Artificial Intelligence in order to facilitate Diagnoses and Treatment. The Opportunity Smart Cities give Subjects


Bachelor Thesis, 2020

110 Pages, Grade: 1,3


Excerpt


Table of Contents

List of Abbreviation

1.0 Introduction
1.1 Background Information
1.2 Thesis Topic
1.3 Thesis & Hypotheses

2.0 Methodology
2.1 Literature Review
2.2 Case study
2.3 Expert Interviews

3.0 Key Theory Definitions & Explanations
3.1 The Wave Approach
3.2 Identities of the Society
3.3 Neuroscience
3.4 The Correlation between Depression, Anxiety and Burn-out

4.0 The Technology behind Artificial Intelligence
4.1 The Internet of Things
4.2 The Rise of Smart Cities and its Impact on Human Life
4.3 Smart Devices as AI & Data Collection
4.4 Challenges of AI in its implementation in Smart Cities

5.0 AI in Smart Cities with the Focus on Health
5.1 The Role of AI in Mental Health

6.0 Societal & Cultural Transformation

7.0 Expert Interview Analysis

8.0 Futuristic Outlook

9.0 Discussion and Conclusion

10.0 References

11.0 Appendix
11.1 Appendix A: Interview questions in German
11.2 Appendix B: Interview Questions in English
11.3 Appendix C: Interview 1 with Michael Dehm
11.4 Appendix D: Interview 2 with Nicoletta Blaschke
11.5 Appendix D: Interview 3 with Lothar Hotz

Abstract

Nowadays society faces challenges in several sectors, because of the shift in society, the growing use of technology as well as rising health issues. Therefore, this bachelor thesis is based on the approach of providing nowadays society with solutions through the advancement of Artificial Intelligence in order to improve their quality of life. For the reason that the obstacles within the health sector are evolving in a negative sense a special focus within this concept is laid on Artificial Intelligence systems impact within the health sector of mental illnesses. Thus being said the structure of the thesis focuses on four key theories in the beginning which arise over and over within the thesis. These four key theories included: The Wave Approach by Toeffler, the definition of three identities, Neuroscience and Mental Illnesses (burn-out, depression and anxiety). Nevertheless, the thesis was examined in several other parts such as Artificial Intelligence and the technology behind it, the implementation of AI in smart cities, pattern recognition and monitoring in the health sector.

By focusing on such areas, analysing and connecting them with past approaches the impact of the data was described and analysed. Thus being said the thesis approach was coming to the conclusion that through the implementation of Artificial Intelligence systems such as voice recognition systems and facial recognition systems the area of diagnosing mental illnesses and improving treatment as well as reaching for quicker response rate — in regards to emergencies — can be reached.

Acknowledgements

Finishing my bachelor degree was a three and a half years journey with a lot of people who stayed close to my side during all these years and making this graduation happen. Therefore, I like to take the opportunity to especially thank my parents and grandparents who did not only supported me during this time financially but also by motivating and rooting for me, my successes and my path ahead. In this setting I also like to thank my younger sister who held up my spirit during stressful times even though she had to study as well. Without you five I would never have been able to finish my degree.

Additionally, I like to thank my research supervisor Prof. Dr. Thomas Santoro. Without their assistance and availability of support in every step through the process — when it was needed— this research paper would miss essentials. Thank you so much for your support over the stressful time of the bachelor thesis, all the ideas and additional books and recommendations.

I would also like to thank Dr. Okan Tansu. Without their inspirational spirit and innovative thinking, I would never have focused on the subject of AI, emotional reading etc.. So, thank you for not only inspiring me but also believing in my futuristic ideas and giving me new and fresh perceptions to the layers of media and communication.

Last but not least I like to thank: Michael Dehm, Nicoletta Blaschke and Lothar Hotz. Without their open mind and agreement of being interviewed the important part of expert interview would be miss from my thesis. Thank you for being so kind and open minded — spending a lot of time with me to generate new important insights — helping to support my thesis and hypotheses.

Key Words

Artificial Intelligence

The Era of AI

Monitoring

Emotions

Mental Illnesses

Health Sector

List of Abbreviation

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1.0 Introduction

In order to introduce the topic of this bachelor's thesis, which can be described as a scientific research paper, some general information have to be pointed too. The following section will focus on this aspect and create an overview of the thesis topic.

1.1 Background Information

By focusing on the implementation of artificial intelligence (AI) in the sector of health, several opportunities to dive deep into the topic arose. For this reason, it was decided to put the center of attention towards the subjects' mental health issues. In order to reflect this approach, a research question was developed.

"How can Artificial Intelligence monitoring aid in the improvement of the quality of life in today's society? "

1.2 Thesis Topic

As the topic of AI and its impact on life improvement covers a significant number of aspects, including the Internet of Things (IoT), emotions, and neuroscience, as well as smart devices and data analysis — it was decided to implement several theories. That includes the presentation of the approach of the Third Wave by Toffler (1980), three different identities, and emotion recognition were used to identify possible conclusions to the research question.

It is believed that through the use of AI, the quality of life of human society can be improved. Specifically, in the health sector focusing on depression detection and improvement of therapy settings (including suicide and anxiety), Through the fast- evolving smart society and smart cities, new possibilities for society open up, making its life more comfortable.

Taking these approaches into account, the decision was made to create a thesis statement, which will be analysed upon the following researched in the upcoming chapters.

"Through the influence of Artificial Intelligence, smart technology, and the Internet of Things, the quality of life can be improved."

When focusing on this specific statement, several more assumptions in regards to the topic of AI and its impact on life can be made. Especially within the health sector in regards to mental health — which started to be a popular topic in recent years triggered the decision to create four hypotheses — in order to reinforce the thesis statement and conduct research on a deeper level.

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As well as the statement beforehand, these hypotheses will be analysed within the bachelor thesis, trying to verify or falsify these approaches and give more specific insight into the thesis topic of Artificial Intelligence.

