AI glossary for content assistants
Plain-English definitions of 13,917 AI terms for branded assistant teams.
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Glossary
13,917 terms. Open one for definitions and related concepts.
GitHub Copilot Launch
GitHub Copilot, launched in 2021, was the first widely adopted AI pair programming tool, using OpenAI Codex to suggest code in real time.
EU AI Act Passage
The EU AI Act, passed in 2024, is the first comprehensive legal framework for regulating artificial intelligence systems by risk level.
Sora Announcement
Sora, announced by OpenAI in February 2024, is an AI model that generates realistic videos from text descriptions, demonstrating advanced world modeling.
Reasoning Models Emergence
The emergence of reasoning models in 2024, starting with OpenAI o1, introduced AI systems that use explicit chain-of-thought reasoning to solve complex problems.
DeepSeek R1 Release
DeepSeek R1, released in January 2025, is an open-source reasoning model from China that matched frontier AI performance at a fraction of the training cost.
John McCarthy
John McCarthy (1927-2011) coined the term "artificial intelligence" and organized the 1956 Dartmouth Conference that founded the field.
Marvin Minsky
Marvin Minsky (1927-2016) was a pioneering cognitive scientist who co-founded the MIT AI Laboratory and made foundational contributions to AI, robotics, and computational theory.
Claude Shannon
Claude Shannon (1916-2001) was the father of information theory, whose mathematical framework for communication laid the groundwork for digital computing and AI.
Ian Goodfellow
Ian Goodfellow is the computer scientist who invented generative adversarial networks (GANs) in 2014, revolutionizing AI-generated content.
Ashish Vaswani
Ashish Vaswani is the lead author of the 2017 "Attention Is All You Need" paper that introduced the transformer architecture powering modern AI.
Richard Sutton
Richard Sutton is a pioneering reinforcement learning researcher whose work and writings shaped modern AI thinking about learning from interaction and scaling.
Attention Mechanism Paper
The 2014 attention mechanism paper by Bahdanau et al. introduced the concept of neural attention, enabling models to focus on relevant parts of input sequences.
Transformer Paper
The 2017 paper "Attention Is All You Need" introduced the transformer architecture that became the foundation for all modern large language models.
Scaling Laws Paper
The 2020 scaling laws paper by Kaplan et al. at OpenAI showed that AI model performance improves predictably with increases in model size, data, and compute.
Chinchilla Paper
The 2022 Chinchilla paper by DeepMind showed that AI models should be trained on far more data than previously thought, redefining optimal training strategies.
Constitutional AI Paper
The 2022 Constitutional AI paper by Anthropic introduced a method for training AI systems to be helpful, harmless, and honest using a set of principles rather than human labelers.
Word2Vec
A neural network technique for learning dense word embeddings from large text corpora, published by Google in 2013.
AI Safety Summit (Bletchley, 2023)
The first international AI safety summit held at Bletchley Park in November 2023, producing the Bletchley Declaration on frontier AI risks.
OpenAI DevDay 2023
OpenAI's first developer conference in November 2023, unveiling GPT-4 Turbo, the Assistants API, and the GPT Store.
OpenAI Founding
The founding of OpenAI in December 2015 as a nonprofit AI safety research lab by Elon Musk, Sam Altman, Greg Brockman, and others.
Anthropic Founding
The founding of Anthropic in 2021 by former OpenAI executives Dario Amodei, Daniela Amodei, and colleagues focused on AI safety.
DeepMind Founding
DeepMind was founded in London in 2010 and acquired by Google in 2014, becoming one of the world's leading AI research labs.
OpenAI Board Crisis (2023)
The November 2023 corporate crisis in which OpenAI's board fired CEO Sam Altman, triggering a staff revolt and his reinstatement within days.
LLaMA 2 Release
Meta's release of LLaMA 2 in July 2023 as an open-weight model free for commercial use, dramatically expanding open-source AI deployment.
Mistral AI & Mistral 7B Release
Mistral AI launched in 2023 and released Mistral 7B, an open-weight model that outperformed LLaMA 2 13B on benchmarks despite being half the size.
AI Safety Letter 2023
An open letter signed by thousands of AI researchers and technologists in March 2023 calling for a 6-month pause on training AI systems more powerful than GPT-4.
RLHF History & Rise
The development and adoption of Reinforcement Learning from Human Feedback as the dominant technique for aligning language models with human preferences.
InstructGPT
OpenAI's 2022 model trained with RLHF to follow instructions, showing that alignment quality matters more than raw model size.
Open-Source AI Movement
The growing effort to release AI model weights, training code, and datasets openly, enabling community development and reducing dependence on closed-source providers.
Microsoft-OpenAI Partnership
Microsoft's multi-billion dollar investment partnership with OpenAI beginning in 2019, integrating AI capabilities across Microsoft's product suite.
GPT-4o
OpenAI's 2024 multimodal model that natively processes and generates text, audio, and images in a single model architecture.
Claude 3 Launch
Anthropic's March 2024 release of the Claude 3 model family (Haiku, Sonnet, Opus), with Opus achieving top benchmark scores across major AI evaluations.
