This page contains a brief explanation of the most common terms encountered in the Artificial Intelligence field.
An algorithm is a set of well-defined instructions or rules designed to perform a specific task or solve a particular problem. These instructions are typically executed in a sequence to achieve the desired outcome. Algorithms are fundamental to various fields, including computer science, mathematics, operations research, artificial intelligence, and data science.
Strong artificial intelligence (AI), also known as artificial general intelligence (AGI) or general AI, is a theoretical form of AI used to describe a certain mindset of AI development. A machine with Strong AI would possess an intelligence equal to humans, and it would have a self-aware consciousness that has the ability to solve problems, learn, and plan for the future. Today AGI only exists as a theoretical concept, where some optimistic individuals consider it possible in a few decades, while more pessimistic views consider it something that can never be developed.
For more information: What is Strong AI? (IBM)
In its most basic essence, artificial intelligence involves the fusion of computer science and extensive datasets to facilitate problem-solving. It encompasses sub-categories like machine learning and deep learning, often associated with AI. These fields consist of algorithms aimed at developing expert systems that predict or classify based on input data.
Compute cost is the cost of processing and transferring data in a computing application. It can also refer to the cost of running applications in the cloud.
Large machine learning (ML) model trained on vast amounts of data in order to be adapted to a wide range of uses.
It refers to deep-learning models that can generate high-quality text, images, and other content based on the data they were trained on.
For more information: What is generative AI? (IBM Research Blog)
A glitch is a short-term malfunction in a software program, computer network, or electronic device. It usually happens unexpectedly and appears as an error or irregular behavior in the system. Glitches sometimes ‘fix themselves’ but may need outside intervention to make them go away.
Phenomenon where a large language model (LLM) like a Generative AI chatbot creates an output that is either inaccurate or nonsensical.
Input data is data added to an artificial intelligence (AI) to explain a problem, situation, or request. Input data may be cleaned, labeled, and organized, or it may be raw data.
Types of foundation models trained extensively on vast datasets, enabling them to comprehend and produce natural language and various forms of content, facilitating a broad spectrum of tasks.
For more information: What are large language models? (IBM)
A branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
Output data is new data an artificial intelligence (AI) creates or synthesizes based on input data and the AI’s algorithm.
Intentionally crafted phrase or instructions given to a Generative AI model to generate a useful response.
See Artificial General Intelligence (AGI)
