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What is Ambient Intelligence (AmI)? Definition of SearchEnterpriseAI

What is Ambient Intelligence (AmI)?

Ambient intelligence (AmI) is the element of a pervasive computing environment that allows it to interact and respond appropriately to humans in that environment.

This capability is enabled by discrete embedded devices in the environment and Natural User Interfaces (NUIs), providing certain services autonomously in response to perceived needs and accepting user input through voice, gestures and d other non-disruptive methods.

Popular examples of ambient intelligence include Google Assistant and Amazon Alexa – devices that automatically respond to a person’s voice.

Ambient intelligence helps power IoT and AI devices

Ambient intelligence at the service of the IoT

AmI communication elements are always on and responsive to human input and other variables.

In addition to home and business settings, ambient intelligence can also be used in a fully automated environment to assess conditions, interact with other devices, perform management functions, and transmit data outdoors.

Some elements of an AmI environment:

Integration: Computers are generally not self-contained devices in the environment, but many man-made and organic systems have built-in intelligence and computing capabilities. The current development of the Internet of Things (IoT), which consists of equipping just about any type of object imaginable with computing capacity and connectivity, leads us to embedded computing.

Transparency: Transparency, in the context of transparent computing, essentially means “invisibility”. People naturally interact with embedded systems – by asking a question rather than, say, picking up a tablet and typing in a search query.

Context awareness: This component is the ability of a system or system component to gather information about its environment at a given time and adapt behaviors accordingly. Contextual or context-aware computing uses software and hardware to automatically collect and analyze data to guide responses. Potential data collection and response systems include sensors, emotion analysis, and affective computing software.

Machine Learning: This capability allows devices in the environment to learn from experience, extrapolate from current data, and extend their knowledge and capabilities autonomously.

AmI, Internet of Things, Artificial Intelligence (AI), Robotics, Nanotechnology and other developing trends are transforming the world to such an extent that the current scenario is sometimes referred to as the Fourth Industrial Revolution.