2.0 Methodology

In order to provide strong evidence for the bachelor thesis, a triangular approach as the research method was chosen. For this thesis, mainly qualitative scientific sources were used in order to understand the behaviour of the thesis phenomenon, as well as the evolution of AI Systems, interactions, or implementations. Therefore, books, as well as several online sources were chosen to analyse the hypothesis and statements made before.

Another focus within the content analysis was put on a handful of market reports from companies such as McKinsey and voicebot.ai, who conducted research over the past decades when AI first arose — creating the advantage of relying on long-term developments as well as social changes. These two non-empirical approaches offer the chance to focus on the "theoretical and general knowledge of science to classify its systematically" (Santoro, 2019, slide 5). In order to strengthen the secondary data, it was decided to implement case study approaches, including experiments & long-term studies conducted e.g., by MIT. To achieve the triangular approach, an empirical method was chosen: expert interviews that focus on the field of Health Care, AI and the implementation of such technology. That approach allowed deriving knowledge from actual experience rather than from theory or belief, as conducted in the scientific data. By choosing these three main methods, different perspectives are brought into the conducted research, looking at every angle of the subject of AI and its impact on the health sector and society's life. It is expected that these methods will either verify or falsify the thesis statement and hypotheses.

2.1 Literature Review

Primarily the thesis was build upon literature review. That is for the reason that literature review has the characteristic of "building research on and relating it to existing knowledge, building a block of all academic research activities, regardless of disciplines" (Snyder, 2019, p.333). According to Snyder, this approach makes literature review to a priority for all academic papers (ibid). Taking a closer look at the characteristics of literature review, it becomes clear that such methodology builds a "firm foundation for advancing knowledge and facilitating theory developments" (ibid). For these reasons, it was decided to include this approach as the primary source of research, gaining an overview of the area AI, mental health, smart cities, et cetera. As literature review can be reflected through a simple summary of sources or be organised through patterns and synthesis — new interpretation can arise ("Literature Reviews - The Writing, n.d.). These new levels give an opportunity for new interpretations and create an "excellent way of synthesising research findings to show the evidence on a meta-level”. While this approach is defined as a quantitive approach rather than qualitative, the next methods are focused on a more specific level.

2.2 Case study

As a second source, it was decided to include case studies within the thesis — in order to include a qualitative research approach. This methodology is mostly used to "investigate and understand complex issues in real-world setting" (Harrison et al., 2017). In other words, "it analyses specific issues with boundaries of a specific environment, situation or organisation" ("Case Studies," n.d.). While case studies can be distinguished into three categories: explanatory case study, descriptive case study, and exploratory case study — this research paper only focuses on exploratory case studies (ibid). This form of case study "often is accompanied by additional data collection methods such as experiments". In this case, that definition reflects the approach in the bachelor thesis in form of e.g., conducted experiments from the MIT University (ibid). In order to include primary data within the methodology approach, the thesis includes a third research method. Therefore, the next subsection will deal with the introduction of expert interviews.

2.3 Expert Interviews

Focusing on expert interviews, primary data as a new approach is included — adding another method to the qualitative research approach (Hamill, 2014). In general, it can be defined as the oldest methodology approach, which consists of a conversation "between two people in which one person has the role of the researcher" (ibid). The probably most important characteristic of expert interviews is the fact that these are "carried out face-to- face, over telephone and internet or in a group setting" (ibid). One of its advantages is that the subject of the interview has no chance of non-response (Valenzuela & Shrivastava, n.d., p.12). Additionally, the high flexibility of personal expert interviews creates another advantage. That is for the reason that interviews can "vary from spontaneous too highly structured" scenarios (Hamill, 2014). While neither of these approaches meets the criteria that want to be met within these expert interviews — a semi structured/open-end approach was chosen. Including an aided interview guide with several questions that could help with the conversation, the interviewer has the advantage of being flexible and responsive throughout the interview (ibid). For the reason, that usually conversations can bring new insights — the sequence of questions can change and adapt to the newly learned/heard material. Therefore, it gives the interviewer the option to probe "more deeply into the initiated response" and getting an in-depth answer.

That flexibility and the empiric approach, are forming a new point of view and give new insights into the topic within the thesis — generating the approach to contribute to the overall statements and analysis. In order to dive into the thesis topic and understand the different areas AI can affect or be implemented, the following chapter will discuss and introduce the key theory definitions and explanations.

3.0 Key Theory Definitions & Explanations

During the last decade, technology has been evolving faster than ever witnessed before and with it society itself. For the reason that "technology never stands still", society and daily life have to evolve constantly as well (Woetzel et al., 2018, p.vi). According to Toffler, "We are the children of the next transformation" (Toffler, pp. 22 -23, 1980), focusing on the transformation into the technological age. Within this transformation, the digitalisation, several new terms, and new inventions arose, such as the Internet of Things (IoT), smart devices and artificial intelligence (AI), and smart cities. Especially at the beginning of the revolution, humans were suspicious about the usage of their private data for the reason that they have not grown up with these new inventions. With the younger generation of Millennials and Generation Z, who started growing up with mobile phones, computers and nowadays AI such as "Alexa", the suspicion to trust or not to trust companies with personal data stayed at an average of 80% in 2015 (Boston Consulting Group, 2016, slide 7). These numbers are still quite high and have to be reduced. Therefore, not only companies have to be more transparent about their data usage, but society has to change/adapt to these new technologies as well. Another downside arising as a consequence of new technologies is a high rate of mental illnesses such as anxiety and depression. According to Twenge et al., over the past ten years, the number for individuals suffering from mental health issues has more than doubled — especially within the younger generations, which would be triggered by technologies (2017). Even though these illnesses were negatively stimulated by technology in the first place — nowadays technology has the possibility to put a focus on these issues that affect the quality of life and improve it by up to "10-30%" (Woetzel, J., Remes, J., et al. 2018). For the reason that the thesis is built on four different key theory definitions and explanations — which will reoccur over and over again within the research paper — the following chapter will introduce the "Wave Approach by Toffler", three identities, neuroscience and lastly mental illnesses including depression, burn-out and anxiety.

