In this short article we are going to define the top 50 Artificial Intelligence terms that everybody needs to know to survive the near future. If you want to understand the artificial intelligence revolution, you will need to understand every one of these AI related definitions. To make that easy, we have developed simply to understand short definitions the AI terms to get you up to speed quickly.

Glossary of AI Terms:

AI Detector: This is a tool designed to detect when a text was AI-generated.

AI Ethics: This is about making sure AI is used in a good and fair way.

AI Safety: This is the study of how AI can be developed and used safely.

Algorithm: This is a set of instructions given to an AI to help it learn on its own.

Alignment: This is the extent to which an AI’s goals are in line with its creators’ goals. This is not a trivial matter. Without “AI Alignment” an Artificial Intelligence could try kill every human because it “thinks” people are the cause of global warming that it has been tasked with fixing. The idea of Alignment is that AI follows not just its coding, but the INTENT of the coding. In the the global warming example, the intent would be to make earth better for humans, not kill them all.

AlphaGo: This is an AI system developed by Google’s DeepMind that plays the board game Go at a super-human level.

Anthropomorphism: This is the attribution of human traits to non-human entities.

Artificial General Intelligence (AGI): This is an AI that has the same flexibility of thought as a human, and possibly even consciousness. AGI is also known as “Strong AI”.

Artificial Narrow Intelligence (ANI): ANI is also known as weak AI because, it is designed to do one specific task. Unlike humans, ANI can’t learn new tasks on its own. Think of it as a very smart robot that can only do one task, like vacuum your floor.

Artificial Super Intelligence (ASI): is a (currently) hypothetical type of self-aware artificial intelligence that goes beyond just understanding human intelligence. ASI’s may learn at an exponential rate and be vastly smarter, faster and more capable than humans.

Artificial Neural Network (ANN): This is a model created to act like a human brain that solves tasks that are too difficult for traditional computer systems to solve.

Automation: This is the handling of a process with machines or software so that less human input is needed.

Autonomic Computing: This is a system’s ability to manage its own resources for high-level computing functions without user input.

Autonomous: This refers to systems that are able to perform tasks without human input.

Bard: This is a chatbot developed by Google.

Bias (AI Bias): This refers to the assumptions that an AI makes to simplify its tasks.

Big Data: This refers to very large datasets which AI can analyze to reveal patterns and trends that normal data-processing software can’t handle.

Bing Chat: This is a chatbot integrated into Microsoft Bing search engine.

Burstiness: This is a measurement of variation in sentence structure and length.

CAPTCHA: This is a test used online to ensure that the user is human.

Chatbot: This is a computer program that can have a conversation with humans through text or voice.

ChatGPT: This is a chatbot released by OpenAI.

Chinese Room: A thought experiment by John Searle arguing that a computer executing a program cannot have a “mind”, “understanding”, or “consciousness”, regardless of how intelligently or human-like the program may make the computer behave.

Classification: Classification algorithms let machines assign a category to a data point based on training data.

Cluster Analysis: This is a type of unsupervised learning used for exploratory data analysis to find hidden patterns or grouping in data.

Clustering: Clustering algorithms let machines group data points or items into groups with similar characteristics.

Cognitive Computing: This is a computer model that tries to think like a human.

Computer Vision: This is a field of study that focuses on how computers can understand images and videos.

Convolutional Neural Network (CNN): This is a type of neural networks that identifies and makes sense of images.

Copilot: Microsoft branding of all things related to Artificial Intelligence including that built into Window 11, and Microsoft 365 products. In 2023 / 2024 nearly all of Copilot functions are using OpenAI’s ChatGPT.

DALL-E: This is an AI image generator released by OpenAI. It is not an acronym, just a blending of “DALL”from Salvador Dali, and “E” referring to Pixar’s animated robot Wall-E24.

Data Mining: This is the examination of data sets to discover and mine patterns from that data that can be of further use.

Data Science: This is a field that combines scientific methods, systems, and processes from statistics, information science, and computer science to provide insight into phenomenon via either structured or unstructured data.

Decision Tree: This is a tree and branch-based model used to map decisions and their possible consequences.

Deep Blue: IBM’s chess playing expert system super-computer. It was the first computer to win a game and a match against a reigning world champion under regular time controls, in the last 1990’s.

Deep Learning: It is a type of Artificial Intelligence that allows computers to learn from experience and understand the world in terms of a hierarchy of concepts. It’s like teaching a computer to be more like a human brain, understanding data through layers of interpretation.

Deepfake: These are AI-generated images and videos designed to look real.

ELIZA: An early chatbot developed in the 1960s at MIT by Joseph Weizenbaum. It simulates a Rogerian psychotherapist using pattern matching and substitution methodology.

Emergent Behavior: This refers to complex behavior resulting from basic processes.

Generative AI: These are AI systems that generate output in response to prompts.

Generative Pre-trained Transformer (GPT): This is a type of Large Language Model used in ChatGPT and other AI applications.
– Generative relating to they can create new things, not just copy existing content.
– Pre-trained meaning that the model already has a notable amount of data in it to learn and create from.
– Transformer: large language models use Google’s open-source deep learning Transformer architecture under the hood to analyze large amounts of text and understand how each word relates to another.

Hallucination: This is the tendency of AI chatbots to confidently present false information.
Large Language Model (LLM): This is a neural net trained on large amounts of text to imitate human language.

Machine Learning (ML): This is the study of how AI acquires knowledge from training data.
Machine Learning: This is a type of AI that allows a computer to learn from data without being explicitly programmed.

Natural Language Processing (NLP): This is a field of AI that gives the machines the ability to read, understand and derive meaning from human languages.

Neural Network: A neural network is a series of algorithms that mimic the neurons in a human brain to recognize underlying relationships in a set of data.

Robotic Process Automation (RPA): A digital assistant for businesses. It uses software ‘bots’ to do repetitive tasks that humans usually do, like filling in forms or moving files but much faster and making fewer mistakes that humans, freeing them to do more complex tasks.

Strong Artificial Intelligence (Strong AI): Synonym for Artificial General Intelligence (AGI) which is defined at the top of this list.

Weak Artificial Intelligence (Weak AI): Synonym for Artificial Narrow Intelligence (ANI) which is defined at the top of this list.


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