A/B testing, also known as split testing, is a method of comparing two versions of a webpage or app against each other to determine which one performs better. It involves showing two variants (A and B) to users at random and using statistical analysis to determine which version is more effective in terms of a predefined metric such as clicks, conversions, or sales.

A/B Testing

AI agents are computer systems capable of autonomous actions to achieve specific objectives. They can be simple, like a chatbot that responds to user inputs, or complex, like a self-driving car that navigates traffic. These agents use artificial intelligence techniques to perceive their environment, make decisions, and perform actions accordingly.

AI Agents

A technique in computer science for quickly finding 'close' or 'similar' items in large datasets, particularly when exact matches are not necessary.

ANN (Approximate Nearest Neighbor)

A set of rules and protocols for building and interacting with software applications. APIs allow different software programs to communicate with each other, enabling the integration and functionality of different software components.

API (Application Programming Interface)

An API (Application Programming Interface) endpoint is a specific point at which an API — the code that allows two software programs to communicate with each other — connects with the software program. Endpoints facilitate the process of inputting and retrieving data from a service, and they are defined by a URL and the type of operation (GET, POST, etc.) that they perform.

API Endpoint

Models used in artificial intelligence that specialize in processing and understanding human language. They can generate, interpret, translate, or summarize text and are used in applications like chatbots, translation services, and content creation tools.

Ai Language Models

Intelligent systems that assist users in online shopping by providing personalized recommendations, price comparisons, and other helpful information.

Ai-Powered Shopping Agents

A statistical technique used to solve matrix factorization problems, especially in the context of recommendation systems for collaborative filtering.

Alternating Least Squares (ALS)

A type of artificial intelligence that has the ability to understand, learn, and apply its intelligence to solve any problem, much like a human being. AGI is characterized by its flexibility and general-purpose learning and reasoning capabilities.

Artificial General Intelligence (AGI)

A field of computer science dedicated to creating systems capable of performing tasks that typically require human intelligence. These tasks include problem-solving, recognizing patterns, understanding natural language, and learning from experience.

Artificial Intelligence (AI)

A decentralized storage network that offers permanent, low-cost, and sustainable data storage for data and applications.

Arweave

A performance measurement for classification problems at various thresholds settings. AUC represents the degree or measure of separability, telling how much the model is capable of distinguishing between classes.

Auc-Roc (Area Under The Receiver Operating Characteristics Curve)

Systems or entities capable of independent action in an environment to meet specified objectives. Examples include robots, drones, and certain software agents.

Autonomous Agents

Bard is a conversational generative artificial intelligence chatbot developed by Google

Bard

Bayesian networks are a type of probabilistic graphical model that uses Bayesian inference for probability computations. They represent a set of variables and their conditional dependencies via a directed acyclic graph and are used in various fields like machine learning, data mining, and decision analysis.

Bayesian Networks

Behavioral analysis involves studying and interpreting data on how individuals behave, particularly in the context of online platforms. This can involve tracking user actions, preferences, and interactions to understand patterns and make data-driven decisions.

Behavioral Analysis

Data sets that are used as a standard of comparison for evaluating or measuring the performance and accuracy of algorithms and systems.

Benchmark Data

A method in natural language processing pre-training that helps models understand the context of words in a sentence by considering the words that come both before and after them.

Bidirectional Encoder Representations From Transformers (Bert)

Refers to extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions. Big data is characterized by its volume, velocity, variety, and sometimes the veracity of the information captured.

Big Data

A decentralized, distributed ledger technology that records transactions across multiple computers in a way that ensures security, transparency, and immutability. Each 'block' in the chain contains a number of transactions, and once a block is completed, it forms a permanent part of the 'chain.' Blockchain is the foundational technology behind cryptocurrencies like Bitcoin too.

Blockchain

A decentralized platform designed for uploading data to the Permaweb, allowing users to pay once for permanent data storage.

Bundlr Network

A network of distributed servers that deliver web content and other web services to users based on their geographic location, the origin of the web page, and a content delivery server.

CDN (Content Delivery Network)

The primary component of a computer that performs most of the processing inside the computer, executing instructions from software to perform basic tasks.

