Ambient Intelligence

Artificial Intelligence has received lots of attention, but a new term called Ambient Intelligence (AmI) is emerging as a cornerstone of the Internet of Things [KC].

The term is a relatively unknown, but not all that new. AmI dates back 16 years—just a few years after the Internet saw its first widespread commercial adoption—when the concept first emerged as a collection of intelligent electronic environments, responsive and sensitive and to our desires, requirements, and needs. It entails ubiquitous sensors embedded into every nook and cranny of our world, heavily populated by gadgets and systems that are capable of powerful capabilities nano- bio- information and communication technology (NBIC).

AmI got its real start in 1998 at Royal Philips of The Netherlands. A consortium of individuals, including Eli Zelkha and Brian Epstein of Palo Alto Ventures (who, with Simon Birrell, coined the name ‘Ambient Intelligence’), described it as “a world where homes will have a distributed intelligent network of devices that provide us with information, communication and entertainment.”

• Sensing. The first element that needs to be in place is the sensor—and not just any sensor. With AmI the network must be able to respond to real-world stimulus. Components must integrate agile agents that perceive and respond intelligently, not simply pick from a series of scenarios in a data base full of theoretical algorithms (which wouldn’t be realistic for dust, or micro-type sensors with limited resources anyway).
• Modeling. One of the features that AmI integrates is the ability to differentiate between general computing algorithms and specific ones that can adapt to or learn about the user. Such “learning” systems do exist and are fairly adept at do this. Even so, the problem with these systems is that, to do it with any amount of efficiency. They require a deep well of hardware and software resources. That works in many cases, and will work to some degree in AmI. Agile systems envisioned in AmI will need to be able to do this, efficiently and accurately in a small form factor, with the ability to refine and adapt itself on the fly.
• Prediction and recognition. These arguably are the two top elements of reasoning in AmI environments. Prediction is accomplished by attestation, from which comes intelligence, which in turn can be used for recognition and, ultimately, prediction. Theoretically, sufficient reiterations of this cycle will increase the intelligence within the networks to near human capability.
• Decision Making. Part of the AmI platform is AI and fuzzy logic. Neural networks are a key element in the decision-making process. Temporal reasoning can be implemented in conjunction with rule-based algorithms to perform any number of functions; from identifying safety concerns to analyzing medical data and adjusting medications, to and diet planning based upon wearable sensor data, to environmental comfort settings.
• Temporal and Spatial Components. The Support Elements. These are crucial elements of AI. There is a wide collection of algorithms that have been developed and honed to deal with the various segments of spatial, temporal, and spatio-temporal reasoning. Such algorithms are another element of the network that allows AmI to understanding of the activities in an AmI application.

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