Artificial Intelligence - Who Is Sherry Turkle?

 

 

 

Sherry Turkle(1948–) has a background in sociology and psychology, and her work focuses on the human-technology interaction.

While her study in the 1980s focused on how technology affects people's thinking, her work in the 2000s has become more critical of how technology is utilized at the expense of building and maintaining meaningful interpersonal connections.

She has employed artificial intelligence in products like children's toys and pets for the elderly to highlight what people lose out on when interacting with such things.

Turkle has been at the vanguard of AI breakthroughs as a professor at the Massachusetts Institute of Technology (MIT) and the creator of the MIT Initiative on Technology and the Self.

She highlights a conceptual change in the understanding of AI that occurs between the 1960s and 1980s in Life on the Screen: Identity inthe Age of the Internet (1995), substantially changing the way humans connect to and interact with AI.

She claims that early AI paradigms depended on extensive preprogramming and employed a rule-based concept of intelligence.

However, this viewpoint has given place to one that considers intelligence to be emergent.

This emergent paradigm, which became the recognized mainstream view by 1990, claims that AI arises from a much simpler set of learning algorithms.

The emergent method, according to Turkle, aims to emulate the way the human brain functions, assisting in the breaking down of barriers between computers and nature, and more generally between the natural and the artificial.

In summary, an emergent approach to AI allows people to connect to the technology more easily, even thinking of AI-based programs and gadgets as children.

Not just for the area of AI, but also for Turkle's study and writing on the subject, the rising acceptance of the emerging paradigm of AI and the enhanced relatability it heralds represents a significant turning point.

Turkle started to employ ethnographic research techniques to study the relationship between humans and their gadgets in two edited collections, Evocative Objects: Things We Think With (2007) and The Inner History of Devices (2008).

She emphasized in her book The Inner History of Devices that her intimate ethnography, or the ability to "listen with a third ear," is required to go past the advertising-based clichés that are often employed when addressing technology.

This method comprises setting up time for silent meditation so that participants may think thoroughly about their interactions with their equipment.

Turkle used similar intimate ethnographic approaches in her second major book, Alone Together: 

Why We Expect More from Technology and Less from Each Other (2011), to argue that the increasing connection between people and the technology they use is harmful.

These issues are connected to the increased usage of social media as a form of communication, as well as the continuous degree of familiarity and relatability with technology gadgets, which stems from the emerging AI paradigm that has become practically omnipresent.

She traced the origins of the dilemma back to early pioneers in the field of cybernetics, citing, for example, Norbert Weiner's speculations on the idea of transmitting a human person across a telegraph line in his book God & Golem, Inc.(1964).

Because it reduces both people and technology to information, this approach to cybernetic thinking blurs the barriers between them.

In terms of AI, this implies that it doesn't matter whether the machines with which we interact are really intelligent.

Turkle claims that by engaging with and caring for these technologies, we may deceive ourselves into feeling we are in a relationship, causing us to treat them as if they were sentient.

In a 2006 presentation titled "Artificial Intelligence at 50: From Building Intelligence to Nurturing Sociabilities" at the Dartmouth Artificial Intelligence Conference, she recognized this trend.

She identified the 1997 Tamagotchi, 1998 Furby, and 2000 MyReal Baby as early versions of what she refers to as relational artifacts, which are more broadly referred to as social machines in the literature.

The main difference between these devices and previous children's toys is that these devices come pre-animated and ready for a relationship, whereas previous children's toys required children to project a relationship onto them.

Turkle argues that this change is about our human weaknesses as much as it is about computer capabilities.

In other words, just caring for an item increases the likelihood of not only seeing it as intelligent but also feeling a connection to it.

This sense of connection is more relevant to the typical person engaging with these technologies than abstract philosophic considerations concerning the nature of their intelligence.

Turkle delves more into the ramifications of people engaging with AI-based technologies in both Alone Together and Reclaiming Conversation: The Power of Talk in a Digital Age (2015).

She provides the example of Adam in Alone Together, who appreciates the appreciation of the AI bots he controls over in the game Civilization.

Adam appreciates the fact that he is able to create something fresh when playing.

Turkle, on the other hand, is skeptical of this interaction, stating that Adam's playing isn't actual creation, but rather the sensation of creation, and that it's problematic since it lacks meaningful pressure or danger.

In Reclaiming Conversation, she expands on this point, suggesting that social partners simply provide a perception of camaraderie.

This is important because of the value of human connection and what may be lost in relationships that simply provide a sensation or perception of friendship rather than true friendship.

Turkle believes that this transition is critical.

She claims that although connections with AI-enabledtechnologies may have certain advantages, they pale in contrast to what is missing: 

• the complete complexity and inherent contradictions that define what it is to be human.

A person's connection with an AI-enabled technology is not as intricate as one's interaction with other individuals.

Turkle claims that as individuals have become more used to and dependent on technology gadgets, the definition of friendship has evolved.

• People's expectations for companionship have been simplified as a result of this transformation, and the advantages that one wants to obtain from partnerships have been reduced.

• People now tend to associate friendship only with the concept of interaction, ignoring the more nuanced sentiments and arguments that are typical in partnerships.

• By engaging with gadgets, one may form a relationship with them.

• Conversations between humans have become merely transactional as human communication has shifted away from face-to-face conversation and toward interaction mediated by devices. 

In other words, the most that can be anticipated is engagement.

Turkle, who has a background in psychoanalysis, claims that this kind of transactional communication allows users to spend less time learning to view the world through the eyes of another person, which is a crucial ability for empathy.

Turkle argues we are in a robotic period in which people yearn for, and in some circumstances prefer, AI-based robotic companionship over that of other humans, drawing together these numerous streams of argument.

For example, some people enjoy conversing with their iPhone's Siri virtual assistant because they aren't afraid of being judged by it, as evidenced by a series of Siri commercials featuring celebrities talking to their phones.

