Image Classification: An AI task that involves assigning a label or category to an input image, commonly used in applications like object recognition or medical diagnosis.

Image Generation: The process of using AI models, such as GANs or VAEs, to create new images based on learned patterns from existing data.

Image Recognition: The process of teaching computers to identify and interpret the content of images or visual data. AI algorithms analyze patterns, shapes, and features to recognize objects, scenes, or even people within images.

Image Segmentation: An AI technique that divides an image into meaningful segments or regions, aiding in object detection, scene understanding, and medical imaging analysis.

Imitation Learning: A machine learning approach where an agent learns by imitating the actions of a demonstrator, often used in robotics and autonomous vehicle control.

Incremental Learning: The ability of AI models to adapt and learn from new data over time without forgetting previously acquired knowledge.

Inductive Logic Programming: A subfield of AI that combines logic programming with machine learning to induce logical rules or knowledge from data.

Inference: Refers to the process of using a trained model to make predictions or decisions based on new or unseen data. It involves applying the learned knowledge to new situations.

Inference Engine: The component of an AI system responsible for applying trained models to new data, and making predictions or decisions based on the learned patterns.

Information Retrieval: The practice of finding and retrieving relevant information from large datasets or databases using AI techniques, often used in search engines or recommendation systems.

In-Memory Computing: A computing approach that performs computations directly within memory, enhancing the speed and efficiency of AI and data processing tasks.

Instance-Based Learning: A machine learning paradigm where predictions are made based on the similarity between a new input and instances or examples from the training data.

Intelligent Agents: An entity, often a software program, that can perceive its environment, process information, and take actions to achieve specific goals. Intelligent agents are a fundamental concept in AI.

Intelligent Automation: The use of AI and robotic process automation to perform repetitive tasks, optimize workflows, and enhance business processes.

Intelligent Chatbot: A chatbot powered by AI that can understand natural language and engage in human-like conversations. Intelligent chatbots can provide information, answer questions, and perform tasks for users.

Intelligent Software as a Service (iSaaS): A form of cloud computing where AI technologies are integrated into Software as a Service (SaaS) applications. This enables the software to provide advanced functionalities, predictions, and insights based on AI analysis.

Intelligent Tutoring Systems: Educational AI systems that provide personalized instruction and feedback to learners, adapting to individual needs and progress.

Interactive Machine Learning: A collaborative approach where humans and machines work together to improve AI models, often used in tasks like data labeling and model refinement. 

Invariance in AI: The property of AI models to produce consistent results despite variations in input, critical for tasks like object recognition in different settings.

Internet of Things (IoT): A network of interconnected physical devices embedded with sensors, software, and connectivity, creating opportunities for AI-enabled data analysis and automation.

Interpretability in AI: The study and development of techniques to make AI models and their decisions more transparent and understandable to humans.

Inverse Reinforcement Learning: A machine learning technique in AI where an agent learns the underlying reward function from observed behaviors, allowing for human-like decision-making in uncertain environments. Commonly used in imitation learning and robotics.

IoT Analytics: The use of AI and data analysis to gain insights from data collected by Internet of Things (IoT) devices, enabling automation and optimization of various processes.

Iterative Deep Learning: A training approach in deep learning where AI models are refined through multiple iterations to enhance their capabilities and reduce errors.

Iterative Training: The process of repeatedly training an AI model with updated data or parameters to improve its performance and accuracy over time.


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