The Augmented Revolution

As technology changes and morphs into new realms of experimental and experiential forms of existence so must the theories that have been accustomed to explaining and examining the structure of technology and its relation to other objects. Actor-Network Theory as defined by Bruno Latour is a notable model by which to assess the emerging existence of Augmented Reality both in theoretical form and practical experience. Augmented Reality, digital content experienced in the physical world, has begun to merge a human’s interactions with digital content for the purpose of altering the physical world - much like the use of holographic technology. When viewed through the lens of Actor-Network Theory, Augmented Reality becomes a new model of viewing both the accuracy of the theory and the theory itself. By analyzing Augmented Reality through Actor-Network Theory is necessary to see the way in which a paradigm shift must take place beyond Actor-Network Theory in order to explain the interactions that humans and nonhumans experience while networking with one another. This will be done by a strict analysis of Actor-Network Theory, Augmented Reality, the advantages of AR, as well as the disadvantages to distinguish the nature of theory as it relates to Actor-Network Theory and the praxis of Augmented Reality. The assertion is simply that Actor-Network Theory is inadequate to explain a world where Augmented Reality has become a part of life and therefore must include new developments in thought and theory. In order to truly grasp the need for a new paradigm, it is imperative that the Actor-Network Theory is thoroughly examined and analyzed so as to determine the paradigmatic shifts that must occur when considering Augmented Reality.

Actor-Network Theory: Explained

Bruno Latour, as well as others, initially envisioned Actor-Network Theory as an entirely different structure with which to model the interactions that objects, whether those are human or nonhuman, translate and are transformed by interactions with each other. Essentially, the network consisted of actors that played a role in one way or another in order to create a working model of a thing, whatever the definition of that thing might be is of no importance to the theory as much as the relationships formed by the actors involved. According to Sergio Sismondo, “the actors of ANT are heterogeneous in that they include both human and non-human entities, with no methodologically significant distinction between them” (81). Sismondo, here, explains ANT as a theory derived from participants within a network that share common bonds, but are seen as separate based on human or non-human objects within the network. By examining a network in such a way, intentionality and other human perceptions are removed from the observable logic of the network so that the network itself behaves as parts moving in tandem to support the network.

ANT logically separates out any human desires or desired outcomes from the network because all entities within a network are equal as objects within it. In doing so, ANT allows the observer to view only the functioning objects within a network, which can be incredibly useful to study a network but can also be problematic in making a judgment or response to the network’s efficiency or ultimate goal. However, according to Bruno Latour, “the apparently reasonable division of material and social becomes what is obfuscating any enquiry on how a collective action is possible” (74). Latour suggests that the examination of any network is actually hindered by allowing the material, or natural, and social elements to be separated from each other. He states that the two need to be combined in order to comprehend the collective action that the network is designed to perform.

Though Actor-Network Theory is actually much more complex then it may seem at first glance. Not only does it state that all elements or entities within the network are heterogeneous, but it also maps out the network in order to show relations that are simultaneously material and semiotic. It makes no distinction between the material and thought processes of the network. Ole Hanseth wrote, “An actor network consists of and links together both technical and non-technical elements. Not only the car's motor capacity, but also your driving training influence your driving” (What is Actor-Network Theory?). It is imperative in understanding ANT to realize that all objects within the network are heterogeneous, not only the actors but all actors’ interactions, also play the part of the actor within the network. The material and semiotic relationships work in tandem to create the network, all of which are actors. As with Hanseth’s brief example, the car, the motor, the motor capacity, the driver, the driver’s training, and so forth will play a strong role within the network in order for the car to actually move and accomplish a task as simple as going from on end of a street to another. This model also shows the overwhelming amount of actors to consider. For instance, driving from one end of the street to another includes the driver’s mobility, any obstacles, like other cars, and the street itself to be material objects within the network. The semiotic elements of this example could easily be the driver’s thought to get to the end of the street, any distractions the driver may be facing, such as talking on the phone or looking for something in the car, etc. would also all be actors within the network. Given that all semiotic and material elements of a network are heterogeneous, in order to examine a complex network via ANT, multitudes of actors must be taken into account, however desire and intentionality do not come into play within this model of examining the network.

