The Symbolic Representation in the Evolution of Computing that Lead to Our Current Technological Advancements in Artificial Intelligence
Abstract
This paper investigates the symbolic history behind computing and technology as it ultimately answers to the how, why and for what we use them today. The long history of computing lies behind the details that go into creating what we, in one word, refer to as software. Some of my guiding research questions were looking into the history of computing while focusing on the importance of human symbolic and semiotic systems as the basis of computing and the depiction of non-technological concepts as cultural and societal fragments that are then reflected on the actual technological innovations and advancement that are occurring in that specific period of time. Understanding how and why our human symbolic systems are the first step towards decoding the “black box” of computing. Highlighting the relationship between cultural and societal characteristics that are embedded in those symbolic systems, establishes the connection between the technological past and present. I further look into theoretical background and research from leaders in the field, and provide an example of how and why the recent presence of Artificial Intelligence that has infiltrated most aspects of our lives, is related to our cultural and binary symbolic systems of representation and why we have adapted to it so well.The evolution of computing has been able to take place as the sets of our very basic symbolic systems and understandings have allowed us to develop a much more complex system compromised of mathematical, computational and encoded information.
Over the last few decades we have witnessed technology completely take over the world as well as most aspects of our lives. For most, technology is a more recent phenomenon that has only existed among and for the newer and younger generations. However, in order to truly understand why and how technology has made such an intricate infiltration in our lives, we need to re-evaluate the origins of how and when it all began. A big part of understanding how it all started, is coming to terms with and understanding human symbolic capacity and systems. We need to deconstruct the idea that being human and having technology and machines are very separate things because in reality, the two latter are a result of the former and are representations and depictions of our physical and cognitive symbolic ideologies and culture (Irvine, 2020). More specifically, even “[o]ur modern technical media are forms of symbolic artefacts developed for large-scale social systems” (Irvine, 2020, 2).
What we call today a computer or a laptop for example, are reflections and projections of machines, software and overall technological innovations and theories of the past. However, the crucial part is understanding how the discoveries of the past are immediately connected to the technology we have today and are the reason behind why it even exists. Often, we find it difficult and foreign to find some sort of connection with these devices and alienate ourselves because it is hard to conceptualize what this modern technology really is. What is the cloud? What is this artificial intelligence? The breakdown of how it all started can explain the answer to those questions. Our connection to our human symbology is what hides the reasoning behind the unexplained, also known as the things that go on behind the scenes. Binary numbers, coding and other symbol systems are used together and interchangeably to create the software and machines that have been built over the decades and have led the path for the ones we know today. In If more people understood the connection of our very symbolic culture and its history as a crucial part of the history and development of technology, we wouldn’t have as many “unexplained” and unanswered questions that further detach in people’s mind cultural patterns from tech.
Michael Mahoney, a science historian, in his piece “The Histories of Computing(s)” (2005), explains the reasons behind why people feel this detachment and loose the cultural subtext that is behind all computers, machines and software. Most connect current or more machines and actual physical computer objects as descendants of the Turing machine, but since the machine wasn’t necessarily confined within the physical limitation of the objects itself but rather depicted a “schema”, that concept “could assume many forms and could develop in many directions” (Mahoney, 2005, 119). In doing so, it “assumed” a depiction of the various cultural meanings and understandings that humans and especially the group of people who were actively working on the development and creations of these applications, had and still give to our symbolic historical attributes that we give to a symbol system (Mahoney, 2005). As an actual physical machine made out of even smaller material and parts, it would not really mean anything, it cannot stand alone. It is a compilation of
“histories derived from the histories of the groups of practitioners who saw in it, or in some yet to be envisioned form of it, the potential to realize their agendas and aspirations […]” the programs we have written for them, reflect not so much the nature of the computer as the purposes and aspirations of the communities who guided those designs and wrote those programs” (Mahoney, 2005, 119).
Connecting back to that idea, we realize how after all we are not so far different from these machines. We gave our own cultural meanings and understandings to symbols in order to benefit our needs. Even before the Turing machine, we can trace computing to what we know it as today, “back to the abacus and the first mechanical calculators and then following its evolution through the generations of mainframe, mini, and micro” (Mahoney, 2005, 121). Each technological era, time period or decade, started somewhere and adapted to the cultural circumstances and needs of the humans.
Our human symbolic systems, which are better understood as our natural language and cultural-symbolic artefacts such as languages, writing, alphabets, mathematics and mathematical symbols, scientific symbols and signs, etc., are the first step in depicting human symbolic-cognitive capabilities. There is a mutual understanding that these were and are the very first methods or representation, safekeeping and external symbol storage of overall human culture and capability (Irvine, 2020). However, this is also the crucial part in understanding that because of these accomplishments and capacities, we were able to transcribe that into a digital system of information, computation, software systems and overall technological advancements of today’s world (Irvine, 2020). The “archaic” and initial symbolic systems that were created by humans are the reason behind why and how we now have the technological luxury to live with and among the systems, softwares and machines that we can no longer live without. These include anything from social media and the depiction of our lifestyles through videos, images, music, to Artificial Intelligence being a part of our day to day lives taking form in our smart phones, smart cars and even smart wearable medical devices.
