ambient intelligence (AmI)

What is ambient intelligence (AmI)?

Ambient intelligence, sometimes referred to as AmI, is the element of a pervasive computing environment that enables it to interact with and respond appropriately to the humans in that environment. That capacity is enabled by unobtrusive embedded devices in the environment and natural user interfaces, which provide some services autonomously in response to perceived needs and accept user input through voice, gesture and other noninterruptive methods.

Popular examples of ambient intelligence include devices that automatically respond to a person's voice, such as Google Assistant and Amazon Alexa. Along with technologies such as the internet of things (IoT), artificial intelligence, robotics and nanotechnology, ambient intelligence is sometimes considered among the transformative technologies underpinning the Fourth Industrial Revolution, or Industry 4.0.

Ambient intelligence powering IoT

Ambient intelligence communication elements are always on and receptive to human input and other variables. In addition to home and business settings, ambient intelligence is also used in completely automated environments to assess conditions, interact with other devices, perform management functions and transmit data externally.

Some elements of an ambient intelligence environment include the following:

• Embeddedness. Technological developments such as the prevalence of IoT devices -- objects outfitted with computing ability and connectivity -- are leading to embedded computing, combinations of hardware and software designed for specific purposes.
• Transparency. In the context of transparent computing, transparency essentially refers to invisibility. People interact with ambient intelligence systems naturally -- for example, by asking a question rather than picking up a tablet and typing a search query.
• Context awareness. Context-aware systems and system components can gather information about their environment at any time and adapt their behavior accordingly. Contextual or context-aware computing automatically collects and analyzes data to guide a system's responses using software and hardware. Potential data collection and response systems include sensors, emotions analytics and affective computing software.
• Machine learning. Machine learning models enable devices in the environment to learn from experience, extrapolate from current data, and autonomously expand on their knowledge and capabilities.