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Deep Learning Gamification and Artificial Intelligence

A Modern Learning Tool or an Agent of Indoctrination

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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 about 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 machine learning (And Deep learning) from human learning behavior, a theory that is still murky. 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 to learn knowledge and use the inherent talent to master it or own the capacity to do it satisfactorily.

They are centered on the environmental impacts on the learning procedure through associations, reinforcements, punishments, and observations. A 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 Gamified learning. This concept has gained the attention of data scientists within the primary learning concepts of the development Artificial Intelligence ( AI) domain.

What is Gamification, and Why Use Games to Teach?

This summary will shine some light on the subject, emphasizing its evolution into the data science space.

I am confident most of you are utterly familiar with the notion that “ 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 curiosity. 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 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 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. Despite 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 other motivational developments. Among them- the notion of self-determination was adopted to research the effects of different compositions between game design components. Competence and autonomy for assignment meaningfulness were affected by symbols, formally reported by Marczewski in 2013 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 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 crossing two of the most crucial possessions in 21st-century healthcare, hence people and 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

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 enhance functions beyond social talent.

Cognitive Assistance, Arts of Persuasion or Manipulation

Deep Learning is a practical 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 According to some researchers, the Cognitive assistance IoT (Internet-Of-Things) toolbox towards AI-driven persuasive technologies for the healthcare system.

The concept of Domain-general and Domain-specific Learning

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 the 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 programming language pattern 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 and computer programming. To do that- computers are absorbing every detail about every individual in real-time as part of Big Data recruitment. So the laptop uses domain-specific learning to memorize everything about a person’s habits and intellectual operations; and Gamify, persuade, and change their minds.

Simulating Neuronal human networks may translate into digital network equivalence. Still, it 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 excellent quality standard fixes, is preordained to jeopardize the sovereignty of human wisdom. Duplicating humans is the next frontier for the modem technocrats, including 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.

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