Active Learning

A type of ML where the algorithm actively queries a human expert to label additional data.

Artificial Intelligence (AI)

The overarching concept of machines mimicking human intelligence, including reasoning, learning, and problem-solving.

Bias

The tendency of an ML model to make systematic errors based on certain characteristics of the input data.

Bot Persona

The personality and characteristics of a chatbot, often designed to align with the brand or target audience.

CaaS

A cloud-based platform that provides the tools and infrastructure needed to build, deploy, and manage chatbots.

Chatbot

A computer program designed to simulate conversation with human users, often via text or voice.

Context

The information surrounding a particular utterance that helps to interpret its meaning.

Contextual Awareness

The ability of a chatbot to understand and maintain context throughout a conversation.

Conversation Design

The process of creating effective and engaging conversational flows for chatbots and other conversational AI applications.

Conversational AI

The broader field of AI that encompasses chatbots and other technologies aimed at facilitating natural language interaction between humans and machines.

Conversational Analytics

The collection and analysis of data from conversational interactions to improve chatbot performance and understand user behavior.

Conversational Flow

A predefined sequence of interactions between a user and a chatbot, designed to guide the conversation and achieve a specific goal.

Conversational UX

The design of the user experience for conversational AI applications.

Conversation History

A record of past interactions between a user and a chatbot.

Conversation Repair

The ability of a chatbot to detect and correct misunderstandings or errors in a conversation.

Deep Learning

A type of ML that uses artificial neural networks to learn from vast amounts of data.

Dialog Management

The process of controlling the flow and structure of a conversation between a user and a chatbot.

Dialog State Tracking

The process of maintaining a representation of the current state of a conversation, including the user's intent, the information provided, and any outstanding tasks or actions.

Disambiguation

The process of resolving ambiguity when a user's query could match multiple intents. The chatbot may ask clarifying questions to determine the correct intent.

Entity

A specific piece of information within a user's message, such as a date, location, or product name.

Entity Extraction

Identifying and extracting relevant information from user input, such as dates, locations, or product names.

Explainability

The ability to understand and interpret the reasoning behind an AI system's decisions.

Fallback Intent

A default intent triggered when a chatbot fails to understand the user's intent. It usually provides a generic response or asks the user to rephrase their query.

Human Handoff

The process of transferring a conversation from a chatbot to a human agent when the chatbot is unable to resolve the issue or the user requests human assistance.

Intent

The underlying purpose or goal of a user's message in a conversation.

Intent Recognition

The process of determining the user's intention from their input.

Machine Learning (ML)

A subset of AI that involves training algorithms on data to improve their performance over time.

Multimodal

The ability to process and respond to multiple forms of input, such as text, voice, and images.

Named Entity Recognition (NER)

The process of identifying and classifying named entities (e.g., people, organizations, locations) within a text.

Natural Language Generation (NLG)

The part of NLP that focuses on producing coherent and natural-sounding language as output.

Natural Language Processing (NLP)

The AI subfield focused on enabling computers to understand, interpret, and generate human language.

Natural Language Search

The ability to search and retrieve information using natural language queries, as opposed to structured keywords.

Natural Language Understanding (NLU)

A subset of NLP dealing with the machine's ability to grasp the meaning and intent behind human language input.

Omnichannel

The ability of a chatbot to operate across multiple channels, such as websites, messaging apps, and social media platforms.

Part-of-Speech (POS) Tagging

The process of assigning grammatical categories (e.g., noun, verb, adjective) to words within a text.

Paraphrasing

The ability of a chatbot to understand different ways of expressing the same meaning.

Personalization

Tailoring a conversation to the individual user's preferences and needs.

Proactive Engagement

The ability of a chatbot to initiate conversations with users based on specific triggers or events.

Reinforcement Learning

A type of ML where an agent learns to take actions in an environment to maximize a reward signal.

Sentiment Analysis

The process of determining the emotional tone of a piece of text.

Sentiment Tracking

The process of monitoring the emotional tone of user interactions with a chatbot.

Slot Filling

The process of collecting information from a user to fulfill a specific intent.

Tokenization

The process of breaking down a text into smaller units, such as words or phrases.

Training Data

The labeled data used to teach ML algorithms how to perform specific tasks.

Utterance

A single unit of speech or text produced by a user or chatbot in a conversation.

Virtual Assistants

Similar to voice assistants but may also use text-based interfaces.

Voice Assistants

AI-powered software that uses voice recognition to respond to user commands and queries.

Webhook

A way for an app to provide other applications with real-time information. In Conversational AI, webhooks are often used to send information from a chatbot to another system.

Zero-Shot Learning

The ability of an ML model to perform a task without any prior training examples.