The field of AI is filled with jargon. This dictionary provides clear, concise definitions for the most important terms you need to know.
A-E
Algorithm: A set of rules or instructions given to an AI, neural network, or other machine to help it learn on its own.
Alignment: The problem of ensuring AI systems have goals that match human values.
Bias: Errors in AI output resulting from prejudices in the training data.
F-L
Fine-tuning: The process of training a pre-trained model on a smaller, specific dataset to specialize it.
Hallucination: When an AI generates false or nonsensical information confidently.
LLM (Large Language Model): A deep learning algorithm that can recognize, summarize, translate, predict, and generate text.
M-Z
Multimodal: AI that can understand and generate multiple types of media (text, images, audio).
Parameters: The internal variables (weights) that the model adjusts during training. GPT-4 has trillions.
Token: The basic unit of text for an LLM (roughly 0.75 words).
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