Commonly Used Terms

Below are some key definitions that help clarify the concepts related to generative artificial intelligence:

• Artificial Intelligence (AI): AI stands for artificial intelligence, which is the simulation of human intelligence processes by machines or computer systems. AI can mimic human capabilities such as communication, learning, and decision-making.
• Deep Learning: Deep learning is a function of AI that imitates the human brain by learning from how it structures and processes information to make decisions. Instead of relying on an algorithm that can only perform one specific task, this subset of machine learning can learn from unstructured data without supervision.
• Generative AI: Generative AI is a type of technology that uses AI to create content, including text, video, code and images. A generative AI system is trained using large amounts of data, so that it can find patterns for generating new content. This is the technology behind platforms like ChatGPT.
• Hallucination: Hallucination refers to an incorrect response from an AI system, or false information in an output that is presented as factual information.
• Large Language Models (LLMs): A large language model (LLM) is an AI model that has been trained on large amounts of text so that it can understand language and generate human-like text.
• Machine Learning: Machine learning is a subset of AI that incorporates aspects of computer science, mathematics, and coding. Machine learning focuses on developing algorithms and models that help machines learn from data and predict trends and behaviors, without human assistance.
• Natural Language Processing (NLP): Natural language processing (NLP) is a type of AI that enables computers to understand spoken and written human language. NLP enables features like text and speech recognition on devices.
• Neural Networks: A neural network is a deep learning technique designed to resemble the human brain’s structure. Neural networks require large data sets to perform calculations and create outputs, which enables features like speech and vision recognition.
• Prompt: A prompt is an input that a user feeds to an AI system in order to get a desired result or output.
• Supervised Learning: Supervised learning is a type of machine learning in which classified output data is used to train the machine and produce the correct algorithms. It is much more common than unsupervised learning.
• Training Data: Training data is the information or examples given to an AI system to enable it to learn, find patterns, and create new content.
• Unsupervised Learning: Unsupervised learning is a type of machine learning in which an algorithm is trained with unclassified and unlabeled data so that it acts without supervision.