| Artificial Intelligence (AI) | AI refers to the simulation of human intelligence by machines, particularly computer systems. These processes include learning, reasoning, problem-solving, and self-correction. |
| Machine Learning (ML) | A subset of AI, machine learning involves the development of algorithms that allow computers to learn from and make decisions based on data, improving their performance without explicit programming. |
| Deep Learning | A branch of machine learning that uses neural networks with many layers (often called artificial neural networks) to model and understand complex patterns in data. It’s particularly useful in areas like image and speech recognition. |
| Neural Networks | A computing system inspired by the structure of the human brain, where layers of connected nodes (or neurons) pass data and make decisions based on the information received. |
| Natural Language Processing (NLP) | The branch of AI that enables computers to understand, interpret, and respond to human language. It’s used in applications like chatbots, language translation, and sentiment analysis. |
| Computer Vision | A field of AI that trains computers to interpret and understand the visual world. Using cameras and algorithms, machines can identify objects, people, and actions in images or videos. |
| Robotics | The study and development of robots, which are AI-powered machines capable of carrying out tasks autonomously or semi-autonomously. |
| Supervised Learning | A type of machine learning where the model is trained on labeled data, meaning the correct output is provided for each example in the training set. |
| Unsupervised Learning | A machine learning approach where the model is trained on data that is not labeled and must find patterns, similarities, or anomalies within the data on its own. |
| Reinforcement Learning | A type of machine learning in which agents learn to make decisions by taking actions in an environment and receiving feedback in the form of rewards or penalties. |
| Artificial General Intelligence (AGI) | A hypothetical form of AI that can understand, learn, and apply knowledge in a way that’s indistinguishable from a human being. AGI would be able to perform any intellectual task a human can. |
| Artificial Narrow Intelligence (ANI) | AI that is specialized in a single task or a narrow range of tasks. It’s the most common form of AI today, such as recommendation algorithms, voice assistants, or self-driving technology. |
| Algorithm | A set of rules or processes a computer follows to solve problems and make decisions. |
| Big Data | Refers to extremely large datasets that can be analyzed computationally to reveal patterns, trends, and associations, often using machine learning and AI. |
| Data Mining | The process of discovering patterns and insights from large amounts of data using methods at the intersection of machine learning, statistics, and database systems. |
| Ethics in AI | A set of moral principles that guide the development and deployment of AI technologies, ensuring they are used responsibly and fairly. |
| Autonomous Systems | Systems or machines that can perform tasks without human intervention, often relying on AI to make real-time decisions. |
| Generative AI | AI systems that can create new content, such as text, images, or music, often using models like GPT (Generative Pre-trained Transformer) or GANs (Generative Adversarial Networks). |
| Chatbot | An AI application that simulates human conversation through voice commands or text chats, often used in customer service or virtual assistants. |
| Predictive Analytics | Using historical data combined with AI and machine learning to make predictions about future outcomes. |
| AI Bias | Refers to the presence of systematic and unfair discrimination in AI systems, often arising from biased training data. |
