The Hierarchy of AI

Business leaders encounter a flood of technical terms. This hierarchy cuts through the noise, organizing the field into clear categories based on business utility and capability rather than academic theory.

• 1. Core Paradigms (How it Learns)
  • Machine Learning (ML)

    The overarching technology where computers learn from data rather than following explicit rules.

    • Supervised Learning

      Learning from labeled examples (e.g., "This is a cat," "This is a fraud"). Business use: Forecasting, Classification.

    • Unsupervised Learning

      Finding hidden patterns in messy, unlabeled data. Business use: Customer Segmentation, Anomaly Detection.

    • Reinforcement Learning (RL)

      Learning by trial and error to maximize a reward. Business use: Robotics, Dynamic Pricing, Logistics Optimization.

• 2. Key Architectures (The Engines)
  • Deep Learning (Neural Networks)

    Complex models inspired by the human brain, capable of handling vast amounts of data.

    • Transformers (e.g., GPT, BERT)

      The architecture behind modern Generative AI. It excels at understanding context and relationships in sequential data like text or code.

    • Computer Vision Models (e.g., CNNs)

      Specialized architectures designed to process and analyze visual data (images and video).

• 3. Business Applications (What it Does)
  • Generative AI

    Creating new content. Tasks include drafting text, generating marketing images, and writing software code.

  • Natural Language Processing (NLP)

    Understanding human communication. Tasks include translation, sentiment analysis, and summarizing documents.

  • Computer Vision

    Seeing and interpreting the physical world. Tasks include quality control inspections, facial recognition, and object detection.

  • Predictive Analytics

    Forecasting future trends based on historical data. Tasks include demand planning and risk assessment.

• 4. Foundations & Governance
  • AI Ethics & Safety

    The framework for managing risk. Focus areas include bias mitigation, explainability (why did the AI do that?), and data privacy.

  • AGI vs. Narrow AI

    The distinction between today's specialized tools (Narrow AI) and the hypothetical future of human-level machine intelligence (AGI).