AI infrastructure (also AI-defined infrastructure, AI-enabled Infrastructure) is the infrastructure of an artificial intelligence system, AI infrastructure, AI infrastructure, for example, AI infrastructure research research in the field of AI infrastructures (see also AI, AI hardware).
AI server is a server with (based on) AI; a server that provides solving AI problems.
AI shopper is a non-human economic entity that receives goods or services in exchange for payment. Examples include virtual personal assistants, smart appliances, connected cars, and IoT-enabled factory equipment. These AIs act on behalf of a human or organization client.
AI supercomputer a supercomputer for artificial intelligence tasks, a supercomputer for AI, characterized by a focus on working with large amounts of data (see also artificial intelligence, supercomputer).
AI term a term from the field of AI (from terminology, AI vocabulary), for example, in AI terms in terms of AI (in AI language) (see also AI terminology).
AI terminology is the terminology of artificial intelligence, a set of technical terms related to the field of AI.
AI TRiSM is the management of an AI model to ensure trust, fairness, efficiency, security, and data protection.
AI vendor is a supplier of AI tools (systems, solutions).
AI winter (Winter of artificial intelligence) is a period of reduced interest in the subject area, reduced research funding. The term was coined by analogy with the idea of nuclear winter. The field of artificial intelligence has gone through several cycles of hype, followed by disappointment and criticism, followed by a strong cooling off of interest, and then followed by renewed interest years or decades later31.
AI workstation is a workstation (PC) with (based on) AI; AI RS, a specialized computer for solving technical or scientific problems, AI tasks; usually connected to a LAN with multi-user operating systems, intended primarily for the individual work of one specialist.
AI-based management system is the process of creating policies, allocating decision-making rights and ensuring organizational responsibility for risk and investment decisions for an application, as well as using artificial intelligence methods.
AI-based systems are information processing technologies that include models and algorithms that provide the ability to learn and perform cognitive tasks, with results in the form of predictive assessment and decision making in a material and virtual environment. AI systems are designed to work with some degree of autonomy through modeling and representation of knowledge, as well as the use of data and the calculation of correlations. AI-based systems can use various methodologies, in particular: machine learning, including deep learning and reinforcement learning; automated reasoning, including planning, dispatching, knowledge representation and reasoning, search and optimization. AI-based systems can be used in cyber-physical systems, including equipment control systems via the Internet, robotic equipment, social robotics and human-machine interface systems that combine the functions of control, recognition, processing of data collected by sensors, as well as the operation of actuators in the environment of functioning of AI systems32.
AI-complete in the field of artificial intelligence, the most difficult problems are informally known as AI-complete or AI-hard, implying that the difficulty of these computational problems is equivalent to that of solving the central artificial intelligence problem making computers as intelligent as people, or strong AI. To call a problem AI-complete reflects an attitude that it would not be solved by a simple specific algorithm33.
AI-enabled are tools with AI, using AI and equipped with AI (see also AI-enabled device). AI-enabled is AI-enabled hardware or software that uses AI-enabled AI, such as AI-enabled tools.
AI-enabled device is a device supported by an artificial intelligence (AI) system, such as an intelligent robot.
AI-enabled healthcare device is an AI-enabled healthcare device.
AIOps (Artificial Intelligence for IT Operations) is the use of machine learning and other AI technologies to automate many processes that are currently done manually in an organization. AIOps is similar to MLOps in that it uses machine learning and other AI technologies to automate IT processes. It is different from MLOps in that the process automation occurs within an organizations IT operations department instead of an organizations machine learning and AI team. AIOps is also different from MLOps because it uses AI to automate many processes, not just one or two tasks like MLOps does.
AI-optimized is one that is optimized for AI tasks or optimized using AI tools, for example, an AI-optimized chip is a chip that is optimized for AI tasks (see also artificial intelligence).
AlexNet is the name of a neural network that won the ImageNet Large Scale Visual Recognition Challenge in 2012. It is named after Alex Krizhevsky, then a computer science PhD student at Stanford University. See ImageNet.
Algorithm an exact prescription for the execution in a certain order of a system of operations for solving any problem from some given class (set) of problems. The term «algorithm» comes from the name of the Uzbek mathematician Musa Al-Khorezmi, who in the 9th century proposed the simplest arithmetic algorithms. In mathematics and cybernetics, a class of problems of a certain type is considered solved when an algorithm is established to solve it. Finding algorithms is a natural human goal in solving various classes of problems. Algorithm is a set of instructions for solving a problem or accomplishing a task. One common example of an algorithm is a recipe, which consists of specific instructions for preparing a dish or meal. Every computerized device uses algorithms to perform its functions in the form of hardware- or software-based routines. In finance, algorithms have become important in developing automated and high-frequency trading (HFT) systems, as well as in the pricing of sophisticated financial instruments like derivatives34.
Algorithm Economy is a term for the evolution of microservices and the functionality of algorithms to drive sophisticated application designs. The term is based on the utility of the algorithm in machine learning, artificial intelligence and other processes where software evolves beyond the limits of its original programming through the use of smart algorithm design. In the algorithm economy, companies can buy, sell or trade individual algorithms or pieces of an application. This decentralization of services is a more precise market than the market for full applications for instance, applications that can share functional algorithms lead to more versatility for developers and more competition in markets35.
Algorithmic Assessment is a technical evaluation that helps identify and address potential risks and unintended consequences of AI systems across your business, to engender trust and build supportive systems around AI decision making.
All.Can initiative to identify ways to optimize the efficiency of cancer care by focusing on improving outcomes for patients and identifying inefficient practices, using technology to demonstrate how efficient care happens36.