AI glossary: 25 AI terms explained

September 3, 2023
Want to leverage AI in your business but not sure where to start? This AI glossary explains technical AI terminology in a way that's easy to digest and understand.

Artificial intelligence (AI)

Artificial intelligence is the field of computer science that focuses on creating systems and algorithms capable of performing tasks that typically require human intelligence.

These tasks can include problem-solving, decision-making, learning from data, and understanding natural language. AI technologies range from rule-based systems to advanced machine learning algorithms, enabling computers to simulate human-like intelligence.

AI assistant

An AI assistant is a virtual or digital assistant powered by artificial intelligence that helps users with tasks like answering questions, setting reminders, and providing information.

There are plenty of AI assistants on the market, from AI scheduling assistants that help individuals to manage their calendars to AI legal assistants that automate repetitive tasks for legal professionals. However, the most recognizable AI assistants include Siri, Alexa, and Google Assistant.


An algorithm is a precise set of step-by-step instructions or rules that a computer program follows to perform a specific task or solve a particular problem. Algorithms are fundamental in AI and are used for data processing, pattern recognition, and decision-making.


Bias in AI refers to the unfair or prejudiced treatment of certain groups or individuals based on factors like race, gender, or age.

Bias can occur due to biased training data, flawed algorithms, or human biases present in the data used to train AI systems. Addressing bias is crucial to ensure fairness and equity in AI applications.

Big data

Big data refers to vast and complex datasets that exceed the capabilities of traditional data processing tools. AI and machine learning techniques are used to analyze and extract valuable insights from these large volumes of data, helping organizations make data-driven decisions.


Clustering is a technique used to group similar data points together based on shared characteristics or features. It's commonly used in data analysis and unsupervised machine learning to discover patterns, segment data, and identify natural groupings within datasets.

Cognitive computing

Cognitive computing involves AI systems that aim to simulate human thought processes, including reasoning, problem-solving, and learning. These systems often use natural language processing and machine learning to understand and interact with humans effectively.

Contract AI

Contract AI uses artificial intelligence to analyze and extract key information from contracts, making it quicker and easier for businesses to manage their legal agreements.

Examples of AI contract tools include AI contract review software, AI contract analysis software, AI contract generation software, and AI contract management software.

Each of these AI contract tools empower legal teams to automate repetitive contract admin, speeding up the contract process as a result. In fact, AI contracting tools like Juro enable teams to agree contracts up to ten times faster than traditional tools and methods.

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Conversational AI

Conversational AI focuses on creating AI systems that can engage in natural language conversations with users. These systems are commonly found in chatbots, virtual assistants, and customer support applications, enhancing human-computer interactions.


A corpus refers to a large and structured collection of texts or data that is used for linguistic analysis, research, and training natural language processing models.

Data mining

Data mining is the process of extracting valuable patterns, insights, and knowledge from large and complex datasets. AI techniques, such as clustering, classification, and association rule mining, are applied to uncover hidden information within the data.

Deep learning

Deep learning is a type of machine learning that involves neural networks with multiple layers (deep neural networks). These networks can automatically learn and represent complex features from data, making them well-suited for tasks like image recognition, natural language processing, and speech recognition.

Generative AI

Generative AI refers to AI models and algorithms capable of generating new content, such as text, images, or music, based on patterns learned from existing data.

ChatGPT is one of the most recognized generative AI platforms in 2023, with more than 100 million users at the time of writing.

Want to find out more about ChatGPT? Check out our guide to ChatGPT for lawyers.


In the context of AI, hallucinations refer to incorrect outputs generated by models. This phenomenon often indicates that AI models require further training or refinement to produce accurate and reliable results. However, in most cases, in simply means that the outputs should be reviewed and verified. This is one of the most common AI risks.

Language modelling

Language modelling involves training AI models to understand and generate human-like text, crucial for tasks like natural language generation and machine translation.

Large Language Models (LLM)

Large language models are advanced AI models like GPT-3 that have been trained on extensive datasets containing text from the internet. These models are capable of performing a wide range of language-related tasks, such as text generation, translation, and summarization.

Legal AI

Legal AI, like Juro, leverages artificial intelligence to assist legal professionals in tasks like document review, legal research, contract analysis, and compliance monitoring. This technology streamlines legal processes and improves efficiency in the legal industry.

Machine Learning (ML)

Machine learning is a subset of AI that focuses on developing algorithms and models that can learn from data and make predictions or decisions without being explicitly programmed.

This can involve various techniques, know as supervised, unsupervised, and reinforcement learning.

Natural language processing (NLP)

Natural language processing is a branch of AI that deals with the interaction between computers and human language. NLP technologies enable machines to understand, interpret, and generate human language, facilitating tasks such as language translation, sentiment analysis, and chatbot interactions.

Neural network

A neural network is a computational model inspired by the human brain's structure. It consists of interconnected nodes (neurons) organized into layers. Neural networks are used in AI for tasks like image recognition, speech recognition, and pattern classification.


OpenAI is a private AI research and deployment company that wants to make AI safe and beneficial for humanity. They are famously known as being the company behind ChatGPT, one of the most popular generative AI tools available right now.

Open source

Open source refers to software or projects whose source code is made freely available to the public, allowing anyone to view, use, modify, and distribute the software. Open source communities encourage collaboration and innovation in AI development.

Predictive analytics

Predictive analytics involves the use of historical and real-time data, along with statistical and machine learning techniques, to predict future trends, outcomes, or events. Organizations use predictive analytics to make informed decisions and optimize their operations.


A prompt is a specific input or instruction provided to AI models to generate desired outputs or responses. The clear and more prescriptive your prompt is, the better the output tends to be. You can find out how to to optimize your prompts in this guide to legal prompt engineering.

Reinforcement learning

Reinforcement learning is a machine learning paradigm where agents learn to make decisions by interacting with an environment. The agent is given feedback based on its actions and learns through trial and error.

Find out more about AI

Still curious and keen to learn more about AI? We'll continue to update this glossary as new developments and terms arise. In the meantime, check out these resources to explore AI risks and applications in more detail:

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