3.1 The Wave Approach

As mentioned in the section before Toffler stated that "We are the children of the next transformation" (1980, pp. 22 -23). Nevertheless, what does he mean by that, and how can it be applied in our society today?

Looking back at former transformations / "Waves," it can be seen that Toffler has divided them into three categories, starting with the "First Wave, which unleashed 10.000 years ago" with the rise of agriculture (p. 25, 1980). This first wave took "thousands of years to play itself out" to manifest itself into society's daily life (Toffler, p.26, 1980). The Second Wave is the so-called industrial wave, which only took 300 years to be integrated into the routine of society (ibid). Back in 1980, Toffler defined the current wave/transformation as the "super industrial society". He explained that the "Third Wave" could not have a specific definition for the reason that it had no specific factor with deep "social upheaval and creative restructuring". Too many factors were included in the "Third Wave", such as the rise of the computers, the first Walkman, radio stations, and television providers. Even though Toffler decided not to specify on a specific name, he stated that the "Third Wave" will be more accelerative as the ones before (p. 26, 1980). According to Toffler, transformation and, therefore, "changes are not independent", meaning that society has to change as well in order for a transformation to work out (p.18, 1980). Therefore, the old assumptions from the previous wave have to be challenged. In this case, Toffler is focusing on the approach that a newly integrated code of behaviour and a clash of the new and old civilisation will form — creating a "fast emerging and evolving technology and lifestyle" (Toffler, p. 18, 1980).

When putting an eye on the older civilisation ("Second Wave"), most of them "do not think about the future and are sure that the world they know will last indefinitely" (Toffler, p.27, 1980). It is even said; they find it difficult even to imagine to form or adapt to a different way of life (ibid). Toffler states that the older generation within a transformational process recognises that "things are changing but assume that the change will pass by them and that nothing will shake their familiar economic framework" (ibid). Another theory the author is bringing to the theory is the assumption that the "Third Wave will sweep across history and complete itself in a few decades,“ bringing a "new way of life" (ibid). Focusing on the point that this assumption was made in 1980, the question arises if our current transformation can still be seen as the "next transformation" from 1980 or if we are already within the next one. The collision of generations is forming again (Millennials and Generation Z vs. the older generations), which forms the theory that humanity is close to another transformation and therefore moving into the "Fourth Wave" — "The Ere of AI". Even though the author already mentioned machine intelligence within his book, the "Ere of AI" reaches another level by adding up small changes to form a transformation in regards to the perspective of life, society and thinking (Toffler, p.28, 1980) (Toffler, p.185, 1980). Moreover, we are right in front of it.

3.2 Identities of the Society

In order to understand on which level AI can interact with the human race and analyse the collected data, several components have to be taken into account. One of these components is the idea of three different identities every individual project on today's society and, therefore, on the AI.

As mentioned before in the previous section — through the Third Wave and the beginning Fourth Wave, many new technologies arrived and were implemented in modern society, changing the lifestyle of the human race in a revolutionary way. Part of this change was the rise of two new identities, additional to our real identity, which includes our real-life accomplishments and persona (Tansu, 2018). The first new identity which was introduced to our daily life is the so-called "Digital Identity" (ibid). It first emerged after Social Media was implemented in the day-to-day life of the human race (ibid). This identity can be described as everything an individual wants to show off on the social media platforms such as Instagram, Facebook, Snapchat, etc.. By posting pictures, statements or anything else — every individual who is using these platforms creates an image of themselves as they want to be perceived by others. The Third Identity, which is the most important for AI, is the "Digital Soul". The main characteristic of this approach is that every individual is not 100% aware of the third identity they create (ibid). It is evolving and building its knowledge every time an individual is using their location-based tools, their credit card, smartphones, and other systems that are connected to Google, Amazon, or similar platforms (ibid). As most consumers are not aware that they give away their data over these actions, applications, and devices, the "Digital Soul" creates probably the most accurate identity that reflects an individual on all levels (emotionally, socially, medically, etc.). The University of Ohio even states that AI probably knows our identity and emotional state better than your own family (Artificial Intelligence: The Insights You Need from Harvard Business Review, 2019, p. 137). Thus being said, a rough picture is created of how the three identities play an important role when interacting with AI — creating data, which will be explained further in the upcoming chapter 3.0 Artificial Intelligence and its impact on the quality of life.

3.3 Neuroscience

Within the last chapter, three identities were introduced in order to realise the impact of AI on the quality of life. However, to understand on which level these identities can be appealed too, an insight into neuroscience has to be given. Neuroscience gives insights into several processes of how we act, why, and with what emotions. This information can be identified and put into AI processing progresses, e.g., emotion detection in order to create a professional interaction with humans in certain emotional states or health conditions.

In general, neuroscience can be described as everything that focuses on the study of the nervous system, which includes the spinal cord, the brain itself, and all networks of the sensory nerves, called neurons (What is Neuroscience?, n.d.). The field integrates several disciplines and deals with knowledge about humans' thoughts, emotions, and behaviour (ibid). It opens up the possibility to understand the processes within the human brain (Cooper, n.d., p.1). For the reason that the brain has a quiet complex antonym consisting of several structural parts (Cronshaw, 2014). Mainly these parts can be distinguished and separated into two main areas. The Neocortex and the limbic system/ reptilian brain.

The Neocortex makes out up to 80% of the human brain. When focusing on its function, this part of the brain is mainly used to coordinate "higher-order thinking", such as using language or more in-depth problem-solving. Therefore, when it comes to decision making, the neocortex is the so-called rational part of the brain, which takes facts such as price and quality into account in order to come to a final choice (cognitive decision making). Even though every individual mostly thinks that their decisions are made consciously, the Limbic System focuses on emotional responsiveness and integration (Kolb and Wishaw, 2016, p. 398). These emotions, such as anger, fear, sadness, jealousy, embarrassment, and joy, can operate outside "our immediate awareness"(ibid). Thus being said, it can be stated that arising subconscious emotions can overlap the rational decision making / rational preferences and change humans behaviour. Even though the Limbic System makes out such a small part of the brain, most processes within it are made emotionally or unconsciously (Lindstrom, 2008). According to Lindstrom, the emotions are the actors in which way our brains encode things of value (2008, p.26).

