AI Knowledge Base 2026

AI Glossary 2026

Clear definitions for the era of Agentic AI and Spatial Intelligence.

Deep Research Agents

Deep Research Agents is a agentic AI concept in modern AI systems that enables autonomous agent capabilities. It plays a key role in enterprise AI deployments where systems must operate independently while maintaining human oversight.

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Agentic Infrastructure

Distributed AI

Distributed AI refers to systems where computing operations, models, and data are spread across multiple computers, edge devices, or data centers rather than running centrally on a single server. This architecture enables faster inference, better scalability, and fault tolerance. Particularly in the context of edge computing and satellite networks like NVIDIA Space Computing, distributed AI is becoming increasingly important. Distribution reduces latency, improves privacy through local processing, and decreases dependence on centralized infrastructure.

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Trust & Sovereignty

Dangerously Skip Permissions

A Claude Code flag (`--dangerously-skip-permissions`) that disables the default confirmation prompts before tool use. Safe only on dedicated, isolated machines (such as a VPS provisioned specifically for agentic tasks) where the blast radius of any runaway process is limited to that server and does not affect the developer's primary environment.

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Reasoning & Reliability

Data

Raw, unorganized facts that need to be processed. Data can be something simple and seemingly random and useless until it is organized. When data is processed, organized, structured or presented in a given context so as to make it useful, it is called information.

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Reasoning & Reliability

Data Scientist

A professional who uses statistical methods, machine learning algorithms, and data visualization techniques to analyze large datasets and extract actionable insights.

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Reasoning & Reliability

Deep Think Mode

Deep Think Mode is a core AI technology concept in modern AI systems that represents fundamental technical capabilities powering modern AI applications. It plays a key role in enterprise AI deployments where choosing the right technology directly determines application performance and capability.

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Trust & Sovereignty

Differential Privacy for ML

Differential Privacy for ML is a mathematical framework that provides formal guarantees about the privacy of individuals whose data is used in machine learning. It ensures that model outputs dont reveal sensitive information about any specific training example.

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Reasoning & Reliability

DOM State

The current condition and data represented within the Document Object Model (DOM) of a web page, reflecting the structure, content, and styling applied to elements.

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Inference & Engineering

DPO (Direct Preference Optimization)

A more efficient alternative to RLHF that eliminates the separate reward model step. Trains the model directly on preference pairs. Simpler to implement, but can also cause Mode Collapse if training data contains Typicality Bias.

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Inference & Engineering

Dual-Model Coding

Dual-Model Coding is an AI development pattern where two language models with complementary strengths collaborate on the same codebase. A high-reasoning model (Claude Opus 4.6, GPT-5) handles architecture decisions and code review, while a fast model (Gemini 3.1 Flash, Claude Haiku) handles rapid code generation and tests. The pattern emerged from the observation that no single model excels at everything. By routing tasks based on complexity, teams achieve both quality and velocity. A pure Opus workflow costs roughly 10x more per token than Flash. A well-tuned dual-model setup achieves 80-90% of the quality at 20-30% of the cost.

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