Intelligent Ambient Technology– Friend or Foe? Minna Kynsilehto
Thomas Olsson
Tampere University of Technology 1st line of address 2nd line of address Telephone number, incl. country code
Tampere University of Technology 1st line of address 2nd line of address Telephone number, incl. country code
[email protected]
[email protected] Generally, dictionaries explain intelligence with a broad definition including learning, understanding and applying knowledge in a new way. The Merriam-Webster [1] online dictionary defines intelligence as follows: “A (1): the ability to learn or understand or to deal with new or trying situations: reason; also: the skilled use of reason (2): the ability to apply knowledge to manipulate one's environment or to think abstractly as measured by objective criteria (as tests)”.
ABSTRACT This paper presents a part of findings from a study carried out to gain insight on user understanding of smart environments and preferred ways and places for interaction with smart services therein. Here we concentrate on qualitative interview data discussing the concept of intelligence with regard to technology and the participants’ perceptions of it. Such understanding of potential users’ expectations is critical in developing novel technologies and launching the first services based on it. Furthermore, naming the offered technology or service smart or intelligent might give wrong impressions of its capabilities, thus leading to experiences of worry or disappointment. The main finding is that the participants understood “intelligence” to mean different things, which are usually related to their own needs or technological novelty. In addition, an intelligent system and ability to act proactively raised concerns of loss of control.
Smartness, on the other hand, has slightly different tone to it, emphasizing mental alertness and eloquence; bright, witty, knowledgeable, clever and shrewd [1]. Nevertheless, it has synonymous uses with intelligence, indicated by both MerriamWebster and Oxford Advanced Learner’s dictionary: “having or showing intelligence; clever” [2] Furthermore, this study was carried out in Finland, where both Smart and Intelligent Environment are translated in the same way: to the expression close to “intelligent” rather than other words closer to additional connotations of “smart”. Thus, we treat the concepts of smart and intelligent interchangeably in this context, because it is about the understandings of users, not about technological distinctions or linguistic finesse.
Categories and Subject Descriptors H5.m. Information interfaces and presentation (e.g., HCI): Miscellaneous.
General Terms
Research in the field of psychology has looked into the phenomenon of intelligence. Researchers, such as Wechsler [4] and Gottfredson [4] have offered following definitions (respectively):
Human Factors, Design, Theory.
Keywords Smart Environments, Intelligence, Ubiquitous Computing, User Studies, Acceptance of Technology, User Expectations
“The aggregate or global capacity of the individual to act purposefully, to think rationally, and to deal effectively with his environment.”
1. INTRODUCTION
“The ability to deal with cognitive complexity.”
What do people talk about when they call something “smart” or “intelligent”? Do users refer to intelligence as humane construct when they evaluate smartness of technologies? In this paper we look at meanings attached to the concepts of smartness and intelligence – especially in the context of smart environments and place-based technology. We treat the concepts of intelligence and smartness interchangeably and we accept loose, common, everyday interpretations of these terms as they reflect the everyday experience of the user.
These definitions and our everyday intuition suggest that intelligence is more than stimulus-response or pre-learned reactions. Intelligence seems to mean new, even creative, purposeful way of behaving or thinking. To contrast with programmed automation: human intelligence seems to refer to being able to apply mental capacity to complex and new situations rather than following pre-set rules. What about intelligence of technology, then? Due to the development of ubiquitous computing technologies, the physical environments and places we perform our daily activities in are gradually becoming smart [5,6]. The physical environment is embedded with various sensor technologies and an infrastructure to provide a network for the users who carry various computing devices with constantly growing capabilities.
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Hermann et al. [7] define smart environments (SE) as seamlessly networked and providing access to ambient resources, devices and services, thus making the systems "intelligent". They
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characterise smart environments by, for example, following technical features: 1) highly integrated and seamlessly available data, services, and resources, 2) exchange of information, access right to objects, ambient resources, and devices, 3) exchange of personal information, 4) location based services and applications, 5) adaptivity and also autonomous system decisions making the systems “intelligent”.
These findings are very much in the same vein than in the paper at hand.
