Core Concepts

AI (Artificial Intelligence)

The simulation of human intelligence processes by machines, particularly computer systems.
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Algorithm

A set of mathematical instructions or rules that a computer follows to perform a specific task efficiently.
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Augmented Intelligence

Enhancing human decision-making with AI capabilities, focusing on collaboration between humans and AI.
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Autonomous

Machines or systems capable of performing tasks and making decisions without human intervention.
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Bias

Preconceived notions or predispositions in AI models that can affect decision-making and fairness.
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Cognitive Computing

AI systems designed to mimic human brain functioning, aiming for natural, human-like interaction.
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Computational Learning Theory

A branch of artificial intelligence focused on understanding the algorithms that drive learning.
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Deep Learning

A subset of machine learning involving neural networks with many layers to analyze data.
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Entities

Specific, identifiable elements in text, such as names, places, dates, often extracted by AI.
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Explainable AI (XAI)

AI systems designed to provide insights into their decision-making processes for transparency.
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General AI

Artificial intelligence that exhibits cognitive functions across a wide range of tasks and domains.
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Generative AI

AI systems capable of generating new, original content or data that mimics real-world examples.
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Hyperparameter

A parameter whose value is set before the learning process begins, influencing the training phase.
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Intent

The underlying purpose or goal that a user aims to achieve through a query or statement.
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Latent Variables

Hidden or unobservable variables inferred from observable data in machine learning models.
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Machine Intelligence

Broad term encompassing the capabilities of machines to learn from data and perform tasks.
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Machine Learning

The science of getting computers to act without being explicitly programmed, through learning.
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Multi-modal AI

AI systems that can process and interpret multiple types of data, such as text, images, and sound.
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Natural Language Generation (NLG)

Generating coherent and contextually relevant text from structured data using AI.
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Natural Language Processing (NLP)

The field of AI focused on the interaction between computers and humans through natural language.
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Natural Language Understanding (NLU)

The ability of AI to understand and interpret human language as it is spoken or written.
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Overfitting

A modeling error in machine learning where a model learns the detail and noise in the training data too well.
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Pattern Recognition

The automated recognition of patterns and regularities in data using machine learning algorithms.
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Reinforcement Learning

A type of machine learning where an agent learns to make decisions by taking actions in an environment to achieve rewards.
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Strong AI

AI with the ability to understand, learn, and apply knowledge in ways indistinguishable from human intelligence.
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Supervised Learning

A machine learning approach where models are trained on labeled data, learning to predict outcomes from inputs.
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Token

The smallest unit of processing in NLP, which could be a word, part of a word, or a character, depending on the model.
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Turing Test

A test of a machine's ability to exhibit intelligent behavior indistinguishable from that of a human.
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Unsupervised Learning

A type of machine learning where models learn patterns from unlabeled data, without explicit instructions.
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Variance

In machine learning, the amount by which the model's predictions vary from the average prediction, reflecting sensitivity to training data.
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Weak AI

AI designed and trained for a specific task, lacking the general cognitive abilities of human intelligence.
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Zone of Proximal Development (ZPD)

A concept from educational psychology applied to AI, referring to tasks an AI can perform with guidance but not independently.