A Glossary of AI Terms and Buzzwords

An A-Z Reference of a Comprehensive List of Terminologies Related to Artificial Intelligence.

If you’ve recently had a conversation about AI or read an article full of AI buzzwords and felt confused, here’s a free glossary of AI terminologies you can bookmark for future reference.

Understanding everything on what is artificial intelligence or what is machine learning can be tricky. But either as a non-technical user or aspiring professional, this resource can help you learn and use AI terminology like a pro.

It’s becoming increasingly important to understand AI terms in today’s digital age, as artificial intelligence is being integrated into more and more aspects of our daily lives. As I explained in my previous article, ‘The AI-Writing Paradox,’ we already use AI in most of our daily technology use. That’s why having a beginner’s guide that can give you answers to the question ‘how does AI works’ is so essential.

Another important benefit of understanding AI is preparing for future developments and taking advantage of opportunities AI can provide. This glossary lists over 60 AI terminologies alphabetically from A to Z. You can quickly find the meaning of any AI word or phrase puzzling you. And this will help you stay up-to-date with the latest AI trends and terminology.

60+ Glossary of AI Terms

1. Algorithm: A set of rules or instructions a computer follows to solve a problem.
2. Artificial Intelligence (AI): The simulation of human intelligence processes by machines, especially computer systems.
3. AGI (Artificial General Intelligence): An AI system capable of understanding, learning, and applying knowledge across diverse tasks and domains, exhibiting human-like cognitive abilities.
4. ASI (Artificial Superintelligence): is an AI system that surpasses human intelligence in all aspects, potentially leading to outcomes beyond human comprehension or control.
5. Artificial Neural Network (ANN): Computing systems inspired by the biological neural networks that constitute animal brains.
6. Autonomous: A machine’s ability to operate and perform tasks without human intervention.
7. Backpropagation: A method used in artificial neural networks to calculate a gradient needed to calculate the weights used in the network.
8. Big Data: Huge data sets that may be analyzed computically to reveal patterns, trends, and associations.
9. Binary Classification: A type of classification task where an instance is classified into one of two classes.
10. Black Box: An AI model or system whose internal workings are opaque or not easily interpretable, making it difficult to understand how it arrives at its decisions or outputs.
11. Chatbot: A software application used to conduct an on-line chat conversation via text or text-to-speech.
12. Clustering: The task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group.
13. Cognitive Computing: A subfield of AI that strives for a natural, human-like interaction with machines.
14. Computer Vision: An interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos.
15. Convolutional Neural Network (CNN): This is a class of deep neural networks most commonly used to analyze visual imagery.
16. Data Mining: The process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.
17. Data Science: An interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.
18. Deepfake: Synthetic media generated using AI techniques, often involving the manipulation of audio, video, or images to depict events or scenarios that did not occur.
19. Deep Learning: A subset of machine learning in AI that has networks capable of learning unsupervised from unstructured or unlabeled data.
20. Dimensionality Reduction: The process of reducing the number of random variables under consideration by obtaining a set of principal variables.
21. Ensemble Learning: A machine learning concept in which multiple models are trained to solve the same problem and combined to get better results.
22. Evolutionary Computation: A family of algorithms for global optimization inspired by biological evolution.
23. Explainable AI or XAI (Explainable Artificial Intelligence): refers to AI systems and models that can provide clear and understandable explanations for their decisions and actions, making the reasoning process transparent and interpretable to humans.
24. Feature Extraction: The process of reducing the amount of resources required to describe a large set of data.
25. Fuzzy Logic: A computing approach based on “degrees of truth” rather than the usual true or false (1 or 0) Boolean logic.
26. Generative Adversarial Network (GAN): This is a class of AI algorithms in which two neural networks, the generator and the discriminator, are trained simultaneously to produce realistic data samples.
27. Generative AI: AI systems capable of creating new content, such as images, text, or music, often through techniques like deep learning and generative models.
28. Genetic Algorithm: A search heuristic that is inspired by Charles Darwin’s theory of natural evolution.
29. GPT (Generative Pre-trained Transformer): A type of large language model based on the Transformer architecture, pre-trained on vast amounts of text data and capable of generating coherent and contextually relevant text.