Related to smart environments, ambient intelligence refers to the kind of services that are sensitive and responsive to the presence and activities of people. According to the ambient intelligence vision, devices work in concert to support people in their everyday life activities in a natural way by using information and intelligence hidden in the network connecting these devices [8]. ETICA project (http://www.etica-project.eu/) lists ambient intelligence to incorporate for example the following features that can be seen to relate to smartness: 1) Adaptiveness: AmI systems can never base their operation on the availability of complete and up-to-date information and services. The degree of service varies with the amount of information available and the reach ability of external services; 2) Personalization: AmI is personalized to specific user needs and preferences; 3) Anticipatory: AmI system is pro-active and anticipates what the user wants or needs ; 4) Context-aware: AmI systems can recognize specific users and their situational context and can adjust to the user and context; 5) Novel human-technology interaction paradigms: systems will utilize new kind of interfaces which should support more seamless user experience with products and services.
Monitoring. The participants disliked the idea of being monitored. They felt it is an invasion of their privacy, even if it would be beneficial. Monitoring the elderly to alert in the case of medical emergency and the hallway for unwanted, potentially malevolent visitors, was better accepted than monitoring for less critical purposes.
Privacy. Throughout all scenarios, the participants expressed various kinds of concerns regarding to privacy and information security. Generally, the participants did not want others, private persons or businesses, to know about their location, purchases and choices.
Control. Control of automation and proactiveness were topics discussed in every focus group. The participants found generally certain instances of automation beneficial, such as lamps going on and off, but they wondered how the service would take different kinds of situations into account, as not wanting the light to be shut down. Autonomy. One major concern arising from proactivity was loss of decision control. In other words, the participants voiced their fears about not being able to make their own choices, if a device or a service would suggest products or actions to them. They used such phrases as “machine decides what to see” or felt forced or controlled if a service reminds them of their exercise program. Effort. Many of the services were considered beneficial in saving effort and making everyday life easier. These include getting heavy items carried for them, automating daily chores and other tasks, getting information easily, saving time and getting other services easily. Some services might require pre-work that was thought to be too demanding, such as making profiles or inserting a large number of contacts into a service.
According to these accounts, intelligence of smart services is characterized, for example, by information availability and exchange, context sensitivity, adaptation to the user and proactivity. Obviously, all these kinds of functions require information on the user and environment, therefore “intelligence” is about information exchange and utilization in pre-defined ways for the most part. Compared to human intelligence, it lacks creativity and adaptation to the novel. Furthermore, intelligence of technology is more precisely defined, whereas human intelligence refers to general roundabout ability and can be given more meanings. Perhaps the reason why scenarios about ambient intelligence have not become concretized is because of the paradigm is so different from the current device-based one: people have the need to be in control of devices, deciding themselves whether to initiate interaction and automatic services or not, and where and when to do so. Perhaps the entire concept of intelligence of technology is so alien to people that it is challenging to approach – even for the visionary designers. All in all, the research knowledge lacks understanding of what kind of expectations people attribute to technological intelligence.
Appropriateness. This category describes how fitting or suitable services are for the user in the context. In this study, those scenarios that were designed for a more specific, defined context were considered more appropriate by the participants than the more general kind of scenarios. Reliability. The comments related to reliability by the participants could be classified in two ways in this study. Firstly, the participants discussed malfunctions, errors, power failures and availability of maintenance and reliability of technology in these terms. On the other hand, reliance to technology was also considered problematic because it is not human, and lacks the capability assesses situations as humans do. To sum up, the findings rather much reflected the division of roles and responsibility between the user and technology. The service concepts having been introduced as smart brought up much discussion about, for example, autonomy and control as well as privacy and monitoring. In the current study, we were not interested of acceptance of smart environments per se. Instead, the above-mentioned findings inspired us to further explore how the participants relate the intelligent services or digital layer to existing place and how the concept of smartness is understood. Therefore, in the concluding interview of the larger study on placing the smart services, we also asked for opinions about smartness of environment and proactivity in specific. This paper reports the findings related to smartness and proactivity, and
1.1 Our Approach and Prior Work In our previous study we looked into user reaction to and acceptance of potential future smart environments [9]. In eight focus group discussions the participants evaluated a comic strip description of one potential smart environment (2 groups per each scenario). Presenting the entire result here would take too much space but findings common with this study are related to themes of effort, privacy, autonomy, control, reliability of technology, appropriateness and feelings of being monitored.
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Forlizzi & DiSalvo [21] looked into participants’ understandings of the intelligence of a vacuuming robot, Roomba. They found that even though initially the robot was not seen as intelligent at all, after teaching the robot about the apartment, their opinions changed. With time, the some participants begun to see some intelligence in the robot. It would appear that some understandings of intelligence can build over time, with effort and commitment.
discusses their implications to designing novel technologies and smart environments.
