AI Glossary
Plain-language explanations of essential AI and machine-learning concepts.
171 terms
An experimental method of exposing two or more versions simultaneously to statistically compare which one is more effective.
Refers to artificial intelligence that can perform intellectual tasks at or above the human level in various domains without explicit human intervention. Unlike conventional AI, which is limited to specific tasks, it refers to systems with general-purpose capabilities that solve complex problems and create new knowledge through self-learning and reasoning.
An intelligent integrated development environment where AI agents understand the context of the entire codebase to autonomously write code, modify multiple files, execute terminal commands, and fix errors.
A collection of software libraries, API clients, documentation, and development tools that support the easy integration of AI model capabilities into applications.
An AI-powered sales solution that autonomously performs top-of-funnel sales tasks, including lead generation, hyper-personalized messaging using real-time data, and meeting scheduling.
A dedicated hardware designed to quickly process large-scale matrix operations of artificial intelligence models, focusing more on parallel processing and power efficiency than a CPU.
An intelligent system that autonomously sets goals and uses tools (APIs, the web, etc.) to perform complex tasks. Moving beyond simply generating answers, it repeats a reasoning, planning, and execution loop to solve problems on its own.
A technology that centrally manages the entire workflow by defining the execution sequence of multiple AI models, data sources, and tools, and automating data flows to achieve complex business goals.
An intelligent development assistance tool based on Large Language Models (LLMs) that helps increase software development productivity by supporting real-time code auto-completion, refactoring, bug fixes, and unit test generation.
A technological methodology that combines artificial intelligence and big data analytics to manage the complexity of IT infrastructure and intelligentize overall operations, from system monitoring to anomaly detection and automated response.
A standard interface for exchanging data and functionality between software applications. In AI, it serves as a core tool to connect cloud-hosted Large Language Models (LLMs) to external apps, enabling immediate implementation of features like text generation and image analysis.
Annual Recurring Revenue (ARR) is the sum of recurring revenues generated over a year in a subscription-based business. Calculated by converting pure subscription revenue (excluding one-time fees) into an annual figure, it is the most critical metric for measuring the growth potential and financial stability of SaaS and AI service companies.
A mechanism that assigns higher weights to the information most important to the current task among all elements in the input sequence. It overcomes the limitations of fixed vector sizes and precisely extracts variable contexts to enhance the AI's understanding.
Refers to the highest level of security environment that is completely physically disconnected from external networks and the internet to fundamentally block data leakage and external intrusion.
A technology that uses AI models to analyze and extract individual sound source components (Stems), such as vocals, drums, and bass, from complex audio signals into independent tracks.
A process that minimizes human intervention and runs tasks independently through technology. It has evolved from classical methods that follow simple, repetitive rules to intelligent automation, where AI assesses situations and processes complex workflows.
Refers to business-to-business transactions. In the AI field, it is a model that provides software, infrastructure, and APIs to improve enterprise productivity or solve business problems. The decision-making process is complex, and data security along with stable Service Level Agreement (SLA) guarantees are key.
A business model where a company sells products or services directly to individual consumers without middle distributors.
A pre-trained language model based on a bidirectional Transformer encoder, released by Google in 2018, which excels at understanding tasks such as search and classification.
A security method that protects data when using cloud or AI services by utilizing encryption keys directly generated and managed by the user, rather than the service provider's encryption keys.
A unique entity combining the values a company aims for with its visual and verbal elements. In an AI environment, it serves as a core standard to maintain brand credibility by enforcing a consistent tone and style across all generated outputs.
A prompting and reasoning technique that improves problem-solving accuracy for complex questions by prompting the AI to go through intermediate logical reasoning steps before outputting the final answer.
A software development automation framework that continuously integrates (CI) code changes and immediately deploys (CD) them to production after automated validation. It is an essential methodology in modern development to ensure both quality and speed by detecting errors early and shortening deployment cycles.
A deep learning neural network architecture specialized in image recognition that learns local patterns in images using convolutional layers.
A strategic system that helps businesses consolidate and manage customer data to strengthen relationships and increase profitability. Modern CRM has evolved beyond simple data tracking into an intelligent platform that uses AI to predict customer intent and automate marketing, sales, and service processes, playing a key role in delivering hyper-personalized customer experiences.
A technology that builds AI personas and virtual environments to simulate the behaviors, decisions, and reactions of real customers, deriving data-driven market insights.
A standard that ensures AI tools use training data legally and guarantees the protection of intellectual property rights and commercial utilization of the outputs generated by users.
