Tech Glossary: Artificial Intelligence
Artificial Intelligence (AI)
Algorithm
A step-by-step set of instructions that a computer program follows to perform a specific task. In the context of AI, algorithms are used to process data and make decisions.
Artificial Intelligence (AI)
The simulation of human intelligence processes by machines, particularly computer systems. AI encompasses tasks such as problem-solving, learning, reasoning, and decision-making.
AI Ethics
The study and practice of ensuring that AI systems are developed and used in ways that are fair, transparent, accountable, and do not harm individuals or society as a whole.
Artificial Neural Network (ANN)
A computational model inspired by biological neural networks. ANNs consist of interconnected nodes (neurons) organized in layers that process information and learn patterns from data.
Autonomous System
A system or agent, often driven by AI, capable of making decisions and performing tasks without continuous human intervention. Examples include self-driving cars and autonomous drones.
Bias in AI
The presence of systematic and unfair preferences or prejudices in AI systems, often reflecting existing biases present in the data used for training. Bias in AI can lead to discriminatory outcomes.
Chatbot
A computer program or AI application designed to simulate conversation with human users, often used for customer service, information retrieval, and interaction.
Computer Vision
A field of AI that involves enabling computers to interpret and understand visual information from the world, similar to how humans perceive and analyze images and videos.
Data Preprocessing
The process of cleaning, transforming, and organizing raw data into a suitable format for training AI models. It includes tasks like data cleaning, normalization, and feature engineering.
Deep Learning
A specialized area of machine learning that uses neural networks with multiple layers to model and solve complex patterns in large datasets, often achieving state-of-the-art performance in tasks like image and speech recognition.
Machine Learning (ML)
A subset of AI that involves developing algorithms and models that allow computers to learn from and make predictions or decisions based on data, without being explicitly programmed.
Natural Language Processing (NLP)
The branch of AI focused on enabling computers to understand, interpret, and generate human language in a way that is both meaningful and contextually relevant.
Neural Network
A computational model inspired by the structure of the human brain's interconnected neurons. Neural networks are used in various AI tasks and consist of layers of interconnected nodes that process and transform data.
Reinforcement Learning
A type of machine learning where an agent learns to perform actions in an environment in order to maximize a reward signal. It involves learning by trial and error, exploring different strategies to achieve the best outcome.
Semi-Supervised Learning
A hybrid approach that combines elements of both supervised and unsupervised learning, using a limited amount of labeled data along with a larger amount of unlabeled data to improve model performance.
Supervised Learning
A type of machine learning in which a model is trained on labeled data, meaning the algorithm is provided with input-output pairs, allowing it to learn the relationship between inputs and desired outputs.
Turing Test
A test proposed by Alan Turing to determine a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human. If a machine can pass the Turing Test, it is considered to possess human-like intelligence.
Unsupervised Learning
A type of machine learning where the algorithm is provided with unlabeled data and is tasked with finding patterns, relationships, or structures within the data without predefined categories or labels.
Other Topics:
---