One of the biggest opponents of ANT is the fact that intentionality and human desire do not play a larger role within than any other actor and are generally dismissed within the framework of the network because they do not play roles as actors at all. In Hanseth’s example, the driver may actually have the desire or intention of going to a specific place, like work. The driver may be concerned about being late for work and therefore not pay as much attention to the obstacles on the road and cause an accident, therefore creating a completely different network that involves another driver and more complex network is derived from this example. In ANT humans and nonhumans are viewed as equal actors within the network, but a nonhuman actor may have no desires or very simple ones, such as a dog wanting to play or a cat that needs food. Latour attempts to explain that within all societies, be it baboon or human, other agencies are acting on the actor at any given point in time. Latour states, “a face-to-face interaction is not a plausible departure point to trace social connections for both humans and monkeys because in both cases they are being constantly interfered with by other agencies” (198). Latour shows that there are so many agencies acting upon both humans and monkeys that even a simple face-to-face interaction between humans or between monkeys may yield a complex network due to the other actors at play within the network that create facial expressions and facial cues by human actors or monkey actors. However, this notion of agency does not easily explain away desire or intention. The fact that humans may have more complex desires or intentions than can be accounted for by other agencies acting upon them does not discount ANT from Latour’s point of view, yet there are several arguments proposed against ANT because of this simple dismissive nature of desire and intention by ANT.

Additionally, Latour has created a new problem by introducing the notion of other agencies acting upon actors. Agency is not necessarily an actor or a non-actor in ANT and therefore creates a strong argument against ANT itself. According to Sergio Sismondo, “On the one hand, it may encourage analyses centered on key figures, and perhaps as a result many of the most prominent examples are of heroic scientists and engineers, or of failed heroes” (89). Sismondo continues by saying that there a multitude of networks that will be missed because they are neither successful or a failure, but rather something in between (89). These networks might not be a perfect success or a perfect failure, but rather have some success with some failure mixed as happens in most human networks and interactions. This strict formulaic view of ANT is dismissive of, possibly, a majority of human endeavors and therefore may better fit a network of nonhuman actors or actually thinking machine actors. While considering Augmented Reality, ANT can be effective to a certain extent showing the network that machine actors can interact with each other, but may be missing the human element of desire or intention as will be discussed in later sections. Augmented Reality needs to readjust ANT in order for the networks to be examined thoroughly, but first, there must be a clear definition of Augmented Reality and the praxis of AR in order to determine how to effectively create a paradigm shift for ANT to analyze AR networks.

Augmented Reality

While the theory of Augmented Reality is easy to understand, once AR is put into practice it becomes a much more complex entity, that is difficult to describe and define. A simple definition of AR is that it is digital content that has been designed to be experienced within the physical world. The simplest example of such an AR type of system is a QR Code. They are commonplace now and can be seen on the back of a box of cereal, billboards, advertisements, and placed in a variety of stores. They can be scanned using a QR reader with a smartphone and then synthesized into legible data that the user of the smartphone can interface with. The most typical types of QR Codes around are that of coupons for specific products or advertisements for films. However, there are several other types of Augmented Reality that are emerging in engineering departments at universities, such as MIT, as well as those that are becoming more popular in artistic scenes in large cities like New York and Tokyo, where the user can interface with the art piece using their smartphone or learn more about the artist. Though this notion isn’t completely new given the context that QR Codes are essentially larger, more complex versions of barcodes seen on every product from food to books, the notion of interactivity via a smartphone, iPad, tablet, PDA, or similar device is becoming increasingly layered and complex. A much more complex example of Augmented Reality is Google Glass, which is used as a visual device to, quite literally, augment the reality of the user by giving more information for a variety of topics using networking capabilities to Google and the internet. Though Google Glass is one of many of these types of devices, it is certainly the most popular currently because it is also a product that someone can actually purchase. Other examples of AR require the user to build or be in contact with the right community in order to use. For instance, MIT has come up with some excellent interfaces for AR systems, but they are not actually available for public consumerism yet unless the consumer is willing to build it.

