W1

Introduction to AI Research

Jun 22–26  ·  Orientation + Python + AI Fundamentals  ·  Deliverable: first pipeline run + 1-page reflection

Learning Goals

Monday, June 22 — Kickoff (Orientation Day)

📋 Topics

What is Research?

  • Research vs. coursework: the difference
  • What is a research question? What is a contribution?
  • What is a publication? What is a conference?
  • What is a PhD and why would someone want one?

What is ComplianceGPT?

  • The problem: healthcare compliance is expensive and error-prone
  • The solution: AI that reads scenarios and gives verdicts
  • Live demo: run the pipeline, see a PERMITTED / DENIED result
  • The pipeline diagram: NL → LLM → Engine → Verdict
🎯 Day 1 Task

Complete the setup checklist. Run the pipeline on 3 rows. Write down what surprised you. Post 1 question in Slack #questions before Wednesday.

Wednesday, June 25 — Main Session: Python + AI Foundations (On campus, NCS 242)

🐍 Topics

Live coding session. Follow along — open a Jupyter notebook and type as I type.

Variables & Types

name = "hospital"
count = 137
is_permitted = True

Lists & Dicts

models = ["gemma3", "llama3"]
result = {
  "verdict": "PERMITTED"
}

Functions

def accuracy(df):
    correct = (df["v"] ==
               df["gt"]).sum()
    return correct / len(df)

Tools: VS Code, Jupyter notebooks, GitHub. We'll commit our first notebook together.

🎯 Day 2 Task

Complete exercises 1–4 in notebooks/week1_python.ipynb. These cover: loading a CSV, filtering rows, writing a function, computing accuracy. Continue independently Thu/Fri — due Friday end of day.

Thursday–Friday, June 26–27 — Remote Work

🤖 Topics

The AI Stack

  • AI — the broad field
  • Machine Learning — learning from data
  • Deep Learning — neural networks
  • Generative AI — creating text/images/code
  • LLMs — what we use in ComplianceGPT

What LLMs Can and Cannot Do

  • Can: read long text, extract structure, reason step-by-step
  • Cannot: guarantee factual accuracy (hallucination)
  • Cannot: reason perfectly about legal edge cases
  • Cannot: reliably follow complex instructions (sometimes)

Discussion: Where does AI fail?

We'll look at 3 real wrong predictions from ComplianceGPT and ask: was this an AI extraction error, a reasoning error, or is the scenario genuinely ambiguous? This question will guide all of your projects.

Week 1 Deliverable

Due: Friday, June 27 (end of day)

1. First pipeline run — Submit the CSV output from running batch_runner.py on 5 rows. The file name should be results/[yourname]_week1.csv.

2. One-page reflection — Answer these 3 questions (roughly 1 paragraph each):

  • What surprised you most about ComplianceGPT today?
  • Looking at your 5 results: did any surprise you? Why?
  • What question about AI and compliance are you most curious about right now?

Graded on effort and engagement, not on correctness. There are no wrong answers here.

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