Demystified · 16 entries

The jargon, decoded.

Plain-language explainers: what each AI term means, and why it matters to your work.

• Foundation model

  The pre-trained base every AI feature sits on. Switching it is harder than vendors admit and easier than internal politics suggests.

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• Embedding

  Picture searching your enterprise knowledge base for “quarterly churn analysis” and retrieving a document titled “customer retention trends Q3” despite neither keyword appearing in the text.

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• Multi-modal AI

  Consider the most recent complex customer complaint your team handled: an angry voicemail, accompanied by a blurry product photo and a fragmented text description.

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• Model Fine-tuning

  Teaching an existing foundation model your domain and dialect: the shortcut to a custom model without building one from scratch.

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• Inference

  The moment a trained model is put to work, turning the expensive thing you built into answers, and into a running cost.

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• Tokens

  The small chunks of text models read and write, the unit that quietly determines AI cost, speed, and context limits.

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• Hallucination

  When a model states something fluent, confident, and entirely made up. Why it happens, and why it matters most in high-stakes work.

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• Federated Learning

  Training shared AI models on data that never leaves each owner's firewall: collective intelligence without surrendering proprietary data.

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• Natural Language Processing (NLP)

  The technology that lets machines read, interpret, and generate human language: the engine under chatbots, search, and summarization.

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• Reinforcement Learning

  Teaching an AI to make good decisions through trial, reward, and error: learning by experience rather than explicit instruction.

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• Human-in-the-Loop versus Governance-in-the-Loop

  Two ways to keep humans in charge of AI: reviewing individual decisions (human-in-the-loop) versus setting the rules the system runs under (governance-in-the-loop).

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• Neural Network

  A pattern-recognition system of layered, interconnected nodes, loosely modeled on the brain. The structure most modern AI is built on.

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• Transformation Architecture

  The Transformer: the 2017 "Attention Is All You Need" design that replaced older sequence models and made today's large language models possible.

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• Telemetry of Work – What It Really Means

  The digital exhaust of how work actually gets done: every click and handoff captured as data, like Formula 1 telemetry for the enterprise.

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• Machine Learning

  Software that learns patterns from data instead of following hand-written rules for every case.

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• Agentic AI

  AI that acts, not just answers.

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