MIT has developed what they refer to as “The Smarter Objects System,” which allows users to interface with different electronic devices, such as a speaker, or door handle, or light switch via interfacing with an iPad in order to control the object. Each of these items has been programmed and contains Wi-Fi connections in order to use the camera function and Wi-Fi connection of an iPad to be able to change the color of the lights or open a locked door via a keypad directly located on the iPad. Though these examples are more complex than QR Codes because each device needs a Wi-Fi connection and additional programming and circuitry to be interacted with, they are ultimately simpler than other endeavors at MIT. According to the Smarter Objects System project at MIT, “[it] can be used with any device that can act as a visual input and output device. This means that instead of using see-through AR technology available on today’s tablets and smartphones we could also use transparent displays…” (6). These types of transparent displays can be better defined by MIT’s Sixth Sense project.

The SixthSense project at MIT poses a new kind of virtual world where a projector, mirror, and camera are used to create a visual interface that can be projected into a room, on a wall, or even on a person’s hands. The SixthSense project is much more complicated than the Smarter Objects System, but it can also be built at home by the user if the user is so inclined to do so. It has several different applications that can be used with it including, “The map application,” which, “lets the user navigate a map displayed on a nearby surface using hand gestures,” as well as, “the drawing application [that] lets the user draw on any surface by tracking the fingertip movements of the user’s index finger” (SixthSense – a wearable gestural interface (MIT Media Lab)). Certainly, these types of technology are interesting, but how does this fit into ANT? The answer is initially simple but builds exponentially to a more convoluted definition as AR is viewed through the lens of ANT. This answer will be explored in-depth. However, before this discussion can take place, an analysis of the positive and negative aspects of Augmented Reality must be further explored.

Augmented Positives

Clearly, AR certainly stands to benefit people in some of the most unique ways the world has ever seen. Some of the applications are easily and readily available such as QR Codes, while others are more expensive and selective to purchase like Google Glass, but there is an obvious upside to AR systems. Google Glass, for instance, allows people to use the web while anywhere without really taking any time to express more than a simple sentence of what a given person wants to know. Google Glass allows people to connect to Wikipedia enabling them to gather more information about a variety of subjects, like more information on locales they may be visiting or on certain restaurants they want to try. Also, there is a clear upside that Google Glass also connects to Google Maps and Google Translate allowing the user to perform complex language translations without too much of a time delay or getting the user exactly where they want to go in a specific city the user hasn't been. Tristan Thielmann states, “The unusual location-based nature of communication in the electronic media is currently leading to a renaissance of cartographic representations, as maps are often indispensable to ‘locative media’ in producing an index for the illustration of spatial relationships” (Aether 2). In this way, Theilmann shows the way Google Glass in conjunction with Google Maps is creating a new system of cartography that would never have existed otherwise, as well as providing a system of understanding where others are at any given moment via social media. Yet, there are other ways in which Augmented Reality is beneficial.

The physically disabled may be able to use Augmented Reality systems to do things they would otherwise be unable to do. For instance, with MIT’s SixthSense project, physically disabled people that may not be able to use a computer can use a system that projects things the way a computer would and allows them to alter their hands in different ways so that those without the ability to use a keyboard could still type. Similarly, Google Glass gives the physically disabled the ability to easily search through the internet and find a plethora of information they may not have had access to in any other way without asking someone else for help.

Lastly, one of the primary positives of Augmented Reality is the gaming and entertainment industries, which may allow the creation of new high-tech jobs as well. Most importantly, there are several different game developers that are attempting to create an AR system that is logical and intuitive for the players. In one instance, Mateas and Stern’s game Façade was introduced in three separate ways, “the last of these had a physically constructed set, including furniture and props, onto which the Façade characters were projected via a head-mounted display, and with support for both spoken and bodily interaction” (Agency Reconsidered 5). According to Agency Reconsidered, this was the closest version of the “Holodeck,” in reference to Star Trek, version of the game Façade that could be created (5). This type of game was envisioned in several different science fiction novels, movies, and TV shows, but only now has it begun to develop into a reality. Though, of course, presently these types of games are not available to the public, they are being created at speed in order to allow a more realistic view of gaming. However, this type of gaming and entertainment is not without its own problems.