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Fig. 1 (Combination of Emotions, personal communication, 2018)

Focusing on Figure 1, it can be seen that all emotions can be categorised in two sections: in primary emotions and secondary combinations. While primary emotions are the ten standard feelings, the secondary combination are emotions that arise when primary emotions are combined. These combinations create then new emotions such as delight, guilt, hope, etc.. As mentioned before, emotions can occur out of an individual's consciousness. These unconscious emotions occur when an individual is not aware of the stimulus or "shift in their emotional state" (Lindstrom, 2008, p.76).

In general, it can be said that emotions are a response to a verbal or physiological appeal, which is "brief in duration" (ibid). Taking this into account, the human mind can be manipulated, appealed to on an emotional base, and driven into behaviour patterns, which also occur unconsciously up to 95% (Kuehn, 2013). Not only emotions alone create a way to trigger specific actions but also senses in combination with these emotions help to

cause moods and responses. Lindstrom states that in order to fully engage the subject, it is more effective to connect a certain smell to the product, a sound to an approach, or even both, rather than relying on older techniques to "manipulate" emotions (2008, p.143). This approach can also be referred to as SensoryBranding (ibid).

An example of this SensoryBranding is the case study of Dunkin' Donuts in South Korea, who used the traffic on busses and sprayed the smell of coffee whenever their jingle came on the radio in order to increase sales in 2012 (Annear, 2012). About 350,000 citizens became part of this advertising approach of SensoryBranding while on their daily ride to work (ibid). At the end of this initiative, which lasted over several months, "the coffee company said that they saw a 16 percent spike in visitors at shops located by bus stops where vehicles were equipped with the smell-technology" (ibid). While at the same time, "coffee sales went up by 29 percent" (ibid). This example supports the SensoryBranding approach from Lindstrom, where emotions and, therefore, the behaviour is triggered subconsciously within the human's mind.

For the reason of the subconscious interference of emotions on individuals, different markets can profit from it. Nowadays, it is mostly used to manipulate society into buying consumer goods or other things in order to generate revenue in form of money, which is frown upon in society. Yet we are moving into the next wave the "Era of AI", where technologies open up so many more opportunities to interfere in a less unethical manner, e.g., within the Health Sector, helping, adapting, and preventing illnesses.

3.4 The Correlation between Depression, Anxiety and Burn-out

When focusing on illnesses that can be diagnosed easily or the therapy can be adapted, most of the time, people think of a rash, Parkinson, etc., basically everything that is visually visible to the human eye. But what about all the silent illnesses, such as depression and anxiety (Powers, n.d.)? For the reason that these three illnesses are the most common ones to arise in today's society and the focus of the three hypotheses rely on depression, anxiety and burn-out — this chapter aims to analyse such diseases.

All individuals who suffer from depression, burn-out, anxiety have to reach out for help by themselves, which means first of all being diagnosed with these issues and secondly being treated within therapy or taking medication. For the reason that most of the time, subjects with such disorders shy away and do not make the first step to getting diagnosed because they are not motivated - new solutions have to be found (Powers, n.d.). Focusing on this problem, the National Institute of Mental Health indicates "that 37% adults" with these sicknesses do not even receive any diagnoses or treatment — mostly for the reason of health care systems (Powers, n.d.). This issue of not being diagnosed and no capabilities for treatments opens up another huge concern in regards to higher suicide rates, less productivity, etc.. When putting the focus of attention on these mental sicknesses in the U.S. — were depression, burn-out and anxiety are a massive issue — it becomes clear that around 43.8 million Americans suffer from at least one of these diseases (Mental Health By the Numbers | NAMI: National Alliance on Mental Illness, n.d.). The NAMI states that every fifth adult is suffering from a mental illness in one year, which means that these illnesses can reoccur, or an individual has been suffering for many years. For this reason, that every year the number of affected individuals is growing, the demand for therapy, doctors, etc. is rising from day to day.

In the following paragraphs, the terms will be explained to understand the connection between these three illnesses. In general, it can be stated that some people are more predisposed to mental issues than others (Powers, n.d.). According to Powers, these conditions can affect any individual at some point in life (ibid). Concentrating on the mental issue of depression, the disease can be defined as the most common disorders in the U.S., with approximately 16.2 Million people suffering from it (ibid). As depression counts to mental illnesses, it has an effect on the human mind. Nevertheless, the condition can be "accompanied by physical symptoms such as fatigue, inability to focus or perform tasks that were easy before" (ibid). Doctors talk about a major depression or depression episode whenever an individual shows these symptoms, including debilitating sadness for weeks, months, or even years (Hanai et al., 2018 p.1716).

Not only depression intervenes with the subjects' life, but also anxiety and burn-out play an essential role in the spectrum of mental illnesses. Anxiety can be characterised on an emotional base, such as "feelings on tension, worried thoughts and physical change" (McFadden, 2019). Not only that the individual usually has recurring intrusive thoughts or concerns, but these characteristics can lead to avoiding certain situations mainly out of worry (ibid). Part of the physical change, which can be visible to others are, for example, "sweating, tumbling, dizziness or rapid heart rate" (ibid). One common trigger of anxiety are "transition periods and moments of change"(Abbas, 2019). Anxiety leads the human body to have arising emotions of, e.g., stress, which is forming into anxiety. In order to understand anxiety on different levels, it has to be distinguished in two different types (Koutsimani et al., 2019). Primary trait anxiety, which is defined as the degree an individual perceives stressful situations or its level of threat (Spielberger, 1966). Secondly, the state anxiety. It can be described as every reaction of an individual towards a situation after "having appraised it as threatening" (ibid).

With similar symptoms, the illness of burnout arises. Burnout can be described as a psychological syndrome, similar to the illnesses of depression and anxiety introduced before (Koutsimani et al., 2019). It can be identified by its symptoms of "emotional exhaustion, feelings of cynicism and reduced personal accomplishments" (ibid). When working too hard or being constantly under stressful conditions at work or even within the private life, burnout can be triggered in each individual's mindset/brain (ibid).

