India spans 3.3 million kilometres spread over 7,900 towns and around 600,000 villages. It has approximately 10 million retailers. The fast moving sector, which, along with automobile, consumer durables and pharmaceuticals, makes up the majority of the consumer industries (CI) sector, retails products from around 21,000 manufacturers producing a quarter of a million stock keeping units (SKUs) (Nielsen 2016). Add the other sectors, and the CI sector in India sells a million products to a billion Indians. Imagine doing that only with human intelligence.

The path to achieve stability for artificial intelligence (AI)-based implementation in the CI sector is nascent but the potential is becoming increasingly clear. The adoption of AI-based solutions in India has seen an upward trend in the past few years. Over the past few years, it has been observed that high-and mid-cap CI companies have benefitted from AI in their various practice lines. AI has delivered excellent results in terms of increased revenues, improved productivity and increased effectiveness of their promotional expenditures. Companies have seen tremendous changes in their transactional, distribution and marketing-based processes, thereby improving both top-line and bottom-line growth. The reason behind this key success is the ability of these organizations to combine AI-based technology with the human-in-the-loop to deliver efficient business outcomes at scale.

Having operated in the market for over two years, Digilytics AI, a category leader of easy-to-use SaaS AI products, finds that players in the consumer industry are extremely receptive to the use of AI and analytics to gain firstmover advantage. However, only a selected few companies understand the real benefits of AI and how to apply it. Despite a huge growth in the technology infrastructure in these companies, most continue to struggle with basic data quality issues. However, there is a consensus among business heads that there lies a huge potential in using AI and in all the major business functions within the company. With this positivity in the business sphere, companies have initiated the roll out of AI-based capabilities with the right steps in mind.

Introduction

As the term artificial intelligence (AI) has become a buzzword in industry and academia in recent years, governments around the world have raced to create national AI strategies. AI can be broadly defined as ‘the study of how to make computers do things at which, at the moment, people are better’ (Rich et al. 2009, 3). Japan was one of the first countries, alongside Canada, to formulate an AI strategy, publicizing its first in early 2017. This first version of Japan’s ‘Artificial Intelligence Technology Strategy’ prioritized ‘health, medical care and welfare’, alongside ‘productivity’ and ‘mobility’ (Strategic Council for AI Technology, Japan 2017). AI technologies are increasingly penetrating our daily lives, and one of the industries that AI is radically transforming is, indeed, healthcare.

AI, alongside the Internet of Things (IoT) and the Internet of Medical Things, is expected to aid the development of new medicines, reduce diagnostic errors and help doctors more efficiently perform complex surgery with AI-assisted medical robotics (AoMRC 2019). The implementation of AI technologies in healthcare is particularly important in Japan because it is the most rapidly ageing society in the world (IIASA 2018). Japan is now facing serious societal challenges, such as the increasing elderly population and an acute workforce shortage, especially in the healthcare industry. The successful application of AI will enable Japan to sustain its healthcare and improve its medical productivity. In order to achieve these goals, however, effective interactions between the public and private sectors are indispensable. This chapter critically examines Japan's strategy to facilitate such interactions, focusing on its national project to develop so-called ‘AI hospitals’.

Japan's Ageing Society and AI in Healthcare

The development of AI in Japan has largely been a government-led process. The Council for Science, Technology and Innovation (CSTI) of the Japanese government launched the cross-ministerial Strategic Innovation Promotion (SIP) programme in 2013 to stimulate technological innovation in the country. Subsequently, SIP has served as a roadmap for AI development in Japan. Before the promulgation of the 2017 AI technology strategy, the development schemes of technological innovation was designed in two phases. The first phase began in 2014 while the second commenced in 2018.

Introduction

This study focusses on artificial intelligence (AI) and its impact on Australian industry. AI is threatening organizations by disruption at a globally level. The value of AI to the global economy is expected to increase by US$16 trillion by 2030 (GEF 2017). Organizational leaders are focusing on answering questions such as ‘where should we target investment, and what kind of capabilities would enable us to perform better?’ (PwC 2017, 4). A report from PricewaterhouseCoopers (PwC 2017) has projected that China will have a 26 per cent gain of GDP in 2030, followed by North America with 14.5 per cent. The report projects a total of 70 per cent (or US$10.7 trillion) of AI's global economic impact. The same report suggests that Europe and developed countries in Asia will benefit significantly with a growth of 9–12 per cent in GDP in 2030. Developing countries in Africa, Latin America and Asia may have a modest gain of about 6 per cent. But what about Australia? Has Australia been late in turning to address the AI challenges?

Like most countries, Australia is experiencing a great change in the way work is performed (ACS 2020; Deloitte 2019; Syam and Sharma 2018). Australia has been a laggard in addressing challenges of AI and creating appropriate policies to deal with AI-disruption (Elliott 2019a, 2019b). This is changing as the Australian government has provided a budget of $29.9 million to enhance AI and machine learning (Future of Life 2020). AI policy initiatives for Australia include: AU$1.4 million for PhD scholarships to support emerging Australian researchers in artificial intelligence and machine learning; an AI Technology Roadmap project recognizing barriers and opportunities to build Australian capability; development and assistance for future government policy; and the development of an Australia AI ethics framework (OECD 2020; DISER 2020).

The Australian tech future or digital economy strategy focuses on integrating businesses, government and community aims to maximize potential benefits and opportunities that are possible by advanced digital technology. In addition, the CSIRO Innovation Fund is a AU$242 million venture capital fund investing in new spin-off companies, existing start-ups and SMEs to foster technological development. The Next Generation Technologies Fund with a AU$730 million budget aims to promote innovation and is managed by the Defence Science and Technology which is part of Australia's Department of Defence.

