AI glossary for content assistants
Plain-English definitions of 13,917 AI terms for branded assistant teams.
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13,917 terms. Open one for definitions and related concepts.
Eigendecomposition
Eigendecomposition factorizes a square matrix into its eigenvalues and eigenvectors, revealing its fundamental geometric properties.
LU Decomposition
LU decomposition factors a matrix into lower and upper triangular matrices, enabling efficient solution of linear systems.
Cholesky Decomposition
Cholesky decomposition factors a symmetric positive definite matrix into the product of a lower triangular matrix and its transpose.
Orthogonal Matrix
An orthogonal matrix has orthonormal columns, meaning its inverse equals its transpose, and it preserves lengths and angles.
Positive Definite Matrix
A positive definite matrix has all positive eigenvalues, ensuring that the quadratic form it defines always yields positive values.
Sparse Matrix
A sparse matrix is a matrix where most elements are zero, allowing specialized storage formats and algorithms that dramatically reduce memory and computation.
Identity Matrix
The identity matrix is a square matrix with ones on the diagonal and zeros elsewhere, serving as the multiplicative identity for matrix operations.
Diagonal Matrix
A diagonal matrix has non-zero elements only on its main diagonal, making multiplication and inversion trivially efficient.
Frobenius Norm
The Frobenius norm is the square root of the sum of squared elements of a matrix, analogous to the L2 norm for vectors.
Spectral Norm
The spectral norm of a matrix is its largest singular value, measuring the maximum amount by which the matrix can stretch a vector.
Condition Number
The condition number measures how sensitive a matrix computation is to input perturbations, indicating numerical stability.
Probability Density Function
A probability density function (PDF) describes the relative likelihood of a continuous random variable taking a given value.
Cumulative Distribution Function
A cumulative distribution function (CDF) gives the probability that a random variable takes a value less than or equal to a given point.
Joint Probability
Joint probability measures the likelihood of two or more events occurring simultaneously.
Marginal Probability
Marginal probability is the probability of an event irrespective of the outcomes of other variables, obtained by summing or integrating out other variables.
Maximum A Posteriori
Maximum a posteriori (MAP) estimation finds the most probable parameter values given observed data and a prior distribution.
Variance (Mathematics)
Variance measures the expected squared deviation of a random variable from its mean, quantifying the spread of a probability distribution.
Covariance (Mathematics)
Covariance measures the joint variability of two random variables, indicating whether they tend to increase or decrease together.
Correlation (Mathematics)
Correlation is a normalized measure of the linear relationship between two variables, ranging from -1 to 1.
Independence (Probability)
Two events or random variables are independent if the occurrence of one does not affect the probability of the other.
Law of Large Numbers
The law of large numbers states that the sample average converges to the expected value as the sample size grows.
Central Limit Theorem
The central limit theorem states that the sum of many independent random variables is approximately normally distributed, regardless of the original distribution.
Markov Chain
A Markov chain is a sequence of random states where each state depends only on the immediately preceding state, not on earlier history.
Markov Property
The Markov property states that the future state of a process depends only on the present state, not on the sequence of events that preceded it.
Binomial Distribution
The binomial distribution models the number of successes in a fixed number of independent yes/no trials with constant success probability.
Exponential Distribution
The exponential distribution models the time between events in a Poisson process, characterized by a constant event rate.
Gamma Distribution
The gamma distribution generalizes the exponential distribution to model the time until k events occur, with applications in Bayesian priors.
Beta Distribution
The beta distribution is defined on [0, 1] and is commonly used as a prior distribution for probabilities in Bayesian inference.
Dirichlet Distribution
The Dirichlet distribution is a multivariate distribution over probability vectors, widely used as a prior for categorical distributions in Bayesian models.
Chi-Squared Distribution
The chi-squared distribution is the distribution of the sum of squared standard normal variables, used extensively in statistical testing.
Student's t-Distribution
Student's t-distribution arises when estimating the mean of a normally distributed population with unknown variance, having heavier tails than the normal distribution.
Gaussian Mixture Distribution
A Gaussian mixture distribution is a weighted combination of multiple Gaussian components, capable of modeling complex multi-modal data distributions.
Optimization Theory
Optimization theory studies methods for finding the best solution from a set of feasible alternatives, forming the mathematical foundation of machine learning training.
Convex Function
A convex function curves upward such that the line segment between any two points on its graph lies above the graph, ensuring any local minimum is global.
KKT Conditions
The Karush-Kuhn-Tucker conditions are necessary conditions for optimality in constrained optimization, generalizing Lagrange multipliers to inequality constraints.
Linear Programming
Linear programming optimizes a linear objective function subject to linear equality and inequality constraints.
Quadratic Programming
Quadratic programming optimizes a quadratic objective function subject to linear constraints, directly underlying support vector machines.
Dynamic Programming
Dynamic programming solves complex problems by breaking them into overlapping subproblems and storing their solutions, avoiding redundant computation.
Information Theory
Information theory quantifies information, uncertainty, and communication efficiency, providing foundational concepts for machine learning loss functions and model evaluation.
Entropy (Mathematics)
Entropy measures the average uncertainty or information content of a random variable, quantifying how unpredictable a distribution is.
Shannon Entropy
Shannon entropy is the foundational information-theoretic measure of average uncertainty in a random variable, named after Claude Shannon.
Cross-Entropy (Mathematics)
Cross-entropy measures the average number of bits needed to encode data from distribution p using a code optimized for distribution q.
Jensen-Shannon Divergence
Jensen-Shannon divergence is a symmetric, bounded measure of similarity between two probability distributions, derived from KL divergence.
Perplexity (Mathematics)
Perplexity is the exponentiation of cross-entropy, representing the effective number of equally likely choices a model considers at each prediction step.
Self-Information
Self-information (or surprisal) measures the information content of a single event, defined as the negative logarithm of its probability.
Coding Theory
Coding theory studies efficient and reliable encoding of information, providing the theoretical foundation for data compression and error correction in ML systems.
Vector Space
A vector space is a mathematical structure where vectors can be added and scaled, providing the algebraic framework for machine learning representations.
Linear Transformation
A linear transformation is a function between vector spaces that preserves addition and scalar multiplication, represented by matrix multiplication.
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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.