A sorting algorithm that distributes elements into a number of buckets, sorts each bucket individually, and then combines the buckets to get the sorted list. Useful for understanding more advanced algorithmic techniques and their applications.
The process of defining and creating algorithms to solve problems and perform tasks efficiently. Fundamental for software development and creating efficient solutions.
A simplified, informal language used to describe the logic and steps of an algorithm or program, without syntax of actual programming languages. Useful for planning and communicating algorithms and program structures before implementation in digital product development.
A problem-solving process that includes logical reasoning, pattern recognition, abstraction, and algorithmic thinking. Important for developing efficient and effective solutions in digital product design and development.
An interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. Essential for driving data-informed decision making, predicting trends, and uncovering valuable insights in digital product design and development.
A set of algorithms, modeled loosely after the human brain, designed to recognize patterns and perform complex tasks. Essential for developing advanced AI applications in various fields.
A machine learning-based search engine algorithm used by Google to help process search queries and provide more relevant results. Important for understanding modern SEO practices and how search engines interpret and rank web content.
An algorithm used by Google Search to rank web pages in their search engine results, based on the number and quality of links to a page. Essential for understanding search engine optimization and improving website visibility.
Systematic errors in AI models that arise from the data or algorithms used, leading to poor outcomes. Important for ensuring fairness and accuracy in AI systems.
Responsive Web Design (RWD) is an approach to web design that makes web pages render well on a variety of devices and window or screen sizes. Essential for creating flexible, adaptive web experiences that maintain functionality and aesthetics across different platforms and devices.
An inference method used in AI and expert systems where reasoning starts from the goal and works backward to determine the necessary conditions. Important for developing intelligent systems that can solve complex problems by working from desired outcomes.
The use of data, algorithms, and machine learning to recommend actions that can achieve desired outcomes. Essential for optimizing decision-making and implementing effective strategies.
A method of splitting a dataset into two subsets: one for training a model and another for testing its performance. Fundamental for developing and evaluating machine learning models in digital product design.
A type of artificial intelligence that enables systems to learn from data and improve over time without being explicitly programmed. Crucial for developing intelligent systems that can make data-driven decisions.
Case-Based Reasoning (CBR) is an AI method that solves new problems based on the solutions of similar past problems. This approach is essential for developing intelligent systems that learn from past experiences to improve problem-solving capabilities.
An iterative design process that uses algorithms and computational tools to generate a wide range of design solutions based on defined constraints and goals. Crucial for exploring innovative and optimized design solutions.
A prioritized list of ideas and potential features for future product development, embodying a collective vision for innovation and improvement. Essential for managing creative input and maintaining an innovation pipeline that aligns with the team's entrepreneurial spirit and shared commitment to product excellence.
The use of algorithms to generate new data samples that resemble a training dataset, often used in AI for creating realistic outputs. Important for developing creative and innovative solutions in digital product design, such as content generation and simulation.
A type of model architecture primarily used in natural language processing tasks, known for its efficiency and scalability. Essential for state-of-the-art NLP applications.
A search method that seeks to improve search accuracy by understanding the contextual meaning of terms in a query rather than just matching keywords. Important for understanding modern search algorithms and optimizing content accordingly.
A type of artificial intelligence capable of generating new content, such as text, images, and music, by learning from existing data. Important for automating creative processes and generating novel outputs.
An inference method used in AI and expert systems where reasoning starts from known facts and applies rules to derive new facts. Important for developing intelligent systems that can build knowledge and solve problems incrementally in digital products.
In AI, the generation of incorrect or nonsensical information by a model, particularly in natural language processing. Important for understanding and mitigating errors in AI systems.
Data that is organized in a predefined manner, making it easier for search engines to understand and display rich snippets in search results. Essential for enhancing search results and improving SEO.
The ability to identify and interpret patterns in data, often used in machine learning and cognitive psychology. Crucial for designing systems that leverage pattern recognition for predictive analytics and user interactions.
AI as a Service (AIaaS) is a service model where AI tools and algorithms are provided over the internet by a third-party provider. Essential for making advanced AI capabilities accessible to businesses.
AI systems that can dynamically adjust their behavior based on new data or changes in the environment. Important for developing systems that can respond to real-time changes and improve over time.
The study of finding the best solution from a set of feasible solutions. Crucial for improving efficiency and performance in design and development processes.
Trust, Risk, and Security Management (TRiSM) is a framework for managing the trust, risk, and security of AI systems to ensure they are safe, reliable, and ethical. Essential for ensuring the responsible deployment and management of AI technologies.
A practice of performing testing activities in the production environment to monitor and validate the behavior and performance of software in real-world conditions. Crucial for ensuring the stability, reliability, and user satisfaction of digital products in a live environment.
