Definition
Computer vision
The branch of AI that lets computers interpret images and video, like recognizing objects, faces, or text.
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Related terms in AI Fundamentals
Chronological Prediction TrainingLightningRod AI's core training method: given all data up to timestamp A in a corpus, predict what will happen at timestamp B. The model is trained iteratively across an entire dataset of chronologically ordered real-world information. The 'label' is simply what actually happened next - no human annotation required. This approach forces the AI to learn causal relationships rather than surface patterns: to get good at predicting timestamp B, the model must understand what factors in timestamp A's data actually drive outcomes. Ben Turtle calls this the mechanism humans use to build world models - we move through time, predict what comes next, feel surprise when we're wrong, and update our beliefs.CitationHow often your specific URL is referenced inside an AI answer — a stronger signal than a mention alone.Citation-Based RetrievalReturning answers tied to specific document sources.Closed sourceProprietary software whose code is kept private, so the public can't see, modify, or reuse it.ConfabulationA human psychology term for the way memory reconstructs rather than records — people create plausible narratives around incomplete memories with no intent to deceive. Geoff draws the parallel to AI hallucinations: both are a system constructing a coherent story from incomplete information. The observation cuts both ways: AI hallucinations aren't as alien as they seem (human memory does the same thing), and human testimony is far less reliable than assumed (well-established in criminal justice research). Working closely with AI has become a useful mirror on human cognition.Constitutional LayerThe rules, values, and ethical principles baked into each agent at instantiation. The foundational constraint set that shapes all downstream behavior before any prompt is processed.Context ArchetypeA named configuration of rules governing how an AI generation system behaves in a specific mode — for example, 'creative exploration' vs. 'compliance review.' The same underlying model produces reliably different outputs depending on which archetype is active. In Knapsack's Intelligent Product Engine, archetypes are one of three components (alongside real-time artifact monitoring and a rules engine) that make AI generation predictable and enterprise-appropriate rather than open-ended and unpredictable.Context engineeringStructuring the inputs and memory an LLM receives. Ross Barnes's position is that the difference between reliable and hallucinatory output is almost always context, not model choice.