Software that engages in natural language conversations with users via text or voice. Recently, it has evolved beyond simple rule-based responses into intelligent assistants that understand complex context and perform professional tasks by combining Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) technologies.
The process of splitting long documents into smaller segments suitable for retrieval and embedding, serving as the foundation of RAG quality.
The maximum range of data (tokens) that an AI model can process and remember at one time in a single request (prompt). It refers to the model's 'short-term memory' and workspace size.
A field of AI technology that enables computers to understand and analyze visual data, such as images and videos, to make decisions like humans do. It goes beyond simple pixel-by-pixel screen analysis to perform object recognition, situational understanding, and 3D spatial comprehension.
The state of demonstrating strict compliance with the laws of the country where the AI tool is introduced, industry-specific regulations, internal security policies, and ethical guidelines.
A technology in which AI automatically writes programming code based on natural language descriptions or the context of existing code.
The response latency that occurs when an AI system is launched from an inactive state, or the phenomenon in recommendation systems where accurate results cannot be provided to new users due to a lack of data.
An automation technology where bots navigate the internet, discover web pages, and collect information. Beyond simple data extraction, it is a core process that serves as the foundation for search engine indexing or building large-scale datasets for AI model training.
An IT model that provides computing resources on-demand over the internet. In the AI field, it serves as a core foundation for flexibly scaling the high-performance computing resources needed for large model training and inference—without building expensive GPU hardware—and instantly leveraging the latest AI models via APIs.
A culture and methodology that integrates software development (Dev) and IT operations (Ops) to automate software deployment and operations.
An automated collaborative framework that integrates security into all stages of software development (Dev) and operations (Ops), detecting and responding to security vulnerabilities early without hindering development speed.
An open-source platform that packages applications together with all necessary libraries and environment settings into independent packages called 'containers,' allowing them to run consistently regardless of OS or hardware constraints. It is utilized as a standard infrastructure technology for cloud-native development and AI model deployment.
A phenomenon where the statistical properties of the data used to train an AI model diverge from the distribution of incoming data in production, leading to a decline in the model's predictive performance.
The process of assigning correct answers (labels) to data for supervised machine learning, serving as the foundation of model quality.
The specific geographical location or country where data collected by an AI service is physically stored and processed, serving as a key benchmark for compliance and security policy development.
A technology that utilizes statistical and machine learning techniques on large datasets to discover meaningful patterns, correlations, and trends. It is a core process that goes beyond simple queries to uncover hidden insights, supporting business forecasting and decision-making.
The process of collecting, cleaning, and interpreting data to derive insights needed for decision-making. It has evolved beyond simple statistics into intelligent analysis that uses AI to predict the future and recommend optimal courses of action.
A technical gateway that allows AI models to access and retrieve information in real time from external data sources such as SaaS, databases, and files.
An artificial intelligence model trained intensively on knowledge and data from a specific industry or domain, allowing it to process specialized terminology and context more precisely and accurately than general-purpose models.
A technology that duplicates physical objects or systems in a virtual world by synchronizing real-time data, helping decision-making through simulation and prediction.
An intelligent virtual being implemented to replicate human appearance, voice, and behavior by combining AI technology and computer graphics, enabling real-time interaction.
An advanced reasoning process where an AI independently formulates a search plan to achieve complex research goals, autonomously analyzes numerous online sources, and generates expert-level, in-depth reports.
A branch of machine learning that learns complex, non-linear relationships in data through multi-layered artificial neural networks. It automatically extracts features from data and serves as a core technology in fields requiring high cognitive abilities, such as image recognition, natural language processing, and generative AI.
A security technology that verifies authenticity and ensures media reliability by analyzing biological signals and subtle data patterns in images, videos, and voices generated or manipulated by AI.
A dominant AI technique for image and video generation that starts with random noise and generates data by removing noise step-by-step.
A system that integrates and manages core business processes (finance, HR, manufacturing, supply chain, etc.) in real time on a single data platform. Recently, it has evolved into intelligent ERP, which combines AI and cloud computing to automate data analysis and support decision-making.
A core data integration process that extracts distributed source data, transforms it into a format suitable for analysis, and loads it into a target repository.
The highest service tier designed for the security, compliance, and administrative efficiency of large organizations. It guarantees data training exclusion, SSO integration, precise permission controls, and dedicated technical support, and is typically adopted through custom contracts.
A distributed computing paradigm that processes data near where it is generated (the edge) to improve response times and save bandwidth costs.
