AI Glossary

AI Terms

Artificial Intelligence, or AI

A computer systems capable of performing tasks that historically required human intelligence, such as recognizing speech, making decisions, or identifying patterns.

Artificial General Intelligence (AGI)

A theoretical concept of AI that can perform any intellectual task that a human can. It remains one of the most significant challenges in AI research.

Artificial Narrow Intelligence (ANI)

Also known as weak AI, it is designed to perform a specific task. Examples include virtual assistants like Siri and Alexa.

Artificial Super Intelligence (ASI)

A hypothetical form of AI that surpasses human intelligence in all aspects, including creativity and problem-solving. It is considered a distant goal.

Algorithm

A step-by-step set of instructions that a computer or a person follows to solve a specific problem or perform a task, such as recognizing patterns.

Bias

The presence of systematic and undesired preferences or imbalances in the output generated by an AI model. Bias can emerge in various forms, such as in the content, language, or perspectives generated by the AI system.

Burstiness

The abrupt shifts in quality, coherence, or relevance often observed in AI generated content, particularly in writing. It refers to the inconsistencies in style, tone, or factual accuracy that can occur within a short span. Identifying burstiness helps distinguish AI-generated content from human-created content.

Business Value of AI

The economic impact and potential of AI to drive growth across various industries. Significant investments and adoption rates highlight its importance. 

Chatbot

A software application or web interface that mimics human conversation through text or voice interactions.

ChatGPT

A chatbot developed by OpenAI, capable of generating humanlike text based on context and past conversations. It is powered by a large language model and is an example of generative AI.

Data Science

The field that involves using scientific methods, processes, algorithms, and systems to extract knowledge and insights from data. It is closely related to AI and ML. 

Deep Learning

A type of machine learning that uses artificial neural networks, which are inspired by the structure and function of the human brain.

Ethical Implications

The considerations and potential consequences of AI development and deployment, including issues like data bias, reliability, and the responsible use of technology. 

Fine-Tuning

A process in machine learning where a pre-trained model (like GPT) is further trained on a new dataset with a smaller amount of data. The purpose of fine-tuning is to adopt the general knowledge of the pre-trained model to a specific task.

Generative AI

A type of AI that “generates” an output, such as text or images. Large language models like ChatGPT are generative AI.

Generative Model

An AI model designed to generate new data that resembles the patterns and characteristics of the training data it has been exposed to.

Generative Pre-trained Transformer (GPT)

A language model that uses deep learning to create realistic text. It is used in many applications, such as translation, question-answering, and text generation. This represents the ‘GPT’ of ChatGPT. 

Hallucinations

Misinformation or made-up information based on a pattern that the AI model has learned as part of its training. For example, the model could create references that do not actually exist.

Heat Map

A visual representation that highlights important elements in the output generated by an AI model. It helps understand where the model focuses and assists in evaluating and improving the generated content.

Internet of Things (IoT)

The network of physical objects embedded with sensors, software, and other technologies to connect and exchange data with other devices and systems over the internet. It generates vast amounts of data valuable for AI applications. 

Language Model (LM)

A type of model in NLP that predicts the next word or character in a sequence. These models are used in speech recognition, text generation, and other NLP tasks. 

Large Language Model (LLM)

A type of software / generative AI that accesses large databases it’s been trained on to predict the next logical word in a sentence, given the task/question it’s been given.

Machine Learning (ML)

A method that helps machines learn from data and get better at doing tasks without being explicitly programmed. It’s like teaching them to make decisions and predictions by themselves based on patterns they discover in information.

Models

AI models or artificial intelligence models are programs that detect specific patterns using a collection of data sets. It is an illustration of a system that can receive data inputs and draw conclusions or conduct actions depending on those conclusions.

Natural Language Processing (NLP)

A subfield of AI that focuses on the interaction between computers and humans through natural language. The ultimate objective of NLP is to read, decipher, understand, and make sense of human language in a valuable way. 

Neural Network

A computer system inspired by the way our brains work. It’s made up of interconnected “artificial neurons” that help computers learn from data and recognize patterns.

Output

The generated content produced by a generative AI system. It can be text, images, audio, music, video, or other data the model is designed to produce.

Perplexity

A measure used to assess the coherence and consistency of AI-generated text. Higher perplexity values suggest the content is more likely to be AI-generated due to unusual patterns or inconsistencies. Content identification systems use perplexity to identify AI-generated content.

Positional Encoding

A technique that assigns a number to each word during training that is used to show the position (or order) of words in a sequence.

Probabilistic

In generative AI, probabilistic means that the models incorporate probability, which is used to estimate the likelihood of different outcomes and generate outputs that align with the learned probabilities.

Prompt

The initial input text or instructions given to a model to generate new content based on that starting point. It provides context and guides the model's output. The prompt can be a few words or sentences that set the tone or specify the desired content.

Prompt Engineering

The practice of crafting clear, specific instructions to elicit the best responses from AI programs.

Reinforcement Learning

A type of machine learning where an AI learns to make decisions by performing actions and receiving rewards or penalties. It is similar to behavioral psychology.

Semi-Supervised Learning

Combines supervised and unsupervised learning by using both labeled and unlabeled data to train AI models.

Supervised Learning

A type of machine learning where the AI is trained on labeled data, meaning the input comes with the correct output. The AI learns to make predictions or decisions based on this data.

Sentient

The capability to possess consciousness, self-awareness, and subjective experiences. Achieving true sentience in AI systems is a topic of scientific exploration and philosophical debate.

Text Classification

This involves assigning categories or labels to text. For example, sorting emails into "spam" and "not spam" is a form of text classification.

Tokens

Discrete units used to represent meaningful components of text, such as words or phrases. Breaking down text into these units allows AI models to process and analyze language at a granular level, enabling tasks like language generation. 

Training Data

The initial dataset, containing the examples used to teach a machine learning application to recognize patterns or perform some function.

Transfer Learning

The application of knowledge gained while solving one problem to a different but related problem. For example, knowledge gained while learning to recognize cars could apply when trying to recognize trucks.

Transformer

A type of model (or robot) that can simultaneously work on several tasks and sequentially build output. The transformer gives AI models the ability to process and learn from data so they can interpret context and place words together to form a cohesive sentence structure.

Unsupervised Learning

A type of machine learning where the AI is given data without explicit instructions on what to do with it. The AI tries to find patterns or relationships within the data on its own.