Artificial Intelligence (AI): AI Glossary

• Anthropomorphism:  the tendency for people to attribute humanlike qualities or characteristics to an AI chatbot.  For example, you may assume it is kind or cruel based on its answers, even though it is not capable of having emotions.
• Bias:  a type of error that can occur in a large language model if its output is skewed by the model's training data.  For example, a model may associate specific traits or professions with a certain race or gender, leading to inaccurate predictions and offensive responses.
• Emergent Behavior:  unexpected or unintended abilities in a large language model, enabled by the model's learning patterns and rules from its training data.  For example, models that are trained on programming and coding sites can write new code.  Other examples include creative abilities like composing poetry, music and fictional stories.
• Generative AI:  technology that creates content - including text, images, video and computer code - by identifying patterns in large quantities of training data, and then creating original material that has similar characteristics.
• Hallucination:  a well-known phenomenon in large language models, in which the system provides an answer that is factually incorrect, irrelevant or nonsensical, because of limitations in its training data and architecture.
• Large Language Model:  a type of neural network that learns skills - including generating prose, conducting conversations and writing computer code - by analyzing vast amounts of text from across the internet.  The basic function is to predict the next word in a sequence, but these models have surprised experts by learning new abilities.

• Natural Language Processing:  techniques used by large language models to understand and generate human language, including text classification and sentiment analysis.  These methods often use a combination of machine learning algorithms, statistical models and linguistic rules.
• Neural Network:  a mathematical system, modeled on the human brain, that learns skills by finding statistical patterns in data.  It consists of layers of artificial neurons:  the first layer receives the input data, and the last layer outputs the results.
• Parameters:  numerical values that define a large language model's structure and behavior, like clues that help it guess what words come next.  Systems like GPT-4 are thought to have hundreds or billions of parameters.
• Reinforcement Learning:  a technique that teaches an AI model to find the best result by trial and error, receiving rewards or punishments from an algorithm based on its results.  This system can be enhanced by humans giving feedback on its performance, in the form of ratings, corrections and suggestions.  
• Transformer Model:  a neural network architecture useful for understanding language that does not have to analyze words one at a time but can look at an entire sentence at once.  Transformers use a technique called self-attention, which allows the model to focus on the particular words that are important in understanding the meaning of a sentence.

(from Artificial Intelligence Glossary: Neural Networks and Other Terms Explained, by Adam Pasick)