Data Annotation: The process of labeling or tagging individual elements of data as images, video, or text so AI algorithms understand what exactly they are being trained on. This data is then used in machine learning.

Data Augmentation: The technique of artificially increasing the size of a dataset by applying various transformations to existing data, often used in image recognition. Using new and different examples to train machine learning models improves performance and outcomes.

Data Fusion: The process of combining data from multiple sources to create a more complete and accurate representation for AI analysis. This helps in improving the accuracy of the algorithm.

Data Governance: Data governance includes all the steps taken to ensure data is secure, private, accurate, available, and usable, along with the processes people must follow and the technology that supports them throughout the data lifecycle. The management and oversight of data ensure its quality, integrity, and compliance with regulations.

Data Imputation: Data imputation refers to the process of substituting missing values from a dataset with different values. The AI engineer must typically specify how these values must be interpreted by the AI to avoid creating inconsistencies in the whole dataset.

Data Labeling: Data labeling involves assigning descriptive labels and annotations to data points that assist machines and developers in identifying the type of data they are dealing with. This can include labeling data points as text, videos, or images to classify individual elements of the dataset into different groups.

Data Leakage: A data leak occurs when unintentional information from the test or validation data gets included in the training process erroneously. Introducing data points like these can lead to inaccurate or overly optimistic performance metrics.

Data Mining: The process of discovering patterns, trends, and other useful insights from large datasets using AI and statistical techniques. It helps to establish rules that can be applied to large amounts of data to teach machines how to comprehend specified parameters.

Data Pipeline: A data pipeline manages the flow of data from its raw stage to the stage where it can be used in the deployment of training models. It involves a series of data processing steps and transformations to prepare, clean, and transform data for AI analysis.

Data Preprocessing: The first step of the data pipeline where raw data is cleaned and transformed to make it ready for analysis. Preprocessing ensures that missing values, noisy data, and other inconsistencies are detected & modified before algorithm execution.

Data Privacy Laws: As a large amount of data is collected for machine learning, data privacy laws have been enforced in some countries to prevent unauthorized collection, storage, and processing of personal and sensitive data for artificial intelligence applications. This prevents AI applications from discriminating or manipulating individuals based on their personal data.

Data Retrieval: The process of finding relevant documents or information from a database based on the query provided by the user or the application. An important application of data retrieval is search engines, which allow users to find information available on the internet. 

Data Science: Data science refers to the process of extracting useful information and patterns from data. It’s an interdisciplinary field that combines the use of statistical methods, advanced analytics, and programming to uncover previously unknown insights in an organization’s data. A data scientist usually works with an AI engineer to create development models or training datasets.

Data Warehouse: A centralized repository that stores large volumes of data, used for AI analytics and reporting.

Data Warehouse as a Service (DWaaS): DWaaS is a cloud service in which the provider configures and manages the hardware and software resources needed for a data warehouse, while the customer provides the data and pays for the managed service provided by the provider.

Decentralized AI: Decentralized AI is a system where artificial intelligence algorithms and processes are distributed across multiple devices or nodes in a network, allowing multiple developers to do AI tasks locally without relying on a central server or cloud infrastructure. This enables collaborative learning and decision-making.

Decision Tree: A decision tree is a model used in machine learning for making decisions. It presents data in a flowchart and asks computers a series of yes-or-no questions to allow them to make choices. It is named such because the process starts with a question and follows branches to reach a final answer.

Defensive AI: Defensive AI is a term from the world of cybersecurity. It refers to strategies and techniques used to defend against cyberattacks and identify and counteract dangers using Defensive AI software that prevents against cyberattacks launched by offensive AIs.

Deep Learning: Deep learning is a type of machine learning where computers learn to make sense of data by imitating the way our brains process information through interconnected layers (known as deep neural networks) of artificial neurons, helping them recognize patterns and make decisions.

Deep Neural Network (DNN): Deep Neural Networks are computer systems inspired by the human brain, with many layers of interconnected nodes that work together to solve complex problems and make predictions, like recognizing objects in images or understanding human language.

Deep Reinforcement Learning: Deep reinforcement learning trains a computer to make decisions based on trial and error. It then learns from its mistakes and experiences until it can get to the desired objective, such as performing tasks like controlling robots, understanding spoken words, or even playing video games. 

Debugging: Debugging is the process of identifying and fixing errors or bugs in AI algorithms or software.

DevOps: A set of practices that combines software development and IT operations to streamline the deployment of AI applications.

Dialog System: An AI system designed to engage in conversations with humans, such as chatbots or virtual assistants.

Dialogue Act: A dialogue act in AI involves categorizing and understanding the purpose or intention behind each statement or message in a conversation, helping AI systems generate appropriate responses and engage effectively with users. This allows AI applications to understand prompts given in plain English.

Digital Assistant: The term “digital assistant” refers to a virtual assistant powered by artificial intelligence, such as Siri, which helps users with tasks, information retrieval, and interactions. AI algorithms enable these assistants to process natural language, recognize speech, and perform tasks like setting reminders, searching the internet, or controlling smart devices. 

Digital Twin: A digital twin is a virtual replica of a real thing or a process, generally used in IoT (Internet of Things). AI is often used to help make them smarter by analyzing data and making predictions to improve their real-world performance and decision-making.

Dimensionality Reduction: It is a technique in data analysis and machine learning that simplifies complex data by reducing the number of variables or features while retaining the most essential information. It helps in visualizing data and improving the efficiency of machine learning models.

Distributed AI: The use of multiple AI models or agents working together on a common task, often used for complex problem-solving. This approach enhances computational power, scalability, and collaborative problem-solving capabilities in AI applications.

Distributed Computing: As an alternative to just using a single centralized computer, distributed computing involves many nodes or interconnected computers working together to accomplish tasks. This helps to work collaboratively, perform parallel processing, and keep things running smoothly, even if some computers run into any problems. It’s used in things like data analysis, scientific simulations, and distributed AI to make them work better and faster.

Document Classification: The task of categorizing text documents into predefined categories or labels using AI algorithms.

Domain Adaptation: Domain adaptation is a technique in machine learning where a model trained on one dataset is adapted or fine-tuned to work well in a different but related dataset.

Domain Knowledge: Specific knowledge about a particular field or industry, which can be integrated into AI systems to make them more effective.

Domain-Specific AI: Domain-specific AI refers to artificial intelligence systems and models that are designed and specialized to excel in a particular industry, field, or domain of knowledge. It’s like having an AI expert in a particular area, such as healthcare or finance who understands and solves problems unique to that industry or field.

Docker: In AI, Docker is like a digital package that holds all the parts an AI program needs to run. It makes it easy to move AI programs between different computers and systems, ensuring they work the same way everywhere.

DaaS (Data as a Service): A cloud-based service that provides access to data for AI and analytics applications.

Dynamic Pricing: Using AI algorithms to adjust prices for products or services in real time based on demand, competition, and other factors.

Dynamic Programming: A technique used in AI and optimization to solve problems by breaking them into smaller subproblems. It then solves each subproblem only once and stores the results to avoid redundant work. This method is particularly useful for optimizing solutions in a systematic and efficient way, especially in tasks like algorithm design and optimization.

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