Resources

Terms and Definitions:

Artificial Intelligence (AI) – The simulation of human intelligence in machines that can perform tasks such as learning, problem-solving, and decision-making.

Machine Learning (ML) – A subset of AI that enables systems to learn from data and improve their performance without explicit programming.

Deep Learning – A branch of ML that uses neural networks with multiple layers to analyze complex data patterns and make decisions.

Neural Network – A computing system inspired by the structure of the human brain, consisting of layers of interconnected nodes (neurons) that process data.

Natural Language Processing (NLP) – A field of AI that focuses on enabling machines to understand, interpret, and generate human language.

Generative AI – AI models, like ChatGPT and DALL·E, that create new content, such as text, images, and music, based on learned patterns.

Computer Vision – A field of AI that enables computers to interpret and analyze visual information from the real world.

Reinforcement Learning – A type of ML where an agent learns by interacting with an environment and receiving rewards or penalties based on its actions.

Supervised Learning – An ML approach where models are trained on labeled datasets, meaning they learn from input-output pairs.

Unsupervised Learning – An ML approach where models find patterns and structures in data without labeled examples.

Semi-Supervised Learning – A hybrid ML approach that uses a small amount of labeled data along with a large amount of unlabeled data.

Explainable AI (XAI) – AI systems designed to provide clear, understandable explanations for their decisions and actions.

Bias in AI – The presence of unfair or prejudiced outcomes in AI models due to biased training data or flawed algorithms.

Ethical AI – The practice of designing and deploying AI systems that are fair, transparent, and aligned with human values.

AI Model – A mathematical framework that enables AI systems to make predictions or decisions based on input data.

Chatbot – A conversational AI system designed to simulate human-like interactions and respond to user queries.

Turing Test – A test developed by Alan Turing to determine if a machine's behavior is indistinguishable from that of a human.

Large Language Model (LLM) – A powerful AI model, like ChatGPT, trained on vast amounts of text data to understand and generate human-like text.

Training Data – The dataset used to teach AI models how to recognize patterns and make predictions.

Fine-Tuning – The process of refining a pre-trained AI model on specific data to improve its performance for a particular task.