Turkle has a problem with this because these devices can only respond as if they understand what is being said.

AI-based gadgets, on the other hand, are confined to comprehending the literal meanings of data stored on the device.

They can decipher the contents of phone calendars and emails, but they have no idea what any of this data means to the user.

There is no discernible difference between a calendar appointment for car maintenance and one for chemotherapy for an AI-based device.

A person may lose sight of what it is to have an authentic dialogue with another human when entangled in a variety of these robotic connections with a growing number of technologies.

While Reclaiming Communication documents deteriorating conversation skills and decreasing empathy, it ultimately ends on a positive note.

Because people are becoming increasingly dissatisfied with their relationships, there may be a chance for face-to-face human communication to reclaim its vital role.

Turkle's ideas focus on reducing the amount of time people spend on their phones, but AI's involvement in this interaction is equally critical.

• Users must accept that their virtual assistant connections will never be able to replace face-to-face interactions.

• This will necessitate being more deliberate in how one uses devices, prioritizing in-person interactions over the faster and easier interactions provided by AI-enabled devices.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

You may also want to read more about Artificial Intelligence here.

See also: 

Blade Runner; Chatbots and Loebner Prize; ELIZA; General and Narrow AI; Moral Turing Test; PARRY; Turing, Alan; 2001: A Space Odyssey.

References And Further Reading

• Haugeland, John. 1997. “What Is Mind Design?” Mind Design II: Philosophy, Psychology, Artificial Intelligence, edited by John Haugeland, 1–28. Cambridge, MA: MIT Press.

• Searle, John R. 1997. “Minds, Brains, and Programs.” Mind Design II: Philosophy, Psychology, Artificial Intelligence, edited by John Haugeland, 183–204. Cambridge, MA: MIT Press.

• Turing, A. M. 1997. “Computing Machinery and Intelligence.” Mind Design II: Philosophy, Psychology, Artificial Intelligence, edited by John Haugeland, 29–56. Cambridge, MA: MIT Press.

Artificial Intelligence - Intelligent Tutoring Systems.

  

Intelligent tutoring systems are artificial intelligence-based instructional systems that adapt instruction based on a variety of learner variables, such as dynamic measures of students' ongoing knowledge growth, personal interest, motivation to learn, affective states, and aspects of how they self-regulate their learning.

For a variety of problem areas, such as STEM, computer programming, language, and culture, intelligent tutoring systems have been created.

Complex problem-solving activities, collaborative learning activities, inquiry learning or other open-ended learning activities, learning through conversations, game-based learning, and working with simulations or virtual reality environments are among the many types of instructional activities they support.

Intelligent tutoring systems arose from a field of study known as AI in Education (AIED).

MATHia® (previously Cognitive Tutor), SQL-Tutor, ALEKS, and Rea soning Mind's Genie system are among the commercially successful and widely used intelligent tutoring systems.

Intelligent tutoring systems are frequently more successful than conventional kinds of training, according to six comprehensive meta-analyses.

This efficiency might be due to a number of things.

First, intelligent tutoring systems give adaptive help inside issues, allowing classroom instructors to scale one-on-one tutoring beyond what they could do without it.

Second, they allow adaptive problem selection based on the understanding of particular pupils.

Third, cognitive task analysis, cognitive theory, and learning sciences ideas are often used in intelligent tutoring systems.

Fourth, the employment of intelligent tutoring tools in so-called blended classrooms may result in favorable cultural adjustments by allowing teachers to spend more time working one-on-one with pupils.

Fifth, more sophisticated tutoring systems are repeatedly developed using new approaches from the area of educational data mining, based on data.

Finally, Open Learner Models (OLMs), which are visual representations of the system's internal student model, are often used in intelligent tutoring systems.

OLMs have the potential to assist learners in productively reflecting on their current level of learning.

Model-tracing tutors, constraint-based tutors, example-tracing tutors, and ASSISTments are some of the most common intelligent tutoring system paradigms.

These paradigms vary in how they are created, as well as in tutoring behaviors and underlying representations of domain knowledge, student knowledge, and pedagogical knowledge.

For domain reasoning (e.g., producing future steps in a problem given a student's partial answer), assessing student solutions and partial solutions, and student modeling, intelligent tutoring systems use a number of AI approaches (i.e., dynamically estimating and maintaining a range of learner vari ables).

To increase systems' student modeling skills, a range of data mining approaches (including Bayesian models, hidden Markov models, and logistic regression models) are increasingly being applied.

Machine learning approaches, such as reinforcement learning, are utilized to build instructional policies to a lesser extent.

Researchers are looking at concepts for the smart classroom of the future that go beyond the capabilities of present intelligent tutoring technologies.

AI systems, in their visions, typically collaborate with instructors and students to provide excellent learning experiences for all pupils.

Recent research suggests that rather than designing intelligent tutoring systems to handle all aspects of adaptation, such as providing teachers with real-time analytics from an intelligent tutoring system to draw their attention to learners who may need additional support, promising approaches that adaptively share regulation of learning processes across students, teachers, and AI systems—rather than designing intelligent tutoring systems to handle all aspects of adaptation, for example—by providing teachers with real-time analytics from an intelligent tutoring system to draw their attention to learners who may need additional support.

~ Jai Krishna Ponnappan

You may also want to read more about Artificial Intelligence here.

See also: 

Natural Language Processing and Speech Understanding; Workplace Automation.

Further Reading:

Aleven, Vincent, Bruce M. McLaren, Jonathan Sewall, Martin van Velsen, Octav Popescu, Sandra Demi, Michael Ringenberg, and Kenneth R. Koedinger. 2016. “Example-Tracing Tutors: Intelligent Tutor Development for Non-Programmers.” International Journal of Artificial Intelligence in Education 26, no. 1 (March): 224–69.