Negatively Augmented Reality

The most notable negative aspect of Augmented Reality is one agency as in the example above. However, that is something that will be returned to shortly. One of the simpler problematic notions of merging reality and Augmented Reality is that of biology. For instance, though most people know of Google Glass and have seen advertisements for it, they have not actually experienced it. It also has not been widely studied by biologists or optometrists. The question remains is the visual cortex capable of actually perceiving two entirely separate systems simultaneously? It may be entirely too confusing for most people to adapt to viewing both reality and virtual reality within the same visual-spatial atmosphere. People have managed to adapt to the complexities of computers, smartphones, and a variety of other devices over the course of human history, but can people adjust to experiencing two or more separate realities simultaneously while constantly having to adjust for both? The answer to this may well lie in the problem of agency for Augmented Reality and more specifically agency within the framework of Actor-Network Theory.

In the example of the game Façade, it was found that the Augmented Reality version created an interesting effect of agency for the players. According to the article, “Dow and his collaborators reached the unexpected conclusion that an increased sense of presence and realism can actually act to decrease agency” (Agency Reconsidered 6). The problem with Augmented Reality in the gaming industry seems to derive from the fact that there is no solution to true robotics or actual artificial intelligence. Augmented Reality systems seem to create an indefinable separation from the user or users and other characters in the game because the other characters are nonhuman actors. This lack of agency within the gaming industry has been fairly well observed and well documented. There are many examples of this in relation to Actor-Network Theory. Another example is shown by Seth Giddings when he states that the chapter in question will draw on, “theoretical positions developed within the Sociology of Science and Technology and Actor-Network Theory (ANT) to explore how social constructivism might be challenged by the consideration of the agency of technologies” (Worlds in Play: International Perspectives on Digital Games Research, 115). Giddings also suggests that the nature of the digital game and playing digital games can be analyzed to suggest ways that digital gaming would challenge the argument of ANT (Worlds in Play: International Perspectives on Digital Games Research 115).

This notion of agency that has such a huge implication of denying the reality of ANT within the framework of Augmented Reality that it is necessary to examine the way in which agency is a problem for ANT again. According to Sergio Sismondo, “to treat humans and non-humans symmetrically, ANT has to deny that intentionality is necessary for action, and thus deny that the differences between humans and non-humans are important for the theory overall” (90). This “reality” of ANT is literally separated from Augmented Reality because there is such a strong logical disconnect between nonhuman actors and characters within an Augmented Reality system that the intentionality of the user has to become an integral part of the theory in order for ANT to remain intact.

This would seem to separate any sense of agency that Actor-Network Theory attempts to maintain into a clear definition of human and nonhuman actors participating in a heterogeneous network because, within Augmented Reality, nonhuman actors do not act like anything in the physical world. Intentionality still remains the heart of the problem for adjusting ANT within AR because within Façade, the intentions of the user are to have fun. In other systems, like Google Glass, the intentions of the user are a functionality of a separate reality that exists outside of true reality. There is an obvious dichotomy that is forming primarily because the fact remains that there is something entirely new about Augmented Reality. It has created its own paradigm. Therefore, does it make logical sense to justify it within the Actor-Network Theory? That remains to be seen.

The Augmented Revolution

The arguments for accepting Actor-Network Theory abound in a variety of people, places, and sciences. The postmodernists, for example, tend toward the viewpoint because it allowed all objects to be perceived as though agency is equal for humans or nonhumans. For instance, David Barton and Mary Hamilton write, “Actor network theory challenges one of the current great divides in Western philosophy and science by insisting that agency resides in combination of both human beings and non-human objects” (Beyond Communities of Practice: Language, Power and Social Context 29). Clearly, Bruno Latour intended this type of agency to be brought into effect within Actor-Network Theory. Latour wanted humans and nonhumans to have equal respect for agency in regards to the network and each other. However, by removing intentionality from the human aspect of the network, Latour created a network of nonhumans interacting with other nonhumans, which is not exactly a model of looking at technology or even technoscience. Though there are many that would argue that point, their voices are becoming fewer and fewer because of the new paradigm of Augmented Reality for which most of the theory has not yet been written. When working within a new framework of thought, theories often need to be remolded, reshaped, or completely done away with. The question is, what happens to Actor-Network Theory when Augmented Reality challenges it? Though developing a cohesive, coherent theory for Augmented Reality in Actor-Network Theory is a complex venture, it is worthwhile to examine what happens to ANT as it delves deeper into AR. However, before that process begins, one must take into account the aforementioned problems of agency and intentionality that ANT has already suffered through as those problems exponentially multiply within more complex AR systems.