A technology that directly runs AI models inside devices to ensure real-time data processing and privacy without going through cloud servers.
A technology that converts unstructured data, such as text and images, into high-dimensional numerical vectors that preserve meaning so that AI can process them. Beyond simple numerical conversion, it mathematically represents contextual relationships and similarity between data points.
A technique that includes 2 to 10 input-output examples in the prompt without retraining the model, enabling the AI to immediately follow the patterns and formats of the task.
A business model that provides basic features for free with no time limit, while selling high-performance model access, higher usage limits, and add-on features for a fee.
A feature that connects an LLM to interpret a user's request and select and call pre-registered external functions or APIs.
The process of additionally training a pre-trained AI model on a specific dataset to optimize it for a particular task or domain. It is used to precisely adjust the required expertise or response style for a specific purpose while maintaining the general knowledge of the foundation model.
A deep learning architecture where a generator and a discriminator compete to generate realistic data. Because its generation speed is significantly faster than diffusion models, it is mainly used for real-time video translation and high-resolution restoration.
A regulation enacted to protect the personal data of EU citizens, serving as a legal standard applicable to all organizations worldwide that process data of data subjects within the EU, regardless of the company's location.
A series of Transformer-based generative AI models developed by OpenAI that understand context, generate natural text, code, and images, and perform complex reasoning and multimodal tasks.
A processor specialized in parallel computing through thousands of cores, serving as the core infrastructure that determines the training and inference performance of AI models. Optimized for deep learning matrix operations beyond graphics processing, it has recently emerged as the standard for running Large Language Models (LLMs) by integrating High Bandwidth Memory (HBM).
A technique that secures the accuracy of AI model outputs by linking them to reliable external data or specific knowledge bases. By allowing the model to reference up-to-date information or internal data not included in its training, it suppresses hallucinations and provides grounds for the responses.
A technical security layer that inspects and controls inputs and outputs in real time to ensure AI models remain within corporate policies and safety standards. It secures business stability by preventing inappropriate answers, data leaks, and hallucinations.
An artificial intelligence technology that learns from large-scale data to understand patterns and structures, and based on this, generates original new content such as text, images, audio, and code.
A US federal law designed to protect patients' protected health information (PHI), serving as a crucial security and privacy standard that AI services processing or storing medical data must comply with.
The world's largest open-source AI platform for sharing and collaborating on AI models, datasets, and demos. It shortens development cycles with millions of pre-trained models and is the hub of the ecosystem, often referred to as the 'GitHub of AI.'
A technology that combines traditional keyword matching (BM25) with AI-based semantic vector search, performing both exact keyword matching and contextual understanding to optimize search precision and recall.
A phenomenon in which an AI model, in the process of predicting the next word based on statistical probability, logically and confidently generates incorrect or groundless information as if it were the truth.
A technology that checks whether responses generated by a large language model (LLM) are grounded in provided reference documents or contradict objective facts to identify potential misinformation.
An interactive process that integrates human intervention into the training, operation, and decision-making processes of AI models to validate the quality of outputs and ensure system safety and accountability.
A technology that embeds sensors and software in physical objects to exchange data in real time over the internet. It is a core infrastructure that connects home appliances, vehicles, and industrial equipment to a network, enabling automated control and data analysis.
A systematic management activity performed according to predefined procedures to minimize the scope of damage and restore normal service operations when a security threat or breach occurs.
A technology in which artificial intelligence analyzes text or reference images to generate new visual outcomes. Moving beyond simple image combinations, it implements original images at the pixel level, reflecting composition, art style, lighting, and more based on trained data.
A technology that combines the execution power of RPA with the judgment of AI to autonomously perform complex decision-making and unstructured data processing beyond simple repetitive tasks.
The speed and efficiency with which an AI model processes user inputs to generate outputs, primarily measured by Time to First Token (TTFT) and Tokens Per Second (TPS).
The total ecosystem of computing hardware and software optimization engines that enables trained AI models to respond to user requests in real time within production services.
A metric that quantifies and objectively evaluates the achievement of core goals set by an organization or project. In AI implementation, it goes beyond simple usage metrics to serve as a vital health indicator and evaluation standard that measures how much the tool has contributed to productivity improvements or cost savings.
A dynamic data repository that stores an enterprise's internal data in a searchable and reason-capable structure, allowing AI to find information in real time and use it as a basis for answers.
An open-source orchestration platform that automates the deployment, scaling, and management of containerized applications.
