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Ai (Hallucination: A response generated by an AI that contains false or…

- - - - Empowerment - Helps individuals navigate AI-driven tools (e.g., smart devices, automated hiring systems).
      - Ethical Engagement - Enables informed participation in debates about AI regulation and policy.
      - Career Readiness - Prepares students and professionals for workplaces increasingly reliant on AI.
      - Guarding Against Risks - Reduces vulnerability to AI-driven scams, deepfakes, or biased decisions.
- - - - • Structured Data: Organized in tables or spreadsheets (e.g., sales records, student grades).
      - • Unstructured Data: Not organized in a pre-defined manner (e.g., emails, social media posts, images).
      - • Semi-Structured Data: Contains tags or markers to separate data elements (e.g., XML files, JSON files).
- - - - Classification Models – Like sorting emails into ‘spam’ or ‘not spam.’
        
        Regression Models – Predicting numbers, like tomorrow’s weather.
        
        Generative Models – Creating new content, like AI art or Deepfake videos.
        
        Recommendation Models – Suggesting things, like Netflix or Amazon.
  - - - Training: The model learns from historical data.
      - Validation: Fine-tuning to improve accuracy.
      - Testing: Evaluating the model’s performance on unseen data.
      - Deployment: Using the model in real-world applications.
      - Maintenance: Updating and improving the model over time.
- - - - Narrow AI: AI designed to complete very specific actions; unable to independently learn.
      - Artificial General Intelligence: AI designed to learn, think and perform at similar levels to humans.
      - Artificial Superintelligence: AI able to surpass the knowledge and capabilities of humans.
      - Reactive Machine AI: AI capable of responding to external stimuli in real time; unable to build memory or store information for future.
      - Limited Memory AI: AI that can store knowledge and use it to learn and train for future tasks.
      - Theory of Mind AI: AI that can sense and respond to human emotions, plus perform the tasks of limited memory machines.
      - Self-Aware AI: AI that can recognize others’ emotions, plus has sense of self and human-level intelligence; the final stage of AI.
      - Machine Learning?
        Machine Learning (ML) is a branch of AI that focuses on enabling computers to learn from data and improve their performance over time without being explicitly programmed. Instead of following rigid, predefined rules, ML systems identify patterns in data and use those patterns to make predictions, decisions, or classifications.
      - Natural Language Processing (NLP)
        Natural Language Processing is a subset of AI that focuses on understanding and generating human language.
      - Computer Vision
        Computer Vision is a subset of AI that focuses on enabling computers to see and understand visual information.
      - Deep Learning?
        Deep Learning is a specialized branch of machine learning inspired by the structure and function of the human brain. It uses artificial neural networks with multiple layers ("deep" networks) to automatically learn patterns and representations from data. Unlike traditional machine learning, which often requires manual feature extraction, deep learning models automatically discover features directly from raw data, making them powerful for complex tasks like image recognition, speech processing, and language translation.
- - - - o Learns by interacting with an environment, receiving rewards or penalties.
        o The agent learns to maximize cumulative rewards through trial and error.
    - - o Works with unlabeled data, finding hidden patterns or relationships.
        o Often used for clustering, association, and dimensionality reduction.
    - - o Learn from labeled data where input-output pairs are provided.
        o The model is trained to predict the output for new inputs based on historical data.
- - - - Extractive AI system: Recognizes patterns and makes predictions that help it connect related documents and retrieve relevant information from data.
      - Generative AI (GenAI) system: Uses a large language model (LLM) that consumes vast volumes of data and learns how to identify patterns and structures that it can use to create new images, text, audio and more.
      - Generative pre-trained transformer: A type of large language model (LLM) that is used for generating human-like text.
      - Large language model: A type of GenAI specifically architected to understand, summarize and generate text-based content.
      - OpenAI: An AI research and deployment company and the developer of ChatGPT.
        
        Prompt engineering/design: The process of carefully constructing prompts or instructions for AI language models to help them understand the intent of your input, as well as your desired output.
        
        RAG (Retrieval-Augmented Generation): A technology that enables a large language model to ground its responses in content from an authoritative knowledge base outside of its training data sources.
        
        RAG (Retrieval-Augmented Generation) Fusion: An enhanced RAG approach that combines the results of multiple searches to improve the detail and diversity of an LLM’s response.
        
        Small language model (SLM): A natural language processing (NLP) model that has fewer parameters than an LLM. SLMs are more lightweight, making them suitable for applications where computational resources are limited or where speed is a priority.
        
        Vector search: A search engine that uses a small language model to convert the meaning and context of unstructured data into numbers or vectors and then find other words, topics or categories that are similar. This allows for more nuanced and context-aware outputs.