CPU (Central Processing Unit)

A metric used to measure the success of an online advertising campaign or the effectiveness of email campaigns. It's calculated by dividing the number of clicks by the number of impressions (views) the ad or email received.

CTR (Click-Through Rate)

A process where new data replaces or updates existing data in a cache, often used to ensure the most current data is available for rapid access.

Cache Override

A software application designed to simulate conversation with human users, especially over the Internet. Chatbots are commonly used in customer service and information acquisition scenarios and can function via text, voice, or both.

Chatbots

Churn rate, often used in business, is a metric that measures the rate at which customers stop doing business with an entity. It is a critical measure for understanding customer retention and loyalty, commonly used by subscription-based companies to understand their customer turnover.

Churn Rate

Claude is a large language model developed by Anthropic.

Claude

A database that runs on a cloud computing platform, accessible over the internet, offering scalability, flexibility, and cost efficiency.

Cloud Database

Collaborative filtering is a method used in recommender systems to predict the preferences of a user by collecting preferences from many users. This method assumes that if a person A has the same opinion as a person B on an issue, A is more likely to have B's opinion on a different issue than that of a random person.

Collaborative Filtering

Collections of facts and information that represent general, widely accepted knowledge about the world, used in AI to enable more human-like understanding and reasoning.

Common Sense Knowledge Bases

The measure of the amount of computational resources (like time and memory) used by an algorithm to perform a task.

Computational Efficiency

Algorithms designed to allow users to modify certain parameters or settings to adapt their functionality to different scenarios or requirements.

Configurable Algorithms

 In computing, this could refer to the ability to customize the conditions or rules that trigger transitions within a system or application.

Configurable Transitions

AI systems designed with core principles and rules that govern their decision-making processes, ensuring that the AI's actions align with legal frameworks, ethical standards, and societal norms.

Constitutional Ai

The method of recommending items based on a comparison between the content of the items and a user profile. The content of each item is represented as a set of descriptors or terms, typically derived from the item's features.

Content-Based Filtering

Contextual analysis in computing involves evaluating a text or dataset in the context of its surrounding data. This can reveal insights that might not be evident from a standalone analysis, particularly in natural language processing and user behavior analysis.

Contextual Analysis

The practice of placing ads on web pages based on the content of the page, ensuring that ads are relevant to the page’s audience. It's a strategy used in digital advertising to increase ad effectiveness.

Contextual Targeting

Differences or changes in data or content that depend on the context or environment in

Contextual Variations

Coral is a knowledge assistant for enterprises to supercharge the productivity of their most strategic teams built by Cohere.

Coral

In linguistics, corpora (singular: corpus) are large and structured sets of texts. They are used for statistical analysis and hypothesis testing, checking occurrences or validating linguistic rules within a specific language territory.

Corpora

A sales strategy where a seller encourages the customer to spend more by purchasing an item that's related to what's being bought already or to purchase complementary items.

Cross-Selling

The practice of obtaining information, ideas, or content by soliciting contributions from a large group of people, especially from an online community, rather than from traditional employees or suppliers. This approach leverages the collective intelligence of a large group to achieve goals.

Crowdsourcing

A specialized algorithm tailored to address specific requirements or problems, often developed for unique applications or tasks.

Custom Algorithm

A variant of the GPT (Generative Pre-trained Transformer) series developed by OpenAI, specifically designed to generate digital images from natural language descriptions. It leverages a deep learning model to create images that match the text input, showing a wide range of artistic and creative capabilities. DALL-E is known for its ability to create novel, detailed, and contextually relevant images based on a wide variety of textual prompts.

Dall-E

Procedures or formulas for solving problems, typically involving the processing and analysis of data. They are fundamental in computer science for data manipulation and are used in various applications like sorting, searching, and pattern recognition.

Data Algorithms

The process of transforming data into a set of features in a lower-dimensional space, often used in machine learning for tasks like natural language processing.

Data Embedding

The process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. It's an essential process where intelligent methods are applied to extract data patterns.

