Glossary

AI Agent Definition An autonomous system that can perceive its environment, reason, and take actions to achieve specific goals. Detailed Explanation In the world of Agents, AI Agent is defined as an autonomous system that can perceive its environment, reason, and take actions to achieve specific goals. Professionals in the field often use AI Agent in conjunction with other technologies to build robust solutions. Why AI Agent MattersFor developers and data scientists, mastering AI Agent unlocks new capabilities in model design. It is particularly relevant for optimizing performance and reducing costs. ...

Alignment Definition The field of AI safety research aiming to ensure AI systems’ goals and behaviors match human values. Detailed Explanation Understanding Alignment is crucial for mastering modern AI. It describes the field of ai safety research aiming to ensure ai systems' goals and behaviors match human values. The significance of Alignment cannot be overstated. As AI systems become more complex, mechanisms like this ensure scalability and accuracy. Applications of AlignmentReal-world applications include advanced natural language processing, computer vision systems, and automated decision-making frameworks. ...

Artificial General Intelligence Definition Hypothetical type of intelligent agent that can learn any intellectual task that human beings or animals can. Detailed Explanation Understanding Artificial General Intelligence is crucial for mastering modern AI. It describes hypothetical type of intelligent agent that can learn any intellectual task that human beings or animals can. The significance of Artificial General Intelligence cannot be overstated. As AI systems become more complex, mechanisms like this ensure scalability and accuracy. ...

Attention Mechanism Definition A technique that allows neural networks to focus on specific parts of the input data while processing it. Detailed Explanation In the world of Architecture, Attention Mechanism is defined as a technique that allows neural networks to focus on specific parts of the input data while processing it. At its core, Attention Mechanism solves a specific problem in the AI landscape. Unlike traditional approaches, it leverages advanced algorithms to process data more efficiently. ...

Bias Definition Systematic errors in AI output that result in unfair outcomes, often reflecting prejudices in training data. Detailed Explanation Bias is a fundamental concept in Ethics that refers to systematic errors in ai output that result in unfair outcomes, often reflecting prejudices in training data. Professionals in the field often use Bias in conjunction with other technologies to build robust solutions. Why Bias MattersFor developers and data scientists, mastering Bias unlocks new capabilities in model design. It is particularly relevant for optimizing performance and reducing costs. ...

Chain-of-Thought Definition A prompting technique that encourages the model to detail its reasoning process before giving a final answer. Detailed Explanation In the world of Nlp, Chain-of-Thought is defined as a prompting technique that encourages the model to detail its reasoning process before giving a final answer. At its core, Chain-of-Thought solves a specific problem in the AI landscape. Unlike traditional approaches, it leverages advanced algorithms to process data more efficiently. ...

Computer Vision Definition A field of artificial intelligence that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs. Last updated: February 2026

Context Window Definition The amount of text an LLM can process at one time, limiting its memory and ability to reference previous parts of a conversation. Detailed Explanation In the world of Nlp, Context Window is defined as the amount of text an llm can process at one time, limiting its memory and ability to reference previous parts of a conversation. Professionals in the field often use Context Window in conjunction with other technologies to build robust solutions. ...

Deep Learning Definition Part of machine learning methods based on artificial neural networks with representation learning. Detailed Explanation Deep Learning is a fundamental concept in Ai Core that refers to part of machine learning methods based on artificial neural networks with representation learning. The significance of Deep Learning cannot be overstated. As AI systems become more complex, mechanisms like this ensure scalability and accuracy. Applications of Deep LearningReal-world applications include advanced natural language processing, computer vision systems, and automated decision-making frameworks. ...

Embedding Definition A vector representation of data (like words) where similar meanings are mapped closer together in vector space. Detailed Explanation In the world of Infrastructure, Embedding is defined as a vector representation of data (like words) where similar meanings are mapped closer together in vector space. At its core, Embedding solves a specific problem in the AI landscape. Unlike traditional approaches, it leverages advanced algorithms to process data more efficiently. ...

