Ambient Intelligence
Ambient Intelligence
History In 1998, Philips and Palo Alto Ventures coined the term ‘ambient intelligence’ (AmI) in order to illustrate a vision of the future where technologies seamlessly interact and adapt to human needs while being none obtrusive. From then on they have further developed the concept, or vision, and designed products such as the Philips Healthcare Ambient Experience[1] that fit into their conception of AmI (de Ruyter & Aarts 2004). AmI gained further substantial attention when the European Commission’s Sixth Framework in Information, Society and Technology (IST) programme set aside a budget to fund research projects that were looking especially at AmI (Aisola, 2005). The funding may have been as a result of advice received from the Information Society and Technology Advisory Group (ISTAG), a group set up to advice the European Commission on overall strategy and implementation of ICT research in Europe who had launched a scenario planning exercise to demonstrate what might be realised through AmI technology (ISTAG, 2005; 2001). Through the exercise, ISTAG set out its vision for AmI by illustrating what living with AmI might be like in 2010 now extended to 2020. As a result, AmI gained a substantial amount of attention, and in the last years several initiatives were started. Research projects in the USA, Canada, Spain, France and the Netherlands were launched. And in 2004, the first European symposium on Ambient Intelligence was held and many other conferences such as the International Symposium on Ambient Intelligence (ISAmI) and the European Conference on Ambient Intelligence among others have been held that address special topics in AmI.
Of course Ambient Intelligence was preceded by other visions and technological developments that had similar kinds of ambitions that AmI has. Aarts (2003) names for example artificial intelligence as such a vision that shares same ambitions like those of AmI. In addition, Weiser work deserves a mention when discussing the history of AmI. For instance, as Ami is very closely related to the earlier concepts of ubiquitous computing, Marc Weiser was already showcasing AmI demos in the early 1980’s/early 1990’s. In light of this, one could claim that Mark Weiser’s vision of ubiquitous computing, closely related to AmI has almost been achieved nowadays when looked at through the developments taking place in AmI. However, his other vision of calm computing has not been achieved yet (Weiser 1991, Weiser and Brown 1995). The biggest difference between earlier visions of AmI and the current ones is according to Aarts (2003) the shift of focus from productivity in business environments to consumer and user centred design approach. In addition the new kind of interaction with technologies in our everyday lives is expected to somehow enhance our experiences and lives with AmI applications.
The most well-known example of AmI is probably a refrigerator that orders products by itself without human intervention (Ducatel, 2010). The fridge ‘knows’ by analyzing previous consuming behavior that there is always milk in the fridge, and it is programmed to order milk when there is only one bottle left. Another often mentioned example is the use of miniaturized biosensor systems that monitor blood pressure, glucose levels, body temperature, heart rate and other bodily variables. The biosensor systems are wirelessly connected to an emergency unit that automatically sends an ambulance when an accident happens, which it presumably automatically ‘assumes’ when certain threshold values are exceeded (Schuurman et al., 2009; Federal Ministry of Education and Research, 2007). Or, to give a final example, consider an interactive screen in one’s mirror that automatically provides its user with customized information about the weather, traffic jams and appointments of the day, so that its user knows what to wear, how to drive and what to prepare for which appointment.
In the previous brief examples the user(s) is provided with applications and services with which he or she interacts in an unobtrusive manner. In the vision of AmI, the sensors are supposed to be supported and utilized in this interaction, and applications and services should be consistent, easy to handle and easy to learn. Furthermore, electric devices are wirelessly connected and form intelligent networks which create environments in which people are surrounded by intelligent and intuitive interfaces that are embedded in all kinds of objects. The ultimate goal is to design an environment that is capable of recognizing and responding to the presence and actions of different individuals in a seamless, unobtrusive and often, invisible way using several sensors (Holmlid & Björklind, 2003).
Application Areas/Examples In this section some scenarios and examples of AmI are given. AmI is a concept that can be applied in basically every societal area such as healthcare, offices, homes, education, transport, entertainment and so on. Therefore, as the scenarios and examples show, AmI is not bound to a particular context, but is meant for society as a whole and is cross-cutting across fields of applications.