When focusing on these three mental illnesses, a few correlations can be seen, e.g., the trigger, the symptoms, or the characteristics. Over the past decades' several scientists and doctors were asking themselves if all three mental illnesses are the same, only overlap on some stages or build each other up to the next level, e.g., anxiety leads sooner or later to depression (ibid).

While an occurring burnout hardly depends on the personality and external factors an individual can stand against, depression and anxiety relate to the personality itself (ibid). Even though these issues arise on different levels, an overlap between depression and burnout can be seen (ibid). According to Freudenberg, "people who suffer from burnout look and act as if they were depressed" (1974). Therefore, they show the same physical characteristics such as loss of interest or pleasure, fatigue, and loss of energy (ibid). According to Koutsimani et al. — the researchers, Bianchi et al., also stand behind the idea of a correlation between depression and anxiety in an emotional state (2019). On the other hand, some studies focus on the differences between those illnesses. While depression is context-free, burnout is work-related (ibid) - still, the symptoms are similar and therefore overlap on some parts. Focusing on the correlation of anxiety and burnout, it can be clearly said that these illnesses are connected/related. According to Cole, anxiety acts as a protective factor against all threatening situations an individual interprets as alarming (2014).

This approach, as well as the two defined anxieties (trait & state), focus on the personality and its limits of accepting certain situations, which collides with the focus of the burnout - what level of stress a particular individual can stand against in its daily life. Therefore, it can be stated that burnout and anxiety are interrelated and correlate on several levels. Similar to the collusion of depression and anxiety. They do not correlate on several levels, but they show the same symptoms. As it can be seen, all illnesses are correlating on several levels. When thinking about triggers for such illnesses, hypothesis 1 comes back to mind: "New technologies originally created the issue of anxiety, burn-out, and depression but now has the potential to create solutions.".

In general, it can be stated that technology made "our lives inexorably more efficient and easier compared to past generations" (McFadden, 2019). It allowed humanity to focus on more important tasks (ibid). Nevertheless, some studies show that several technologies "making us less happy" (ibid). An example for that are computers. If these are used over hours and hours a day (such as, e.g., at work), they will increase the probability of depression (ibid). Another factor is the internet, with all its information. The reason why it could have such a massive impact on the humans' mind is that the brain is "not laid out to be bathed in so much information all the time" (ibid). As the brain can adapt to a certain amount of data and information, an overload changes the behaviour, e.g., individuals start "feeling that the real-life is boring and slow" compared to the internet with continuous stimuli (ibid). This so-called "popcorn brain" can lead to "serious mental health issues," such as anxiety (Fear of missing out), and depression (ibid). However, the probably most interruptive technology that had an impact on our life, on a positive but also negative sense, is the smartphone. For the reason that the smartphone is widely adapted in our society and humanity always carries these with them, a separation anxiety forms (ibid). The smartphone does not only trigger anxiety but also the possibility for an individual to be contacted 24/7 around the world. This reachability can lead to worsening anxiety disorder and burnout syndrome (ibid). Thus being said, technology and its impact can be defined as a double-edged sword. Without a doubt, it can trigger certain sicknesses such as depression, anxiety, and burnout, but only if those are used excessively. For this reason, the first statement of hypothesis 1 that technology triggers such illnesses could be supported.

Yes, technology can have a negative impact on individuals, but can it also create solutions by diagnosing or better the situation with mental health issues as suggested by hypothesis 1 and 2? In order to understand today's technologies impact on nowadays life, the next chapter will explain arising new technologies such as Artificial Intelligence (AI) and the small processes behind it.

4.0 The Technology behind Artificial Intelligence

Currently, a significant amount of research is focusing on the potentialities of Artificial Intelligence (AI). It could be even stated that this is the next significant paradigm shift for humanity after the last great invention of the mobile phone and, therefore, the "Ere of AI". In order to understand how AI can impact the health sector several aspects and the technology behind it have to be understood.

Today AI already has the possibility to improve certain aspects of humans’ lives, such as work, driving cars, or just our smartphone, which are highly implemented and accepted in our society (Eadicicco, 2019). Nearly every household has at least one AI in their home, e.g., Alexa, Google Home, Netflix, or just even Siri or the Google Assistant (ibid). AI will not only make the world more efficient and more effective for individuals, but it will also improve the quality of life on many levels (ibid). Even though humans use these technologies in their daily life to enhance their lifestyle or just to have more fun, the process behind the AI seems to be a mystery. In general, AI can be defined as a collection of similar technologies, which a computer can use in order to make data-based and humanlike decisions (ibid). For the AI to understand and process this data, several small processes are implemented before they can interact with other machines or individuals. For humans to understand these interactions, the history of AI, as well as several terms, have to be explained.

After the term of Artificial Intelligence arose first in 1956 on the Campus Dartmouth College, it was perceived differently "as its actually being put to use" nowadays, and it is still evolving (Pathak & Bhandari, 2018, pp. 3 - 5). The definition of AI back in the days was described as "every aspect of learning or any other feature of intelligence which can be so precisely described that a machine can be made to simulate it" (Pathak & Bhandari, 2018, p.5). For the reason that this "basic concept of AI has not changed in a wider sense," the evolvement focuses on its application and new layers (Pathak & Bhandari, 2018, p.3). Therefore, AI can be described as a machine learning process in today's society. These robotic systems are understood as a programming paradigm, "where the engineer provides examples comprising what the expected output of the program should be, given the input" (Taulli, n.d.). Machine Learning (ML) systems then take all the information into account to explore a variety of possible outcomes (ibid). According to Chanchaichujit et al., ML is the core part of AI, dealing with the simulation of intelligent behaviour in computers (2019, p.64). It allows the AI to mimic human behaviour, which led the machine to learn and accumulate information and solve problems (ibid). Through all the given inputs by interactions or programming, the AI has the possibility to find and learn an individual's pattern and learn even further to "classify and use regression to determine a suitable output" (ibid). Pattern recognition, in this case, is an essential characteristic of such processes. As ML enables the AI to learn an individual's "normal behaviour" pattern, it is to detect abnormalities by comparing newly collected data with the learned pattern model (Brugnara et al., n.d.).