Artificial Intelligence of Things (AIoT) is the integration of AI with the Internet of Things (IoT) to create smart systems that can learn and adapt. Crucial for developing advanced, intelligent products that offer enhanced user experiences and operational efficiencies.
Natural Language Processing (NLP) is a field of AI focused on the interaction between computers and humans using natural language. Essential for developing applications like chatbots, language translation, and sentiment analysis.
A scheduling term that indicates a delay in the project timeline that cannot be recovered. Important for identifying and addressing potential project delays, ensuring timely delivery of digital products.
Lifetime Value (LTV) is a metric that estimates the total revenue a business can expect from a single customer account throughout their relationship. Crucial for informing customer acquisition strategies, retention efforts, and overall business planning by providing insights into long-term customer profitability.
A statistical method used to assess the generalizability of a model to unseen data, involving partitioning a dataset into subsets for training and validation. Essential for evaluating model performance and preventing overfitting in digital product analytics.
The process by which search engines systematically browse the internet to index and retrieve information from websites. Essential for understanding how search engines discover and index web content.
The preferred version of a web page that search engines should index, used to avoid duplicate content issues and improve SEO. Essential for managing SEO and ensuring the correct indexing of web pages.
An automated program used by search engines to browse the internet and index web pages, aiding in the retrieval of relevant information during a search query. Crucial for understanding how search engines discover and index web content.
The process of optimizing content and website structure to improve visibility and ranking in voice search results. Important for adapting to the growing use of voice search and ensuring content is accessible to voice queries.
In AI and machine learning, a prompt that specifies what should be avoided or excluded in the generated output, guiding the system to produce more accurate and relevant results. Crucial for refining AI-generated content by providing clear instructions on undesired elements, improving output quality and relevance.
A statistical technique that uses several explanatory variables to predict the outcome of a response variable, extending simple linear regression to include multiple input variables. Crucial for analyzing complex relationships in digital product data.
Artificially generated data that mimics real data, used for training machine learning models. Crucial for training models when real data is scarce or sensitive.
A theory in economics that models how rational individuals make decisions under risk by maximizing the expected utility of their choices. Essential for understanding decision-making under risk.
A learning phenomenon where information is better retained when study sessions are spaced out over time rather than crammed in a short period. Crucial for designing educational tools and content that optimize long-term retention.
The use of natural language processing to identify and extract subjective information from text, determining the sentiment expressed. Crucial for understanding public opinion and customer feedback.
The SEO value or authority passed from one website to another through hyperlinks, influencing the search engine ranking of the linked site. Important for understanding and leveraging the impact of links on SEO performance.
Knowledge Organization System (KOS) refers to a structured framework for organizing, managing, and retrieving information within a specific domain or across multiple domains. Essential for improving information findability, enhancing semantic interoperability, and supporting effective knowledge management in digital environments.
A recommendation system technique that suggests items similar to those a user has shown interest in, based on item features. Important for providing personalized recommendations and improving user satisfaction.
Reinforcement Learning from Human Feedback (RLHF) is a machine learning technique that uses human input to guide the training of AI models. Essential for improving the alignment and performance of AI systems in real-world applications.
The theory that users search for information in a manner similar to animals foraging for food, aiming to maximize value while minimizing effort. Important for designing efficient and user-centered information retrieval systems.
The process by which search engines organize and store web content to facilitate fast and accurate information retrieval. Crucial for understanding how search engines work and ensuring that web content is accessible and searchable.
A system that suggests products, services, or content to users based on their preferences and behavior. Essential for personalizing user experiences and increasing engagement and conversion rates.
The practice of two websites agreeing to link to each other's content, often used to build relationships and improve SEO. Important for understanding link-building strategies and their impact on SEO.
Retrieval-Augmented Generation (RAG) is an AI approach that combines retrieval of relevant documents with generative models to produce accurate and contextually relevant responses. Essential for improving the accuracy and reliability of AI-generated content.
The study of how psychological influences affect financial behaviors and decision-making. Essential for understanding and influencing financial decision-making and behavior.
A behavior where users repeatedly bounce back and forth between a search engine results page and individual search results. Important for identifying issues in search result relevancy and user satisfaction.
Data points that differ significantly from other observations and may indicate variability in a measurement, experimental errors, or novelty. Crucial for identifying anomalies and ensuring the accuracy and reliability of data in digital product design.
A phenomenon where the winner of an auction tends to overpay due to emotional competition, leading to a less favorable outcome than anticipated. Important for understanding decision-making biases and designing systems that mitigate overbidding risks.
A symmetrical, bell-shaped distribution of data where most observations cluster around the mean. Fundamental in statistics and crucial for many analytical techniques used in digital product design and data-driven decision making.
A metric used to evaluate the trustworthiness of a website based on the quality of links pointing to it, often used in SEO. Crucial for improving website credibility and search engine rankings.