AI glossary for content assistants
Plain-English definitions of 13,917 AI terms for branded assistant teams.
Search glossary terms
13,917 glossary pages match your filters.
Category
Browse by letter
Glossary
13,917 terms. Open one for definitions and related concepts.
Conditional Probability
Conditional probability is the probability of an event occurring given that another event has already occurred, forming the basis of Bayesian reasoning and sequential prediction.
Bayes' Theorem
Bayes' theorem describes how to update the probability of a hypothesis based on new evidence, providing the mathematical foundation for Bayesian inference and learning from data.
Prior Probability
Prior probability represents the initial belief about the likelihood of a hypothesis before observing new evidence, serving as the starting point for Bayesian inference.
Posterior Probability
Posterior probability is the updated probability of a hypothesis after incorporating new evidence, computed from the prior probability and the likelihood of the observed data.
Likelihood
Likelihood is a function that measures how probable the observed data is under different parameter values, guiding parameter estimation in statistical and machine learning models.
Maximum Likelihood Estimation
Maximum Likelihood Estimation (MLE) is a method for estimating model parameters by finding the values that maximize the probability of the observed data under the model.
Bayesian Inference
Bayesian inference is a statistical method that updates probability estimates as new evidence arrives, using prior knowledge combined with observed data to compute posterior beliefs.
Random Variable
A random variable is a numerical outcome of a random process, providing the mathematical bridge between uncertain real-world events and probability distributions.
Expectation
Expectation (expected value) is the weighted average of all possible values of a random variable, representing the long-run average outcome of a random process.
Variance
Variance measures how spread out the values of a random variable are around the mean, quantifying the degree of uncertainty or variability in a distribution.
Standard Deviation
Standard deviation is the square root of variance, measuring data spread in the same units as the original data and providing an intuitive sense of typical deviation from the mean.
Covariance
Covariance measures the joint variability of two random variables, indicating whether they tend to increase together, decrease together, or vary independently.
Correlation
Correlation is a standardized measure of the linear relationship between two variables, ranging from -1 (perfect negative) to +1 (perfect positive), with 0 indicating no linear relationship.
Normal Distribution
The normal distribution is a bell-shaped probability distribution characterized by its mean and standard deviation, appearing throughout nature and forming the basis of many statistical methods.
Gaussian Distribution
The Gaussian distribution is another name for the normal distribution, named after mathematician Carl Friedrich Gauss, widely used in probability theory and machine learning.
Uniform Distribution
The uniform distribution assigns equal probability to all values in its range, used when no outcome is more likely than any other and for non-informative priors.
Bernoulli Distribution
The Bernoulli distribution models a single binary outcome (success/failure) with a fixed probability, the simplest probability distribution used in classification and dropout.
Poisson Distribution
The Poisson distribution models the number of events occurring in a fixed interval of time or space, given a known average rate and independent events.
Categorical Distribution
The categorical distribution models a single trial with k possible outcomes, each with its own probability, used for multi-class classification and language model token prediction.
Optimization
Optimization is the mathematical process of finding the best parameters that minimize (or maximize) an objective function, the core mechanism behind training machine learning models.
Objective Function
An objective function is the mathematical function that an optimization algorithm seeks to minimize or maximize, defining the goal of the optimization problem.
Convex Optimization
Convex optimization deals with minimizing convex functions over convex sets, guaranteeing that any local minimum is the global minimum and enabling efficient, reliable solutions.
Non-Convex Optimization
Non-convex optimization involves minimizing functions that may have multiple local minima and saddle points, characterizing the challenging optimization landscape of neural networks.
Gradient
The gradient is a vector of partial derivatives that points in the direction of steepest increase of a function, used in optimization to determine how to update model parameters.
Hessian Matrix
The Hessian matrix contains all second-order partial derivatives of a function, providing information about the curvature of the loss landscape for optimization analysis.
Jacobian Matrix
The Jacobian matrix contains all first-order partial derivatives of a vector-valued function, describing how multi-dimensional outputs change with respect to multi-dimensional inputs.
Saddle Point
A saddle point is a critical point where the gradient is zero but the point is neither a local minimum nor maximum, being a minimum in some directions and a maximum in others.
Local Minimum
A local minimum is a point where the function value is lower than all nearby points, though not necessarily the lowest overall, relevant to understanding neural network optimization.
Global Minimum
A global minimum is the point where a function achieves its absolute lowest value over its entire domain, the ideal but often unreachable target of optimization.
Lagrange Multiplier
Lagrange multipliers are a method for finding the extrema of a function subject to constraints, used in support vector machines and constrained optimization problems.
Entropy
Entropy measures the uncertainty or information content of a probability distribution, with higher entropy indicating more randomness and lower entropy indicating more predictability.
Cross-Entropy
Cross-entropy measures the difference between two probability distributions, serving as the standard loss function for training classification models and language models.
Mutual Information
Mutual information measures the amount of information that one random variable provides about another, quantifying the statistical dependence between two variables.
KL Divergence
KL divergence measures how one probability distribution differs from a reference distribution, used in variational inference, knowledge distillation, and generative model training.
Information Gain
Information gain measures the reduction in entropy achieved by splitting data on a particular feature, used as the criterion for building decision trees.
Perplexity
Perplexity is an evaluation metric for language models that measures how well the model predicts text, with lower perplexity indicating better prediction quality.
P-Value
A p-value is the probability of observing results at least as extreme as the actual results, assuming the null hypothesis is true, used to assess statistical significance.
Confidence Interval
A confidence interval is a range of values that likely contains the true population parameter, providing a measure of estimate precision alongside point estimates.
T-Test
A t-test is a statistical test that determines whether there is a significant difference between the means of two groups, commonly used for A/B testing and model comparison.
ANOVA
ANOVA (Analysis of Variance) is a statistical test that compares means across three or more groups simultaneously, determining if at least one group differs significantly from the others.
Effect Size
Effect size is a quantitative measure of the magnitude of a phenomenon or the strength of a relationship, complementing p-values by indicating practical significance.
Vector (Mathematics)
A vector is an ordered list of numbers representing a point or direction in multi-dimensional space, fundamental to machine learning computations.
Tensor (Mathematics)
A tensor is a multi-dimensional generalization of scalars, vectors, and matrices used as the core data structure in deep learning frameworks.
Inner Product
An inner product is a generalization of the dot product that defines geometric concepts like length, angle, and orthogonality in vector spaces.
Outer Product
The outer product of two vectors produces a matrix where each element is the product of one element from each vector.
Pseudo-Inverse
The pseudo-inverse (Moore-Penrose inverse) generalizes the matrix inverse to non-square and singular matrices, enabling least-squares solutions.
Trace
The trace of a square matrix is the sum of its diagonal elements, providing a simple scalar summary used in optimization and matrix calculus.
Matrix Rank
The rank of a matrix is the number of linearly independent rows or columns, indicating the dimensionality of information it contains.
Turn owned content into answers
Use InsertChat to launch a branded assistant visitors can ask directly.
7-day free trial · No card required
Try the FAQ like a visitor.
Open product, pricing, security, integration, and free-tool questions in the same chat your visitors use.
InsertChat
Interactive FAQ
Hey. Pick a question below and see how InsertChat turns FAQs into clear, source-backed answers.
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.