AI Terminology

Artificial Intelligence (AI): The simulation of human intelligence in machines that are programmed to think and learn like humans.

Machine Learning (ML): A subset of AI that enables computers to learn and improve from experience without being explicitly programmed.

Deep Learning: A subset of machine learning where artificial neural networks, inspired by the human brain, learn from vast amounts of data.

Natural Language Processing (NLP): The ability of machines to understand, interpret, and generate human language.

Neural Network: A system of algorithms designed to recognize patterns, inspired by the structure of the human brain.

Algorithm: A set of rules or instructions followed by a computer to perform a specific task or solve a particular problem.

Data Science: Field that uses scientific methods, algorithms & systems to extract insights and knowledge from structured and unstructured data

Big Data: Massive datasets that may be analyzed computationally to reveal patterns, trends & associations, especially relating to human behavior

Supervised Learning: A type of machine learning where the algorithm is trained on a labeled dataset, with input-output pairs.

Unsupervised Learning: A type of machine learning where the algorithm is given data without explicit instructions on what to do with it and must find patterns on its own.

Reinforcement Learning: A type of machine learning where an agent learns how to behave in an environment by performing actions and receiving rewards or penalties.

Chatbot: A computer program designed to simulate conversation with human users, especially over the Internet (e.g., Bard, Claude, and ChatGPT)

Computer Vision: A field of AI that enables computers to interpret and make decisions based on visual data from the world.

Generative Adversarial Network (GAN): A class of machine learning systems where two neural networks, a generator and a discriminator, are trained together to create realistic outputs.

Ethical AI: The practice of ensuring that artificial intelligence systems are designed and used in a way that aligns with human values and ethical principles.

Explainable AI (XAI): The ability to provide clear and understandable explanations for how AI algorithms reach their decisions.

AI Ethics: The study of the ethical issues arising from the use of artificial intelligence and the development of guidelines for responsible AI use.

Singularity: A hypothetical future point in time when technological growth becomes uncontrollable and irreversible, resulting in unforeseeable changes to human civilization.

AGI (Artificial General Intelligence) Related Terms:

Artificial General Intelligence (AGI): A type of artificial intelligence that possesses the ability to understand, learn, and apply knowledge across a wide range of tasks at a human level.

Superintelligence: An AI system that surpasses human intelligence in every aspect, including creativity, problem-solving, and social skills.

Narrow AI: AI designed for a specific task or a narrow set of tasks, as opposed to AGI, which aims to understand and perform any intellectual task a human can.

Singularity: A theoretical point in the future when technological growth becomes uncontrollable, leading to unprecedented changes in human civilization, potentially associated with the advent of AGI.

Ethics of AGI: Considerations and discussions surrounding the ethical implications and challenges posed by the development and deployment of AGI.

Prompting Related Terms:

Prompt Engineering: The process of crafting specific instructions or queries to guide the behavior of language models and other AI systems.

Prompt Design: The practice of carefully constructing prompts to elicit desired responses from AI models, ensuring clarity and precision.

Prompting Strategies: Techniques and methodologies for effectively instructing AI models to generate desired outputs or perform specific tasks.

Prompt Tuning: The iterative process of adjusting and refining prompts to improve the performance and reliability of AI models.

Human-in-the-Loop (HITL): An approach in which human input is integrated into the AI system, often through prompts, to enhance and guide its decision-making.

Prompt Responsiveness: The ability of an AI system to generate accurate and relevant responses based on well-crafted prompts.

Prompting for Creativity: Techniques and methods for encouraging AI models to exhibit creative thinking in their outputs.