ii. Generative AI works by analyzing large datasets to learn patterns and features, which it then uses to generate new, original content. It uses complex algorithms, often based on neural networks, to make predictions based on the input data it has processed; thereby enabling it to create a wide range of outputs, from text and images to music and code, that mimic the style or characteristics of the data on which it was trained.

iii. At its core, generative AI predicts the flows of language. Trained on massive amounts of text taken from publicly available internet sources to recognize the relationships that most commonly exist between individual units of meaning (including full or partial words, phrases, and sentences), LLMs can, with great frequency, generate replies to users’ prompts that are contextually appropriate, linguistically facile, and factually correct.

c. Natural Language Processing (NLP)

i. A field of artificial intelligence that focuses on enabling computers to understand, interpret, and respond to human language in a meaningful way. Examples of NLP include, but are not limited to, Grammarly, GPT-Based APIs, Google Cloud Natural Language AI, Microsoft Azure Text Analytics, IBM Watson NLP, Amazon Comprehend, etc.

d. Large Language Model (LLM)

i. A sophisticated AI system trained on extensive text data to process and produce language; recognize patterns, grammar, and nuances. It can perform tasks like text generation, question answering, and language translation.

e. Algorithm

i. A set of rules or instructions guiding AI operations and decision-making.

f. Personally Identifiable Data/Personal Data

Refers to any information that can directly or indirectly identify an individual including, but not limited to, names, addresses, student records, and health information.

Proprietary Information/Data