2. RELATED WORK Psychologists have tried to understand and explain intelligence for decades but there is yet a consensus on what intelligence is [10]. Some emphasize underlying mental ability [11], some researchers have recognized different kinds of intelligence. According to these accounts, there are at least seven kinds of intelligence. These are linguistic, logic-mathematical, musical, spatial, bodily-kinesthetic and personal kinds of intelligence [12]. Yet another line of inquiry has looked into the efficiency of processes underlying mental performance, such as accuracy and speed [11]. Thus, it is not easy to know what exactly we are talking about, when we use the term “intelligent” without definition.
In several experiments Nass and Reeves [22] show that people can treat computers as social actors. Assigning human attributions to computers can happen with minimal social cues (e.g. such as labeling a computer a team member or giving it a female or male voice) and irrespective of the conscious realization that computers are not human. These studies showed how people are polite to computers, treat computers with male and female voices differently and that people like computers with imitating a personality resembling their own. Particularly interesting is a study where same instruction given by male and female voice is regarded less or more expert depending on the subject matter, e.g. male voice instruction on computers is seen more valid [22].
Probably the most well-known academic approach on intelligence to the majority of western population is represented by intelligence tests. Testing for intelligence began in the beginning of previous century, to assess children’s problem solving skills [11Tests has since been developed and revised to cater for adults and different abilities, for instance WAIS [11]. IQ tests predict success in schools but not necessarily in everyday life [11].
From the more technological development point of view we can also discuss features that might simulate intelligent behavior, such as context awareness. In many definitions context awareness has become somewhat synonymous with other terms: adaptive, reactive, responsive, situated, context-sensitive and environment directed [23, 24]. By the definition of Pascoe [25], context-awareness is the ability of devices to detect and sense, interpret and respond to aspects of the user's local environment and the computing devices themselves. Dey [23] defines context awareness: “A system is context-aware if it uses context to provide relevant information and/or services to the user, where relevancy depends on the user’s task.” This definition includes only the response to context, allowing the detection and interpretation to be performed by other systems. By Korkea-aho’s studies one definition is “A system is context-aware if it can extract, interpret and use context information and adapt its functionality to the current context of use.” [26]
Intelligence quotient is not such straight indicator of success in everyday life [11]as it might seem, nor does it remain unchanged. Learning, for instance, has to do with success in intelligence testing. The testing itself requires often some learning with the regard to symbol systems, such as the alphabet or numeric system and creates cultural differences. Furthermore, there is indication that following generations do better in the tests than the previous ones e.g. the Flynn Effect [13]. All in all, intelligence is a multifaceted phenomenon and it would be beyond the need and scope of this paper to delve more into the general theme of human intelligence. For the purposes of this work, we look into general, lay-man understandings of intelligence. What is more, we are interested in the ways people see intelligence in machines and devices. Taylor [14] gives a discussion of different ways to understand intelligence in field of human-computer interaction. Firstly, we can look into machine intelligence and see how it has developed through decades. Second, and more interestingly, he discusses the ways people see intelligence in devices and systems, as a kind of anthropomorphism. Examples of various kinds of AI are Deep Blue chess machine [15], such robots as Kismet [16] and Aibo [17] and more generally the work done in the field of neural networks [18].
To summarize, these studies indicate that there is much more to an assessment of capability of a computer than mere processor speed and amount of memory. Actually, everyday appraisal of intelligence of technology is very much driven by social cues. Perception of intelligence in machines can come about at least two ways; anthropomorphic or social cues and building a relationship to the machine by teaching and doing.
3. METHODOLOGY
A classic work done in the field of AI is known as the Turing’s test [19] and it nicely combines both human and machine intelligence. Or, to put it in another way, presents the question of intelligence in another way. In Turing’s test a human judge tries to tell by exchange of textual messages which one of their discussion partners is a computer. Some people would mistake the computer for a human. The first program to claimed do so, ELIZA, was coded in the sixties [20].Thus, it is relatively easy to project an image of some level of human intelligence. As such, we are not interested in the capability of a machine to imitate intelligence but the human tendency to see human-like capabilities in object and non-human beings.
We carried out a semi-controlled field experiment with 20 sessions and altogether 40 participants. The participants acted in pairs, adding up to five pairs in four different types of places that represent potential future smart environments. The participants were interviewed in situ, asked to design and place smart services in the area, and finally draw a mental map of the new “smart environment”. Mental maps were utilized to collect data on conceptions and experiences of existing physical places, especially to identify how smart services are placed in the space, and what are the most semantically relevant aspects and mental borders of the places overall. The section containing results and analysis of drawn map will be presented in another, yet unpublished paper. The concluding interviewing elicited
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understanding of the participants’ relationships to the four studied places, and addressed the research questions of what smartness is and what makes space smart.