Aleven, Vincent, Elizabeth A. McLaughlin, R. Amos Glenn, and Kenneth R. Koedinger. 2017. “Instruction Based on Adaptive Learning Technologies.” In Handbook of Research on Learning and Instruction, Second edition, edited by Richard E. Mayer and Patricia Alexander, 522–60. New York: Routledge.

du Boulay, Benedict. 2016. “Recent Meta-Reviews and Meta-Analyses of AIED Systems.” International Journal of Artificial Intelligence in Education 26, no. 1: 536–37.

du Boulay, Benedict. 2019. “Escape from the Skinner Box: The Case for Contemporary Intelligent Learning Environments.” British Journal of Educational Technology, 50, no. 6: 2902–19.

Heffernan, Neil T., and Cristina Lindquist Heffernan. 2014. “The ASSISTments Ecosystem: Building a Platform that Brings Scientists and Teachers Together for Minimally Invasive Research on Human Learning and Teaching.” International Journal of Artificial Intelligence in Education 24, no. 4: 470–97.

Koedinger, Kenneth R., and Albert T. Corbett. 2006. “Cognitive Tutors: Technology Bringing Learning Sciences to the Classroom.” In The Cambridge Handbook of the Learning Sciences, edited by Robert K. Sawyer, 61–78. New York: Cambridge University Press.

Mitrovic, Antonija. 2012. “Fifteen Years of Constraint-Based Tutors: What We Have Achieved and Where We Are Going.” User Modeling and User-Adapted Interaction 22, no. 1–2: 39–72.

Nye, Benjamin D., Arthur C. Graesser, and Xiangen Hu. 2014. “AutoTutor and Family: A Review of 17 Years of Natural Language Tutoring.” International Journal of Artificial Intelligence in Education 24, no. 4: 427–69.

Pane, John F., Beth Ann Griffin, Daniel F. McCaffrey, and Rita Karam. 2014. “Effectiveness of Cognitive Tutor Algebra I at Scale.” Educational Evaluation and Policy Analysis 36, no. 2: 127–44.

Schofield, Janet W., Rebecca Eurich-Fulcer, and Chen L. Britt. 1994. “Teachers, Computer Tutors, and Teaching: The Artificially Intelligent Tutor as an Agent for Classroom Change.” American Educational Research Journal 31, no. 3: 579–607.

VanLehn, Kurt. 2016. “Regulative Loops, Step Loops, and Task Loops.” International Journal of Artificial Intelligence in Education 26, no. 1: 107–12.

AI - Technological Singularity

 

The emergence of technologies that could fundamentally change humans' role in society, challenge human epistemic agency and ontological status, and trigger unprecedented and unforeseen developments in all aspects of life, whether biological, social, cultural, or technological, is referred to as the Technological Singularity.

The Singularity of Technology is most often connected with artificial intelligence, particularly artificial general intelligence (AGI).

As a result, it's frequently depicted as an intelligence explosion that's pushing advancements in fields like biotechnology, nanotechnology, and information technologies, as well as inventing new innovations.

The Technological Singularity is sometimes referred to as the Singularity, however it should not be confused with a mathematical singularity, since it has only a passing similarity.

This singularity, on the other hand, is a loosely defined term that may be interpreted in a variety of ways, each highlighting distinct elements of the technological advances.

The thoughts and writings of John von Neumann (1903–1957), Irving John Good (1916–2009), and Vernor Vinge (1944–) are commonly connected with the Technological Singularity notion, which dates back to the second half of the twentieth century.

Several universities, as well as governmental and corporate research institutes, have financed current Technological Singularity research in order to better understand the future of technology and society.

Despite the fact that it is the topic of profound philosophical and technical arguments, the Technological Singularity remains a hypothesis, a guess, and a pretty open hypothetical idea.

While numerous scholars think that the Technological Singularity is unavoidable, the date of its occurrence is continuously pushed back.

Nonetheless, many studies agree that the issue is not whether or whether the Technological Singularity will occur, but rather when and how it will occur.

Ray Kurzweil proposed a more exact timeline for the emergence of the Technological Singularity in the mid-twentieth century.

Others have sought to give a date to this event, but there are no well-founded grounds in support of any such proposal.

Furthermore, without applicable measures or signs, mankind would have no way of knowing when the Technological Singularity has occurred.

The history of artificial intelligence's unmet promises exemplifies the dangers of attempting to predict the future of technology.

The themes of superintelligence, acceleration, and discontinuity are often used to describe the Technological Singularity.

The term "superintelligence" refers to a quantitative jump in artificial systems' cognitive abilities, putting them much beyond the capabilities of typical human cognition (as measured by standard IQ tests).

Superintelligence, on the other hand, may not be restricted to AI and computer technology.

Through genetic engineering, biological computing systems, or hybrid artificial–natural systems, it may manifest in human agents.

Superintelligence, according to some academics, has boundless intellectual capabilities.

The curvature of the time curve for the advent of certain key events is referred to as acceleration.

Stone tools, the pottery wheel, the steam engine, electricity, atomic power, computers, and the internet are all examples of technological advancement portrayed as a curve across time emphasizing the discovery of major innovations.

Moore's law, which is more precisely an observation that has been viewed as a law, represents the increase in computer capacity.

"Every two years, the number of transistors in a dense integrated circuit doubles," it says.

People think that the emergence of key technical advances and new technological and scientific paradigms will follow a super-exponential curve in the event of the Technological Singularity.

One prediction regarding the Technological Singularity, for example, is that superintelligent systems would be able to self-improve (and self-replicate) in previously unimaginable ways at an unprecedented pace, pushing the technological development curve far beyond what has ever been witnessed.

The Technological Singularity discontinuity is referred to as an event horizon, and it is similar to a physical idea linked with black holes.

The analogy to this physical phenomena, on the other hand, should be used with care rather than being used to credit the physical world's regularity and predictability to technological singularity.

The limit of our knowledge about physical occurrences beyond a specific point in time is defined by an event horizon (also known as a prediction horizon).