Artificial Intelligence has been one of the most, if not the most, nuance concept(s) of the past few decades technological advancements. A.I. however, can characterize something very specific such as the artificial intelligence that is a part of our smart phones, or something more general such as data analytics, machine automation and more. Although a complicated concept to grasp, as most things related to AI still remain in the technological and computational “black box”, some aspects of AI have made it possible to bridge that gap between humans and machine without necessarily realizing it. Specifically, AI that is used is in our day-to-day devices such as smart phones and smart wearable tech, that have become not only permanent, but also highly dependable parts of our lives. What is behind this type of AI is all those binary, semiotics and symbolic figures, structures and meanings that humans have ascribed to what we constitute as computing and software. Artificial Intelligence in the form of IPAs (Intelligent Personal Assistants), overpowers its “black box” with the distinct anthropomorphic disposition it embodies. These IPAs have a daily presence in our lives because they also reflect certain societal and cultural concepts and notions.
Researchers Goksel-Canbek and Mutlu (2016) who have investigated the topic of IPAs as part of our regular habits, explain the various connections that can be established between the users and the IPAs that rely less on the actual physical machine and more so on the AI, usually the female voice or unseen presence. The evolution of our technology from that of pure binary code and symbols has progressed to such an extent that software can freely interact with their user/human without the need of having another human monitoring the program. Goksel-Canbek and Mutlu, as well as other experts in the human-tech field platform, have assigned different reasoning as of why we find such a strong connection and normalcy in IPAs, yet often struggle with other adaptations, forms and applications of Artificial Intelligence. The humanoid form attributed to these intelligent assistants, such as Apple’s Siri, Google’s Google Now, Microsoft’s Cortana, Amazon’s Alexa, etc. is according to Goksel-Canbek and Mutlu, partially due to Three-Factor Theory that makes us more comfortable with understanding this type of software and devices (Goksel-Canbek & Mutlu, 2016). The Three-Factor Theory justifies with the use of psychological evidence, peoples’ tendency to ascribe anthropomorphic forms, features and characteristics to non-living and non-human entities (Goksel-Canbek & Mutlu, 2016; Theocharaki, 2020; Cao et al., 2019; Nass et al., 1999). An evolutionary achievement that has allowed IPAs to develop into what they are and have the capabilities that they do, is Natural Language Processing (NLP). NLP is a great example of human symbolic capacity that has evolved over the decades as have our own societal and cultural understanding, perceptions and needs. Goksel-Canbek and Mutlu highlight the importance of NLP as it “the most crucial element for creating computer software that provides the human-computer interaction for storing initial information, solving specific problems, and doing repetitive tasks demanded by the user” (Goksel-Canbek and Mutlu, 2016, p. 594; Theocharaki, 2020) as they focus on how these IPAs are used for foreign language learning. Their software intelligence allows for such “machines” to work independently and interact on their own will (to some extent), knowledge and capability, while using natural human language and semantics (Goksel-Canbek & Mutlu, 2016).
Goksel-Canbek’s and Mutlu’s research (2016) constituted of performing a variety of test interactions between IPAs (specifically Siri, Google Now and Cortana) and students who want to learn a new language. They recorded and monitored multiple instances where students were asked to address questions towards the device and see how the IPAs interact, react and “behave” (Goksel-Canbek & Mutlu, 2016). They also compare the performance between the three different assistants that not only highlights the weakness and strength of each, but also perfectly illustrates how even though the software for all three IPAs might have a similar “story of origin” and definitely overlaps in many feature, criteria and “black-box content”, the billions of complex possibilities that our semiotic systems allow for, create differentiation and promote adaptability into multiple forms and usages. Even though they still lack the same potential that a real life language tutor would have, IPAs have gained the trust of so many people who use them on a daily basis to mostly facilitate their busy lives or even teach them something new, because they provide the extra humanistic feature that for example lacked from the Turing machine yet the former is the continuation of the latter. The software behind the IPAs, use the same symbolic systems and capabilities that lead to the abacus or the first physical calculator, but have evolved, developed and adapted to each level or stage of history they came across and reflect the human values and belief systems the time.
Conclusion
The evolution of computing has been established and executed through the presence of human symbolic and semiotic systems that have adapted through out the decades allowing for the technological improvements and advancements that have led to the tech, machines and softwares that we use today. The technology that is available to us today isn’t a new invention nor a futuristic phenomenon. We often neglect to remember or realize that, it is rather a continuation of our primary symbolic systems that combined with the cultural, societal and contextual understanding of that time. Those two things work together to form the software, machines and technology that has evolved through out time. It is both a result and a reflection of our need to create and fill up the gaps or find solutions for the specific time’s needs and problems. In a way, the extreme could be to consider that even tech prototypes are no longer a thing, since nothing in tech arises from ground zero and one way or another, all findings are a continuation, improvement or expansion of another.
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Web Sources & Links
Map/Table 1: Irvine, Martin. (2020). CCTP-711: Week 3: Introduction: The Human Symbolic Capacity: From Language and Symbol Systems to Technologies. CCT Program (course notes).
Image 1: ComputerHope. “When Was the First Computer Invented?” Computer Hope, 30 June 2020, www.computerhope.com/issues/ch000984.htm.
Image 2: ComputerHope. “When Was the First Computer Invented?” Computer Hope, 30 June 2020, www.computerhope.com/issues/ch000984.htm.
Image 3: ComputerHope. “When Was the First Computer Invented?” Computer Hope, 30 June 2020, www.computerhope.com/issues/ch000984.htm.
Image 4: ComputerHope. “When Was the First Computer Invented?” Computer Hope, 30 June 2020, www.computerhope.com/issues/ch000984.htm.
Image 5: Goksel-Canbek, N. & Mutlu, M. E. (2016). On the track of artificial intelligence: Learning with intelligent personal assistants. Journal of Human Sciences, 13(1), 592-601.
Image 6: Goksel-Canbek, N. & Mutlu, M. E. (2016). On the track of artificial intelligence: Learning with intelligent personal assistants. Journal of Human Sciences, 13(1), 592-601.
