About vs. Demo: This demo folder now uses a standardized launch page. Start with About Demo, then choose the run mode that fits your class.

AI/ML Workflows • Browser • 20-30 min

Probabilistic Decision Engine

| Application | Benefit | |-------------|---------| | **Healthcare** | Treatment recommendations with uncertainty | | **Finance** | Risk assessment with scenario analysis | | **Robotics** | Real-time decision making under uncertainty |.

Open About Demo Read README View Course Path

Amortized Conditioning Engine (ACE) - Change assumptions and queries in real-time to see probabilistic predictions update instantly.

Demo: Healthcare treatment decision support with probabilistic inference.

Patient Profile

Treatment Options

Probabilistic Predictions (Updated in Real-Time)

Recovery Probability

72%

Risk Assessment

28%

Treatment Recommendation

Standard Treatment recommended

How Amortized Inference Works

  • Forward Pass: Neural network predicts posterior in milliseconds
  • Real-time Updates: Change assumptions → instant predictions
  • Multiple Queries: Compare scenarios without retraining
  • Applications: Healthcare, finance, robotics, active learning

Based on research from: Helsinki Institute, Aalto University, ELLIS Institute Finland

Standard demo guide

Use this demo in a logical learning sequence

Starts immediately in browser with no installs, no API keys, and classroom-safe defaults.

What this demo is about

| Application | Benefit | |-------------|---------| | **Healthcare** | Treatment recommendations with uncertainty | | **Finance** | Risk assessment with scenario analysis | | **Robotics** | Real-time decision making under uncertainty |.

Learning objectives

  • Explain the main ai/ml decision that Probabilistic Decision Engine is designed to support.
  • Change input assumptions and predict how the output should respond before running the demo.
  • Interpret the result in plain language, not just as a number, chart, or AI recommendation.

Run mode and expectations

  • Supported modes: Browser
  • Starts immediately in browser with no installs, no API keys, and classroom-safe defaults.

Step 1: Inputs

  • Start with the default assumptions, then change one variable at a time so students can isolate cause and effect.
  • Treat each input as a lever that changes the scenario, baseline, or business context behind the result.

Step 2: Decision buttons

  • Use the main run or simulate action to compute the scenario after inputs are set.
  • Use export or reset actions, when present, to compare runs or return to a classroom-safe baseline.

Step 3: Outputs and what to notice

  • Read the top-line result first, then look for supporting metrics, tables, or narratives that explain why it changed.
  • Students should explain whether the output is descriptive, predictive, simulated, or recommended.
  • Look for probability estimates, decision thresholds, and expected value calculations
  • Observe how changing risk tolerance changes the recommended action

Available run modes

  • Browser: available for this demo.

How to proceed

  1. Choose the run mode that fits the class: Browser.
  2. Review the default assumptions before changing anything.
  3. Change one or two inputs, then use `Run the main action`.
  4. Read the output first, then compare any supporting metrics, charts, or AI text.
  5. Capture one insight, one limitation, and one action recommendation.