Environmental cost of training AI models in AI for Everyone - Mini Project: Build & Apply

Environmental Cost of Training AI Models

📖 Scenario: You are part of a team raising awareness about the environmental impact of artificial intelligence (AI). Your goal is to create a simple data summary that shows the energy consumption and carbon emissions of training different AI models.

🎯 Goal: Build a clear data summary that lists AI models with their training energy use and carbon emissions, then calculate total energy and emissions to understand the environmental cost.

📋 What You'll Learn

Create a dictionary with AI model names as keys and their training energy consumption (in kWh) as values

Add a variable for the average carbon emission factor (kg CO2 per kWh)

Calculate a new dictionary with AI model names and their estimated carbon emissions

Calculate total energy consumption and total carbon emissions for all models

💡 Why This Matters

🌍 Real World

Understanding the environmental cost of AI training helps researchers and companies make greener choices and raise awareness about energy use.

💼 Career

Data analysts, AI researchers, and sustainability experts use such data to measure and reduce carbon footprints in technology projects.

1

Create AI models energy data

Create a dictionary called ai_models_energy with these exact entries: 'GPT-3': 1287000, 'BERT': 150000, 'ResNet': 50000, 'Transformer XL': 300000

AI for Everyone

# Create the dictionary ai_models_energy with the specified AI models and their energy consumption
# Your code here

Need a hint?

Use curly braces {} to create a dictionary with the model names as keys and energy values as integers.

2

Set carbon emission factor

Create a variable called carbon_per_kwh and set it to 0.475 representing average kg CO2 emitted per kWh of electricity

AI for Everyone

ai_models_energy = {
    'GPT-3': 1287000,
    'BERT': 150000,
    'ResNet': 50000,
    'Transformer XL': 300000
}
# Set the carbon emission factor per kWh
# Your code here

Need a hint?

Assign the value 0.475 to the variable carbon_per_kwh using the equals sign.

3

Calculate carbon emissions per model

Create a new dictionary called ai_models_carbon using dictionary comprehension that multiplies each model's energy in ai_models_energy by carbon_per_kwh

AI for Everyone

ai_models_energy = {
    'GPT-3': 1287000,
    'BERT': 150000,
    'ResNet': 50000,
    'Transformer XL': 300000
}
carbon_per_kwh = 0.475
# Calculate ai_models_carbon using dictionary comprehension
# Your code here

Need a hint?

Use {model: energy * carbon_per_kwh for model, energy in ai_models_energy.items()} to create the new dictionary.

4

Calculate total energy and carbon emissions

Create two variables: total_energy as the sum of all values in ai_models_energy, and total_carbon as the sum of all values in ai_models_carbon

AI for Everyone

ai_models_energy = {
    'GPT-3': 1287000,
    'BERT': 150000,
    'ResNet': 50000,
    'Transformer XL': 300000
}
carbon_per_kwh = 0.475
ai_models_carbon = {model: energy * carbon_per_kwh for model, energy in ai_models_energy.items()}
# Calculate total_energy and total_carbon
# Your code here

Need a hint?

Use the sum() function on the dictionary values to get totals.