An open-source framework that reduces the complexity of developing LLM-based applications. It provides key interfaces needed to build production-grade AI services by combining large language models with external tools, such as connecting external data sources, constructing complex chains, and building autonomous agents.
An artificial intelligence model trained on vast amounts of data using billions or more parameters. It serves as the core engine of modern generative AI services, performing various intellectual tasks such as complex reasoning, summarization, and coding, in addition to natural language understanding and generation.
An efficient fine-tuning (PEFT) technique that drastically reduces training parameters and hardware requirements by freezing the existing weights of a large model and adding only trainable low-rank matrices.
The time it takes from the moment a user sends a request to the AI until the first response appears on the screen or the full result is completed.
A connection server that provides a standardized pathway for AI models to access external data sources such as local files, databases, and SaaS tools.
A framework for automating and managing the lifecycle of machine learning models. It is an extension of DevOps that combines software engineering's CI/CD with 'Continuous Training (CT)' to maintain model performance and stability in production environments.
A neural network architecture that selectively activates only a subset of 'expert' subnetworks required to process input tokens out of the model's total parameters, simultaneously securing the massive knowledge capacity of large models and efficient computation speeds.
The sum of recurring revenues generated each month in a subscription business. Calculated by normalizing regular subscription payments while excluding one-time fees, it is a key metric demonstrating the business's predictability and growth potential.
A cloud-native architecture that builds and deploys an application as a collection of independent, small-scale services centered around business capabilities.
A core AI technology that learns from data and discovers patterns to make optimal predictions or decisions. It extracts statistical rules from vast amounts of data without explicit programming, and is the most common mechanism used by modern AI to solve complex problems.
An intelligent system where multiple AI agents with specialized roles collaborate to accomplish complex goals that are difficult for a single AI to solve.
An AI model that understands and processes two or more data formats, such as text, images, audio, and video, together.
An objective, quantified metric that measures specific capabilities of an artificial intelligence model—such as knowledge, reasoning, and coding—using standardized datasets. It serves as a benchmark to assess the model's absolute technical level and fairly compare performance across different models.
A technology where AI analyzes medical images such as X-rays, CTs, and MRIs to detect the location of diseases and precisely measure lesions, helping medical staff interpret the images.
A core AI technology that enables machines to understand and generate human language. It includes NLU to capture context and intent, and NLG to generate sentences. Currently, it performs advanced translation, summarization, and reasoning based on Large Language Models (LLMs). It is an essential engine for implementing business automation by converting unstructured data into valuable information.
A dedicated AI processor that accelerates large-scale matrix operations—the core of deep learning—at the hardware level, helping improve on-device AI performance with low power consumption.
A method of developing software using visual tools (such as drag-and-drop) without coding knowledge. It enables non-experts to quickly build websites, apps, and automation workflows.
An open standard protocol that securely delegates access permissions for specific data or features to third-party services without directly sharing passwords. It primarily provides a token-based authorization framework.
A technology that converts text in images, scanned documents, or photos into computer-editable and searchable text data.
An AI research and deployment company aiming to develop artificial general intelligence (AGI) that benefits all of humanity. It sets global standards in multimodal interaction and advanced reasoning performance through leading generative AI technologies such as ChatGPT, GPT-4o, and the o1 model.
The ability to understand and diagnose the internal state, performance, quality, and root causes of costs in an AI system through its external outputs: logs, metrics, and traces.
A model deployment method that publishes the weights (parameters), which are the core values of an AI model, allowing users to directly install and run them on their own servers or local environments without external APIs.
A development methodology where source code is made public, allowing anyone to freely view, modify, and distribute it. In the AI field, according to the 'Open Source AI Definition (OSAID)' announced in 2024, it refers to systems where not only model weights but also training data information and training code are transparently disclosed, enabling replication and improvement.
A technology that helps improve security and real-time responsiveness by directly running AI models through hardware (NPU) inside user devices such as smartphones and PCs, without going through cloud servers.
A model where an enterprise directly installs and operates hardware in its own data center or physical server room. It is an essential model for the financial and public sectors where data sovereignty and security are critical, and has recently gained renewed attention as key infrastructure due to the growing demand for 'private AI' using sensitive internal data and local inference of open-source LLMs.
Refers to information that can identify a specific individual on its own or in combination with other details, such as names, resident registration numbers, and contact details. It is a critical management subject for preventing data leakage and ensuring legal compliance during AI adoption.
A security technology that replaces Personally Identifiable Information (PII) such as names, phone numbers, and resident registration numbers within data with specific symbols or dummy data to prevent privacy exposure during AI model processing.
