Glossary

The fifth-generation technology standard for cellular networks, which cellular phone companies began deploying worldwide in 2019, and is the successor to 4G technology that provides connectivity to most current mobile phones.

Stands for artificial intelligence, which is the simulation of human intelligence processes by machines or computer systems. AI can mimic human capabilities such as communication, learning, and decision-making.

An application programming interface, is a set of protocols that determine how two software applications will interact with each other. APIs tend to be written in programming languages such as C++ or JavaScript.

A sequence of rules given to an AI machine to perform a task or solve a problem. Common algorithms include classification, regression, and clustering.

The delivery of computing services—including servers, storage, databases, networking, software, analytics, and intelligence—over the internet (“the cloud”) to offer faster innovation, flexible resources, and economies of scale.

Includes the vast network of highways, connecting bridges and tunnels, railways, utilities and buildings necessary to maintain normalcy in daily life. Transportation, commerce, clean water and electricity all rely on these vital systems.

Is the process of developing a company-specific AI solution targeting a particular problem. Since custom AI software is developed for a single business, it needs to satisfy the business' specifications and expectations.

The art of protecting networks, devices, and data from unauthorized access or criminal use and the practice of ensuring confidentiality, integrity, and availability of information.

A centralized repository designed to store, process, and secure large amounts of structured, semi structured, and unstructured data. It can store data in its native format and process any variety of it, ignoring size limits. Learn more about modernizing your data lake on Google Cloud.

The process of sorting through large data sets to identify patterns that can improve models or solve problems.

A collection of related, discrete items of related data that may be accessed individually or in combination or managed as a whole entity.

A function of AI that imitates the human brain by learning from how it structures and processes information to make decisions. Instead of relying on an algorithm that can only perform one specific task, this subset of machine learning can learn from unstructured data without supervision.

Risk brought on by sudden and unpredictable changes in the economy

Refers to technologies that are currently developing, or that are expected to be available within the next five to ten years, and is usually reserved for technologies that are creating, or are expected to create, significant social or economic effects.

Is the process by which multiple models, such as classifiers or experts, are strategically generated and combined to solve a particular computational intelligence problem.

Generative AI refers to deep-learning models that can generate high-quality text, images, and other content based on the data they were trained on.

Refers to the collective network of connected devices and the technology that facilitates communication between devices and the cloud, as well as between the devices themselves.

Type of language model notable for its ability to achieve general-purpose language understanding and generation.

A subset of AI that incorporates aspects of computer science, mathematics, and coding. Machine learning focuses on developing algorithms and models that help machines learn from data and predict trends and behaviors, without human assistance.

Multipolarity is a distribution of power in which more than two states have similar amounts of power.

(NLP) is a type of AI that enables computers to understand spoken and written human language. NLP enables features like text and speech recognition on devices.

A subset of AI that incorporates aspects of computer science, mathematics, and coding. Machine learning focuses on developing algorithms and models that help machines learn from data and predict trends and behaviors, without human assistance.

A deep learning technique designed to resemble the human brain’s structure. Neural networks require large data sets to perform calculations and create outputs, which enables features like speech and vision recognition.

A type of analytics that uses technology to predict what will happen in a specific time frame based on historical data and patterns.

a type of analytics that uses technology to analyze data for factors such as possible situations and scenarios, past and present performance, and other resources to help organizations make better strategic decisions.

The process of using quantum-mechanical phenomena such as entanglement and superposition to perform calculations. Quantum machine learning uses these algorithms on quantum computers to expedite work because it performs much faster than a classic machine learning program and computer.

A type of machine learning in which an algorithm learns by interacting with its environment and then is either rewarded or penalized based on its actions.

Data that is defined and searchable. This includes data like phone numbers, dates, and product SKUs.

A type of machine learning in which classified output data is used to train the machine and produce the correct algorithms. It is much more common than unsupervised learning.

A machine learning system that takes existing, previously learned data and applies it to new tasks and activities.

Data that is undefined and difficult to search. This includes audio, photo, and video content. Most of the data in the world is unstructured.

A type of machine learning in which an algorithm is trained with unclassified and unlabeled data so that it acts without supervision.