Science and technology are on a fast track towards the elusive embodiment on the infinite horizon- destination, painted with the idealistic resemblance coating the edge of a deep cliff. Over-idealized technocracy is evolving the typical viewpoint of millennial, as they are motivated to place their one hundred percent faith in science and technology, supplant their stake of exertions with what machine can do for them, yet anticipate the favorable, justified while sustaining the competitive retrieval of the 21st-century scientific evolution. Not long ago, I divulged an essay with the title “The Paradox of Empathetic Transference in Medicine: Empathic Technology vs. Algorithmic Sympathy.” Within the context, I strove to magnify on the intuition of humanizing the robotics through simulation of empathy. Here within, I would like to bring a varied approach to the related, yet critical theme by shining light on another simulation of human attitude by the enduring technocratic voyage of corporate-driven ambitions as the fallout of the millennial stance.
While reading through some of the latest trends on Deep learning and artificial intelligence, I arrived across a prevailing learning hypothesis; called the Gamification of learning behavior. The latter is primarily in reference to the application of game mode factors and recreation doctrines in none-recreation contexts. Gamification toils by employing engaging game techniques to leverage a person’s natural yearnings to enhance activities or processes. Before we buy into the landmark, we must first orchestrate our psyches around skills, utilities, talents, and knowledge by understanding their precise interactions and how they, in turn, are influenced by the means we learn. Once we built upon that thought, we must also differentiate the machine learning (And Deep learning) from human learning behavior, a theory that is still murky. Even so, it seems though the applied science is troubled to build a human being by understanding the biological and mentality behind the learning process.
Any fraction of work a person does undertake is a task. The prerequisite to fulfill that particular chore is for a person to learn knowledge and use the inherent talent to master it or own the capacity to do satisfactorily.
The theory of learning is a sophisticated one, and so are techniques on maximizing the pace of human intellect retention. But in general, it utterly depicts how brains infer, operate, and conserve data during the learning process. Mental, volatile, and environmental leverages, as well as prior acquaintances, all fiddle in how social belief is attained or altered, and knowledge and skills retained.
The psychology of learning as a theoretical science relating to knowledge acquisition relies primarily on unique subjects’ experiences leading to long-term shifts in behavior perspective. It measures on the environment influencers, such as the social context, conditioning, and reinforcement, as they sway how behavior ensues and shifts. In contrast, short term behavior modifications are merely caused by circumstances like fatigue. Learning theories involve undertakings that adequately discern and clarify the cognition of processes. Ways exist to convey learning behavior. Three of those theories include: 1) the behaviorism, which views knowledge as a collection of behavioral rejoinders to different stimuli in the environment by facilitating the learning mechanism through positive reinforcement and reiteration. 2) Cognitive constructivism discerns acquiring proficiency as adding new information to cognitive configurations that are already there, as part of the hypothesis that knowledge is actively assembled on the mental structures that have previously been inserted. 3) Social constructivism, as it suggests, speculates that education and learning are earned within the social circumstances through communication with a knowledgeable community.
The primary learning concepts of development are centered on the environmental impacts on the learning procedure by way of associations, reinforcements, punishments, and observations. Handful of such includes Classical conditioning, Operand conditioning, Social learning. I don’t recognize myself as an expert in behavioral learning. Therefore I have no intention of lending deep into the details of the subject. Besides, I feel it would be out of the scope of this discussion to do so. Still, one of the newly sent out theories of learning is about the subject of Gamified learning, the concept that has gained the attention of data scientists within the Artificial Intelligence (AI) domain.
What is Gamification, and Why Use Games to Teach?
This summary will shine some light on the subject with emphasis on its evolution into the data science space.
I am confident most of you are utterly familiar with the notion of ” playing should be fun.” Based on such theorem, comprehending would be fun if we look for educational toys (for children), gadgets, games with built-in illustrations, books with a compelling message. Often academic tools are less attractive and alleviate the person’s inquisitiveness. The fiddle is, by essence, educational; meanwhile, it should breathe as pleasant. When the fun goes out of a game, often so does the learning.