It signifies that there is no way of knowing what will happen beyond the event horizon.

The discontinuity or event horizon in the context of technological singularity suggests that the technologies that precipitate technological singularity would cause disruptive changes in all areas of human life, developments about which experts cannot even conjecture.

The end of humanity and the end of human civilization are often related with technological singularity.

According to some research, social order will collapse, people will cease to be major actors, and epistemic agency and primacy would be lost.

Humans, it seems, will not be required by superintelligent systems.

These systems will be able to self-replicate, develop, and build their own living places, and humans will be seen as either barriers or unimportant, outdated things, similar to how humans now consider lesser species.

One such situation is represented by Nick Bostrom's Paperclip Maximizer.

AI is included as a possible danger to humanity's existence in the Global Catastrophic Risks Survey, with a reasonably high likelihood of human extinction, placing it on par with global pandemics, nuclear war, and global nanotech catastrophes.

However, the AI-related apocalyptic scenario is not a foregone conclusion of the Technological Singularity.

In other more utopian scenarios, technology singularity would usher in a new period of endless bliss by opening up new opportunities for humanity's infinite expansion.

Another element of technological singularity that requires serious consideration is how the arrival of superintelligence may imply the emergence of superethical capabilities in an all-knowing ethical agent.

Nobody knows, however, what superethical abilities might entail.

The fundamental problem, however, is that superintelligent entities' higher intellectual abilities do not ensure a high degree of ethical probity, or even any level of ethical probity.

As a result, having a superintelligent machine with almost infinite (but not quite) capacities but no ethics seems to be dangerous to say the least.

A sizable number of scholars are skeptical about the development of the Technological Singularity, notably of superintelligence.

They rule out the possibility of developing artificial systems with superhuman cognitive abilities, either on philosophical or scientific grounds.

Some contend that while artificial intelligence is often at the heart of technological singularity claims, achieving human-level intelligence in artificial systems is impossible, and hence superintelligence, and thus the Technological Singularity, is a dream.

Such barriers, however, do not exclude the development of superhuman brains via the genetic modification of regular people, paving the door for transhumans, human-machine hybrids, and superhuman agents.

More scholars question the validity of the notion of the Technological Singularity, pointing out that such forecasts about future civilizations are based on speculation and guesswork.

Others argue that the promises of unrestrained technological advancement and limitless intellectual capacities made by the Technological Singularity legend are unfounded, since physical and informational processing resources are plainly limited in the cosmos, particularly on Earth.

Any promises of self-replicating, self-improving artificial agents capable of super-exponential technological advancement are false, since such systems will lack the creativity, will, and incentive to drive their own evolution.

Meanwhile, social opponents point out that superintelligence's boundless technological advancement would not alleviate issues like overpopulation, environmental degradation, poverty, and unparalleled inequality.

Indeed, the widespread unemployment projected as a consequence of AI-assisted mass automation of labor, barring significant segments of the population from contributing to society, would result in unparalleled social upheaval, delaying the development of new technologies.

As a result, rather than speeding up, political or societal pressures will stifle technological advancement.

While technological singularity cannot be ruled out on logical grounds, the technical hurdles that it faces, even if limited to those that can presently be determined, are considerable.

Nobody expects the technological singularity to happen with today's computers and other technology, but proponents of the concept consider these obstacles as "technical challenges to be overcome" rather than possible show-stoppers.

However, there is a large list of technological issues to be overcome, and Murray Shanahan's The Technological Singularity (2015) gives a fair overview of some of them.

There are also some significant nontechnical issues, such as the problem of superintelligent system training, the ontology of artificial or machine consciousness and self-aware artificial systems, the embodiment of artificial minds or vicarious embodiment processes, and the rights granted to superintelligent systems, as well as their role in society and any limitations placed on their actions, if this is even possible.

These issues are currently confined to the realms of technological and philosophical discussion.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

You may also want to read more about Artificial Intelligence here.

See also: 

Bostrom, Nick; de Garis, Hugo; Diamandis, Peter; Digital Immortality; Goertzel, Ben; Kurzweil, Ray; Moravec, Hans; Post-Scarcity, AI and; Superintelligence.

References And Further Reading

Bostrom, Nick. 2014. Superintelligence: Path, Dangers, Strategies. Oxford, UK: Oxford University Press.

Chalmers, David. 2010. “The Singularity: A Philosophical Analysis.” Journal of Consciousness Studies 17: 7–65.

Eden, Amnon H. 2016. The Singularity Controversy. Sapience Project. Technical Report STR 2016-1. January 2016.

Eden, Amnon H., Eric Steinhart, David Pearce, and James H. Moor. 2012. “Singularity Hypotheses: An Overview.” In Singularity Hypotheses: A Scientific and Philosophical Assessment, edited by Amnon H. Eden, James H. Moor, Johnny H. Søraker, and Eric Steinhart, 1–12. Heidelberg, Germany: Springer.

Good, I. J. 1966. “Speculations Concerning the First Ultraintelligent Machine.” Advances in Computers 6: 31–88.

Kurzweil, Ray. 2005. The Singularity Is Near: When Humans Transcend Biology. New York: Viking.

Sandberg, Anders, and Nick Bostrom. 2008. Global Catastrophic Risks Survey. Technical Report #2008/1. Oxford University, Future of Humanity Institute.

Shanahan, Murray. 2015. The Technological Singularity. Cambridge, MA: The MIT Press.

Ulam, Stanislaw. 1958. “Tribute to John von Neumann.” Bulletin of the American Mathematical Society 64, no. 3, pt. 2 (May): 1–49.

Vinge, Vernor. 1993. “The Coming Technological Singularity: How to Survive in the Post-Human Era.” In Vision 21: Interdisciplinary Science and Engineering in the Era of Cyberspace, 11–22. Cleveland, OH: NASA Lewis Research Center.

Cyber Security - Location-Based Services.

 

Location-Based Services (LBSs) are systems that deliver information based on the location of people or devices [54]. 