A technical process where AI analyzes user behavior data, preferences, and real-time context to automatically deliver optimized content, products, and services to individual users.
A framework for calculating the cost of using AI services, evolving from traditional flat-rate, seat-based subscriptions toward dynamic structures that bill based on token consumption or the outcome of tasks completed by the AI.
The initial stage preceding fine-tuning, where a model is first trained on large-scale data to acquire foundational language and knowledge capabilities.
Internal variables (weights and biases) that an AI model saves through learning. Larger numbers allow the model to learn complex patterns, but also increase computational and memory costs.
A system that connects a sequence of processes—from data collection and processing to model training and deployment—into an automated workflow. It improves operational efficiency by maintaining data integrity and building a reproducible experimental environment.
The technique of designing and refining instructions, context, and examples in input prompts to get desired outputs from AI models.
A security attack where a user manipulates an AI to bypass its original system instructions using malicious inputs or corrupted external data, leading to unintended actions or confidential data leaks.
An LLM optimization technique that reduces response costs and latency by caching and reusing the repeating prefix of a prompt.
RAG is a technology that merges real-time database search results with AI responses. It has evolved beyond 'Naive RAG,' which simply locates documents, into 'Agentic RAG,' where the AI autonomously repeats search and verification cycles. It has become a key standard for enterprise knowledge management, leveraging large-scale data without retraining while reducing hallucinations and generating well-sourced answers.
A software architectural style that manages resources on the web by leveraging the strengths of the HTTP protocol. It identifies each resource with a unique URI and transfers states via standard HTTP methods, making it the most widely used API design standard for data integration and model deployment in AI services.
A reinforcement learning technique that uses human preferences as reward signals to fine-tune AI models to align with user intent and values.
A deep learning neural network that processes sequential data by feeding back the output of previous time steps, with LSTM and GRU being representative examples.
The ratio of net profit generated to investment cost, serving as a key metric to measure the economic viability of a project. When adopting AI, it quantifies and evaluates multifaceted business value, including productivity gains, revenue growth, and risk reduction, rather than just simple cost savings.
A technology where software robots automate repetitive and rule-based tasks in business processes. By mimicking human actions, they input or extract data and transfer information between systems. Recently, it has evolved into intelligent automation that handles unstructured data by integrating with generative AI.
An independent verification process that performs intentional adversarial attacks from an attacker's perspective to proactively identify security vulnerabilities, biases, and harmful outputs of AI systems.
A model that precisely re-orders candidate documents retrieved from a primary search based on their relevance to the query.
A security management method that systematically controls access to AI models, data, and system functions by defining job-based 'roles' rather than granting individual permissions to users within an organization.
An AI model specialized in complex reasoning, trained to go through a step-by-step thinking process internally before generating an answer.
A service model where software is subscribed to and used over the internet as needed. It is a cloud-based approach that allows users to instantly utilize the latest AI features through a web browser or API without separate installation or server setup.
A package that bundles the tools, libraries, and documentation required to develop software for a specific platform or service. In the AI field, it is a core tool that abstracts model API calls to help implement complex authentication, streaming, and error handling with concise code.
A strategy to optimize websites for higher exposure at the top of search results. Beyond simply boosting rankings, it is a key digital marketing technology that drives high-quality traffic by satisfying user intent and E-E-A-T (Experience, Expertise, Authoritativeness, and Trustworthiness).
An efficient model with billions of parameters that delivers high performance even on low-spec hardware, optimized for on-device AI and task-specific specialization.
A security control standard established by the American Institute of Certified Public Accountants (AICPA). It is a report verified by an independent auditor confirming whether cloud and AI service providers have designed and are actually operating appropriate internal control systems to protect customer data.
A unified authentication method that allows users to access multiple integrated software services with a single login (ID/PW) without additional sign-ins. As a core component of enterprise security frameworks, it eliminates the hassle of managing individual accounts for each service and enables centralized access control.
An open-source text-to-image generation model released by Stability AI, based on latent diffusion models.
An AI technology that converts voice signals into computer-readable text data. Recently, deep learning models such as Whisper and Gemini offer high accuracy even in noisy environments, making it useful for automatic subtitling and meeting transcription.
A framework that protects the integrity of data, model weights, third-party libraries, and infrastructure, and manages vulnerabilities across the entire process from AI model development to deployment.
A feature that forces LLM responses to conform to a predefined schema format like JSON, facilitating easy system integration.