3.2 Procedure and Materials The procedure of the session comprised of five distinct phases: 1) Background questionnaires (outside the place), 2) Brief interview on the felt experience of the place instantly after entering it, 3) Task to design and place smart services in the place, 4) Transition to another location near-by and the mapdrawing task, and 5) Interview for both participants.
3.1 Places The places representing potential smart environments were selected on the basis of two dimensions that were presumed to affect how people regard places, and thus what kind of smart services could be embedded: 1) public access and 2) task agenda heterogeny. The public access refers to how publicly available the places are, how much people are able to influence on the place and its activities, and how public the activities performed there are in nature. The task agenda heterogeny refers to the extent to which visitors in the place have homo- or heterogenic agendas, in other words, have similar/dissimilar tasks, behavior, and routines. With these principles we chose four places: 1) A university guest residence, a three-room flat close to down-town area, 2) A Gym at university campus, open for students and staff, 3) A public market place by a lake, in downtown, 4) An office environment at university campus. All of the places are situated in the same Finnish city with approximately 200 000 inhabitants.
Design of the study was brought about from the view point of actual environment, not user needs in them. This is not to say that user needs are neither important nor related to the environment, quite the opposite. We purposefully created a method that would leave open space to detect other contextual factors other than said user needs. The first part included permissions to record audio and video, a short questionnaire with statements regarding demographics and technology attitudes, and a short place-specific questionnaire regarding the participant’s view on familiarity, agenda heterogeny, privacy, and prior experiences of the place in question. All these were filled in outside the place, and the target place was not revealed until handing out the place-specific questionnaire.
Figure 1 exemplifies these two dimensions by roughly placing the four selected places into a fourfold table. The guest residence was a homely, furnished residence. It is accessible by very limited amount of people, intended for same activities as performed in homes. The gym is an example of a rather public place with homogenic agenda: basically everyone is there to do some exercises and follows a routine with similarities, and everyone (having paid necessary fees) can have access to the place. The market place represents a very public place (open access to the area) with rather heterogenic agendas: the place is used for resale, social gatherings, venue for events, boat trips from the attached inland port, passing the time etc. Although having little restrictions for pass, the office was rather private by nature, and the tasks performed limit to normal activities in office and university environments: meetings, research and teaching (presentations), and social interaction.
Second, after transition to the place, a short interview was carried out, focusing on the instant feeling of the place and prior experiences of it. Regarding the third phase, the task was set up by first introducing the basic idea of device interoperability with an illustrated scenario that described various smart services in another type of context (e.g. proactive features, multimodal interaction, easy transportation of data between devices). The predefined services included various elements related to the normal activities in the place, performed in various parts of it (e.g. concerning privacy, entering and leaving the place, interacting with the physical environment, social interaction with local or remote people, and proactive or automatic features). The task design was based on the results of our earlier work where we gathered and analyzed user needs in and acceptance of smart environments in various potential environments [9]. Table 1 (below) describes the predefined services as they were presented for the participants (translated from Finnish). The participants were given the permission to put also their own ideas into the design but the emphasis was on the five pre-set services. Approx. 20 minutes to was given to complete the task.
Private
Guest residence
Office
Heterogeny
In the fourth phase, the participants were taken again elsewhere to a near-by location, and were introduced the mental map drawing task. They were first requested to draw a map of the place they designed digital services into, using as much as possible of the paper area.
Homogeny Gym
Market place
In the final phase the participants were interviewed as a pair. Interview topics touched various aspects of the context, which were considered when designing the smart services in the place, such as privacy and social effects. For the subject matter of this paper, the more important questions concerned understanding smartness from the participants’ perspective and general expectations regarding proactivity and smartness in physical environment.
Public
Figure 1. Placement of the four selected areas on the two controlled dimensions
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couples, 7 friends or close colleagues, 1 mother & daughter. The participants were recruited with posters in various public places, as well as from various e-mail lists targeted to university students and staff and people interested in user testing.