Consequently, ML within the AI brings the prospect to establish correlations between already collected data, making suggestions, which gives the human race a massive advantage in several sectors (Chanchaichujit et al., 2019, p.64). To adapt to an even higher level, the algorithms of AI have to be taken into account. Algorithms, in general, are a "series of computations from the most simple and most complex areas" (Taulli, n.d.). When focusing on ML, algorithms are being used to "process data and encode them in a model which can be used to make predictions on new data" (ibid). Machine Learning uses the regression of the algorithms in order to improve its interactions and predictions on a regular basis (Chanchaichujit et al., 2019, p.65). As mentioned in the previous sector, the load of information through the internet (digital information) are increasing, so the implementation of AI can help with the amount of data even more efficient than humans since the machines are not dedicated to illnesses such as "popcorn brain" (ibid). Therefore, ML primary aims "to allow machines itself to understand information without human intervention" (ibid).

Another vital characteristic to understand the importance and capabilities of AI are its "Neural Networks" (Taulli, n.d.). The neural networks have many connections within each AI, mimicking the structure of a human brain — creating the capacity to "summarise complex information into simple, tangible results (ibid). Neural Networks allow AI to be trained and construct to learning processes (ibid). Therefore, the next step for AI after ML is the implementation of Neuro Learning (NL) (Chanchaichujit et al., 2019, p.65). Two different approaches of learning arise within the machine learning: supervised and unsupervised. These approaches create unseen errors for the reason of unseen sequences "of input events," only focusing on specific areas (ibid). NL uses this basic approach of machine learning, concentrating on the two ideas, connecting it to neural networks, which again create algorithms that work similarly "to the human brain" (ibid). The unique quality NL brings to the bigger picture is that it can "extract information more efficiently, the more it learns" (ibid). In addition, NL brings the characteristic to sense and notice if the information is wrong, which gives the AI space to adapt on a new level and mimic humans' communication and processes (ibid). While earlier systems were able to read pure implemented data, through Neuro Systems, the algorithms today are "better suited to analyse disordered and complicated data" (Chanchaichujit et al., 2019, p.66).

Through the approach of NL, the integrated system of deep learning (DL) can be introduced. This system is used in order to analyse all the data which was captured through ML & NL (ibid). In general, it can be viewed as a "generalisation of classical pattern recognition" either of the environment or individual systems or subjects (Pedrycz & Shyi-Ming, 2018, p. 3). DL creates the capacity for AI systems to predict outcomes, using the collected & analysed data (Chanchaichujit et al., 2019, p.66). While NL creates the decision-making process of an AI accurate, DL processes "break down all layers within the Neuro Learning and lead them towards the Machine Learning Systems" in order to get better outcomes (ibid).

Through all this information collected by the different learning layers, the AI creates a decision-making function (ibid). The DL process "begins by going through the Machine Learning process", starting to transform the binary data "into multi-layer processes with neural networks" (ibid). All information/ data that make it through this layer are classified into predicted outcomes of the DL processes, which are the responses individuals get from AI nowadays when, e.g., asking them to order food, making a call, or predicting car accidents (ibid).

Therefore, the statement can be made that these three learning systems led the AI to its capability to answer our questions, predict certain situations, and intervene in our life within certain aspects.

4.1 The Internet of Things

With all the data, AI is producing, analysing, collecting, and recognising one aspect plays an important role: The Internet of Things (IoT). The term of IoT first came up back in the 80s (Pathak & Bhandari, 2018, p. 26). Primary in the early 2000s, the phrase started being mentioned in "scientific journals, conferences, and magazines" (ibid). It was not until 2009, only 11 years ago, that IoT officially was recognised after the number of devices that were connected to the internet exceeded the number of living people on earth (ibid). It was alone in 2014 the IoT started gaining recognition all over the industries (ibid). Thus being said, it can be seen that IoT is quite new to the world, connecting it to AI and giving it the possibility to communicate/exist in our world (ibid). Therefore the statement can be made that IoT "works all around us" (Woetzel et al., 2018, p.24).

Taking a step back, the internet was initially invented to connect two computers — "sitting in two different parts of the world" — with each other (Pathak & Bhandari, 2018, p.6). While most individuals connect the term internet with the association www. (web), it has to be defined as two separate things (ibid). The web can be described as a "pet of the internet," meaning certain other aspects create the internet itself (ibid). According to Chanchaichujit et al., the internet allowed us to advance on a technical basis and impacted our style of living on the level of communication systems as well as data-sharing systems (2019, p.3). Looking back at the original task of the internet, nowadays several new technologies were introduced that are connected with it. This includes technologies such as smartphones, tablets, Smart TVs, Smart Watches, Smart Fridges, AI in general, etc., supporting the statement from Pathak & Bandari mentioned before. A high number of devices joined the internet over the past decade. Till today five billion of "non-computer items" are implemented in the immense network of the internet (Pathak & Bhandari, 2018, p.12) (Woetzel et al., 2018, p.23). This number will grow drastically within the next year connecting up to 20.4 Billion smart devices (Woetzel et al., 2018, p.24). All these gadgets are defined as things, which then build the wide world of the "Internet of Things" (IoT). Woetzel et al. state that by this year (2020), 6.1 Billion mobile phone users will be connected to the internet, which shows a constant growing the IoT network (Woetzel et al., 2018, p.23).

Within this context, the IoT can be described as the "global network of "smart" versions of regular physical objects" (Pathak & Bhandari, 2018, p.12). In the context of the IoT and today's society, smart objects are everything that is able to connect to the internet — creating new possibilities for these objects to offer applications and evolve (ibid). This does not mean that all IoT devices have to be connected to the internet all the time to identify as such (Pathak & Bhandari, 2018, p. 36). They can "operate offline most of the time and only connect to the internet" in order to update the data with the Cloud (ibid). Most of the time, these objects of IoT are used for data collection, which are gathered by its implemented sensors for measuring specific parameters such as, e.g., the heart rate through smartwatches (Pathak & Bhandari, 2018, p.14). These data pieces are then stored within the device and saved in the cloud later on for further analysis within the AI itself or other AI circles — generating meaningful information about individuals, reflecting their third identity, which they not aware of (Chapter 2.3) (ibid). An important characteristic of IoT devices is not only their daily task of collecting and analysing data but also the ability to communicate with each other, giving them new opportunities to learn, develop new knowledge and understand patterns (Aher, 2018).

