Analysing the performance of leading Conversational AI Companies in Countries Based on Open Datasets
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Аннотация
INTRODUCTION 8
CHAPTER I. UNDERSTANDING OF CONVERSATIONAL AI AND DIGITAL TECHNOLOGIES
1.1 The concept of conversational AI 10
1.2 Chatbots & Voice Assistant 11
1.3 Literature Review 14
1.3.1 The First AI Standard 14
1.3.2 The AI Readiness Index 17
Summary
CHAPTER II. THE METHODOLOGY AND PLAN OF STUDY
2.1 Relevance of study 20
2.2 Research gap 21
2.3 Research question 22
2.4 Methodology 23
2.5 The search process 23
2.6 The study selection process 23
2.6.1 Inclusion criteria 25
2.6.2 Exclusion criteria 26
2.7 Quality assessment 26
2.8 data extraction and analysis 26
Summary
CHAPTER III. FINDINGS AND DISCUSSION
3.1 Research Question 1 27
3.2 Research Question 2 39
3.3 Research Question 3 48
Summary
CONCLUSION 52
REFERENCES 54
APPENDIX 59
INTRODUCTION 8
CHAPTER I. UNDERSTANDING OF CONVERSATIONAL AI AND DIGITAL TECHNOLOGIES
1.1 The concept of conversational AI 10
1.2 Chatbots & Voice Assistant 11
1.3 Literature Review 14
1.3.1 The First AI Standard 14
1.3.2 The AI Readiness Index 17
Summary
CHAPTER II. THE METHODOLOGY AND PLAN OF STUDY
2.1 Relevance of study 20
2.2 Research gap 21
2.3 Research question 22
2.4 Methodology 23
2.5 The search process 23
2.6 The study selection process 23
2.6.1 Inclusion criteria 25
2.6.2 Exclusion criteria 26
2.7 Quality assessment 26
2.8 data extraction and analysis 26
Summary
CHAPTER III. FINDINGS AND DISCUSSION
3.1 Research Question 1 27
3.2 Research Question 2 39
3.3 Research Question 3 48
Summary
CONCLUSION 52
REFERENCES 54
APPENDIX 59
Artificial Intelligence (AI) is a concept that for decades has been part of public discourses, often featured in science fiction films or debates on how intelligent machines will take the world placing the human race in a life of slavery in support of a new AI order. Although this image is a specific AI image, the reality is that artificial intelligence has arrived now and many of us are constantly in touch with technology in our daily lives. AI technology is no longer the field of future scientists but an integral part of the multi-agency business model and an important strategic element in the plans of many business, medical and government sectors around the world. This transformational impact from AI has led to greater interest in education with the latest research examining the effects and effects of technology than the effects of AI performance, which seems to be an important research center for some years.
Conversational Artificial Intelligence (CAI) systems and intelligent assistants (IA), such as Alexa, Cortana, Google Home and Siri are becoming ubiquitous in our lives, including those of children, their impact of gaining more attention, especially in relation to the effects of these programs on children's mental development, socialization and language. Recent developments address CAI's implications for privacy, security, safety, and accessibility. However, there is a need for connecting and embedding ethical and technical aspects in design and development. Using a case-study of a research and development project focused on the use of CAI in leading countries, this research work highlights the social context within a particular case of technological development, as evidenced and supported by contradictions within the literature. It describes the decision-making process behind the recommendations made in this case for adoption in the industry. Further research involving developers and stakeholders in CAI behaviour are highlighted as a matter of urgency.
Russell and Norvig (2016) defined the term AI to describe systems that mimic cognitive functions generally associated with human attributes such as learning, speech and problem solving.
The motivation for this study is twofold as it is based on an increase in publications on Artificial intelligence and the importance of investment in AI for global economy (Maria Cubric, 2020). Companies around the world have tried to implement artificial intelligence techniques adopted by different country strategies.
The success of AI depends on its correct execution. To ensure AI success, organizations often need to excel in a wide variety of applications, including creating strategies, finding the right use cases, building a database, and developing strong experimental capabilities. These capabilities are critical at this time because the window to differentiate oneself from competitors is likely to narrow as AI becomes easier to use.
Early users from different countries show different levels of AI maturity. The enthusiasm and experience of early adopters varies from country to country. While some are actively using AI, others are taking a more cautious approach. In some cases, subscribers use AI to improve certain processes and products; others are using artificial intelligence to transform their entire organization.
No matter how mature AI is in countries, we can learn from its approach. By looking at country problems and how to solve them, we can collect some key innovative practices. For example, leaders in some countries are more concerned with filling skills gaps. Others focus on how AI can improve decision-making or cybersecurity capabilities.
There are many paths to great AI, and success doesn't mean that the winner takes it all. Looking at early AI users from a holistic perspective can provide a broader perspective. By doing this, anyone can find a more balanced AI-based approach to their journey.
The main goal of the study is to explore whether there were clear differences in how Conversational AI is impacting their businesses and how different countries are promoting AI efforts. In order to achieve the goal, we focussed the research on following questions:
RQ1. What are the most developed AI countries? What are their policies and approaches to state regulation of Conversational AI?
RQ2. What are the most representative Conversational AI Tech companies, according to the Open datasets? What are the analysis of performance of leading companies??
RQ3. What are typical investors of Conversational AI leading companies in India (which type of investor and on what stage of funding)?