Data Mining

This refers to the collection, manipulation, and processing of data to transform it into a meaningful format. Techniques involve various processes including validation, sorting, summarization, aggregation, analysis, reporting, and classification of data.

Data Processing

The documentation or tracing of the origin and changes made to data throughout its lifecycle. It provides a historical record of the data and its origins, enabling the understanding and verification of the data's authenticity and quality.

Data Provenance

In the context of computer science and data analysis, a dataset is a collection of data. Most commonly, a dataset groups information in a way that supports data processing and analysis.

Dataset

Systems designed without a central controlling node, where components operate independently and coordinate among themselves, commonly used in blockchain and peer-to-peer networks.

Decentralized Architectures

A database system where data is stored as key-value pairs, distributed across a decentralized network to enhance scalability, fault tolerance, and data sovereignty.

Decentralized Key-Value Database

A storage system where data is stored across multiple nodes or locations, often spread across different devices or networks, to enhance data security, accessibility, and fault tolerance.

Decentralized Storage

A family of formal knowledge representation languages in AI, used to represent and reason about the knowledge of an application domain in a structured and formally well-understood way.

Description Logics

An algorithm designed to identify patterns, trends, or relationships within data sets, often used in data mining and machine learning.

Discovery Algorithm

A model in which components of a software system are shared among multiple computers to improve efficiency and performance. It involves dividing a complex problem into smaller parts, distributing those parts to multiple computers, and then combining the results. This approach is used in various applications like cloud computing, grid computing, and parallel computing.

Distributed Computing

The creation of a concise and coherent summary of a longer text document, maintaining the essential information and overall meaning.

Document Summarization

A suite of AI technologies developed by Salesforce, designed to provide predictions and recommendations based on business data, automate tasks, and improve customer service.

Einstein AI

The dimensionality of the vector space in which entities (like words, users, or items) are represented as vectors, in machine learning models.

Embedding Size

Personalization techniques that use embedding vectors to represent users and items, facilitating more accurate recommendations and content targeting.

Embedding-Based Personalization

In the context of machine learning, embeddings are a form of feature learning where high-dimensional data (like text or images) is converted into lower-dimensional vectors while preserving relevant information. This technique is often used to process natural language data, enabling algorithms to understand and manipulate words or phrases based on their context in a dataset.

Embeddings

A process in natural language processing that identifies and classifies named entities (like names of people, organizations, locations, expressions of times, quantities, monetary values, percentages, etc.) present in text.

Entity Recognition

An open-source library developed by Facebook AI Research for efficient similarity search and clustering of dense vectors.

Faiss (Facebook Ai Similarity Search)

A term that might refer to a specialized form of embeddings used for flagging or highlighting specific features or aspects in data representation.

Flagembeddings

A specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display device. It's also used for complex computations in AI and machine learning.

GPU (Graphics Processing Unit)

A type of artificial intelligence that is capable of creating new content, including text, images, and music, based on the patterns it learns from training data. This includes technologies like LLAMA for text generation and DALL-E for image generation.

Generative AI (Gen-AI)

A type of language model that uses deep learning to produce human-like text. It's pre-trained on a large corpus of text and then fine-tuned for specific tasks. GPT models are known for their ability to generate coherent and contextually relevant text based on a given prompt.

Generative Pre-Trained Transformer

"A type of artificial intelligence model designed by OpenAI to generate human-like text. GPT models are trained on a vast amount of text data, enabling them to generate coherent and "

Generative Pre-trained Transformer (GPT)

A concept in data retrieval and recommendation systems where the relevance of information or content to a user's query or interest becomes increasingly precise over time or through interactions.

Gradual Relevance

A mathematical space with many dimensions used to represent complex data (like words, images, or user profiles) in a form that can be processed by algorithms.

High-Dimensional Vector Space

A graph-based algorithm used for efficient approximate nearest neighbor (ANN) search in high-dimensional spaces.

Hnsw (Hierarchical Navigable Small World)

A term that might refer to a specific type of database or data storage system, potentially characterized by certain features like efficiency, scalability, or a unique data processing method.

Hollowdb

The use of data, analytics, AI, and automation to deliver more personalized products, services, and content to individual consumers.