As these definitions describe all IoT devices, a distinction can be made between customer IoT and industrial IoT (Pathak & Bhandari, 2018, p.28). The customer IoT consists of already developed devices, which are ready for consumption by each individual. These devices are connected to each other, e.g.; smartphones are connected to a watch or another smartphone — or to other local networks via, e.g., Bluetooth or the WiFi (ibid). On the other hand, the industrial IoT exists in today's society, which includes all devices that are "custom-made for specific enterprises and industrial scenarios," e.g., within the government (ibid). These devices are then only connected directly to the internet and not to other smart devices or networks (ibid).

Taking all these information into account, it can be declared that IoT can be summarised as a tool that "facilitated the exchange and access to real-time data from any place in the world at any given time", promoting its environment "where individuals can be connected to the web services provided by such technologies" (IoT devices, e.g., AI) (Chanchaichujit et al., 2019, p.3).

These services and the constant access to the internet and other IoT services can be provided in so-called smart cities, which will be taken into account in the next subchapter, giving insights into the evolvement of smart cities and their possibilities when introduced to society.

4.2 The Rise of Smart Cities and its Impact on Human Life

Over the last decades, cities got smarter and smarter, meaning more livable and more responsive (Woetzel et al., 2018, p. v). Even though humanity evolved immensely in regards to technology — today reflects only a preview of what technology can do and will do in the future (ibid). For the reason that technology is no longer a constraint in the present and future society, humanity had the opportunity to make "rapid advances such as IoT, machine learning" etc. which paved the way for them to evolve and create innovations (Woetzel et al., 2018, p.21). As mentioned before, inventions and change are moving faster than ever before, expanding in ways humanity previously never imagined. Looking for example at the invention of smartphones, it becomes clear that through its widespread use and acceptance in society the smartphones become the key of today's smart cities (Woetzel et al., 2018, p.1). The advantage of smartphones is that a wide range of society is able to use them (Woetzel et al., 2018, p.21). With one tap, several information, vital services, etc. are available for the consumer (ibid). For that reason that smartphones have the opportunity to deliver instant information about several aspects such as transit, traffic, safety alerts, payment methods, and health services, the smartphone becomes part of AI systems (Woetzel et al., 2018, p.1). Therefore, it has the opportunity to deliver information and transfer a "river of information" to the government, companies, etc. for further analysis and pattern recognition in order to predict outcomes (Woetzel et al., 2018, p.23). Mostly this data is collected by the layers of sensors (voice, heart rate) on the smartphone and other devices — taking in every aspect of their physical environment (ibid).

When talking about smart cities, individuals mostly think about all technologies that are implemented within it. However, the statement of "smart" also indicates the use of technology and the use of its collected data (ibid). Thus being said, "smart" in the context of cities should be implemented "to make better decisions and deliver" a better standard of life which can be reached by the usage/implementation of technology as well as the aspects mentioned above (ibid). Smartness, in this case, is simply just a tool to help the cities improving living standards and serve the citizens (Woetzel et al., 2018, p. 33). By collecting different real-time data from all smart devices such as phones, smartwatches, etc., the city can become more and more comprehensive (Woetzel et al., 2018, p.23). The city can, for example, watch "events as they unfold" and use this information to understand how "demand patterns change," which gives the city and other individuals the possibility to react to it faster at lower costs (ibid). Nevertheless, "smart" can not only be defined on this level but on three different layers.

1. The Technology Layer

This layer is defined as the "critical masses of smartphones and other sensors," which are connected by high-speed networks, allowing the communication between individuals and devices itself (Woetzel et al., 2018, p.2). The sensors within the devices then have the ability to take constant readings of different variables such as traffic via GPS, energy consumption, heart rate, air consumption (ibid).

2. The Specific Application Layer

The Layer of Specific Applications concentrates on the task of translating raw data into alerts, insights, preventions, etc., which can be or are already introduced into sectors such as security, health, and community (ibid).

3. The Public Usage Layer

The third and last layer focuses on the implementation of applications within the society (ibid). It states that applications are only successful if widely used by society and managed to change individuals behaviour (ibid). This includes the transparency of applications in order for humans to make better choices in their daily life (ibid).

In order to complete the operation of all smart applications — companies and residents play an essential and active role in shaping the "city's performance" (ibid). Hence to the ever-growing human race, which is drawn to cities because of better jobs and perspectives — cities nowadays face a lot of pressure (ibid). With the "Era of AI" and other new technologies, the cities have a new set of tools, and a new gained digital intelligence, which can help with the information flow and improvements in daily life. With all-new technologies, it can even be stated that the relationship between the government and the citizens they serve changes (Woetzel et al., 2018, p.15). The cities can use this insight and the technology within it to take the "pulse of the public opinion on a wide range of issues" (ibid). Nevertheless, in order for them to focus on issues such as Health Care (HC), more of the population has to be online (ibid). This task of bringing more people online should be perceived as priority No. 1. That is for the reason that without humans being connected, no information would be generated, and no analysis could be taken place within devices. Consequently, no input for the AI would be generated, excluding the possibility of improving daily life. Therefore, the goal of every smart city should focus on public services such as free WiFi and a general cheaper distribution of smartphones on the companies' site in order to begin the improvement of the quality of life/living standards.

When taking all this information about smart cities into account, their image from Sci-Fi movies with flying cars, hoverboards, etc. have to be redefined. By using the information gathered and including "an overlay of intelligence," the city can not only expand on capacity within the city walls but also on the lifespan of existing assets as for example elderly care, health care, pollution (Woetzel et al., 2018, p.22). Therefore, the perceived image of smart cities has to be reconsidered and newly introduced as a "places where different actors amply technology and data to make better decisions and achieve a better quality of life" (ibid).

