Key Artificial Intelligence Terminology: Essential AI Terms Explained Simply
Before you dive into the world of artificial intelligence, first understanding of some common terms and definitions would be helpful. The list below outlines key AI concepts and machine learning terms −
| Artificial Intelligence (AI) | The technology that allows computers and machines to copy human intelligence. |
| Machine Learning (ML) | A branch of AI that enable systems to learn from data and improve their performance over time. |
| Deep Learning | A specific area of machine learning that employs neural networks with multiple layers to analyze different types of data. |
| Neural Networks | Computational models inspired by the human brain's structure, using neurons. These models consist of interconnected nodes to process information. |
| Natural Language Processing (NLP) | The field in AI that focuses on the interaction between computers and humans through natural language. |
| Computer Vision | An area of AI that enables machines to understand and make decisions based on visual information.. |
| Reinforcement Learning | This is a type of machine learning in which an agent learns to make decisions based on actions in an environment. |
| Supervised Learning | A form of machine learning where the model is trained on labeled data to forecast outcomes. |
| Unsupervised Learning | A type of machine learning where the model discovers patterns and relationships from unlabeled data. |
| Semi-supervised Learning | A mixed machine learning approach that uses a small amount of labeled data along with a large amount of unlabeled data to predict outcomes. |
| Data Mining | The process of finding patterns and insights from large datasets using various techniques. |
| Agent | An entity that observes its surroundings and takes actions to achieve specific objectives. |
| Algorithm | A systematic procedure or set of processes followed in calculations or problem-solving tasks by a computer. |
| Training Data | The dataset utilized to train a machine learning model to identify patterns and make predictions. |
| Model | A mathematical model that represents a process and captures data relationships for prediction tasks. |
| Overfitting | A modeling mistake that happens when a model learns the training data too thoroughly, picking up noise instead of the real patterns. |
| Underfitting | A modeling mistake that occurs when a model is too simplistic to recognize the true trend in the data. |
| Cognitive Computing | An AI method that imitates human processes in a complex, human-like manner. |
| Autonomous | Systems that function on their own without needing human help. |
| Large Language Models | AI models like GPT that are trained on vast amounts of text data to comprehend and produce human-like content. |
| Artificial General Intelligence (AGI) | A theoretical type of AI that can understand, learn, and exhibit general intelligence across nearly any task, similar to human capabilities. |
| Generative AI | AI that can create new content, whether text, images, or music, based on learned patterns. |
| Transfer Learning | A method where a model trained on one task is modified to work on another related task. |
| Chatbot | A program created to mimic conversation with human users. |
| Backward Chaining | A reasoning method that starts from the goal and works backward to find supporting information. |
| Forward Chaining | A reasoning method that begins with available data and applies rules to gather more data until a goal is achieved. |
| Environment | The context or situation in which an agent operates and makes decisions. |
| Heuristics | Problem-solving techniques that use practical approaches to find solutions that may not be perfect but are good enough for immediate needs. |
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