Before you deep dive into the concepts of artificial intelligence, it can be useful to first get familiar with some of the common terminology and definitions. The following list of AI words will provide a foundation on the key concepts of AI and machine learning −
| Term | Definition |
|---|---|
| Artificial Intelligence (AI) | The technology that enable computers and machine to replicate human intelligence. |
| Machine Learning (ML) | A subset of AI that allows systems to learn from data and improve their performance over time. |
| Deep Learning | A specialized domain of machine learning that uses neural networks with many layers to analyze various forms of data. |
| Neural Networks | Computational models inspired by the functioning of human brain using neurons. This models consist of interconnected nodes to process the data. |
| Natural Language Processing (NLP) | The domain in AI which deals with interaction between computers and humans through natural language. |
| Computer Vision | A field of AI that allows machines to interpret and make decisions over visual data. |
| 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 type of machine learning where the model is trained on labeled data to predict outcomes. |
| Unsupervised Learning | A type of machine learning where the model identifies patters and relationships from unlabeled data. |
| Semi-Supervised Learning | A hybrid machine learning method that combines small amount of labeled data and a large amount of unlabeled data to predict outcomes. |
| Data Mining | The process of discovering patterns and knowledge from large amounts of data using various techniques. |
| Agent | An entity that perceives its environment and takes actions to achieve specific goals. |
| Algorithm | A step-wise procedure or processes followed in calculations or problem-solving operations by a computer. |
| Training Data | The dataset used to train a machine learning model to recognize patterns and make predictions. |
| Model | A mathematical representation of a process which captures relationships in the data for predictive tasks. |
| Overfitting | A modeling error that occurs when a model learns the training data too well, capturing noise instead of the underlying patterns. |
| Underfitting | A modeling error that occurs when a model is too simple to capture the underlying trend in the data. |
| Cognitive Computing | An AI approach that mimics human through processes in a complex, human-like way. |
| Autonomous | Systems that operate independently without human intervention. |
| Large Language Models | AI models like GPT that are trained on large amounts of text data to understand and generate human-like data. |
| Artificial General Intelligence (AGI) | A theoretical form of AI that is capable of understanding, learning, and general intelligence throughout almost any task, quite similar to that of a human being. |
| Generative AI | AI capable of generating new content, be it text, images, or music, based on learned patterns. |
| Transfer Learning | A technique where the model trained on one task is adapted to work on a different related tasks. |
| Chatbot | A program designed to simulate conversation with human users. |
| Backward Chaining | An inference method where reasoning started from the goal and works backwards to find supporting data. |
| Forward Chaining | An inference method that started with available data and applied rules to extract more data until a goal is reached. |
| Environment | The surrounding context or scenario in which an agent operates and makes decisions. |
| Heuristics | Problem-solving strategies that use practical methods to produce solutions that may not be optimal but are sufficient for immediate goals. |
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