AmI in the home environment. ‘Ellen returns home after a long day’s work. At the front door she is recognized by an intelligent surveillance camera, the door alarm is switched off, and the door unlocks and opens. When she enters the hall the house map indicates that her husband Peter is at an art fair in Paris, and that her daughter Charlotte is in the children’s playroom, where she is playing with an interactive screen. The remote children surveillance service is notified that she is at home, and subsequently the on-line connection is switched off. When she enters the kitchen the family memo frame lights up to indicate that there are new messages. The shopping list that has been composed needs confirmation before it is sent to the supermarket for delivery. There is also a message notifying that the home information system has found new information on the semantic Web about economic holiday cottages with sea sight in Spain. She briefly connects to the playroom to say hello to Charlotte, and her video picture automatically appears on the flat screen that is currently used by Charlotte. Next, she connects to Peter at the art fair in Paris. He shows her through his contact lens camera some of the sculptures he intends to buy, and she confirms his choice. In the mean time she selects one of the displayed menus that indicate what can be prepared with the food that is currently available from the pantry and the refrigerator. Next, she switches to the video on demand channel to watch the latest news program. Through the follow me, she switches over to the flat screen in the bedroom where she is going to have her personalized workout session. Later that evening, after Peter has returned home, they are chatting with a friend in the living room with their personalized ambient lighting switched on. They watch the virtual presenter that informs them about the programs and the information that have been recorded by the home storage server earlier that day’ (Wikipedia, 2010).
AmI in healthcare. ‘Ambient Intelligence could provide a basis for integrating intelligent health care technology into an individual’s personal surroundings. Computers around you, on your body and even in your body could monitor your health status at all times and, when the need arose, alert your carer or intervene directly’ (Schuurman, El-Hadidy, Krom & Walhout, 2009, p. 15).
AmI for disabled persons. ‘A personal communication device can be worn or fitted to a wheelchair or a blind person’s cane. These can be programmed to communicate with barriers, ticket machines and gates to allow access or more time. Smart tags, embedded in a floor, can receive and send information that will guide a person to a destination. A person with low-vision could hear guidance signals’ (Gill, 2008, p 6).
AmI for industry. ‘Intelligent and autonomous networked sensors systems offer possible uses among others in precise, low-cost control of chemical processes, in monitoring and linking machines, in the tracking and management of security-relevant objects, in registering ambient conditions and in testing product quality of the condition of building fabric’ (Federal Ministry of Education and Research, 2007, p 39).
AmI for business. ‘Hélène calls Ralph in New York from their company’s home office in Paris. Ralph’s E21, connected to his phone, recognizes Hélène’s telephone number; it answers in her native French, reports that Ralph is away on vacation, and asks if her call is urgent. The E21′s multilingual speech and automation systems, which Ralph has scripted to handle urgent calls from people such as Hélène, recognize the word “décisif” in Hélène’s reply and transfer the call to Ralph’s H21 in his hotel. When Ralph speaks with Hélène, he decides to bring George, now at home in London, into the conversation. All three decide to meet next week in Paris. Conversing with their E21s, they ask their automated calendars to compare their schedules and check the availability of flights from New York and London to Paris. Next Tuesday at 11am looks good. All three say “OK” and their automation systems make the necessary reservations. Ralph and George arrive at Paris headquarters. At the front desk, they pick up H21s, which recognize their faces and connect to their E21s in New York and London. Ralph asks his H21 where they can find Hélène. It tells them she’s across the street, and it provides an indoor/outdoor navigation system to guide them to her. George asks his H21 for “last week’s technical drawings,” which he forgot to bring. The H21 finds and fetches the drawings just as they meet Hélène’ (MIT Project Oxygen, 2010).
Definition and Defining Features From the five examples described in the previous section, one can infer a definition and deduce some defining features. The general idea of AmI is that electronic devices in our homes, offices, hospitals, cars and public spaces will be embedded, interconnected, adaptive, personalized, anticipatory and context-aware. These six features are now described in more detail.
Embedded It is important to note that AmI is not the outcome of any single technology or application. It is rather an emergent property of several interconnected computational devices, sensors and ICT systems. The devices, sensors and ICT systems are distributed and embedded in the surroundings. The technology disappears into the background and is usually not consciously experienced.
Interconnected The devices, sensors and ICT systems are not only embedded in the surroundings, they are (wirelessly) interconnected as well, thereby forming a ubiquitous system of large-scale distributed networks of interconnected computing devices. For example, the miniaturized biosensor systems that monitor vital body variables are connected to an emergency unit which is connected to the ambulance. All the devices, sensors and ICT systems are connected and form one ambient intelligence system.