Table 1. Predefined digital services instructed in each place Area
Digital services to be placed in each location
Guest residence
- exit watch: automatic decrease of temperature and AC, reminder of appliances still on - ad board between neighbors: for sale, need / offer help or tools etc. - making a shopping list and ordering supplies - reminding service: take medicine, feed the cat etc. - checking an aged relative’s health or bank status - a new customer registering and paying - automatic supervision and guidance of the workout - informing about broken equipment - after workout, reviewing and recording the activity - leader board: funny or pragmatic statistics - gathering (preferred) company for lunch - reserving meeting rooms or other facilities - automatic sharing of business cards in meetings - adding/revising an idea in a collaborative idea bank - status update of what has happened since last visit - introduction of market place digital host at arrival - reserving a stall for resale - bidding or responding to electronic sales adverts - sharing digital memories (e.g. photos, local stories) - information about events and services in the area
Gym
Office
Market place
On average, the participants had mostly positive attitudes towards new technology and rather high expertise but the variation was also high. Statement “I’m using several different mobile services” received responses with median of 4 (Likert scale 1-5), and “I’m interested in using technology with novelty value” with median of 4. Overall, the ratings to these (and other attitude statements not reported here) varied highly between the participant sets in the four different places. Those who had not visited the place before were instructed to rate according to their expectations, based on their evaluations of similar places they have experience on (e.g. similar offices, own home). This concerned mostly Residence and Office as most had not been in the place in question. Therefore, the ratings especially related to home describe the participants’ thoughts related to the other place queried. For example, controlling the access is naturally high in own home although the participants could not control the access to this particular residence at all.
4. RESULTS The interview materials were analyzed in terms of the concept of intelligence and the participants’ reaction towards it. These are treated separately here, introducing main findings. We first cover the understandings of intelligence of technology. Second, we present the participants’ reactions and expectations intelligence and the related concept of proactivity.
In general, all the instructions were given both orally and as text. The entire sessions were audio taped and the participants’ explanations of the placements were also videotaped. Sessions lasted roughly from 75min to 110min, with a mean of about 95min. As an expression of gratitude, each participant was offered two movie tickets.
4.1 Intelligence of Technology The material suggests classes of expressed views on intelligence of technology. These classes are overlapping and mostly the case of difference reflects difference in the view point. Because of the overlapping quality of statements and small numbers in differences, no numerical or statistical analysis is offered. The classes are presented in the order of growth of demands for technology. Below we represent each definition for intelligence with quoted statements from the participants.
3.3 Analysis of data Of the interview data we concentrated on three questions: 1.
Is this design (that you did) intelligent?
2.
What do you think intelligence of technology means in this context?
3.
What do you think about proactivity? How could it work this design of yours?
One set of statements labeled the system intelligent when it practically helped the user in his/her activities and in saving resources and effort, such as time, mental effort or avoiding mistakes:
The interview materials were analyzed by using Nvivo, coding participants’ statements to identify common themes. Nvivo provides a flexible way to construct, examine and revisit the themes and categorize the data. The statement contents were coded with regard to meaning, attitude or acceptance and emotional content. Per each question, these codings were then examined and recoded into different classes if need be. For the meaning of intelligence there were clearly more definite and different classifications. With regard to proactivity, the main results were about different ways of acceptable proactivity. The quotes presented in the Results section have been translated from Finnish by the authors.
“If it helps in your activities in that environment” (Office, male) “Especially that it saves time and effort […] that it reminds you of important things and things have a priority order” (Office, female) “So it does things for you” (Gym, male) “So that it makes your life easier” (Home, female) A set of descriptions of an intelligent system state that if the system knew what the user needed (even before they knew what they wanted). This set reflects the lack of user initiative or effort:
3.4 Participants We had altogether 40 participants, 16 of them male and 24 female. Ages varied between 14 and 62, with a mean of 29. All participants were Finnish. In general, the participants were highly educated; most of them were studying in a university (22) or had a university degree (8). The pairs in the sessions had varying mutual relationships: 4 pairs unknown to each other, 8
“If it knows what you need without you telling it” (Market place, female) “Well, if the environment can give you something without you giving it anything” (Office, male)
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“Something that thinks on your behalf, you don’t have to do much anything, the device knows from some hints” (Market place, male)
4.2 Adverse Reactions to Intelligence and Proactivity Reactions to intelligence and proactivity were rather cautious and mistrustful. The participants mentioned negative feelings related to intelligent services. These were mostly anxiousness or confusion or that a flood of proactive messages would be irritating.