Some LBSs go a step further in delivering valuable services by using the users' location to infer additional information about the area. 

To get there, existing IMSs track users' whereabouts so that they may be taken into account during management operations. 

An LBS may be either person-oriented or device-oriented, depending on the emphasis of services. 

The emphasis on applications in the person-oriented approach exploits a person's position to improve service. 

A friend-finder application is an example of an LBS that fits this concept. 

Device-oriented LBSs, on the other hand, may concentrate on a person's location but do not need it. 

Objects such as vehicles in navigation systems might also be found using this method. 

There are two types of application designs: push and pull services, in addition to this categorization. 

Users obtain information through push services without having to explicitly request it. 

Pull services, on the other hand, imply that users actively seek for information. 

The majority of early location services were pull services, however push services have gained prominence in particular areas in recent years. 

The ubiquitous computer paradigm evolved in the early 1990s, aided by the location information supplied by the mobile paradigm. 

Weiser [55] used the term "pervasive" to describe the seamless integration of electronics into consumers' daily lives. 

The invisibility or full disappearance of computer technology from the user's viewpoint, the number of users and computing resources that form the environment and the system's scalability, and context-awareness were the key aims of ubiquitous computing. 

People were prioritized above computer devices and technological difficulties in the ubiquitous paradigm. 

LBSs offered the users' movement records, which were critical pieces of information for this paradigm. 

IMSs took into account not just the users' location, but also information about their lives. 

Because ubiquitous computing is still a highly active and dynamic topic, the pace of penetration into daily life varies greatly depending on technological and nontechnical aspects including infrastructure, compute resources, security, and economics. 

The ubiquitous computing paradigm [55] is built on the context-awareness idea [56]. 

Context-awareness is a mobile paradigm in which services learn about the context or environment in which users are placed and adjust their behavior based on that knowledge without requiring users to engage. 

It's vital to note that consumers don't engage with context-aware systems, but they do agree to their data being collected and managed. 

Context-aware solutions rely on location-based systems to determine the users' position and acquire data about the people, objects, and factors that make up the context or environment in which they are situated. 

There are various definitions of context in the literature. 

Schilit et al. [57] were the first to publish one in 1994. They defined context as location, adjacent people's identities, things, and changes in those items. 

Following that, other writers added additional elements such as identity and time (Ryan et al. [58]), emotional states and focus of attention (Dey [59]), context dimensions (Prekop and Burnett [60]), and any environmental data (Gustavsen [61]). 

(Tajd and Ngantchaha [62]). Previous solutions' advancements have added to the complexity of the information management process. 

This fact suggests an increase in the amount of data evaluated during management processes, and hence an increase in the complexity of such procedures. 

Furthermore, the security of location information is a vital factor to consider. 

In another scenario, users' whereabouts may be recorded without their knowledge or agreement, and this information could be used maliciously. 

One example of this circumstance may be the ability to determine a user's present position in order to determine whether or not he or she is at home. 

It is now necessary to regulate not just the privacy of location information, as in location-based services, but also other sensitive pieces of information about the users' lives. 

In the other situation, and continuing with the previous example, it would be feasible to determine not only if a certain user is at home, but also his or her identity, whether he or she is married, his or her emails, and other details about his or her personal life. 

The quantity of heterogeneous bits of information maintained by the context-aware paradigm is quite large, as mentioned by the preceding definitions of context. 

This fact has been affected by the transition from established paradigms to new ones. 

For starters, the mobile paradigm included user and element location into IMS management procedures. 

Pervasive systems later integrated additional key aspects of the users' lives, such as their identities, emails, alerts, and information about smart places. 

Finally, the combination of information regarding the context in which users are positioned with information considered by earlier paradigms has enhanced the complexity of management operations. 

Figure depicts the development of IMSs through time, taking into account the effect of new emerging paradigms and the most representative bits of data that they typically handle. 

Due to the fact that a significant portion of this data is sensitive or private, contemporary IMSs must take into account the privacy of this data. 

In this regard, further research on automated techniques for safeguarding the privacy of sensitive data handled during IMS management activities is required. 

Otherwise, malevolent people might have access to additional information about a certain user's life. 

Context-awareness is a paradigm that may be useful in a variety of situations, including network administration. 

Contextual information may be used by network managers to manage network resources dynamically by adjusting their behavior to the network state. 

The location of connected devices and network resources, the types of devices connected to the network infrastructure, and the network's status and statistics have all been identified as valuable pieces of context information that network administrators should consider before making management decisions. 

These options might range from reconfiguring a network component that isn't being utilized in an energy-efficient manner to deploying additional resources in a specific location to assure the QoS of certain users. 

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

You may also want to read and learn more Technology and Engineering here.

You may also want to read and learn more Cyber Security Systems here.

References & Further Reading:

1. OSI. Information Processing Systems-Open System Inteconnection-Systems Management Overview. ISO 10040, 1991.

2. Jefatura del Estado. Ley Orgánica de Protección de Datos de Carácter Personal. www.boe.es/boe/dias/1999/12/14/pdfs/A43088-43099.pdf.

3. D. W. Samuel, and D. B. Louis. The right to privacy. Harvard Law Review, 4(5): 193–220, 1890.

4. A. Westerinen, J. Schnizlein, J. Strassner, M. Scherling, B. Quinn, S. Herzog, A. Huynh, M. Carlson, J. Perry, and S. Waldbusser. Terminology for Policy-Based Management. IETF Request for Comments 3198, November 2001.

5. B. Moore. Policy Core Information Model (PCIM) Extensions. IETF Request for Comments 3460, January 2003.

6. S. Godik, and T. Moses. OASIS EXtensible Access Control Markup Language (XACML). OASIS Committee Specification, 2002.

7. A. Dardenne, A. Van Lamsweerde and S. Fickas. Goal-directed requirements acquisition. Science of Computer Programming, 20(1–2): 3–50, 1993.

