CellPress Reviews, September
2018. |
Dr. Melissa Ko, PhD, Cancer Biology, Stanford |
Week #9 5/27/2023 |
Memorial
Holiday |
|
|
5/29/2023 |
Consciousness
& Mind, Part 2 & 3 of 3: Putting all together: EVINCE [Slides] |
1. Cocomo, Computational Consciousness Modeling for Generative and Ethical AI, Edward Y.
Chang, arXiv eprint
2304.02438 [PDF], March 2023 2. SocraSynth:
Socratic Synthesis with Multiple Large Language Models --- Principles and
Practices, March 2025. 3. Integrating Emotional and Linguistic Models for
Ethical Compliance in Large Language Models, Edward Y. Chang
, arXiv:2405.07076 [PDF],
May 2024 4. Ensuring Diagnosis Accuracy in Healthcare AI with
the EVINCE framework, Edward Y. Chang, arXiv:2405.15808 [PDF], May 2024 5. EVINCE (Entropy Variation and INformation CompetencE),
Stanford InfoLab Technical Report [PDF], June 6th,
2024 |
E. Chang |
6/3/2023 |
Project
Presentations I |
Please sign up |
E. Chang, CA |
Week #10 6/5 (last
day of class) |
Project
Presentations II |
Presentation time is flexible to arrange between
5/31 and 6/5. |
E. Chang CA |