Enhance Your Understanding with Artificial Intelligence Concept Cards for quick learning
The simulation of human intelligence in machines that are programmed to think and learn like humans.
A subset of AI that enables machines to learn from data and improve their performance without being explicitly programmed.
A subfield of machine learning that uses artificial neural networks to model and understand complex patterns and relationships in data.
The ability of a computer to understand, interpret, and generate human language, enabling interactions between humans and machines through natural language.
The field of AI that focuses on enabling computers to understand and interpret visual information from images or videos.
Computer systems that emulate the decision-making ability of a human expert in a specific domain, providing expert-level advice or solutions.
The interdisciplinary field that combines AI, engineering, and computer science to design, build, and program robots capable of performing tasks autonomously or with human assistance.
The study and application of moral principles and values in the development and use of AI systems, addressing concerns related to bias, privacy, transparency, and accountability.
The various domains and industries where AI technologies are applied, including healthcare, finance, transportation, gaming, and more.
Programming languages and frameworks used for developing AI applications, such as Python, R, TensorFlow, and PyTorch.
A step-by-step procedure or set of rules for solving a specific problem or accomplishing a specific task, often used in AI for data analysis and decision-making.
A computational model inspired by the structure and function of the human brain, consisting of interconnected artificial neurons that process and transmit information.
A type of machine learning where an algorithm learns from labeled training data to make predictions or decisions based on input examples.
A type of machine learning where an algorithm learns from unlabeled data to discover patterns, relationships, or structures without specific guidance or predefined outcomes.
A type of machine learning where an agent learns to interact with an environment and maximize rewards by taking actions and receiving feedback or reinforcement signals.
A computational model composed of interconnected artificial neurons that can learn and perform tasks by adjusting the strengths of connections between neurons.
A type of artificial neural network commonly used in computer vision tasks, designed to automatically and hierarchically learn visual features from input images.
A type of artificial neural network that can process sequential data by maintaining internal memory, making it suitable for tasks like natural language processing and speech recognition.
The ability of a computer to comprehend and interpret human language, including syntactic and semantic analysis, enabling it to understand and respond to user queries or commands.
The process of determining the sentiment or emotional tone of a piece of text, often used in AI applications to analyze social media posts, customer reviews, and feedback.
The task of identifying and localizing objects within an image or video, often used in computer vision applications for tasks like autonomous driving, surveillance, and object recognition.
A software framework or platform that provides tools and libraries for building expert systems, allowing developers to focus on domain-specific knowledge representation and inference.
The hypothetical ability of an AI system to understand, learn, and apply knowledge across different domains and tasks, similar to human intelligence.
The ability of a machine or computer system to see and interpret visual information, enabling tasks like object recognition, image analysis, and quality control in manufacturing.
The presence of systematic errors or prejudices in AI systems, often resulting from biased training data or biased algorithms, leading to unfair or discriminatory outcomes.
The protection of personal data and privacy rights in the development and use of AI systems, addressing concerns related to data collection, storage, and usage.
The openness and explainability of AI systems, allowing users and stakeholders to understand how decisions are made and ensuring accountability and trustworthiness.
The responsibility and liability of individuals, organizations, or systems for the actions, decisions, and consequences of AI technologies, ensuring ethical and legal compliance.
The application of AI technologies in healthcare settings, including medical diagnosis, drug discovery, personalized medicine, and patient monitoring.
The use of AI technologies in financial services, such as fraud detection, algorithmic trading, risk assessment, credit scoring, and customer service.
The integration of AI technologies in transportation systems, enabling autonomous vehicles, traffic management, route optimization, and predictive maintenance.
The implementation of AI techniques in video games, including game playing agents, procedural content generation, character behavior, and player experience optimization.
A popular programming language widely used in AI development due to its simplicity, readability, and extensive libraries for scientific computing and machine learning.
A programming language and environment for statistical computing and graphics, commonly used in data analysis, machine learning, and data visualization.
An open-source machine learning framework developed by Google, widely used for building and deploying AI models, especially in deep learning applications.
An open-source machine learning library developed by Facebook's AI Research lab, known for its dynamic computational graph and ease of use in building neural networks.
The process of cleaning, transforming, and organizing raw data to make it suitable for analysis and machine learning, including tasks like data cleaning, feature scaling, and data splitting.
A phenomenon in machine learning where a model performs well on training data but fails to generalize to new, unseen data, often caused by excessive complexity or lack of regularization.
A phenomenon in machine learning where a model is too simple or lacks the capacity to capture the underlying patterns in the data, resulting in poor performance on both training and test data.
A technique used to assess the performance and generalization ability of a machine learning model by splitting the data into multiple subsets for training and evaluation.
A parameter that is not learned from the data but set before the learning process, influencing the behavior and performance of a machine learning algorithm, such as learning rate, regularization strength, or number of hidden units.
An optimization algorithm used to minimize the loss or error of a machine learning model by iteratively adjusting the model's parameters in the direction of steepest descent.
An algorithm used to train artificial neural networks by computing the gradients of the loss function with respect to the network's weights, enabling efficient learning through error propagation.
A mathematical function applied to the output of a neuron in an artificial neural network, introducing non-linearity and enabling the network to learn complex patterns and representations.
A mathematical function that measures the discrepancy between the predicted output of a machine learning model and the true output, guiding the learning process and optimization.
A complete pass through the entire training dataset during the training of a machine learning model, consisting of multiple iterations or updates of the model's parameters.
The number of training examples used in a single iteration or update of a machine learning model, affecting the speed of training and memory requirements.