8. F. L. Gandon, and N. M. Sadeh. Semantic web technologies to reconcile privacy and context awareness. Web Semantics: Science, Services and Agents on the World Wide Web, 1(3): 241–260, April 2004.

9. I. Horrocks. Ontologies and the semantic web. Communications ACM, 51(12): 58–67, December 2008.

10. R. Boutaba and I. Aib. Policy-based management: A historical perspective. Journal of Network and Systems Management, 15(4): 447–480, 2007.

11. P. A. Carter. Policy-Based Management, In Pro SQL Server Administration, pages 859–886. Apress, Berkeley, CA, 2015.

12. D. Florencio, and C. Herley. Where do security policies come from? In Proceedings of the 6th Symposium on Usable Privacy and Security, pages 10:1–10:14, 2010.

13. K. Yang, and X. Jia. DAC-MACS: Effective data access control for multi-authority Cloud storage systems, IEEE Transactions on Information Forensics and Security, 8(11): 1790–1801, 2014.

14. B. W. Lampson. Dynamic protection structures. In Proceedings of the Fall Joint Computer Conference, pages 27–38, 1969.

15. B. W. Lampson. Protection. ACM SIGOPS Operating Systems Review, 8(1): 18–24, January 1974.

16. D. E. Bell and L. J. LaPadula. Secure Computer Systems: Mathematical Foundations. Technical report, DTIC Document, 1973.

17. D. F. Ferraiolo, and D. R. Kuhn. Role-based access controls. In Proceedings of the 15th NIST-NCSC National Computer Security Conference, pages 554–563, 1992.

18. V. P. Astakhov. Surface integrity: Definition and importance in functional performance, In Surface Integrity in Machining, pages 1–35. Springer, London, 2010.

19. K. J. Biba. Integrity Considerations for Secure Computer Systems. Technical report, DTIC Document, 1977.

20. M. J. Culnan, and P. K. Armstrong. Information privacy concerns, procedural fairness, and impersonal trust: An empirical investigation. Organization Science, 10(1): 104–115, 1999.

21. A. I. Antón, E. Bertino, N. Li, and T. Yu. A roadmap for comprehensive online privacy policy management. Communications ACM, 50(7): 109–116, July 2007.

22. J. Karat, C. M. Karat, C. Brodie, and J. Feng. Privacy in information technology: Designing to enable privacy policy management in organizations. International Journal of Human Computer Studies, 63(1–2): 153–174, 2005.

23. M. Jafari, R. Safavi-Naini, P. W. L. Fong, and K. Barker. A framework for expressing and enforcing purpose-based privacy policies. ACM Transaction Information Systesms Security, 17(1): 3:1–3:31, August 2014.

24. G. Karjoth, M. Schunter, and M. Waidner. Platform for enterprise privacy practices: Privacy-enabled management of customer data, In Proceedings of the International Workshop on Privacy Enhancing Technologies, pages 69–84, 2003.

25. S. R. Blenner, M. Kollmer, A. J. Rouse, N. Daneshvar, C. Williams, and L. B. Andrews. Privacy policies of android diabetes apps and sharing of health information. JAMA, 315(10): 1051–1052, 2016.

26. R. Ramanath, F. Liu, N. Sadeh, and N. A. Smith. Unsupervised alignment of privacy policies using hidden Markov models. In Proceedings of the Annual Meeting of the Association of Computational Linguistics, pages 605–610, June 2014.

27. J. Gerlach, T. Widjaja, and P. Buxmann. Handle with care: How online social network providers’ privacy policies impact users’ information sharing behavior. The Journal of Strategic Information Systems, 24(1): 33–43, 2015.

28. O. Badve, B. B. Gupta, and S. Gupta. Reviewing the Security Features in Contemporary Security Policies and Models for Multiple Platforms. In Handbook of Research on Modern Cryptographic Solutions for Computer and Cyber Security, pages 479–504. IGI Global, Hershey, PA, 2016.

29. K. Zkik, G. Orhanou, and S. El Hajji. Secure mobile multi cloud architecture for authentication and data storage. International Journal of Cloud Applications and Computing 7(2): 62–76, 2017.

30. C. Stergiou, K. E. Psannis, B. Kim, and B. Gupta. Secure integration of IoT and cloud computing. In Future Generation Computer Systems, 78(3): 964–975, 2018.

31. D. C. Verma. Simplifying network administration using policy-based management. IEEE Network, 16(2): 20–26, March 2002.

32. D. C. Verma. Policy-Based Networking: Architecture and Algorithms. New Riders Publishing, Thousand Oaks, CA, 2000.

33. J. Rubio-Loyola, J. Serrat, M. Charalambides, P. Flegkas, and G. Pavlou. A methodological approach toward the refinement problem in policy-based management systems. IEEE Communications Magazine, 44(10): 60–68, October 2006.

34. F. Perich. Policy-based network management for next generation spectrum access control. In Proceedings of International Symposium on New Frontiers in Dynamic Spectrum Access Networks, pages 496–506, April 2007.

35. S. Shin, P. A. Porras, V. Yegneswaran, M. W. Fong, G. Gu, and M. Tyson. FRESCO: Modular composable security services for Software-Defined Networks. In Proceedings of the 20th Annual Network and Distributed System Security Symposium, pages 1–16, 2013.

36. K. Odagiri, S. Shimizu, N. Ishii, and M. Takizawa. Functional experiment of virtual policy based network management scheme in Cloud environment. In International Conference on Network-Based Information Systems, pages 208–214, September 2014.

37. M. Casado, M. J. Freedman, J. Pettit, J. Luo, N. McKeown, and S. Shenker. Ethane: Taking control of the enterprise. In Proceedings of Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications, pages 1–12, August 2007.

38. M. Wichtlhuber, R. Reinecke, and D. Hausheer. An SDN-based CDN/ISP collaboration architecture for managing high-volume flows. IEEE Transactions on Network and Service Management, 12(1): 48–60, March 2015.

