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We are updating the Artificial Intelligence AI Terms and Concepts, Check again to see more.

Artificial Intelligence (AI):

The simulation of human intelligence in machines to perform tasks, make decisions, and learn from data.

Automation :

The process of using AI systems or machines to perform tasks or processes without human intervention.

Adversarial Examples :

Inputs or data specifically designed to deceive or mislead AI systems, highlighting vulnerabilities and potential weaknesses.

Augmented Intelligence :

The use of AI systems to enhance human intelligence, capabilities, and decision-making rather than replacing humans.

Artificial Neural Network (ANN) :

A computational model inspired by the structure and function of the human brain, used for pattern recognition and learning in AI.

AI Ethics :

The study and consideration of moral and ethical principles surrounding the development, deployment, and use of AI technologies.

AI Governance :

The policies, regulations, and frameworks that govern the development, use, and impact of AI systems.

AI Model :

A mathematical representation or algorithm designed to perform specific tasks or make predictions based on input data.

AI Training :

The process of feeding data into an AI system and adjusting its parameters or weights to improve its performance and accuracy.

Artificial General Intelligence (AGI) :

AI systems capable of performing any intellectual task that a human being can do.

Artificial Narrow Intelligence (ANI) :

AI systems designed for specific tasks or domains, lacking general intelligence.

Association Rule Learning :

A technique in AI and data mining that discovers interesting relationships or associations among variables in large datasets.

Anomaly Detection :

The identification of patterns or data points that deviate significantly from the norm or expected behavior.

Automated Reasoning :

The process of using logic and inference rules to derive new information or make deductions automatically.

Ambient Intelligence :

The integration of AI systems into the environment to create smart and responsive spaces that adapt to human needs.

Agent :

An entity, such as a software program or robot, that can perceive its environment and take actions to achieve goals.

Artificial Life (ALife) :

The study and simulation of life-like behaviors and processes in AI systems, often inspired by biological systems.

AI Chip :

A specialized hardware component designed to accelerate AI computations, such as training or inference tasks.

Active Learning :

A machine learning approach where an AI system interacts with a human expert to acquire labeled data and improve its performance.

Ambient Intelligence :

The integration of AI systems into the environment to create smart and responsive spaces that adapt to human needs.

Association Rule Learning :

A technique in AI and data mining that discovers interesting relationships or associations among variables in large datasets.

Accuracy :

Refers to the correctness or precision of a model's predictions compared to the actual or expected outcomes.

Activation :

Mathematical functions applied to the output of a neuron in a neural network, introducing non-linearities and aiding in learning complex patterns.

Adversarial Machine Learning :

Focuses on studying and defending against attacks on machine learning models, aiming to develop robust models that can withstand intentional manipulation.

Anchor box :

Pre-defined bounding boxes of different scales and aspect ratios used as reference points in object detection algorithms.

Annotations :

Additional information or labels associated with data points, often used in supervised machine learning tasks.

Annotations Format :

Specific structures or organizations used to represent and store annotations, such as Pascal VOC format, COCO format, or YOLO format.

Annotations Group :

A collection or set of annotations grouped together based on a specific criterion or purpose.

Application Programming Interface (API) :

Set of rules and protocols that enable different software applications to communicate and interact with each other.

Architecture :

Design and structure of a machine learning model or network, including the arrangement of layers, connectivity patterns, and parameters.

Artificial General Intelligence (AGI) :

Refers to highly autonomous systems or machines possessing human-level intelligence and capability to perform intellectual tasks.

Augmented Reality :

Technology that overlays virtual information or digital content onto the real-world environment, enhancing user perception and interaction.

Automation Bias :

Tendency to attribute excessive trust or reliance on automated systems, potentially impacting decision-making processes.

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We are updating the Artificial Intelligence AI Terms and Concepts, Check again to see more.

Artificial Intelligence (AI):

The simulation of human intelligence in machines to perform tasks, make decisions, and learn from data.

Automation :

The process of using AI systems or machines to perform tasks or processes without human intervention.

Adversarial Examples :

Inputs or data specifically designed to deceive or mislead AI systems, highlighting vulnerabilities and potential weaknesses.

Augmented Intelligence :

The use of AI systems to enhance human intelligence, capabilities, and decision-making rather than replacing humans.

Artificial Neural Network (ANN) :

A computational model inspired by the structure and function of the human brain, used for pattern recognition and learning in AI.

AI Ethics :

The study and consideration of moral and ethical principles surrounding the development, deployment, and use of AI technologies.

AI Governance :

The policies, regulations, and frameworks that govern the development, use, and impact of AI systems.

AI Model :

A mathematical representation or algorithm designed to perform specific tasks or make predictions based on input data.

AI Training :

The process of feeding data into an AI system and adjusting its parameters or weights to improve its performance and accuracy.

Artificial General Intelligence (AGI) :

AI systems capable of performing any intellectual task that a human being can do.

Artificial Narrow Intelligence (ANI) :

AI systems designed for specific tasks or domains, lacking general intelligence.

Association Rule Learning :

A technique in AI and data mining that discovers interesting relationships or associations among variables in large datasets.

Anomaly Detection :

The identification of patterns or data points that deviate significantly from the norm or expected behavior.

Automated Reasoning :

The process of using logic and inference rules to derive new information or make deductions automatically.

Ambient Intelligence :

The integration of AI systems into the environment to create smart and responsive spaces that adapt to human needs.

Agent :

An entity, such as a software program or robot, that can perceive its environment and take actions to achieve goals.

Artificial Life (ALife) :

The study and simulation of life-like behaviors and processes in AI systems, often inspired by biological systems.

AI Chip :

A specialized hardware component designed to accelerate AI computations, such as training or inference tasks.

Active Learning :

A machine learning approach where an AI system interacts with a human expert to acquire labeled data and improve its performance.

Ambient Intelligence :

The integration of AI systems into the environment to create smart and responsive spaces that adapt to human needs.

Association Rule Learning :

A technique in AI and data mining that discovers interesting relationships or associations among variables in large datasets.

Accuracy :

Refers to the correctness or precision of a model's predictions compared to the actual or expected outcomes.

Activation :

Mathematical functions applied to the output of a neuron in a neural network, introducing non-linearities and aiding in learning complex patterns.

Adversarial Machine Learning :

Focuses on studying and defending against attacks on machine learning models, aiming to develop robust models that can withstand intentional manipulation.

Anchor box :

Pre-defined bounding boxes of different scales and aspect ratios used as reference points in object detection algorithms.

Annotations :

Additional information or labels associated with data points, often used in supervised machine learning tasks.

Annotations Format :

Specific structures or organizations used to represent and store annotations, such as Pascal VOC format, COCO format, or YOLO format.

Annotations Group :

A collection or set of annotations grouped together based on a specific criterion or purpose.

Application Programming Interface (API) :

Set of rules and protocols that enable different software applications to communicate and interact with each other.

Architecture :

Design and structure of a machine learning model or network, including the arrangement of layers, connectivity patterns, and parameters.

Artificial General Intelligence (AGI) :

Refers to highly autonomous systems or machines possessing human-level intelligence and capability to perform intellectual tasks.

Augmented Reality :

Technology that overlays virtual information or digital content onto the real-world environment, enhancing user perception and interaction.

Automation Bias :

Tendency to attribute excessive trust or reliance on automated systems, potentially impacting decision-making processes.

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