Generative AI: The Evolution of Thoughtful Online Search

 

AI Glossary

This glossary covers essential AI terms and definitions to help you upskill your team effectively. It's here to support your understanding and your use of AI technologies in driving learning and development.

algorithm: a set of rules or instructions given to an AI machine to perform a task or solve a problem

application programming interface (API): a set of protocols that allow different software applications to communicate with each other

artificial general intelligence (AGI): a theoretical form of AI that possesses the ability to understand, learn, and apply knowledge across a wide range of tasks; similar to human intelligence

artificial intelligence (AI): the simulation of human intelligence in machines that are programmed to think and learn like humans

bias: systematic errors in AI models that can lead to unfair or inaccurate outcomes; often due to biased training data

big data: large and complex data sets that can be analyzed to reveal patterns, trends, and associations

chain-of-thought: a reasoning approach where an AI model generates intermediate steps to solve complex problems

chatbot: a software application designed to simulate human conversation through text or voice commands

cognitive computing: a subset of AI that aims to mimic human thought processes in a computerized model

data mining: the process of discovering patterns and knowledge from large amounts of data

deep learning: a type of machine learning that uses neural networks with many layers to analyze various levels of data abstraction

few-shot learning: an AI model's ability to learn and make predictions from a limited number of examples.

generative pretrained transformer (GPT): a type of AI model that generates humanlike text by predicting the next word in a sentence.

graphics processing unit (GPU): a specialized processor that accelerates the training and operation of AI models by handling many calculations simultaneously.

hallucinations: instances where an AI model generates incorrect or nonsensical information that often appears plausible

hyperparameter tuning: the process of optimizing the parameters that govern the training of an AI model to improve its performance

inference: the process of using a trained AI model to make predictions or decisions based on new data

large language model (LLM): a type of AI that can understand and generate human-like text based on vast amounts of data

machine learning (ML): a subset of AI where machines learn from data to improve their performance on tasks without being explicitly programmed

natural language processing (NLP): a field of AI focused on enabling machines to understand, interpret, and respond to human language

neural network: a computational model inspired by the human brain; consisting of interconnected nodes (neurons) that process information

overfitting: a modeling error where an AI model learns the training data too well, including noise and outliers; results in poor performance on new data

prompt engineering: the process of designing and refining prompts to get the best responses from AI models

retrieval-augmented generation (RAG): an AI technique that combines retrieving relevant information from a database with generating text to provide accurate and informative responses

reinforcement learning: a type of machine learning where an agent learns to make decisions by receiving rewards or penalties for its actions

single-shot learning: an AI model's ability to learn from just one example or a very small number of examples

supervised learning: a type of machine learning where the model is trained on labeled data; the input data is paired with the correct output

tokens: the smallest units of text (like words or characters) that an AI model processes

tensor processing unit (TPU): a specialized processor designed by Google to accelerate machine learning tasks

training data: the dataset used to teach an AI model to recognize patterns and make decisions

unsupervised learning: a type of machine learning where the model is trained on unlabeled data; must find patterns and relationships on its own

zero-shot learning: an AI model's ability to make predictions on tasks it has never seen before without any specific training