Hadoop: An open-source framework for distributed storage and processing of large datasets, commonly used in big data analytics and machine learning.

Hardware Acceleration: The use of specialized hardware, such as GPUs (Graphics Processing Units) or TPUs (Tensor Processing Units), to speed up AI and machine learning tasks.

Healthcare AI: The application of AI in healthcare settings to improve diagnosis, treatment, patient care, and administrative processes.

Heterogeneous Data: Data that comes from diverse sources and formats, often requiring specialized AI techniques for processing and analysis.

Heuristic Algorithms: Problem-solving algorithms that use rules of thumb or approximation techniques to find solutions when an exhaustive search is impractical.

Heuristic Search: Refers to the problem-solving technique in AI that uses practical experience and rules of thumb to find solutions, often employed when a precise algorithmic solution is not feasible.

High-Performance Computing (HPC): The use of powerful computing systems, often with parallel processing capabilities, to perform complex AI and scientific calculations.

HMM (Hidden Markov Model): a statistical model used in AI to describe sequential data, where it assumes that there are underlying hidden states responsible for generating observable data. HMMs are commonly used in applications like speech recognition and part-of-speech tagging.

Human-AI Collaboration: The interaction and cooperation between humans and AI systems to enhance decision-making, creativity, and problem-solving in various domains.

Human-AI Ethics: The study and consideration of ethical issues related to the development, deployment, and impact of AI technologies on society and individuals.

Humanoid AI Artists: AI systems that create art, music, or other creative works in a manner that emulates human artistic expression.

Human-Centered AI: The design and development of AI systems with a primary focus on enhancing and benefiting human experiences and capabilities.

Human-in-the-Loop (HITL): An AI system that involves human intervention or oversight in decision-making or data processing to ensure accuracy, ethics, and safety. Commonly used in applications like content moderation, data labeling, quality control, and decision refinement.

Human-level AI (HLAI): Used to describe AI systems that have human-like reasoning, learning, perception, and problem-solving abilities.

Human-Machine Collaboration: The interaction and cooperation between humans and AI systems to achieve tasks that neither can perform optimally alone, leveraging the strengths of both.

Human Pose Estimation: Human pose estimation in AI involves the process of determining the spatial positions of key body joints or landmarks in images or videos. It’s used for tasks like tracking human movement, gesture recognition, and human-computer interaction.

Humanoid Robots: Robots designed to resemble and mimic human form and movements, often used in research, entertainment, and assistance applications. These are ai-powered to perform tasks such as speech recognition, facial recognition, and ai-based path planning.

Human-Robot Interaction (HRI): The study and development of interfaces and interactions between humans and robots, focusing on making robots more intuitive and user-friendly in various settings, including healthcare and manufacturing.

Hybrid AI: AI systems that combine multiple AI techniques, such as rule-based systems and machine learning, to achieve improved performance and flexibility.

Hybrid Recommendation Systems: Recommendation systems that combine collaborative filtering and content-based filtering techniques to provide more accurate and personalized recommendations.

Hyperautomation: The use of AI and automation technologies to enhance and streamline processes across various business operations, leading to increased efficiency and productivity.

Hyperparameter: Refers to the parameters set before the machine learning algorithm starts training, influencing its learning process. Examples include learning rate, batch size, and number of hidden units in a neural network.

Hyperparameter Tuning: The process of optimizing the hyperparameters of a machine learning model to improve its performance on a specific task.

Hypervisor: Software that creates and manages virtual machines on a physical host. It allows multiple operating systems to run on a single physical machine simultaneously.

Hypothesis Testing: Statistical methods used in AI and data analysis to assess the significance of observed results and make inferences about data populations.


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