Glossary of AI Terms
Glossary of AI Terms

Glossary of AI Terms

Artificial Intelligence isn't a novel concept; its roots dates all the way back to ancient Greece. Talos, a giant bronze automaton created by Hephaestus, the greek god of crafts, was given to King Minos of Crete. Talos served as a guardian of the island, circling it three times daily and throwing rocks at approaching ships to protect Crete from invaders. Fast forward to 1955, and John McCarthy officially coins the term "artificial intelligence."

For years, AI has been a staple in sci-fi novels and films, but it didn't hit mainstream chatter until the end of 2022. The real game-changer? The introduction of ChatGPT, an AI chatbot that's shaking up how we write, brainstorm, and even think. Its sudden rise, along with other AI innovations, is revolutionizing everything from education to daily life. Students are now leveraging AI in innovative ways for learning, while teachers are using it to craft lessons and prep classroom material.

As AI buzzwords become increasingly common, we want to simplify the jargon for you. We're here to help you get a solid grip on AI concepts, making sure you're up to speed and ready to dive into those conversations.

icon
Artificial General Intelligence (AGI) AI systems that can perform a wide range of intellectual tasks at a human-level or higher. Unlike narrow AI systems that are designed to perform specific tasks, AGI systems are intended to be domain-general and capable of learning and adapting to new situations. In 2023, Microsoft researchers concluded that GPT-4 could reasonably be viewed as an early (yet still incomplete) version of an AGI system.

icon
Artificial Intelligence (AI) A term coined in 1955 by Stanford University computer science professor John McCarthy, PhD, who defined it as “the science and engineering of making intelligent machines.” Today it is understood as a branch of computer science that focuses on creating machines that can perform tasks typically requiring human intelligence.

icon
Machine Learning A method that helps machines learn from data and get better at doing tasks without being explicitly programmed. It’s like teaching them to make decisions and predictions by themselves based on patterns they discover in information.

icon
Deep Learning A type of machine learning that uses artificial neural networks, which are inspired by the structure and function of the human brain. Deep learning models can recognize complex patterns in pictures, text, sounds, and other data to produce accurate insights and predictions.

icon
Large Language Models (LLMs) A type of AI model known for their size, complexity, and ability to understand and generate human-like text. They learn from large amounts of text data and have been used in various applications, such as writing content, language translation, and text completion.

icon
Prompts An input provided to a language model to generate a specific response or complete a task. It can be a question, a statement, or a set of instructions that guide the model's language generation. Prompts play a crucial role in directing the behavior and output of AI models.

icon
Generative AI Generative AI refers to a class of AI systems that can create new and creative content, such as images, texts, music, video, and other forms of information. Generative AI has potential applications in multiple knowledge industries, including software engineering, medical diagnosis and academic research.

icon
ChatGPT A chatbot developed by OpenAI, capable of generating humanlike text based on context and past conversations. It is powered by a large language model and is an example of generative AI.

icon
Hallucinations The phenomenon where generative AI systems, such as large language models (LLMs), create inaccurate or fictional results, often because they cannot distinguish between real and fake information.

icon
Responsible AI A set of principles or approaches to developing, deploying, and using artificial intelligence (AI) systems in a safe, trustworthy, and ethical manner. These principles include fairness, transparency, accountability, inclusiveness, reliability, safety, privacy, and security.

icon
Multimodal systems Systems that can process, understand, and generate outputs for various types of data, such as images, text, speech, and numerical data. By simultaneously incorporating data from multiple modalities, AI systems can enhance their accuracy, efficiency, and human-like reasoning.

icon
Agents AI programs that act autonomously on behalf of a user or another program. Agents can perceive their environment, reason about it, and take actions to achieve specific goals.