Other description of system intelligence was linked to context sensitivity and awareness, e.g. the system knows what is happening in the surrounding environment and acts accordingly: “It’s context sensitive so that it knows what the person is doing in that environment” (Gym, male)
“When the system reads sensor data and starts using it in a new way that could be vexing.” (Home, male)
“Adapts to the user, things happen only when they are needed” (Home, male)
“If it tries to emulate a personality that would be eerie.”(Home, male)
“A device knows when it must be on and when to switch off”(Home, male)
“It would be highly irritating if something is heckling at you every time you go by.”(Market place, female)
To some, an intelligent system was proactive, for instance approached the user with suggestions and could foresee user needs and wishes. Here the emphasis is on proactive and initiative way of functioning of technology:
In addition, some expressed the wish to be rid of problems that malfunctioning or complex technology can bring, and rather keep devices and services simple.
“That the services can initiate interaction with you and suggest something” (Office, male)
“It is difficult to estimate how intelligent a house appliance should be; when it develops a problem it turns against it, so simple is beautiful in many things” (Home, male)
“That they solve problems that the user might have before the user even knows they have a problem, which it can kind of foresee what people need” (Office, female)
Fear of loss of control was a major topic here, whether discussing intelligence or proactivity. This was both indicated directly by direct statements and the way the participants wished the system to work. Some were worried about the system revealing their choices or actions to the environment in an embarrassing way.
“That it thinks ahead and is proactive and shows you are doing it the wrong way, you don’t have to find out afterwards” (Gym) And finally, intelligent system could learn from interaction with the user(s):
“Mostly touch based, or you can see it on a screen, not so that it starts (publicly) announcing that you are doing something wrong” (Gym, male)
“I would link it to learning, it is intelligent when it learns from the user, independently deduces and learns” (Home, female)
“It could have a loudspeaker but it wouldn’t be nice that everyone hears the system’s comments.” (Gym, male)
“If it were intelligent it would wait before it knows more about me before it starts giving advice” (Gym, male)
Another set of concerns were expressed more directly. “Feeling of losing control, the place controls things for my wellbeing ostensibly …” (Home, male)
Some of the participants brought up the idea of relativity of intelligence, using wordings such as “intelligent for a machine”, the place they were in or in comparison to current state of technology. In addition, some noted, that it was not a matter of intelligence but complexity of automation or programming.
“When it [technology] intervenes my life too much, I’ll be at the control board” (Home, male) “Intelligent device can act on its own accord and you cannot control it anymore” (Home, female)
When asked if the designed and placed smart services the participants divided roughly in three groups. One group agreed immediately “yes, it is”. Another group labeled it jokingly or in a dismissing way “idiotic” or said that interacting with it could make the person feel confused or waste their time, which is stupid. Yet another group said it is not intelligent but offered another term to emphasize relative advantage over other existing systems or current state of art.
In summary, the participants expected the services to help them in daily matters but we’re unwilling to relinquish control.
4.3 Suggestions for Proactivity Proactivity was very conditionally and cautiously accepted in some cases and environments. Some participants refused it directly:
“What they really mean (with intelligence) is that the system or device is more automated.” (Home, male)
“Proactivity sounds so bad that I would not wish any device to approach me there” (Gym, male)
“When related to technology intelligence means advanced technology that works appropriately.” (Office, male)
Yet another view underlines the relativity and contextual factors:
“This is intelligent enough for a gym.” (Gym, female)
“It depends on the content and topic of the service, for instance a poster like that could advertise itself.” (Office, Male)
In summary, the understandings of intelligence of technology are very much overlapping and inseparable. An intelligent system provides effortlessly the user something valuable, with little or no direct input from the user. Thus, the system must be context sensitive and monitor and/or learn from the user behavior.
Loss of privacy and the fear of being bombarded by unwanted messages were the main concerns of the participants. Many statements the participants expressed that they wanted to stay in control of what is revealed of them to the immediate surroundings. This means that the vast majority of participants
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various tasks, by well known parameters. With general human intelligence description - adaptive, creative cognitive ability applied to cope effectively with novel or complex situations- this had relatively little to do. The practical value and ability to produce the right kind of result, action or response was identified as intelligent. As some participants put it:
preferred more personal nature of messages and suggestion delivered to them to their personal devices. “When you have visitors at your home you don’t want the system shout that take your depression pill, it should be a simple sound and you can check the message somewhere” (Home, female) Another choice was having a signal (voice or light) to indicate that they have a message to see at a touch screen, for instance. Voice control was suggested only at the apartment setting, voice advice or feedback was conceivable at the ear phones.
