Glossary of AI Terms¶
To aid in your understanding of artificial intelligence (AI) concepts discussed throughout this book, here are key terms and definitions:
Artificial Intelligence (AI): The simulation of human intelligence processes by machines, especially computer systems, to perform tasks that typically require human intelligence.
Machine Learning: A subset of AI that enables systems to learn and improve from experience without being explicitly programmed, often using statistical techniques to identify patterns in data.
Deep Learning: A branch of machine learning that uses neural networks with many layers (deep neural networks) to learn from large amounts of data, achieving state-of-the-art accuracy in tasks such as image and speech recognition.
Neural Networks: Computational models inspired by the human brain’s structure and function, capable of learning to perform tasks by processing data through interconnected layers of nodes (neurons).
Natural Language Processing (NLP): AI techniques used to enable computers to understand, interpret, and generate human language, including text and speech.
Computer Vision: The field of AI that enables computers to interpret and understand visual information from the world, often used for tasks such as image and video recognition.
Algorithm: A set of rules or instructions designed to perform a specific task or solve a particular problem, often used in AI to process data and make decisions.
Big Data: Large and complex datasets that are difficult to process using traditional data-processing applications, often used in AI to train machine learning models.
Generative Adversarial Networks (GANs): A class of machine learning frameworks where two neural networks compete against each other, used to generate new content (e.g., images, text) that resembles the training data.
Ethical AI: The practice of designing and deploying AI systems that uphold ethical principles, including fairness, transparency, accountability, and respect for human rights.
AI Ethics: The study and application of ethical principles and guidelines governing the development and deployment of AI technologies, addressing societal impacts, bias mitigation, and responsible AI use.
Bias in AI: Systematic errors or prejudices in AI systems that can result in unfair outcomes, often related to data biases, algorithmic biases, or unintended consequences in decision-making.
Autonomous Systems: AI-driven systems capable of performing tasks or making decisions without direct human intervention, ranging from autonomous vehicles to robotic process automation.
Quantum Computing: A computing paradigm that uses quantum-mechanical phenomena, such as superposition and entanglement, to perform computations exponentially faster than classical computers, with potential applications in AI optimization and simulation.
AI Governance: Policies, regulations, and frameworks that guide the development, deployment, and ethical use of AI technologies to ensure their responsible and beneficial integration into society.
This glossary provides foundational terms to support your comprehension and engagement with AI concepts discussed throughout this book. Continuously expanding your knowledge of these terms and staying informed about AI advancements will empower you to navigate and contribute to the evolving landscape of artificial intelligence effectively.
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