The term; Gamification is modern, hence often used with a myriad of meanings. The concept has been around for over ten years. It initially emerged in the 2000s, as formally reported by Marczewski in 2013, since it has lived the increasing focus of attention in the 2010s; Deterding et al., 2011; Werbach and Hunter, 2012). Gamification is the role of bringing an already prevailing factor or prevailing such as website, business applications, social media, and integrating game act in them to inspire participation, engagement, and liking. Gamification solely exploits the methods that game designers used to engage players via manipulating data sets and applying them to non-game ordeals to motivate activities that stand valuable to a business cycle. In spite of significant controversies on what the distinct role of Gamification entails about the learning experience, one can point to the importance of its” effect” on the technique. It’s not directly vital, but game design elements can incite different motivational developments. Among them- the notion of self-determination was adopted to research the effects of different compositions between game design components, under which Competence and autonomy for assignment meaningfulness were affected by symbols, ledger boards, and performance charts. Concomitantly Social interactions were positively influenced by avatars, meaningful stories, and teammates.
Changing Behaviors with the Gamification of Healthcare delivery
Those deeply involved in healthcare delivery and management must be aware of the sentiments of personalized care, patient engagement, and lifestyle changes in boosting patients to take control of their health habits and clinical decisions.
According to a report published by Huron, the effectiveness of games in the global healthcare gamification market is projected to reach $13.5 billion by 2025. The learning process leans at the crossing of two of the most crucial possessions in 21st-century healthcare, hence people and the technology. Wearable technologies such as Fitbit, Apple Watch, and apps aimed at tracing and awarding exercise, diet, and general wellness have been the first major front lines of health Gamification. The successive epoch of Gamification in healthcare is predicted to tackle more in-depth monitoring and management of chronic disorders such as hypertension and diabetes.
The persuasive technologies in learning behavior
Gamification has been applied to application realms with the sole intention of persuading the public to meet a particular goal. Yet, such influential technologies also amass capacities that urge players to engage and sustain new behaviors and sustain them for an extended period. If left unaddressed or without a close validation for legitimacy, it can potentially come to be the instrument of future ethical and legal crises. One of the thoughtful approaches to tackle this issue is to put in place adequate personification of the gamified quest while each challenge tailored to every individual player profiles and game histories, devoid of biased external influence. A large amount of data (Big data) is required for optimal execution of machine learning (ML) technology along with well-executed high-level Transparency and accountability of the system it is designed to represent.
Industries such as DeepMind are the kind of artificial intelligence (AI) solutions that have prompted gameplay computation and have gained successive mastery for the AI domain by winning victory on means that are often beyond the ability scope of the ordinary human mind. Simplistically deep learning utilizes predetermined algorithms to memorize human intelligence patterns and use them to enhance functions beyond social talent.
For another instance, Data61 has been able to use AI, and Gamification to help clinicians accurately diagnose patients with mental disorders, thus help enhance treatment options. The Data61 is equipped to show the patterns of how we enact between choices, navigate between them, and stick with one. Since neuroscience defines that most of the mental health ailments involve how we make decisions thus, one of the lenient ways to examine that interchange is through wielding ML to analyze detailed data bundles by conducting an easy task, which would allow physicians to record the patient’s behavior. Gamification of the indicated cycle provides the clinician with an easier route to access and analyze underlying pathology and diagnosis of mental health disorders.
Cognitive assistance, Arts of persuasion or manipulation
According to some researchers, the Cognitive assistance is a valuable application to curtail costs and improve quality in healthcare. It relies on the methods of persuasion, through the design, development, and evaluation of interactive technologies aimed at changing learner behavior through persuasive modalities. The author has tried to differentiate the latter from coercion or deception while pointing out the implication of motivating persuasiveness for healthcare systems using artificial intelligence (AI) perspectives for conceptual design and system implementation. The fundamental goal supports the development of an IoT (Internet-Of-Things) toolbox towards AI-driven persuasive technologies for the healthcare system.
The concept of domain-general and domain-specific learning
Humans are born with thought mechanisms that occur to boost and guide learning on a broad tier, regardless of the information uncovered, hence called domain-general learning theories of development. It distinguishes that-from the Domain-Specific learning on that; despite differences in learning types, the new data in the later is processed independently by different domains in the same way and the same areas of the brain.
Domain-general learning theories are in contrast to domain-specific learning theories. (Also called methods of Modularity) It holds that humans have independent, specialized knowledge configurations, rather than one cohesive knowledge pattern. Thus, training in one domain may not impact another independent realm. This Method of Modularity believes people have highly specialized functions that are independent of one another.