“If it would be able to offer right kind of information, even then it should work exceedingly well; otherwise it’s just information technology that doesn’t work well.” (Office, female)
“A light or symbol that indicates that you can touch and get more information.” (Office, female)
“With technology I would consider it intelligent when an advanced technology works as it should.” (Home, female)
“Maybe a sound and then you could go somewhere to read the message.” (Home, female)
Obviously, as we didn’t run any tests with prototypes or such, evaluating reaction to anthropomorphic cues cannot be profoundly argued. However, intelligence seemed to be related to agent-like behaviors of technology; taking initiative, acting proactively and “thinking” on your behalf. Amount or exchange of information, processing capabilities or logic (as such) was not mentioned at all. To further understand apperception of machine intelligence in smart environments, more work needs to be done to fathom how various agent-like qualities and social cues affect perception and acceptance. As further study indicates the number of humanizing cues increases anthropomorphism in participants [27], their practical effect on perception and acceptance of “intelligence” still needs more research.
“You could go close enough to activate it but it should not invite you to go there in any way.” (Market place, female) “If I would have recommendations by personal profile I would like to have them in my personal device and then I could decide whether to go see it or not.” (Office, male) To conclude, proactivity can be accepted in certain environments and with a humble attitude; technology can express availability for interaction but not force it on the user. Even though proactive functions can be helpful in many activities, chores and tasks, the participants did not want technology to reveal personal, even embarrassing facts about them to the surroundings.
From the smartness of technology perspective, one definition seems to be above the others: context sensitivity. In way or another, the participants indicated context sensitivity as the sign of intelligence. A machine that is able to adjust adequately to user and environmental needs was regarded intelligent. The participants were not bothered about availability or exchange of information as such, and autonomous system decision seemed to worry them.
5. DISCUSSION The main findings for intelligence of technology in this study were making the user’s life easier, context sensitivity and technological novelty and complexity. Unwanted signs of intelligence – independence and making unforeseen decisions – were feared. Proactivity was accepted only conditionally and cautiously. The participants came up with suggestions how to design acceptable proactivity: let the user decide whether to interact and affordances should be discrete and not disturbing.
To proactivity to be accepted, there are at least two factors to consider; the place and manner of interaction. According to this study, the main worries of participants are loss of privacy by badly timed or initiated messages, fear of losing control over their own lives and irritation by unwanted, repeated messages. Some places are more forgiving when it comes to privacy; at home a voice message might be acceptable. In public, the participants feared that their activities and personal matters could be spread to immediate surroundings in an embarrassing way. We suggest that the interaction should give the control to the user, so that a minor, unobtrusive sign indicates the possibilities but the choice whether to interact should be left to the user.
With the regard of the method a more thorough discussion will be presented in a yet unpublished paper, which presents the main findings of the study. Generally, we found the method beneficial in eliciting the participants’ understandings of the environment and how the activities are shaped by the actual facilities. What is more, the method opens a discussion of acceptability of interaction and digital services in each location with respect to social, task and physical environment. The method is under development and will be refined for more accuracy and richer description of the users’ views on the environment. Reflecting on the results, the participants saw “intelligent” technology making their life easier and problem-free almost magically thinking ahead. With that respect there seems to be a conflict of wishes and fears. Basically, the intelligence of technology was evaluated in contrast to human needs and advances of technology in the other hand. Mainly the participants’ understanding of “intelligence” comes close to context sensitivity and gathering information about the user in an environment. The system knows the user so well, that it can predict happenings and actions and act in a helpful way.
Methodologically the results should be evaluated considering that this presents a part of a study where assessment of intelligence perceptions was not in the main focus. Thus, the structure of the interview was first and foremost designed to support map-explanations and general acceptance factors of smart environments. Furthermore, quite a number of the participants were technology professionals or students, thus their knowledge of the subject matter was rather good. Interviewing less technologically orientated people might give different kinds of results. Another point of consideration is that the participants designed the lay-out and interaction with the services in the environment. It could be difficult to evaluate on what their
With regard to different understandings of intelligence as lined out in the introduction, the participants emphasized more the practical kind of intelligence, e.g. technology helping them in
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opinion of “intelligence” of their own design is based on. As one participant commented:
[12] Gardner, H. 1993. Multiple Intelligence: The Theory in Practice. N.Y: Basic Books.
“I think this is not yet intelligent, I am not creative enough to invent an intelligent environment but this is intelligent in some way at least.” (Office, female)
[13] Flynn, J. R. 1994. IQ gains over time. In R. J. Sternberg (Ed.), Encyclopedia of human intelligence. New York: Macmillan.