A safe virtual environment isolated from external systems, providing an independent testing space where AI-generated code can be executed safely to block security threats, or where new services can be tested without legal or technological constraints.
A cloud computing model where you run code or services without managing infrastructure, featuring automatic scaling and billing based on actual usage (number of invocations, execution time).
A deployment model where AI models and software are installed and run on servers directly owned by an enterprise or individual, or on an independent cloud instance, rather than using an external vendor's cloud service (SaaS).
A technology that automatically detects sensitive credentials such as API keys, authentication tokens, and passwords exposed in source code, datasets, and configuration files to prevent security leaks.
A natural language processing (NLP) technology that compresses vast amounts of information focusing on key content to increase the efficiency of acquiring information.
A search technology that understands the meaning and intent of a query to find highly relevant results, rather than relying on exact keyword matches.
A technology that analyzes the structure and logic of source code without executing the software to detect security vulnerabilities, bugs, and coding standard violations early.
Artificially generated data, rather than real-world measurements, created through AI algorithms or statistical models that retains the statistical characteristics and patterns of the original data to be used for analysis and model training.
A technology that segments an audio recording with multiple speakers to identify 'who spoke when' for each segment. In meetings with multiple participants, it separates each utterance by speaker, allowing transcription results to be organized into a dialogue format.
A custom AI accelerator chip designed by Google for machine learning matrix operations, used through Google Cloud.
An AI technology that converts input text into natural-sounding speech, as if spoken directly by a human.
A value that controls the degree of randomness when an LLM selects the next word, determining the consistency and creativity of the output.
The process of listening to spoken words in audio or video and converting them directly into text. Historically performed manually by humans, it is now automated using STT technology to convert recordings of meetings, interviews, and lectures into text, serving as the foundation for search and summarization.
A technique that reuses the knowledge of a model trained on a specific task (Source Task) for a similar or new task (Target Task). It is a core paradigm of modern AI development that enables rapid construction of high-performance models even in data-scarce environments.
An AI's technological ability to interpret signals such as pressure, friction, and vibration generated during physical contact to understand the state of an object and perform precise physical interactions.
A technology in which Large Language Models (LLMs) understand context to automatically generate natural, human-like text. Beyond simply completing sentences, it can execute complex instructions or write creative content and code.
A generative AI technology that analyzes text descriptions and converts them into visual images. Moving beyond simple image generation, it is used throughout practical workflows such as design drafts and marketing asset creation. Recently, typography expression in images and commercial copyright safety have become key metrics for tool selection.
The basic unit through which an LLM recognizes and generates text, created by breaking sentences into semantic pieces smaller than words or characters. It is a key metric that determines the computational cost of the AI model, response speed, and the amount of information it can remember at once (context window).
An optimization process that strategically manages the usage of tokens, which are the basic units of data processed by LLMs, to reduce API call costs and improve the model's Time to First Token (TTFT) and overall inference performance.
A deep learning architecture that parallelizes input sequence processing with self-attention at its core, serving as the foundation for most modern LLMs.
The process of interpreting unstructured data that lacks a fixed structure, such as text, images, and audio, using AI technologies like NLP and computer vision to extract insights with business value.
A model where costs are paid in proportion to the actual resources consumed (e.g., number of tokens, API calls, compute time), allowing users to pay only for what they use without upfront fixed costs.
A technology where AI analyzes pixel data from low-resolution images or videos using deep learning models to intricately infer details and convert them into high-resolution quality.
A multimodal artificial intelligence model that understands and processes images and text simultaneously to perform natural language description, question answering, and complex reasoning on visual information.
A specialized database that stores high-dimensional vector data and supports high-speed similarity-based searches. By utilizing embedding values that quantify unstructured data, it enables AI to quickly locate semantically similar information and serves as a core infrastructure for Retrieval-Augmented Generation (RAG) systems.
A technology where AI automatically generates videos with consistent frames and natural movement based on text or image prompts. Recently, the Diffusion Transformer (DiT) architecture is primarily used to simulate physical laws and maintain spatiotemporal consistency.
A technology that goes beyond simply identifying objects in images or videos, enabling AI to analyze visual context and reason logically to demonstrate human-level understanding.
A technology that learns a specific person's voice and intonation from a brief voice sample to synthesize arbitrary text into that voice.
An HTTP-based event notification mechanism where a server sends real-time data to a specified URL when a specific event occurs.
A business model where an adopting company replaces the brand name and logo of an AI software or platform developed by a third-party vendor with their own, offering it to end users as if it were their own proprietary solution.