How does domain specificity theory apply to machine learning?
The domain-speciﬁc language (DSL) approach is gaining significant popularity in machine learning, as well. Deep learning in using DSL refers to the pattern of programming language that pertains to a modeling language by which domain professionals can interpret a model utilizing inscriptions and intuitions. From a programming language perspective, a DSL refers to reusable formulations using which experts within a particular space can define an algorithm with high-level language specifications,
The standard and diversified gamification agenda between human and the robot
Gamification is beginning to be recognized as a powerful instrument, rather than a time-waster. It is recognized for grasping people’s intellects and retaining their engagement through gamifying academic analyses, henceforth fitting self into the future of schooling. Software, apps, and even robotics are commencing to fascinate the millennial. One such case is “Nao”, the talking robot, which interacts with users while teaching them literacy as well as computer programming. To do that- the computers are absorbing every detail about every individual in real-time as part of big data recruitment. So the computer uses domain-speciﬁc learning to memorize everything about a person’s habits and intellectual operations; and can Gamify, persuade, and change their minds.
Data and Computer Science has advanced to the point of shadow learning from their human counterparts, but they differ on the domain-general ability of learning theory; because humans have independent, specialized knowledge configurations. In spite of the overwhelming luxury of efficiency and convenience, machine learning will ultimately follow the mathematical formulation of its scientists and the validation of its domain experts. According to a study; is postulated that brain neurons are shaped like trees, with ‘roots’ deep in the brain and ‘branches’ close to the surface, where roots receive a different set of inputs than the branches that allow certain functionalities have the required separation. Using the latter knowledge, technological researchers have been able to build a computer prototype using the same hypothesis. It turns out that these sections allowed simulated neurons in different layers to collaborate, achieving deep learning. Deep learning has brought about machines that can “see” the world more like humans can, and recognize the language. But still not sure if the brain truly learns that way.
I would like to differentiate human learning from machine learning by the theory of pyramidal construction, as ML follows reverse Pyramidal learning process and human uses both pyramidal learning and reverses versions. Latter differentiation is substantial, as pyramidal learning onsets with broad-based domain-general learning scheme and progressively develops into domain-speciﬁc learning reaching the peak. Pertaining to the Deep learning direction of learning is reversed, as the machine must get the most specific data beforehand prior to attaining broader knowledge about variations of the given operation. Thus what is tactically required to promote ML in cord with reinforcing human learning capabilities may fictitiously alter through strategic pivoting in line with corporate fiscal refinements.
Simulating human Neuronal network may translate into digital network equivalence but can also be effortlessly capitalized on to work as a brainwashing tool instead of enhancing the individual talent founded on personal preferences.
Deep learning is not necessarily equal to human learning, as it requires a large amount of data (in most part profound and personal ) that can be processed to extreme technical precision collected from thousands of resources and delivered to the learner through the efficiency of gamification learning.
The take-home message
Common sense is the ultimate sound judgment relating to everyday concerns, or an essential ability to perceive, understand, learn, and judge what is conveyed by the public to nearly all humans. Common sense determines the position to draw the line between Game, manipulation, and indoctrination. Hence to assure artificial intelligence collects the exact data, learns impartially, and teaches what is meant to acquaint compels Transparency, but Are corporations and big data industry willing to conform such responsibility?!
Have laws advanced enough to ensure Transparency and accountability?!
Undoubtedly, the Gamification of the healthcare learning process has the conceivable potential of Empowering Consumers, engaging physicians and patients. Therefore, it will help Prioritizes Consumer Experience through domain-specific learning. But- We are living in an era riddled with technocracy, an over-reliance on technology. The overwhelming trust in emerging tools short of more than the ability to devote them to the reasonable quality standard fixes is preordained to jeopardize the sovereignty of human wisdom. Duplicating humans is the next frontier for the modem technocrats that include made-up fabrication of how a person can learn efficiently. But within the path, the mission to conquer the efficient learning process at its most basic can be swiveled into the instrument of involuntary conditioning and manipulation. Henceforth, whether workable or not, we must be critical not to recreate demonized rendition of our selves borrowing the rationale of modernizing knowledge accession, Because in technological meaning Deep learning is utterly different from its benevolent, humanistic imitation.