Adaptive Because there is no stable connectivity to services and information sources in ad-hoc networks, AmI systems can never base their operation on the availability of complete and up-to-date information and services. This has the consequence that AmI systems have to organize their services in an adaptive way, i.e. the degree of service varies with the amount of information available and the reach-ability of external services.
Personalized AmI is personalized to specific user needs and preferences. Remember the example of an interactive interface in one’s mirror which provides its user with personalized information about the weather, traffic jams and appointments. In other words, AmI is user-centered.
Anticipatory AmI can anticipate the desires of its user(s). Consider an AmI system in the context of one’s home that monitors behavioral patterns, infers one’s mood from the behavioral patterns and adjusts the light and music accordingly. The system is pro-active and anticipates to what the user wants or needs.
Context-aware AmI systems can recognize specific users and its situational context and can adjust to the user and context. It may know that some users like classical music and others like jazz. Or that some users prefer it to be warm in the house and others like it cool. To give a contemporary example, car navigation systems can adjust the level of their lighting when it becomes dark, and the user benefits from more light on the screen. The system ‘knows’ that its user benefits from more or less light in different contexts.
Novel human-technology interaction paradigms AmI systems will utilize new kind of interfaces which should support more seamless user experience with products and services. It is assumed that interaction paradigms like speech or haptics could lead to the more intuitive or natural interfaces.
Furthermore, it is important to note that not all features are equally present in all AmI systems. For example, some AmI systems are very personalized, anticipatory and adaptive whereas others are not. It may also be helpful to note that there is some overlap between adaptive, personalized, anticipatory and context-aware. The adaptive and anticipatory aspects, for example, make sure that the system can be personalized.
Timeline As stated previously, AmI is a vision of the future of ICT and does not exist yet, at least not in the sense of the definitions described in the introduction. Some contemporary devices are to some extent context-aware, personalized and adaptive, but not on the scale of the AmI vision, according to which, many devices are embedded in the environment, interconnected and context-aware so that they can be adaptive, anticipatory and personalized. Contemporary computing systems do not confirm with this description. Finally, AmI is a view and prediction of the future and it is claimed to be realized in approximately 10 years from now, that is to say, in 2020 (Philips Research, 2010; Gasson & Warwick, 2007; Federal Ministry of Education and Research, 2007). It is, however, not clear from the literature when exactly or to what extent AmI will become a reality.
Relation to other Technologies AmI is related to other ICT concepts such as ubiquitous computing (the idea that computing devices are everywhere, they are ubiquitous) and internet of things (the idea that computing devices are increasingly interconnected and form intelligent networks). These concepts are often mentioned as synonyms for AmI and there are indeed more similarities than differences. One could also argue that ubiquitous computing and internet of things are enabling technologies for AmI. Without computing devices that are ubiquitous and interconnected AmI would not be possible. Other enabling technologies are sensor systems, usually cameras that can recognize different objects and biosensors. Radio Frequency Identification (RFID) chips are also mentioned as enabling technologies as well as other electrical devices such as interactive screens, televisions, radios, fridges, lighting systems, the Internet and so on. Basically every electric device could be incorporated into the AmI system.
Critical Issues Although some predict that AmI will be existent within 15 years, others claim it will be an extremely difficult task to build, provide and maintain the infrastructure required to support the AmI environment. Both functional (related to the specific system operation) and non-functional (security, scalability, performance, robustness, availability, reliability, license issues, etc) requirements of an AmI system pose strict demands on the computational and communication resources and in general on the underlying infrastructure (Gasson & Warwick, 2007). Moreover, AmI systems must be able to ‘infer the goals of the users without giving them the impression that they are under control (big brother), and must be able to support the users without giving them the impression that they are forcing them. The system must offer possible solutions, not impose them. This requires a lot of ingenuity also on the part of human beings, and appears particularly difficult for a machine’ (Gill, 2008, p 5). Finally, the AmI environment will be very complex and stimulating, from both a sensorial and cognitive perspective. It is not clear whether people will be able to cope with the hyper stimulation and the corresponding cognitive load. This is particularly true for people with reduced abilities, and principally for people with cognitive limitations (Gill, 2008). Thus, there are substantial technical issues to resolve as well as human-centered design issues.
There are several critical issues mentioned in the literature on AmI. It may be helpful to keep in mind that these critical issues are raised by the authors of scientific and governmental/political reports on AmI and not by ethicists. These issues are now briefly summarized.