In summary, the participants’ understandings of machine intelligence and the general concept of intelligence come closer to each other. In a way, they both talk about adaptation; technology adapts to human being and an intelligent human can adapt to the ever changing environmental demands. In the core of intelligence seems to be the capability, either by innate ability or advanced programming, to foresee and react to contextual and environmental demands. Perhaps, it would be better to talk about adaptive technology rather than intelligent to avoid the unwanted connotations to intelligence.
[14] Taylor, A.S. Machine intelligence. In Proceedings of CHI 2009 (Boston, Massachusetts, USA, April 4 – 9, 2009). ACM, NY. [15] Feng-hsiung, H. 2002. Behind Deep Blue: Building the Computer that Defeated the World Chess Champion. Princeton University Press. [16] Breazeal, C. 2003. Emotion and sociable humanoid robots. Int J Hum Comp Std 59, 1-2, 119-155. [17] Kaplan, F. and Oudeyer, P.-Y. 2007. Intrinsically motivated machines. In Lungarella, M., Iida, F., Bongard, J. and Pfeider, R. (Eds.), Intrinsically Motivated Machines. Springer-Verlag, Berlin.
6. REFERENCES [1] www.m-w.com, accessed 5.8.2011
[18] Haykin, S. 1999. Neural Networks: A Comprehensive Foundation. Prentice Hall.
[2] Hornby, A. S (ed.). 1995. Oxford Advanced Learner’s Dictionary of Current English. Oxford: Oxford University Press.
[19] Turing, A. M. 1950. Computing machinery and intelligence. Mind 59, 433-460
[3] Wechsler, D. 1944. The Measurement of Adult Intelligence. Baltimore: Williams and Wilkins.
[20] Weizenbaum, J. 1966. ELIZA - A computer program for the study of natural language communication between man and machine. Communications of the ACM 9, 36-45. DOI=10.1145/365153.365168
[4] Gotfredson, L. 1998. The General Intelligence Factor. Scientific American Presents 9, 4, 24-29. http://www.udel.edu/educ/gottfredson/reprints/1998generali ntelligencefactor.pdf.
[21] Forlizzi, J. and DiSalvo, C. 2007. Service robots in the domestic environment: a study of the Roomba vacuum in the home. In the Proceedings of CHI’07 (San Jose, California, USA, April 28 – May 3). ACM, NY..
[5] Satyanarayanan, M. 2001. Pervasive Computing: Vision and Challenges, IEEE Pers. Comm, 10-17. [6] Weiser, M.. 199l. The Computer for the 21st Century, Scientific American 265,3,94-104.
[22] Reeves, B. and Nass, C. 1996. The Media Equation: How People treat computers, television and new media like real people and places. CSLI publications.
[7] Hermann, F., Blach, R., Janssen, D., Klein, T., Schuller, A., and Spath, D. 2009. Challenges for user centered smart environments. In: Human-Computer Interaction. Ambient, Ubiquitous and Intelligent Interaction. Lecture Notes in Computer Science. Springer, Heidelberg.
[23] Dey, A., Abowd, G., and Wood, A. 1999. CyberDesk: a framework for providing self-integrating context-aware services. Knowledge-Based Systems, 11, 3-13. [24] Dey, A. 2001. Understanding and using context. Personal and Ubiquituos Computing 5 (Feb. 2001), 4-7.
[8] Aarts, E., Harwig, R., and Schuurmans, M. 2001. Ambient intelligence. In: The Invisible Future: The Seamless Integration Of Technology Into Everyday Life. McGrawHill Companies.
[25] Pascoe, J. 1998. Adding generic contextual capabilities to wearable computers. In 2nd International Symposium on Wearable Computers, (Pittsburgh, PA, USA, October 1998).
[9] Kynsilehto, M., Olsson, T., and Niemelä, M. Issues on user acceptance and experience in smart interoperability environments. In proceedings of 14th MindTrek Conference (Tampere, Finland, October 6 -7, 2010). ACM, NY.
[26] Korkea-aho, M. 2001. Location Information in the Internet. Licentiate’s thesis. Helsinki University of Technology. 80 pp., Cambridge. [27] Gong, L. 2008. How social is responses to computers? The function of the degree of anthropomorphism in computer representations. Computers in Human Behavior, 24, 14941509.
[10] Tamez, E., Myerson, J. and Hale, S. 2008. Learning, working memory, and intelligence revisited. Behavioural processes 78, 240-245. [11] Atkinson, R. L., Atkinson, R.C., Smith, E. E., Bem, D. J., and Nolen-Hoeksema, S. 1996. Hilgard’s Introduction to Psychology. London: Harcourt College Publishers.
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