AI & Technical Terms Glossary
Find definitions for artificial intelligence and technical terms used across our website.
Last updated: June 15, 2024
A
- AGI (Artificial General Intelligence)
- Hypothetical AI with the ability to understand, learn, and apply knowledge across a wide range of tasks at a level equal to or exceeding human capabilities.
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- AI (Artificial Intelligence)
- Computer systems that can perform tasks requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.
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- AI Alignment
- The challenge of ensuring AI systems act in accordance with human intentions, values, and preferences, even as they become increasingly autonomous.
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- AI Applications
- Practical implementations of AI technology to solve specific business or consumer problems.
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- AI Ethics
- Field concerned with ensuring AI systems are designed and deployed in ways that align with human values and ethical principles.
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- AI Governance
- Structures, processes, and policies organizations implement to manage AI development and deployment responsibly.
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- AI Infrastructure
- Hardware, software, and networking components that support the development, training, and deployment of AI systems at scale.
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- AI Reasoning
- Capability of AI systems to process information logically, draw inferences, and reach conclusions based on available data and knowledge.
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- AI Safety
- Research field focused on ensuring advanced AI systems remain beneficial, controllable, and aligned with human values as they become more capable.
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- Attention Mechanism
- Neural network component that allows models to focus on different parts of the input when producing outputs, crucial for understanding context in language.
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B
- Business Automation
- Use of technology to execute recurring tasks or processes where manual effort can be replaced with automated systems.
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C
- Chain of Thought
- Prompting technique that encourages AI models to show their reasoning process step-by-step, leading to more accurate and explainable results.
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- Clustering
- Unsupervised learning technique that groups similar data points based on their features without prior labeling.
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- Computer Vision
- Field of AI focused on enabling computers to interpret and understand visual information from the world, such as images and videos.
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D
- Data Science
- Interdisciplinary field using scientific methods, processes, algorithms and systems to extract knowledge from structured and unstructured data.
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- Deep Learning
- A machine learning technique based on artificial neural networks with multiple layers that progressively extract higher-level features from raw input.
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- Diffusion Models
- Type of generative AI model that creates images by gradually removing noise from random data, powering tools like DALL-E, Midjourney, and Stable Diffusion.
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- Distributed Computing
- System where components located on networked computers communicate and coordinate actions to appear as a single coherent system, often used in large-scale AI.
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E
- Embeddings
- Dense numerical vector representations that capture semantic meaning of text, images, or other data, allowing machines to understand similarities between concepts.
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F
- Federated Learning
- ML approach that trains algorithms across multiple devices or servers holding local data samples, without exchanging the data itself, enhancing privacy.
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- Fine-tuning
- Process of further training a pre-trained model on a specific dataset to adapt it for particular tasks or domains, improving performance for specialized applications.
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G
- Generative AI
- AI systems that can generate new content including text, images, audio, code, or other media in response to prompts.
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I
- Image Recognition
- AI technology that identifies objects, people, scenes, and actions in images, enabling applications from facial recognition to medical diagnostics.
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K
- Knowledge Base
- Organized repository of information that AI systems can access to retrieve facts, rules, and domain-specific knowledge when generating responses.
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L
- LLM (Large Language Model)
- Advanced AI systems trained on vast text datasets that can understand and generate human-like text, code, and other content.
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M
- Machine Learning
- A subset of AI focused on building systems that learn from data rather than following explicit programming instructions.
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- Multimodal AI
- AI systems capable of processing and generating multiple types of data (text, images, audio, video) simultaneously, understanding connections between different modalities.
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N
- Narrow AI
- AI systems designed to perform specific tasks within a limited domain, as opposed to general intelligence across multiple domains.
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- Neural Networks
- Computing systems inspired by the biological neural networks of animal brains, designed to recognize patterns and solve complex problems.
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- NLP (Natural Language Processing)
- Technology that enables computers to understand, interpret, and generate human language in useful ways.
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P
- Pre-training
- Initial phase of model training on large general datasets before fine-tuning, allowing models to learn broad language patterns and knowledge.
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- Privacy-Preserving AI
- AI technologies and methods designed to protect personal data while still delivering useful insights and functionality.
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- Process Efficiency
- Measure of how effectively resources are used to achieve desired outcomes in business operations.
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- Prompt Engineering
- Practice of designing and optimizing input prompts to AI systems to elicit desired outputs, improving the quality and relevance of generated content.
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R
- RAG (Retrieval-Augmented Generation)
- A technique that enhances LLM outputs by retrieving relevant information from external knowledge sources before generating responses, improving accuracy and reducing hallucinations.
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- Responsible AI
- Framework for developing AI systems that are ethical, transparent, fair, and accountable throughout their lifecycle.
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S
- Semantic Search
- Search technique that understands the intent and contextual meaning of queries rather than just matching keywords, delivering more relevant results.
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- Sentiment Analysis
- Use of NLP to identify and extract subjective information from text, determining whether the writer's attitude is positive, negative, or neutral.
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- Superintelligence
- Theoretical form of AI that would surpass human cognitive capabilities across virtually all domains, potentially leading to rapid self-improvement.
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- Supervised Learning
- ML technique where the model is trained on labeled data, learning to map inputs to outputs based on example input-output pairs.
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T
- Text Analytics
- Process of deriving meaningful information from text data using various techniques including statistical analysis and ML.
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- Training Data
- The dataset used to train an ML model, containing examples the system learns from to make predictions or decisions.
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- Transfer Learning
- ML technique where knowledge gained from training on one task is applied to a different but related task, reducing the need for extensive new training data.
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- Transformer
- Neural network architecture that uses self-attention mechanisms to process sequential data, forming the foundation of modern LLMs like GPT and BERT.
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U
- Unsupervised Learning
- ML technique where the model learns patterns from unlabeled data without explicit guidance or target outcomes.
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V
- Vector Database
- Specialized database that stores data as high-dimensional vectors, enabling efficient similarity searches crucial for RAG systems and other AI applications.
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W
- Workflow Optimization
- Process of improving efficiency by streamlining operations, reducing bottlenecks, and automating repetitive tasks.
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