39. A. Lara, and B. Ramamurthy. OpenSec: Policy-based security using Software-Defined Networking. IEEE Transactions on Network and Service Management, 13(1): 30–42, March 2016.

40. W. Jingjin, Z. Yujing, M. Zukerman, and E. K. N. Yung. Energy-efficient base stations sleep-mode techniques in green cellular networks: A survey. IEEE Communications Surveys Tutorials, 17(2): 803–826, 2015.

41. G. Auer, V. Giannini, C. Desset, I. Godor, P. Skillermark, M. Olsson, M. A. Imran, D. Sabella, M. J. Gonzalez, O. Blume, and A. Fehske. How much energy is needed to run a wireless network?IEEE Wireless Communications, 18(5): 40–49, 2011.

42. W. Yun, J. Staudinger, and M. Miller. High efficiency linear GaAs MMIC amplifier for wireless base station and Femto cell applications. In IEEE Topical Conference on Power Amplifiers for Wireless and Radio Applications, pages 49–52, January 2012.

43. M. A. Marsan, L. Chiaraviglio, D. Ciullo, and M. Meo. Optimal energy savings in cellular access networks. In IEEE International Conference on Communications Workshops, pages 1–5, June 2009.

44. H. Claussen, I. Ashraf, and L. T. W. Ho. Dynamic idle mode procedures for femtocells. Bell Labs Technical Journal, 15(2): 95–116, 2010.

45. L. Rongpeng, Z. Zhifeng, C. Xianfu, J. Palicot, and Z. Honggang. TACT: A transfer actor-critic

learning framework for energy saving in cellular radio access networks. IEEE Transactions on Wireless Communications, 13(4): 2000–2011, 2014.

46. G. C. Januario, C. H. A. Costa, M. C. Amarai, A. C. Riekstin, T. C. M. B. Carvalho, and C. Meirosu. Evaluation of a policy-based network management system for energy-efficiency. In IFIP/IEEE International Symposium on Integrated Network Management, pages 596–602, May 2013.

47. C. Dsouza, G. J. Ahn, and M. Taguinod. Policy-driven security management for fog computing: Preliminary framework and a case study. In Conference on Information Reuse and Integration, pages 16–23, August 2014.

48. H. Kim and N. Feamster. Improving network management with Software Defined Networking. IEEE Communications Magazine, 51(2): 114–119, February 2013.

49. O. Gaddour, A. Koubaa, and M. Abid. Quality-of-service aware routing for static and mobile IPv6-based low-power and loss sensor networks using RPL. Ad Hoc Networks, 33: 233–256, 2015.

50. Q. Zhao, D. Grace, and T. Clarke. Transfer learning and cooperation management: Balancing the quality of service and information exchange overhead in cognitive radio networks. Transactions on Emerging Telecommunications Technologies, 26(2): 290–301, 2015.

51. M. Charalambides, P. Flegkas, G. Pavlou, A. K. Bandara, E. C. Lupu, A. Russo, N. Dulav, M. Sloman, and J. Rubio-Loyola. Policy conflict analysis for quality of service management. In Proceedings of the 6th IEEE International Workshop on Policies for Distributed Systems and Networks, pages 99–108, June 2005.

52. M. F. Bari, S. R. Chowdhury, R. Ahmed, and R. Boutaba. PolicyCop: An autonomic QoS policy enforcement framework for software defined networks. In 2013 IEEE SDN for Future Networks and Services, pages 1–7, November 2013.

53. C. Bennewith and R. Wickers. The mobile paradigm for content development, In Multimedia and E-Content Trends, pages 101–109. Vieweg+Teubner Verlag, 2009.

54. I. A. Junglas, and R. T. Watson. Location-based services. Communications ACM, 51(3): 65–69, March 2008.

55. M. Weiser. The computer for the 21st century. Scientific American, 265(3): 94–104, 1991.

56. G. D. Abowd, A. K. Dey, P. J. Brown, N. Davies, M. Smith, and P. Steggles. Towards a better understanding of context and context-awareness. In Handheld and Ubiquitous Computing, pages 304–307, September 1999.

57. B. Schilit, N. Adams, and R. Want. Context-aware computing applications. In Proceeding of the 1st Workshop Mobile Computing Systems and Applications, pages 85–90, December 1994.

58. N. Ryan, J. Pascoe, and D. Morse. Enhanced reality fieldwork: The context aware archaeological assistant. In Proceedings of the 25th Anniversary Computer Applications in Archaeology, pages 85–90, December 1997.

59. A. K. Dey. Context-aware computing: The CyberDesk project. In Proceedings of the AAAI 1998 Spring Symposium on Intelligent Environments, pages 51–54, 1998.

60. P. Prekop and M. Burnett. Activities, context and ubiquitous computing. Computer Communications, 26(11): 1168–1176, July 2003.

61. R. M. Gustavsen. Condor-an application framework for mobility-based context-aware applications. In Proceedings of the Workshop on Concepts and Models for Ubiquitous Computing, volume 39, September 2002.

62. C. Tadj and G. Ngantchaha. Context handling in a pervasive computing system framework. In 

Proceedings of the 3rd International Conference on Mobile Technology, Applications and Systems, 

pages 1–6, October 2006.

63. S. Dhar and U. Varshney. Challenges and business models for mobile location-based services and advertising. Communications ACM, 54(5): 121–128, May 2011.

64. F. Ricci, L. Rokach, and B. Shapira. Recommender Systems: Introduction and Challenges, pages In Recommender Systems Handbook, pages 1–34. Springer, Boston, MA, 2015.

65. J. B. Schafer, D. Frankowski, J. Herlocker, and S. Sen. Collaborative Filtering Recommender Systems, In The Adaptive Web, pages 291–324. Springer, Berlin, Heidelberg, 2007.