However, what exactly is meant by the quality of life?

Concentrating on its definition, many dimensions have to be taken into account (Woetzel et al., 2018, p.2). This includes, for example, if the citizens feel safe if they can breathe the air without a health hazard if health services are provided in every city district, etc. (ibid). According to the MGI, gathered data in smart cities could improve the quality of life in regards to health, time, and convenience (Woetzel et al., 2018, p.4 & p.33). That indicates about 10 - 30 % of all indicators that have an impact on societies' daily life can be improved (ibid). In order to collect and make use of important data, several devices have to be implemented in society to deliver data that improves their lives. Therefore, a significant focus has to rely on smart devices and data collection within the cities, which will be reflected in the following section.

4.3 Smart Devices as AI & Data Collection

Humanity nowadays uses smart devices on a daily basis, but most of the time, the subjects cannot define what exactly these devices are compromised of and what they are able to do with all the data they collect.

In general smart devices are all objects that can interact with human beings, analyse data, run through learning processes by themselves, and recognise a pattern. Therefore smart devices are an Artificial Intelligence device that brings several inventions and possibilities into society and smart cities. Nevertheless, before scientists, as well as society itself, decided to implement AI and, therefore, smart devices in today's manner, it started off with the idea to build "human-like robots that could understand us" (Pathak & Bhandari, 2018, p. 6). In some aspects, precisely this happened - AI understands us and exceeds expectations by learning and predicting specific outcomes. Therefore, nowadays, our society and especially cities can be described as virtual market places, which have an impact on the influence of connections in the physical world - meaning people, products, machines, and systems. All these factors form one virtual world, which can be analysed by Artificial Intelligences (Chanchaichujit et al., 2019, p.3). For smart devices/AI to track and collect this data and form the market place we expect, sensors are being used to take in all information. These sensors distracted information from the environment the device is interacting with, e.g., microphones, measurement of heart rate. Thus being said, sensors can be generalised as "small electronic components designed to sense/detect a specific parameter" — sound, temperature, etc. (ibid). Through these sensors, the devices have the ability to "continuously record and store data in devices" (Pathak & Bhandari, 2018, p.14). According to Pathak & Bhandari, the sensors are the core element of all AI devices — besides ML, NL, and DL — for the reason that otherwise, no data would be collected and generated (2019, p.29). When a sensor comes to action, the physical phenomenon they measure formed into electoral signals, e.g., microphones are converting sound vibration into a signal for the device, which can be analysed (ibid).

When focusing on current trends within the industry of Artificial Intelligences, the most popular and probably also the most common object adapted and accepted in society are voice recognition systems such as Alexa, Siri, Google Home, Google Assistant, etc. (Eadicicco, 2019). They are not only the most common AI devices used in a human's normal life, but it is also the most advanced device in this sector (Artificial Intelligence: The Insights You Need from Harvard Business Review, 2019, p.6). These voice assistant devices hit the market in 2017 with a rising popularity "out spacing predictions" (Kinsella, 2018a). According to Kinsella, smart speaker technology has "grown faster than any other consumer technology" — even faster than mobile phones (2018a). Not only with its fast implementation in the market of AI but also with its endless possibilities to be integrated and used, gave the technology a push in sales (ibid). The Harvard Business Review even states that voice recognition systems are equal to human performances/interactions — reflecting the newly implemented technologies of AI (ML, NL and DL) (Artificial Intelligence: The Insights You Need from Harvard Business Review, 2019, p. 8). According to Kinsella, the shipments of such smart speakers/assistants rose up to 35 % in the whole world (Kinsella, 2019a). Meaning a whole of 92 Million smart assistants are placed in homes, apartments, and other facilities - collecting data, analysing it, and getting a picture of every individual living there (ibid).

Even though the sales growth of smart speakers slew down in 2019, sales will still "continue to grow quickly" (ibid). The volume though "will be suppressed due to the substitute effect of voice assistants and its access" to coming and already existing applications (ibid). The more smart speakers are adapted and implemented in society /households; more and more consumers will buy more voice assistants, which they can access in their home (ibid). The probably most important technology introduced within smart assistant devices, which made it possible for them to interact with human beings are the natural language processing processes (NLP). With the NLP, several aspects are brought together to achieve the maximum in the technology of voice recognition. This includes AI, computer science, and linguistic (Taulli, n.d.). All these technologies are focusing together on the goal of teaching machines to understand and process human language (ibid). For the reason that the language of humans has evolved and changed over the previous millennia, challenges for the NLP System arise (ibid). Therefore, the system has to focus on different levels of dexterity, precision, and discernment in order to form into a fully functional voice recognition system. Nevertheless, NLP has "entered a thrilling period of new possibilities" (ibid). NLP brings not only the opportunity for voice recognition systems to analyse the language of individuals but also to build tools that can engage with "a level of expressive intricacy," which was not imaginable just a decade ago (ibid). Even though the technology of smart assistants evolved immensely over the past three years at a fast pace, no other invention ever developed before, it still is in a time transition, and several players of the economic system are investing in multiple layers in this technology (Kinsella, 2018a).

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Title
Artificial Intelligence in order to facilitate Diagnoses and Treatment. The Opportunity Smart Cities give Subjects
College
University of Applied Sciences Hamburg
Grade
1,3
Author
Year
2020
Pages
110
Catalog Number
V994741
ISBN (eBook)
9783346397652
ISBN (Book)
9783346397669
Language
English
Notes
Ich würde das E-Book gerne für 14.99€ anbieten und das gedruckte Buch für 17.99€
Keywords
AI, depression, Emotion AI, Emotion, Internet of Things, Machine Learning, Neuro Learning, Deep Learning, Smart Cities, Medicine
Quote paper
Sina Kiene (Author), 2020, Artificial Intelligence in order to facilitate Diagnoses and Treatment. The Opportunity Smart Cities give Subjects, Munich, GRIN Verlag, https://www.grin.com/document/994741

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