Top Ten (10) Concepts in Artificial Intelligence

Cox & Mazumdar (2024). Definitions of artificial intelligence gathered by librarians Compiled by Dean Giustini, UBC Biomed librarian, dean.giustini@ubc.ca Updated 3 January 2026 | Part of Knowledge Synthesis (KS) & AI Search Wiki 2026 & A to Z Listing See also Artificial intelligence (AI) glossary of terms Note: OpenAI leads the general AI space, but AI companies are developing deep research tools and experimenting with AI-powered academic searching in support of research. Perhaps you have faculty or students asking you to present these tools to classes. How will AI tools affect our traditional bibliographic databases? Will we see GenAI being put into our search platforms? Can we stop it? Algorithms | Which companies are behind AI search tools? Large language models (LLMs) | Machine learning | Neural networks | Prompt engineering | Reasoning models | Semantic searching Top Ten (10) Concepts in Artificial Intelligence 1) Artificial intelligence (AI) vs. Machine learning (ML) vs. Deep Learning (DL) The hierarchy of terms: AI (broad field) → ML (subset: systems learn patterns from data) → DL (subset of ML using neural networks). 2) Supervised, Unsupervised, and Reinforcement Learning The three major learning paradigms: Supervised: labeled data (e.g., training models to recognize cancer images). Unsupervised: unlabeled data (e.g., clustering articles by topic). Reinforcement: learning by trial and error with rewards (e.g., game-playing AI). 3) Neural networks The structure of deep learning models (layers, weights, activations). Key to understanding natural language processing (NLP) and computer vision. 4) Natural language processing (NLP) Core to AI in libraries/research: text classification, summarization, entity recognition, embeddings, search/retrieval. 5) Large language models (LLMs) Modern NLP systems (GPT, LLaMA, Claude). Concepts: transformers, tokens, context windows, fine-tuning, hallucination. 6) Searching & information retrieval (IR) Embeddings, vector search, semantic searching vs. keyword search. Essential for understanding AI-powered discovery tools. 7) Bias, Fairness, and Explainability How training data shapes outcomes. Why transparency and interpretability matter in scholarly and clinical contexts. 8) Evaluation metrics Accuracy, precision, recall, F1 score, ROC curves—how we measure “good” AI. Important for comparing search tools or summarization systems. 9) Generative AI Text, image, audio, and multimodal generation. Key concept: diffusion models (images), transformer-based models (text). 10) Ethics, Governance, and Policy in AI Issues of copyright, misinformation, reproducibility, accountability. Especially relevant for librarians, publishers, and educators. References Chatterjee R. Fundamental concepts of artificial intelligence and its applications. Journal of Mathematical Problems, Equations and Statistics. 2020;1(2):13-24. Schwalbe G, Finzel B. A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts. Data Mining and Knowledge Discovery. 2024 Sep;38(5):3043-101. Touretzky D, Gardner-McCune C, Seehorn D. Machine learning and the five big ideas in AI. International journal of artificial intelligence in education. 2023 Jun;33(2):233-66. Disclaimer Note: Please use your critical reading skills while reading entries. No warranties, implied or actual, are granted for any health or medical search or AI information obtained while using these pages. Check with your librarian for more contextual, accurate information.