What actually is AI, and why can't it be governed like ordinary software? In this foundational episode of Checks & Algorithms, we unpack the technical building blocks, architectural layers, and governance principles behind modern artificial intelligence. It's built for boot camp candidates preparing for the IAPP AIGP exam, and useful for anyone who needs a clear, jargon-free grounding in how AI works.

In this episode:

• Core definitions: The NIST definition of an AI system, what makes machine learning "learn," and how knowledge-based expert systems codify human expertise instead of relying on statistical patterns.
• Automation vs. AI: Deterministic rules vs. systems that learn, reason, and adapt, and the essential human role in setting objectives, curating data, defining guardrails, and judging whether outputs are safe and appropriate.
• Predictive vs. generative AI: Forecasting from historical patterns vs. creating new text, code, images, and audio, with the risks each brings, from rigidity in novel scenarios to hallucinations and heavy resource demands.
• Narrow AI vs. AGI: Single-task systems like spam filters and translators vs. the still-theoretical goal of human-level general intelligence.
• How machines learn: Supervised learning (classification and regression), unsupervised learning (clustering and dimension reduction), semi-supervised learning, and reinforcement learning.
• Deep learning and foundation models: Neural networks, transformers and self-attention, open- vs. closed-source foundation models, and how multimodal AI fuses text, vision, audio, and sensor data.
• The AI technology stack: Application and platform frameworks (LangChain, Vertex AI, Semantic Kernel), the model layer, and the compute infrastructure underneath: Python, PyTorch, TensorFlow, GPUs, and TPUs, on-premise or in the cloud.
• AI as a socio-technical system: Why AI's inputs and outputs affect people, groups, and institutions, and why governance needs UX designers, sociologists, linguists, and legal counsel alongside engineers.
• The OECD Framework for Classifying AI Systems: Its five dimensions, People & Planet, Economic Context, Data & Input, AI Model, and Task & Output, and how they support shared risk assessment and policy alignment.

Who should listen:

• AIGP candidates covering Domain I, Understanding the Foundations of AI Governance.
• AI governance officers and legal counsel who want a plain-language tour of the technology stack, model types, and infrastructure constraints.
• Enterprise product managers working through developer vs. deployer roles and socio-technical risk mapping.

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Disclaimer: AIGP official source information + Notebook LLM + Human Reviewer