AI terminology

This is the name given to a sequence of rules given to an AI machine to perform a task or solve a problem. Common types include classification, regression, and clustering.

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This term refers to the large data sets that can be studied to reveal patterns and trends to support business decisions. Organizations can now gather massive amounts of complex data using data collection tools and systems. It can be collected very quickly and stored in a variety of formats. 

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This is a software application that is designed to imitate human conversation through text or voice commands.

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This is a function of AI that imitates the human brain by learning from how it structures and processes information to make decisions. Instead of relying on an algorithm that can only perform one specific task, this subset of machine learning can learn from unstructured data without supervision.

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This is a type of technology that uses AI to create content, including text, video, code and images. Such a system is trained using large amounts of data, so that it can find patterns for generating new content.

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This term refers to restrictions and rules placed on AI systems to make sure that they handle data appropriately and don't generate unethical content.

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This refers to an incorrect response from an AI system, or false information in an output that is presented as factual information.

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This is the method of using computer algorithms to analyze, detect, and label regularities in data. This informs how the data gets classified into different categories.

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This is a subset of AI that incorporates aspects of computer science, mathematics, and coding. It focuses on developing algorithms and models that help machines learn from data and predict trends and behaviors, without human assistance.

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This is a computing system designed to function like the human brain. Existing examples can perform many tasks involvng speech, vision and board game strategy

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