Epoch Definition One complete pass of the training dataset through the algorithm. Detailed Explanation In the world of Training, Epoch is defined as one complete pass of the training dataset through the algorithm. Professionals in the field often use Epoch in conjunction with other technologies to build robust solutions. Applications of EpochReal-world applications include advanced natural language processing, computer vision systems, and automated decision-making frameworks. Last updated: February 2026

Explainability Definition The extent to which the internal mechanics of a machine or deep learning system can be explained to human experts. Detailed Explanation Understanding Explainability is crucial for mastering modern AI. It describes the extent to which the internal mechanics of a machine or deep learning system can be explained to human experts. The significance of Explainability cannot be overstated. As AI systems become more complex, mechanisms like this ensure scalability and accuracy. ...

Few-Shot Learning Definition Providing a model with a small number of examples to help it understand the task. Detailed Explanation In the world of Training, Few-Shot Learning is defined as providing a model with a small number of examples to help it understand the task. At its core, Few-Shot Learning solves a specific problem in the AI landscape. Unlike traditional approaches, it leverages advanced algorithms to process data more efficiently. ...

Fine-tuning Definition The process of taking a pre-trained model and training it further on a specific dataset to improve performance on a particular task. Detailed Explanation Fine-tuning is a fundamental concept in Training that refers to the process of taking a pre-trained model and training it further on a specific dataset to improve performance on a particular task. The significance of Fine-tuning cannot be overstated. As AI systems become more complex, mechanisms like this ensure scalability and accuracy. ...

Generative Adversarial Network Definition A class of machine learning frameworks designed by Ian Goodfellow and his colleagues in 2014. Last updated: February 2026

GPU Definition Graphics Processing Unit, a specialized electronic circuit designed to manipulate and alter memory to accelerate the creation of images and AI calculations. Detailed Explanation Understanding GPU is crucial for mastering modern AI. It describes graphics processing unit, a specialized electronic circuit designed to manipulate and alter memory to accelerate the creation of images and ai calculations. Professionals in the field often use GPU in conjunction with other technologies to build robust solutions. ...

H100 Definition Nvidia’s flagship GPU architecture designed specifically for massive AI workloads. Detailed Explanation In the world of Hardware, H100 is defined as nvidia's flagship gpu architecture designed specifically for massive ai workloads. At its core, H100 solves a specific problem in the AI landscape. Unlike traditional approaches, it leverages advanced algorithms to process data more efficiently. Applications of H100Real-world applications include advanced natural language processing, computer vision systems, and automated decision-making frameworks. ...

Hallucination Definition A phenomenon where an AI model generates incorrect or nonsensical information confidently. Detailed Explanation Understanding Hallucination is crucial for mastering modern AI. It describes a phenomenon where an ai model generates incorrect or nonsensical information confidently. Professionals in the field often use Hallucination in conjunction with other technologies to build robust solutions. Why Hallucination MattersFor developers and data scientists, mastering Hallucination unlocks new capabilities in model design. It is particularly relevant for optimizing performance and reducing costs. ...

Inference Definition The process of running a trained model to make predictions on new data. Detailed Explanation In the world of Infrastructure, Inference is defined as the process of running a trained model to make predictions on new data. Professionals in the field often use Inference in conjunction with other technologies to build robust solutions. Applications of InferenceReal-world applications include advanced natural language processing, computer vision systems, and automated decision-making frameworks. ...

Large Language Model Definition A type of AI algorithm that uses deep learning techniques and massively large data sets to understand, summarize, generate and predict new content. Detailed Explanation Understanding Large Language Model is crucial for mastering modern AI. It describes a type of ai algorithm that uses deep learning techniques and massively large data sets to understand, summarize, generate and predict new content. The significance of Large Language Model cannot be overstated. As AI systems become more complex, mechanisms like this ensure scalability and accuracy. ...