Since everything has already been defined thus far, there is no need for further definition, but rather analysis is the next step. Augmented Reality as a useful tool in getting coupons or advertisements from QR Codes using smartphones represents a slight threat to Actor-Network Theory, but not much more than the problem of intentionality. For instance, a store may use a QR Code on one of its advertisements to allow those with smartphones to get a certain percentage sale price off the normal price. Here, the problem of intentionality primarily exists in that humans designed the advertisement using nonhuman objects to create a human response, which would be to sell a specific product. Advertisers in this sense act as both human and nonhuman entities in this model, where the human advertisers should have intentionality. The same goes for the user of the QR Code, the consumer. The consumer may want to buy a specific product, but not be able to afford it and therefore discover that by using the QR Code coupon that the consumer can then afford the product. They may also not want the product that much, but perhaps a little bit, and use the QR Code as an excuse to buy something the consumer slightly desired. None of this is new. Advertisers have been using strategies like this to sell products since there have been advertisements. The question in this example is, does the desire and intentionality of the human actors in the network cause a problem for ANT? Based on the fact that ANT has suffered problems like this since its inception, it would appear that there will always be those arguing against ANT and those arguing for it. Therefore, the Augmented Reality of the QR Code does not have any effect on ANT, except to exaggerate a weakness that already exists within the theory. In order to get more of a solution to the question, the Augmented Reality system must be more complex.

The aforementioned example of MIT’s Smarter Objects System would appear to be a more complex example at first because of the way the system uses an iPad to interact with different nonhuman objects such as a light switch or door lock or stereo system. Though the iPad does, in fact, interact with these systems in a user-directed way, intention and desire-based, the ANT model holds up reasonably well. While the systems have all been programmed and connected with Wi-Fi devices in order to be used with the iPad and its camera, the network consists of similarly based actors of both humans nonhumans. The problem ANT faces here is that of desire and intention. If the user of the iPad wants to go through the door, that creates a desire or intentional input from the user, which forces the network into a system where users are directing objects based on desires and intentions. If the user wants to operate the light switch via the iPad in order to turn on the lights, turn them off, or change the color of them, the decision making process for the user cannot be equal to that of a nonhuman because it is entirely based on desire and intent. This is where the slippery slope for Actor-Network Theory begins in Augmented Reality systems. ANT can easily argue that intentions and desires don't matter in this example, but it is not so easy to accept such an argument as anything more than fallacious when it comes to this example. This is also one of the more simplistic examples of Augmented Reality. Complexity is key when it comes to AR.

MIT’s other project, the SixthSense project works in relatively the same manner as the previous example in regards to ANT, however, it adds on more complex intentions and desires. SixthSense allows users or actors within the network to interact with a myriad of different tools and applications such as maps and dialing phone numbers on one’s own hand. This creates an added complexity for ANT because while the user is still interfacing with nonhuman objects, the human user has very specific intentions of what they may want to do or look up or if they may need to use a map to find a specific place that they are looking for. This added dimension of intention multiplies the problems ANT is now facing because as the more complex intentions are added, it is increasingly more difficult for ANT to treat human and nonhuman actors in a homogeneous way.