30. Hallucination: AI Hallucination is a phenomenon where an AI model generates inaccurate or unrealistic outputs, often due to biases or limitations in the training data or algorithm.
31. Heuristic: A technique designed for solving a problem more quickly when classic methods are too slow.
32. Image Recognition: The ability of software to identify objects, places, people, writing, and actions in images.
33. Knowledge Graph: A knowledge base used by Google to enhance its search engine results with information gathered from various sources.
34. Large Language Model (LLM): An AI model trained on extensive text data, such as GPT, capable of understanding and generating human-like text.
35. Linear Regression: A basic predictive analytics technique that uses historical data to predict an output variable.
36. Machine Learning (ML): A type of artificial intelligence that allows software applications to become more accurate in predicting outcomes without being explicitly programmed.
37. Multilayer Perceptron (MLP): An artificial neural network characterized by multiple layers of interconnected neurons, commonly used in supervised learning tasks like classification and regression.
38. Natural Language Processing (NLP): The ability of a computer program to understand human language as it is spoken.
39. Neural Network: A series of algorithms that endeavours to recognize underlying relationships in a data set through a process that mimics how the human brain operates.
40. Outlier Detection: The process of identifying rare items, events, or observations that raise suspicions by differing significantly from the majority of the data.
41. Pattern Recognition: The automated recognition of patterns and regularities in data.
42. Predictive Analytics: The use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data.
43. Quantum Computing: An area of computing focused on developing computer-based technologies centred around the principles of quantum theory.
44. RAG (Retrieve and Generate): A model architecture that combines retrieval-based and generative approaches, enabling AI systems to generate text based on retrieved knowledge or context.
45. Random Forest: A versatile machine learning method capable of performing both regression and classification tasks.
46. Recurrent Neural Network (RNN):
47. Reinforcement Learning: An area of machine learning where an agent learns to behave in an environment by performing certain actions and observing the results.
48. Robotics: A field of engineering focused on the design and manufacturing of robots.
49. Semantic Analysis: The process of relating syntactic structures, from the levels of phrases, clauses, sentences, and paragraphs to the level of the writing as a whole, to their language-independent meanings.
50. Sentient: An AI system possessing consciousness, self-awareness, and subjective experiences, similar to sentient beings like humans.
51. Sentiment Analysis: The use of natural language processing to identify, extract, and quantify subjective information from source materials.
52. Supervised Learning: A type of machine learning algorithm that uses a known dataset (called the training dataset) to make predictions.
53. Swarm Intelligence: The collective behaviour of decentralized, self-organized, natural or artificial systems.
54. TTS (Text-to-Speech): AI technology that converts written text into spoken language, synthesizing human-like speech output.
55. Text Mining: The process of deriving high-quality information from text.
56. Time Series Analysis: A statistical technique that deals with time series data or trend analysis.
57. Transformers: A type of deep learning model architecture based on self-attention mechanisms, widely used in natural language processing tasks like translation and summarization.
58. Unsupervised Learning: A type of machine learning algorithm used to draw inferences from datasets consisting of input data without labelled responses.
59. Virtual Reality (VR): A simulated experience that can be similar to or completely different from the real world.
60. Voice Recognition: The ability of a machine or program to receive and interpret dictation or to understand and carry out spoken commands.
61. Web Scraping: A method used to extract large amounts of data from websites whereby the data is extracted and saved to a local file in your computer or a database in table (spreadsheet) format.
62. Word Embedding: A type of mapping where words or phrases from the vocabulary are mapped to vectors of real numbers. It involves a mathematical embedding from a space with one dimension per word to a continuous vector space with a much lower dimension.
63. XGBoost: An optimized distributed gradient boosting library designed to be highly efficient, flexible, and portable.
64. Yann LeCun: A computer scientist who has contributed to machine learning, computer vision, mobile robotics, and computational neuroscience. He is a founding father of convolutional nets, a type of deep-learning model.
65. Zero-shot Learning: The ability of a machine learning model to correctly infer or classify instances that have not been encountered during training.
66. Zeta Architecture: An enterprise-grade, globally distributed, multi-model, real-time data-processing architecture.