Artificial Intelligence (AI) is a rapidly evolving field filled with complex concepts and jargon. To help you navigate this landscape, we’ve put together a simple guide to some of the key terms you’ll encounter.
The simulation of human intelligence in machines that are programmed to think like humans and mimic their actions.
A set of rules or procedures for solving a problem.
The use of technology to perform tasks without human intervention.
The study of ethical and societal implications of artificial intelligence.
A systematic error in a model that can lead to unfair outcomes.
A computer program designed to stimulate conversation with a human user.
The process of examining large datasets to uncover patterns, trends, and insights.
The process of combining data from multiple sources into a unified view.
A subfield of machine learning that uses artificial neural networks with multiple layers to learn from data.
The principles and regulations governing the development and use of AI, ensuring fairness, accountability, and transparency.
A subset of AI that allows systems to learn and improve from experience without being explicitly programmed.
A mathematical representation of a real-world system.
The ability of computers to understand, interpret, and generate human language.
The ability of computers to generate human-like text in response to a wide range of prompts and situations.
Computing systems inspired by the human brain, capable of learning and making decisions.
When a model is too complex and performs well on training data but poorly on new data.
Using data to predict future outcomes.
Using data to recommend actions to optimise outcomes.
A system that suggests items or content to users based on their preferences.
A type of machine learning that allows an agent to learn by trial and error in an interactive environment.
Determining the emotional tone of text data.
Monitoring online conversations to understand public opinion.
The process of establishing and implementing agreed-upon norms and technical specifications.
When a model is too simple and cannot capture the underlying patterns in the data.
For more insights on how to integrate AI into your company, check out our A Brokers Guide to AI guide and The Best AI Tools for Mortgage Brokers.
by Jeremy Duncombe
Added 09/09/24 - min read
by Jeremy Duncombe
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by Jeremy Duncombe
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