Logic Theorist
The first AI program, created by Allen Newell and Herbert Simon in 1956, capable of proving mathematical theorems using symbolic reasoning.
Google Brain Founding
Google Brain was founded in 2011 by Andrew Ng and Jeff Dean as Google's internal deep learning research team, pioneering large-scale neural network training.
LSTMs and Recurrent Neural Networks
Long Short-Term Memory networks, invented by Hochreiter and Schmidhuber in 1997, became the dominant architecture for sequence modeling before Transformers.
AI Arms Race
The competitive acceleration of AI capabilities between major tech companies and nation-states, characterized by rapid capability scaling and geopolitical competition.
DeepSeek R1 Release
DeepSeek's January 2025 release of R1, an open-weight reasoning model that matched OpenAI o1 on benchmarks at a fraction of the training cost.
Adaptive Feature Engineering
Adaptive Feature Engineering describes how machine learning teams structure feature engineering so the work stays repeatable, measurable, and production-ready.
Advanced Feature Engineering
Advanced Feature Engineering describes how machine learning teams structure feature engineering so the work stays repeatable, measurable, and production-ready.
Applied Feature Engineering
Applied Feature Engineering is a production-minded way to organize feature engineering for machine learning teams in multi-system reviews.
Autonomous Feature Engineering
Autonomous Feature Engineering describes how machine learning teams structure feature engineering so the work stays repeatable, measurable, and production-ready.
Collaborative Feature Engineering
Collaborative Feature Engineering is a production-minded way to organize feature engineering for machine learning teams in multi-system reviews.
Context-Aware Feature Engineering
Context-Aware Feature Engineering describes how machine learning teams structure feature engineering so the work stays repeatable, measurable, and production-ready.
Cross-Domain Feature Engineering
Cross-Domain Feature Engineering is a production-minded way to organize feature engineering for machine learning teams in multi-system reviews.
Data-Centric Feature Engineering
Data-Centric Feature Engineering is a production-minded way to organize feature engineering for machine learning teams in multi-system reviews.
Dynamic Feature Engineering
Dynamic Feature Engineering is an dynamic operating pattern for teams managing feature engineering across production AI workflows.
Enterprise Feature Engineering
Enterprise Feature Engineering is an enterprise operating pattern for teams managing feature engineering across production AI workflows.
Foundation Feature Engineering
Foundation Feature Engineering is a production-minded way to organize feature engineering for machine learning teams in multi-system reviews.
Turn owned content into answers
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Interactive FAQ
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Product FAQ
What is InsertChat?
InsertChat is a white-label AI assistant for your website. Train it, brand it, publish it, and learn from visitor questions.
How does InsertChat use my website content?
Connect approved pages, docs, videos, FAQs, policies, and other sources. InsertChat turns them into source-backed answers and next steps.
Can I control the assistant's tone and sources?
Yes. Choose its sources, tone, welcome message, and prompts so it stays on brand.
How does InsertChat stay accurate?
Answers use approved content and source links. Analytics show unclear or missing answers so you can improve coverage.
Can it collect leads or route support questions?
Yes. InsertChat can collect details, qualify intent, add context, and send chats to the right inbox, CRM, workflow, or person.
Can I control how the assistant behaves?
Yes. Control prompts, model choice, tool access, and the branded assistant experience so behavior stays consistent.
Which AI models can I use?
InsertChat supports multiple model providers. Choose each assistant's model for quality, speed, and cost, or use BYOK.
Can I pick different models for different workflows?
Yes. Use a faster model for common questions and a stronger model for complex reasoning. InsertChat supports that balance per conversation.
Where can I deploy an assistant?
Use a widget, embed, full-page assistant, custom domain, in-app embed, or API. Reuse one setup across surfaces.
Do I need coding skills?
No. Build and deploy AI assistants using our visual builder. The embed code is one line of JavaScript.
Can I customize the branding and UI?
Yes. Customize the assistant name, logo, colors, welcome message, suggested prompts, tone, domain, and white-label presentation.
Can I use my own domain?
Yes. Custom domains are supported, typically via enterprise options.
Does InsertChat support voice?
Yes. Voice dictation and text-to-speech let users speak instead of type.
Does InsertChat support vision?
Yes. Enable vision for assistants when images help clarify a request or context.
What tools and integrations are supported?
Zendesk, HubSpot, Shopify, WooCommerce, calendar booking, web search, Perplexity, and webhooks for your own systems.
Can I control which tools the assistant is allowed to use?
Yes. Tool access is controlled per assistant so you enable only what you need.
Can the agent hand off to a human?
Yes. Configure human handoff so the agent escalates when needed. Full conversation history is passed along.
Do you provide analytics?
Yes. Track chats, leads, feedback, top questions, unanswered questions, most-used sources, and content gaps.
Is it mobile friendly?
Yes. The widget and embeds work well on desktop and mobile with no separate experience needed.
What's the fastest path to a successful deployment?
Start with one assistant and a small set of high-value sources. Iterate using real questions from analytics.
What is the fastest way to get started?
Create an account. Connect one key source. Ask a test question, brand the assistant, then publish it on one page.