Sustainability & AI Cheatsheet

Cheatsheet Content

Green Skills & Environmental Concepts Green Skills: Knowledge, abilities, values, and attitudes needed to live in, develop, and support a sustainable and resource-efficient society. Essential for green jobs. Green Jobs: Employment that contributes to preserving or restoring environmental quality. Green Economy: An economy that aims at reducing environmental risks and ecological scarcities, and that aims for sustainable development without degrading the environment. Green Project: A project designed with environmental sustainability in mind, aiming to minimize negative impacts and maximize positive ones. Pollution: Introduction of harmful substances or products into the environment. Greenhouse Effect: Natural process where atmospheric gases trap heat, warming the Earth. Enhanced by human emissions, leading to global warming. Ozone Layer Depletion: Thinning of the ozone layer in the stratosphere, primarily due to CFCs, increasing UV radiation reaching Earth. 3R's: Reduce: Decrease the amount of waste produced. Reuse: Use items multiple times before discarding. Recycle: Process used materials into new products. Conservation: Protection, preservation, management, or restoration of natural environments and wildlife. SDGs (Sustainable Development Goals): 17 global goals set by the UN to achieve a better and more sustainable future for all by 2030. Probability: Fundamentals & Applications Definition: A measure of the likelihood of an event occurring. Expressed as a number between 0 (impossible) and 1 (certain). Basic Formula: $P(A) = \frac{\text{Number of favorable outcomes}}{\text{Total number of possible outcomes}}$ Key Concepts: Sample Space ($S$): The set of all possible outcomes. Event ($A$): A subset of the sample space. Mutually Exclusive Events: Events that cannot occur at the same time ($P(A \cap B) = 0$). Independent Events: The occurrence of one event does not affect the probability of another ($P(A \cap B) = P(A)P(B)$). Conditional Probability: $P(A|B) = \frac{P(A \cap B)}{P(B)}$, the probability of event A given event B has occurred. Applications & Uses: Data Science/Machine Learning: Risk assessment, classification, prediction models (e.g., Naive Bayes, Logistic Regression). Finance: Stock market predictions, insurance premium calculations, portfolio management. Engineering: Reliability analysis, quality control, signal processing. Medicine: Disease diagnosis, clinical trial design, epidemiology. Gaming/Gambling: Odds calculation, strategy development. Weather Forecasting: Predicting likelihood of rain, storms, etc. Generative AI Definition: A type of Artificial Intelligence that can produce new and original content, such as text, images, audio, video, or code, rather than just classifying or analyzing existing data. Types of Generative AI Models: Generative Adversarial Networks (GANs): Two neural networks (Generator and Discriminator) compete to create realistic data. Variational Autoencoders (VAEs): Learn a compressed representation (latent space) of data and generate new data from it. Transformers: Neural network architecture, often used in Large Language Models (LLMs), capable of processing sequential data like text. Diffusion Models: Generate data by iteratively denoising a random signal. Benefits: Content Creation: Automates generation of text, images, music, code, reducing manual effort. Innovation: Enables new forms of creative expression and problem-solving. Personalization: Creates tailored content for individual users. Efficiency: Speeds up design cycles, prototyping, and data augmentation. Data Augmentation: Generates synthetic data for training other AI models. Popular Tools & Examples: Text: ChatGPT, Bard, LLaMA, GPT-3/4. Images: DALL-E 2/3, Midjourney, Stable Diffusion. Code: GitHub Copilot. Music: Google Magenta. Video: RunwayML. Generative AI vs. Conventional AI: Feature Conventional AI (Discriminative) Generative AI Primary Task Classification, Prediction, Recognition Creation, Generation, Synthesis Output Labels, Scores, Decisions New data (text, images, code) Learning Focus Distinguishing between data points Learning data distribution to create new points Examples Spam detection, facial recognition, sentiment analysis Image generation, text writing, code generation Impact: Job Market: Automation of certain tasks, creation of new roles (AI prompt engineers, AI ethicists). Creativity: Democratizes content creation, empowers artists and writers. Economy: Drives productivity gains, creates new industries and services. Ethics & Society: Raises concerns about misinformation (deepfakes), copyright, bias, and job displacement. Education: Changes how students learn and how educators teach.