The research is based on number of common methods with different level of implication - Theoretical
The work structure consists of several parts:
• Introduction: background and motivation for the thesis development, general description of the problem, research questions, structure and volume of the work;
• Chapter 1: literature review with the most relevant information related to the analyzed main topics: conversational AI, Chatbots and voice assistants, connection links between these topics;
• Chapter 2: general description of the potential research methods, followed by explanation for the chosen research strategy;
• Chapter 3: Findings and discussion.
• Conclusion: summarization of the results of the work performed and evaluating the results obtained.
Conversational Artificial Intelligence (CAI) systems and intelligent assistants (IA), such as Alexa, Cortana, Google Home and Siri are becoming ubiquitous in our lives, including those of children, their impact of gaining more attention, especially in relation to the effects of these programs on children's mental development, socialization and language. Recent developments address CAI's implications for privacy, security, safety, and accessibility. However, there is a need for connecting and embedding ethical and technical aspects in design and development. Using a case-study of a research and development project focused on the use of CAI in leading countries, this research work highlights the social context within a particular case of technological development, as evidenced and supported by contradictions within the literature. It describes the decision-making process behind the recommendations made in this case for adoption in the industry. Further research involving developers and stakeholders in CAI behaviour are highlighted as a matter of urgency.
Russell and Norvig (2016) defined the term AI to describe systems that mimic cognitive functions generally associated with human attributes such as learning, speech and problem solving.
The motivation for this study is twofold as it is based on an increase in publications on Artificial intelligence and the importance of investment in AI for global economy (Maria Cubric, 2020). Companies around the world have tried to implement artificial intelligence techniques adopted by different country strategies.
The success of AI depends on its correct execution. To ensure AI success, organizations often need to excel in a wide variety of applications, including creating strategies, finding the right use cases, building a database, and developing strong experimental capabilities. These capabilities are critical at this time because the window to differentiate oneself from competitors is likely to narrow as AI becomes easier to use.
Early users from different countries show different levels of AI maturity. The enthusiasm and experience of early adopters varies from country to country. While some are actively using AI, others are taking a more cautious approach. In some cases, subscribers use AI to improve certain processes and products; others are using artificial intelligence to transform their entire organization.
No matter how mature AI is in countries, we can learn from its approach. By looking at country problems and how to solve them, we can collect some key innovative practices. For example, leaders in some countries are more concerned with filling skills gaps. Others focus on how AI can improve decision-making or cybersecurity capabilities.
There are many paths to great AI, and success doesn't mean that the winner takes it all. Looking at early AI users from a holistic perspective can provide a broader perspective. By doing this, anyone can find a more balanced AI-based approach to their journey.
The main goal of the study is to explore whether there were clear differences in how Conversational AI is impacting their businesses and how different countries are promoting AI efforts. In order to achieve the goal, we focussed the research on following questions:
RQ1. What are the most developed AI countries? What are their policies and approaches to state regulation of Conversational AI?
RQ2. What are the most representative Conversational AI Tech companies, according to the Open datasets? What are the analysis of performance of leading companies??
RQ3. What are typical investors of Conversational AI leading companies in India (which type of investor and on what stage of funding)?
The research is based on number of common methods with different level of implication - Theoretical
The work structure consists of several parts:
• Introduction: background and motivation for the thesis development, general description of the problem, research questions, structure and volume of the work;
• Chapter 1: literature review with the most relevant information related to the analyzed main topics: conversational AI, Chatbots and voice assistants, connection links between these topics;
• Chapter 2: general description of the potential research methods, followed by explanation for the chosen research strategy;
• Chapter 3: Findings and discussion.
• Conclusion: summarization of the results of the work performed and evaluating the results obtained.
This Chapter is the final part of the master thesis and contains the main conclusions of the work. During the research, we got acquainted with the main conversational AI leading countries and leading companies. In order to successfully complete the research tasks and be able to draw conclusions, we used the literature review and analysis of open datasets as the main method of our research. This work also presented a systematic literature review about the use of conversational agents in business domains. The work answered three focused research question based on a selected literature corpus and analysing the open datasets. The corpus was created through the search of articles in research databases, with inclusion and exclusion criteria followed by filtering steps. Each article of the corpus was used in one way or other to analyse or draw conclusions to build ground to answer the proposed research questions. The research questions covered the business domains that received studies and described primary goals and future challenges. The selected literature corpus and analysing the open datasets it is understood that the AI standards and AI readiness index is important to understand when it comes to determine which country is leading in conversational AI. It is not necessarily that the country leading in Artificial Intelligence overall will also lead in conversational AI Except the case of United States of America. AI readiness Index 2020 shows the top leading AI countries are USA, UK, Finland, Germany and Sweden but excluding United States of America none of the countries from top five are leading in conversational AI. India is the leading country in conversational AI with several conversational AI companies and startups are leading either in India or headquartered in USA with India operations. In addition to providing unique opportunities, India provides a complete "playground" for businesses and institutions around the world to develop solutions that can be easily used in all developing countries and economies. Simply put, Resolving India means resolving 40% of the world's AI garage. India's strengths in IT combined with opportunities, provide much needed impetus to find uncontrollable solutions to customer service problems, and developing chatbots and voice assistant. Coming to the findings of second research questions there are several conversational AI leading companies in India and USA out which the Uniphore a leading Conversational AI company tops the list with 2.5 billion market valuation. The analysis of performance of top 5 leading conversational AI companies is categorized in two parts first is innovation and second is growth of the company .Third research question explains the type of investors and round of funding they have done in companies leading in conversational AI technologies where NEA and march capital tops the list by investing 400M USD in uniphore.