Data Protection/Surveillance/Privacy AmI systems use profiling to adapt to user preferences. A large amount of sensors and data mining is needed to adapt to user preferences. The main question is: Who is in control of the personal data collected by the sensors and who has access to it? An operator? A hacker? AmI has great surveillance potential which can therefore cause privacy issues. As such the user profiles need to be protected (Gasson & Warwick, 2007; Gill, 2008; Schuurman et al, 2009).
Autonomy & Trust In some cases AmI systems can be said to have the ability to make decisions for people. Recall the fridge that automatically orders milk when there is one bottle left. We delegate tasks to the system and trust that it does the tasks well. But, what if we do not want milk but orange juice? Who is to blame? The system or its user? Or what if the fridge orders soft drinks, but it should have ordered orange juice? The point is that AmI can reduce one’s autonomy (Schuurman et al, 2009; Gill, 2007).
Reliability ‘One central challenge consists of achieving reliability and fail-safe stability in autonomous systems with ambient intelligence’ (Federal Ministry of Education and Research, 2007, p 39). For example, a user relies on an AmI system to open the door when he or she stands in front of it. But what happens when the door remains closed? We heavily rely on the system and things may go wrong if the system fails or malfunctions.
References
Aarts, E. (2003)Technological Issues in Ambient Intelligence; Emile Aarts and Stefano Marzano (eds.), The New Everyday Visions of Ambient Intelligence, pp. 12-17; Aarts, Emile; 010 Publishers Rotterdam; 2003
Aisola, K. (2005) The European Union’s Sixth Framework Programme: a Laypersons Guide to Funding. Tactical Technology Collective, Amsterdam
Ducatel, K., Bogdanowicz, M., Scapolo, F., Leijten, J. & Burgelman, J-C. (2010). That’s what friends are for. Ambient Intelligence (AmI) and the IS in 2010. Retrieved May 4, 2010, from http://www.itas.fzk.de/eng/e-society/preprints/esociety/Ducatel%20et%20al.pdf
de Ruyter, B., Aarts, E. (2004): Ambient intelligence: visualizing the future, Proceedings of the Working Conference on Advanced Visual Interfaces, AVI 2004, pp 203-208; May 28, 2004
Federal Ministry of Education and Research (BMBF). (2007). ICT 2020 – Research for Innovations.
Gasson, M. & Warwick, K. (2007). D12.1 Study On Emerging AmI Technologies. FIDIS – Future of Identity in the Information Society.
Gill, J. (2008). Ambient Intelligence – Paving the way. Cost.
Holmlid, S. & Björklind, A. (2003). Ambient Intelligence to go, AmiGo white paper on mobile intelligent ambience. Research Report SAR-03-03.
ISTAG. (2005). Ambient Intelligence: from Vision to Reality. Retrieved May 4, 2010, from http://www.vepsy.com/communication/book5/04_AMI_istag.pdf
ISTAG. (2001). ISATG Scenarios for Ambient Intelligence 2010. Retrieved May 4, 2010, from ftp://ftp.cordis.europa.eu/pub/ist/docs/istagscenarios2010.pdf
MIT Project Oxygen. (2010). Retrieved March 26, 2010, from http://oxygen.lcs.mit.edu/
Philips Research. Retrieved January 31, 2010, from http://www.research.philips.com/technologies/projects/ambintel.html
Schuurman, J.G., El-Hadidy, F.M., Krom, A. & Walhout, B. (2009). Ambient Intelligence – Viable Future or Dangerous Illusion? Rathenau Institute, The Hague.
Weiser, M. (1991) The Computer for the 21st Century, Scientific American, vol. 265, no. 3, September 1991 (reprinted in IEEE Pervasive Computing: Mobile and Ubiquitous Systems, vol. 1, no. 1, January-March 2002).
Weiser, M. and Brown, J.S. (1995) Designing Calm Technology. Accessed April 7, 2006, from: http://www.ubiq.com/weiser/calmtech/calmtech.htm
Wikipedia entry on Ambient Intelligence. Retrieved February 2, 2010, from http://en.wikipedia.org/wiki/Ambient_intelligence#CITEREFAartsHarwigSchuurmans2001
[1] http://www.healthcare.philips.com/wpd.aspx?p=/resources/news.wpd&id=1683&c=main
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