66. P. Lops, M. de Gemmis, and G. Semeraro. Content-Based Recommender Systems: State of the Art and Trends, In Recommender Systems Handbook, pages 73–105. Springer, Boston, MA, 2011.

67. D. Slamanig and C. Stingl. Privacy aspects of eHealth. In Proceedings of Conference on Availability, Reliability and Security, pages 1226–1233, March 2008.

68. C. Wang. Policy-based network management. In Proceedings of the International Conference on Communication Technology, volume 1, pages 101–105, 2000.

69. R. Want, A. Hopper, V. Falcao, and J. Gibbons. The active badge location system. ACM Transactions on Information Systems, 10(1): 91–102, January 1992.

70. K. R. Wood, T. Richardson, F. Bennett, A. Harter, and A. Hopper. Global teleporting with Java: Toward ubiquitous personalized computing. Computer, 30(2): 53–59, February 1997.

71. C. Perera, A. Zaslavsky, P. Christen, and D. Georgakopoulos. Context aware computing for the Internet of Things: A survey. IEEE Communications Surveys Tutorials, 16(1): 414–454, 2014.

72. B. Guo, L. Sun, and D. Zhang. The architecture design of a cross-domain context management system. In Proceedings of Conference Pervasive Computing and Communications Workshops, pages 499–504, April 2010.

73. A. Badii, M. Crouch, and C. Lallah. A context-awareness framework for intelligent networked embedded systems. In Proceedings of Conference on Advances in Human-Oriented and Personalized Mechanisms, Technologies and Services, pages 105–110, August 2010.

74. S. Pietschmann, A. Mitschick, R. Winkler, and K. Meissner. CroCo: Ontology-based, crossapplication context management. In Proceedings of Workshop on Semantic Media Adaptation and Personalization, pages 88–93, December 2008.

75. T. Gu, X. H. Wang, H. K. Pung, and D. Q. Zhang. An ontology-based context model in intelligent environments. In Proceedings of Communication Networks and Distributed Systems Modeling and Simulation Conference, pages 270–275, January 2004.

76. H. Chen, T. Finin, and A. Joshi. An ontology for context-aware pervasive computing environments. The Knowledge Engineering Review, 18(03): 197–207, September 2003.

77. D. Ejigu, M. Scuturici, and L. Brunie. CoCA: A collaborative context-aware service platform for pervasive computing. In Proceedings of Conference Information Technologies, pages 297–302, April 2007.

78. R. Yus, E. Mena, S. Ilarri, and A. Illarramendi. SHERLOCK: Semantic management of location based services in wireless environments. Pervasive and Mobile Computing, 15: 87–99, 2014.

79. L. Tang, Z. Yu, H. Wang, X. Zhou, and Z. Duan. Methodology and tools for pervasive application development. International Journal of Distributed Sensor Networks, 10(4): 1–16, 2014.

80. B. Bertran, J. Bruneau, D. Cassou, N. Loriant, E. Balland, and C. Consel. DiaSuite: A tool suite to develop sense/compute/control applications. Science of Computer Programming, 79: 39–51, 2014.

81. P. Jagtap, A. Joshi, T. Finin, and L. Zavala. Preserving privacy in context-aware systems. In Proceedings of Conference on Semantic Computing, pages 149–153, September 2011.

82. V. Sacramento, M. Endler, and F. N. Nascimento. A privacy service for context-aware mobile computing. In Proceedings of Conference on Security and Privacy for Emergency Areas in Communication Networks, pages 182–193, September 2005.

83. A. Huertas Celdrán, F. J. García Clemente, M. Gil Pérez, and G. Martínez Pérez. SeCoMan: A 

semantic-aware policy framework for developing privacy-preserving and context-aware smart applications. IEEE Systems Journal, 10(3): 1111–1124, September 2016.

84. J. Qu, G. Zhang, and Z. Fang. Prophet: A context-aware location privacy-preserving scheme in location sharing service. Discrete Dynamics in Nature and Society, 2017, 1–11, Article ID 6814832, 2017.

85. A. Huertas Celdrán, M. Gil Pérez, F. J. García Clemente, and G. Martínez Pérez. PRECISE: Privacy-aware recommender based on context information for Cloud service environments. IEEE Communications Magazine, 52(8): 90–96, August 2014.

86. S. Chitkara, N. Gothoskar, S. Harish, J.I. Hong, and Y. Agarwal. Does this app really need my location? Context-aware privacy management for smartphones. In Proceedings of the ACM Interactive Mobile, Wearable and Ubiquitous Technologies, 1(3): 42:1–42:22, September 2017.

87. A. Huertas Celdrán, M. Gil Pérez, F. J. García Clemente, and G. Martínez Pérez. What private information are you disclosing? A privacy-preserving system supervised by yourself. In Proceedings of the 6th International Symposium on Cyberspace Safety and Security, pages 1221–1228, August 2014.

88. A. Huertas Celdrán, M. Gil Pérez, F. J. García Clemente, and G. Martínez Pérez. MASTERY: A multicontext-aware system that preserves the users’ privacy. In IEEE/IFIP Network Operations and Management Symposium, pages 523–528, April 2016.

89. A. Huertas Celdrán, M. Gil Pérez, F. J. García Clemente, and G. Martínez Pérez. Preserving patients’ privacy in health scenarios through a multicontext-aware system. Annals of Telecommunications, 72(9–10): 577–587, October 2017.

90. A. Huertas Celdrán, M. Gil Pérez, F. J. García Clemente, and G. Martínez Pérez. Policy-based management for green mobile networks through software-defined networking. Mobile Networks and Applications, In Press, 2016.

91. A. Huertas Celdrán, M. Gil Pérez, F. J. García Clemente, and G. Martínez Pérez. Enabling highly dynamic mobile scenarios with software defined networking. IEEE Communications Magazine, Feature Topics Issue on SDN Use Cases for Service Provider Networks, 55(4): 108–113, April 2017.