Next, the examination of Google Glass needs to take place. Google Glass creates another dimension of problems for Actor-Network Theory to deal with because even though there is only one human actor in the network of a human using Google Glass, there are thousands, if not many more, of programmers, cartographers, translators, and other actors that compiled the information that is now available to the user. Also, because Google Glass can connect to social media networks, the network itself becomes exponential because the user can communicate with others across the entire planet. This is where ANT genuinely seems to lose its logical connection to the network because there are, possibly, millions of human actors interacting with millions of nonhuman actors in order to explain, experience, understand, or feel something, which requires the human actors to be attributed desire and intentions. With whom do certain users communicate with and what do those communications have in the place of nonhuman actors within the network? When even viewing Facebook through this lens, it’s easy to see that the human and nonhuman actors are so increasingly large in number that it becomes virtually impossible to differentiate what the actors are. Is something as simple as a voicemail or text a nonhuman actor? How does a human actor that sent a text with a specific desired response in mind fit into Actor-Network Theory? Because Google Glass allows such a myriad of possibilities to communicate with both human and nonhuman actors, the network becomes too complex to map if intentions and desires from human actors are not considered with more weight than those from nonhuman actors. The most complex example, however, is that of virtual reality gaming.

In the example explained previously of the game Façade in which players used sets and visual devices in order to engage both other players and nonhuman characters, the human players had a specific desire to have fun, as well as to experience the virtual reality world they were involved in. In this type of Augmented Reality, Actor-Network Theory almost completely falls apart because there are several different aspects that the human actors are experiencing which the nonhuman actors aren’t able to experience or synthesize information in the same way at all. This type of network creates a need for human desires and intentions to be accurately considered and weighted against nonhuman actors. Nonhuman actors may also have substantial artificial intelligence programming to the effect that they may have intentions, such as preventing the players from doing certain things within the game or attempting to be adversarial to the human players. Artificial intelligence technology only has come so far and is not actually similar to a human's advancing intelligence in any regard. Therefore, nonhuman actors with semi-artificial intelligence may only be able to be understood within the network by speaking the programmers that created them. Something as simple as a cat may create a complex web of intentions even if it is artificially programmed. Whereas, the human players have far more complex intentions and desires, which simply does not fit into Actor-Network Theory as equal, heterogeneous actors to nonhuman actors. Another problem, and in many ways the most difficult problem, is that when human actors played Façade via Augmented Reality there was seen a depreciable level of agency for the players (Agency Reconsidered 6). They simply did not enjoy the game as much as players that were playing via a computer, mouse, and keyboard. The decreased sense of agency is highly problematic for Actor-Network Theory because it does not allow anything, but a heterogeneous sense of agency. If human actors experience deflated agency while experiencing Augmented Reality, then Actor-Network Theory simply does not allow for the possibility of AR. This is the shift in paradigms from the age of computers to the age of Augmented Reality. It has, quite literally, created its own paradigm, which requires a new methodology and hegemony of theory to explain it, which Actor-Network Theory can’t account for.

The New Paradigm

Though it is virtually impossible to actually create a new paradigm in a paper such as this, it is imperative that the notion is explored. Because it has become clear that the Actor-Network theory no longer remains valid when applied to Augmented Reality, the theory must be altered or eradicated. The best way to deal with this problem is to develop a coherent weighted system that allows agency, desire, and intention to be involved within the network. Especially as artificial intelligence continues making strides to lead to more complex forms of nonhuman actors that can interact with human actors in any given network. Agency must certainly be given a specific definition for human actors, nonhuman objects, nonhuman animals, and nonhuman forms of artificial intelligence at present. Then desire and intention must be attributed to human actors in contrast to nonhuman actors because “all things being equal” is not the causal relationship networks have. Though this creates a more complex version of Actor-Network Theory, complexity is not inherently bad. When explaining the depths of social networks, as well as any other network, complexity should be invited, not ignored. Therefore, Bruno Latour’s work must be adjusted to compensate for reality and Augmented Reality.

To conclude, this paper has defined Actor-Network Theory clearly and explicitly. It has also clearly defined and given examples of Augmented Reality. In giving examples, there has been evidence given of both the positive and negative sides of merging Augmented Reality with reality and human actors. Finally, this paper has proven that Augmented Reality shows the need for a new theoretical framework from which to work either outside of Actor-Network Theory or by readjusting the theory itself to become more valid and more complex. This analysis has lead to the conclusion that Actor-Network Theory no longer remains a valid theory and thus needs multifaceted aspects to examine it within the framework of Augmented Reality. AR is a paradigm shift, a revolution of new thought and design, and theory should follow along with its own revolution.

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