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31 of 31 terms
Active learning is a machine learning method where models selectively choose data to improve learning efficiency.
Analytical AI analyzes large datasets to discover patterns, generate insights, and support data-driven decision-making.
Artificial intelligence (AI) allows machines to learn from information, identify trends, make decisions, and complete activities that would typically need human intelligence.
Artificial Life studies life-like behaviors using computer simulations, robotics, and AI-based evolutionary systems.
Autonomous decision system uses AI to analyze data and make decisions without human intervention.
Batch processing handles large data sets by processing them in groups at scheduled times, improving efficiency, scalability, and system performance.
Batch size defines how many samples a model processes per iteration, affecting training speed, memory usage, and overall performance.
Bayesian inference combines prior beliefs with new evidence to calculate updated probabilities, improving predictions in machine learning and statistical analysis.
Big Data refers to large, complex datasets processed using advanced tools to extract insights, improve decisions, and drive business innovation.
Backpropagation is a neural network training algorithm that reduces errors by adjusting weights using gradients to improve model accuracy.
A chatbot is software that simulates human conversation automatically.
Cognitive computing uses AI to mimic human thinking, analyze data, learn patterns, and deliver intelligent insights for improved decision-making processes.
A CNN is a deep learning model that analyzes images by detecting patterns, features, and objects through layered neural network architecture.
Data refers to raw facts, figures, observations, or information collected for analysis and decision-making. It can be in the form of text, numbers, images, audio, or video.
Data mining is the process of analyzing large datasets to discover patterns, trends, and insights that help organizations improve decision-making, predict outcomes, and optimize business performance.
A dataset is a collection of data used to train, test, and improve AI and machine learning models. It may include text, images, audio, video, or numerical information.
Edge AI enables artificial intelligence models to run directly on devices, allowing faster processing, real-time decision-making, improved privacy, and reduced reliance on cloud computing infrastructure.
Embeddings are numerical vector representations of data that help AI models understand meaning, relationships, and similarities, enabling semantic search, recommendations, and advanced language processing.
Explainable AI (XAI) makes AI decisions transparent and understandable by showing how models reach conclusions, helping improve trust, accountability, fairness, and regulatory compliance.
Feature Engineering is the process of transforming raw data into meaningful features that improve machine learning model accuracy, efficiency, and predictive performance across various AI applications
Fine-tuning is the process of adapting a pre-trained AI model using specialized data to improve accuracy, relevance, and performance for specific tasks or industries.
Fuzzy Logic is an AI technique that handles uncertainty by using degrees of truth instead of binary values, enabling more flexible and human-like decision-making.
Game Theory in AI applies strategic decision-making principles to help intelligent systems predict behaviors, optimize actions, and achieve goals in competitive or cooperative environments.
Generative AI is an artificial intelligence technology that creates original content, including text, images, audio, video, and code, using patterns learned from large datasets.
Geospatial AI combines artificial intelligence and geographic data to analyze locations, identify spatial patterns, automate mapping, and generate insights for better decision-making.
AI hallucination occurs when an artificial intelligence system generates inaccurate, fabricated, or misleading information that appears credible despite lacking factual support or real-world evidence.
Hard Voting is an ensemble learning method that combines predictions from multiple classifiers and selects the final result using a majority-vote decision process.
Holistic AI is a comprehensive approach to artificial intelligence that balances performance, ethics, governance, transparency, security, and business value throughout the AI lifecycle.
Industrial AI applies artificial intelligence to industrial operations, enabling predictive maintenance, process optimization, automation, and smarter decision-making to improve efficiency and productivity.
Internet of Things (IoT) is a network of connected devices that collect, exchange, and analyze data to enable automation, monitoring, and smarter decision-making.
Interpretable AI focuses on creating transparent AI models whose decision-making processes are easy for humans to understand, verify, and trust.
Artificial Intelligence is the simulation of human intelligence in machines programmed to think and learn.
AI is the broader concept of intelligent machines, while Machine Learning is a subset that enables systems to learn from data.