Hyper-Personalization

This is the process of optimizing the parameters that govern the overall behavior of a machine learning model. The goal is to find the best combination of hyperparameters that minimizes a predefined loss function on given data.

Hyperparameter Tuning

The administration of identifying data and authentication credentials, ensuring the right individuals can access the right resources at the right times for the right reasons.

ID Management

Data collected about a user's activities and interactions during a single session on a website or application, used for real-time analysis or personalization.

In-Session Data

Components of AI systems that apply logical rules and algorithms to the knowledge base to derive new information or make decisions.

Inference Mechanisms

An algorithm used to map and understand a user's progression of interests over time, often used in marketing and recommendation systems to personalize content and offers.

Interest Journey Algorithm

Representations of items (like products, movies, articles) as vectors in a multi-dimensional space, capturing their features and relationships for use in recommendation systems.

Item Embeddings

A recommendation system approach that makes predictions about a user's interests by comparing the similarities between items based on users' previous interactions or ratings.

Item-Based Collaborative Filtering

Databases that store data as a collection of key-value pairs, where a key serves as a unique identifier to access its corresponding value. Key-value databases are optimized for retrieval and storing large amounts of data and are commonly used for caching and real-time web applications.

Key-Value Databases

The process of comparing and matching keywords or metadata tags in search and retrieval systems to find relevant content or information.

Keyword/Metadata Matching

The transformation of knowledge into a vector format, enabling its use in computational models, particularly in machine learning and AI.

Knowledge Vectorization

AI systems that utilize a knowledge base of facts and relationships and apply inferential logic to solve complex problems, answer questions, or make recommendations.

Knowledge-Based Ai

A term that could refer to a caching mechanism specifically designed for large language models, aiming to improve performance and efficiency.

LLMCache

LangChain is a framework for developing applications powered by language models.

Langchain

An advanced type of machine learning model specifically designed to process, understand, and generate human language. These models are 'large' due to the immense size of their training data and the complexity of their neural network architectures. They are capable of tasks like translation, question answering, and text generation.

Large Language Model (LLM)

A fast, compact, and reliable embedded data store designed for high-performance, read-heavy applications.

Lightning Memory-Mapped Database (Lmdb)

LLaMA is a family of large language models, released by Meta AI

Llama

LMDB is a fast, memory-efficient database. It is designed to provide high performance and reliability for data-intensive applications. LMDB uses a memory-mapped file, which makes data retrieval fast and efficient.

Lmdb (Lightning Memory-Mapped Database)

Probability distributions with a high frequency of occurrence for a small number of events and a long "tail" of lower frequency occurrences for many events, commonly seen in retail and online business models.

Long-Tail Distributions

A subset of artificial intelligence that involves the development of algorithms that can learn from and make predictions or decisions based on data. Machine learning models improve their performance as they are exposed to more data over time.

Machine Learning

A technique used in machine learning to decompose a matrix into multiple constituent matrices. It's often used in recommendation systems to discover latent features underlying the interactions between two different kinds of entities (like users and movies).

Matrix Factorization

This is a measure used in information retrieval, representing the average of the precision scores at each threshold in a precision-recall curve. It's a standard metric for evaluating models in tasks like object detection and ranking problems.

Mean Average Precision

"Data that provides information about other data. It can include elements like description, a summary of the content, authorship, and file type. In a digital context, metadata can help organize, find, and understand data.  "

Metadata

In machine learning, model ensembling involves combining multiple models to improve the accuracy of predictions. By aggregating the predictions of several models, ensemble methods can achieve better performance than any single model.

Model Ensembling

A method in AI that involves making multiple inferential steps to arrive at a conclusion, often used in complex question-answering systems.

Multi-Hop Reasoning

A tool or algorithm in AI that retrieves relevant data or content from multiple vectorized representations, enhancing the accuracy and relevance of search results.

Multi-Vector Retriever

 A field of artificial intelligence that focuses on the interaction between computers and human language. It involves the development of algorithms and systems that can understand, interpret, and generate human language in a useful way.

Developer Dictionary