Introduction

It's very difficult to summarize a topic as deep as AI in a place a big as China. The objective of this chapter is to provide a snapshot of the current industry status as well as potential future developments, drawing heavily on the direct experience of the author. AI's 60-plus year history has been characterized by five to six waves of development and stagnation. Each wave has been driven by a new approach (such as expert systems or symbolic AI) which achieves breakthroughs in a specific area. This is followed by excitement, investment, and overexpectation followed almost inevitably by disappointment and abandonment. China has largely been absent from these waves of development, except the most recent cycle that started in the early 2010s. This wave has been driven by a new technology known as machine learning. It is unclear whether this new wave of development will meet the same fate as earlier cycles. Whatever the case, it's clear that China has already made big inroads in this area, both in terms of applications as well as research and development. China's entry into this phase of AI development has less to do with any particular expertise in machine learning and much more to do with the overall market and economic conditions of the country, which has enabled both private enterprises and government to invest significantly. No one could have anticipated that China would factor so heavily in the development of AI globally. Like many mega-trends, things began in a modest way.

Humble Beginnings

Unlike the West, AI developments in China were initially driven by start-ups much more than the technology giants. This seems counterintuitive given the level of technological complexity and investment required. But China's tech landscape has historically been driven more by business models and operational innovation than investment in technology. In fact given the legal environment and often high degree of employee churn, it was often very difficult to protect any intellectual property or investments in research and development. Therefore, the first wave of Chinese technology companies were mainly consumer-focused internet companies such as Baidu, Alibaba, and Tencent (collectively known as “BAT”) where sustained competitive advantage came through operations and implementation more than technology.

First Decades: The Soviet Era

As of now, Russian researchers do not typically make the lists of major newsmakers when it comes to Artificial Intelligence (AI). However, this trend is being rapidly reversed, and the home country of the first world champion in computer chess games and inventors of the mathematical learning theory will soon very likely catch up with the very best in both the academia and the industry.

The first attempts at what can be called “AI before AI” can be traced back to the 1820s to 1830s Russia, when, concurrently with Ada Lovelace, Semyon N. Korsakov proposed a series of mechanical machines for “enhancing natural intelligence” through “comparison of ideas” (Karsakoff 1832) to the Imperial Academy of Science in St. Petersburg. In modern terms, Korsakov’s ideoscope could compute set-theoretic intersection and complement (which gives a complete set of Boolean functions) over data given by object-attribute tables implemented by punched cards. Unlike Jaccard machines driven by programs on punched cards— the precursors of machines with numerical control— Korsakov's ideoscope was intended for information processing with symbolic computation, such as checking the similarity, difference, search, and classification. Like toy steam engines designed in ancient Greece, these inventions were hardly technologically scalable and did not meet societal needs of the day, so they sank into oblivion till the rise of the computer era in the 1950s.

It is worth mentioning that Soviet and Russian researchers of the twentieth century would often not claim that they “were doing AI,” so our classification follows the modern view of what is AI. AI, as a striking term motivating better fund raising in Western countries, was under suspicion (not always ideological) in Soviet science, which tried to keep to deeper-grounded nomenclature of science branches, with a sort of Arbor Porphyriana as the archetype of classification of things. In 1954, two years before the now-famous Dortmund seminar, where the name Artificial Intelligence was coined, A. A. Lyapunov started his seminar “Automata and Thinking” at Moscow State University. The event featured physiologists, linguists, psychologists, and mathematicians, and arguably marked the start of AI research in Soviet Russia. Back in the day, AI was considered as a branch of cybernetics.

Introduction

Since time immemorial, the small island states of the Caribbean have continuously developed regional integration schemes to achieve greater independence and development among themselves. This has resulted in several multipronged approaches to integration covering a wide scope. The Caribbean Community and Common Market (CARICOM) was established by the Treaty of Chaguaramas, which was signed by Barbados, Guyana, Jamaica and Trinidad and Tobago and came into effect on 1 August 1973, after which 11 other territories joined.

CARICOM is a unique arrangement. According to Payne (1994), writing in the second decade of its existence, CARICOM's survival was secured on the basis that it would steer clear of Caribbean political integration and all its facets, namely supranationalism, which threatens national independence and sovereignty. With respect to its governance mechanisms, Payne's discussion describes the CARICOM system as being managed by a chain of organs comprised of member states’ politicians and is merely ‘serviced by its secretariat’. Decision-making within the Community has to be by unanimous agreement but the implementation of all decisions is up to the individual member state and ‘its own constitutional procedures’ (1994).

In following the trends of regional integration worldwide, the European Union has launched its AI strategy and roadmap. However, in the conceptualization of AI within the Caribbean, the author affirms the belief that as developed countries increase their adoption of AI, the gap between the developing and developed countries will widen (Szcepanski 2019).

This research aims to examine the utilization of artificial intelligence (AI) within the Caribbean. This topic was selected with the aim of identifying key trends in AI within CARICOM. An assessment of key private sectors, regional organizations and civil society representatives across four CARICOM territories as well as national policymakers involved in the information and communication technology (ICT) arena was done to identify related opportunities in AI within CARICOM and the subsequent prospects for increasing economic growth, ethical governance and cross-border flows.

Introduction of the CARICOM Single Market and Economy (CSME)

As with previous integration attempts, upon realizing the limitations of the regional initiative, Caribbean heads of governments agreed to deepen their state of regional integration and thus conceptualized a Caribbean Single Market and Economy.

Introduction

The above words have never been truer than in a world rendered uncertain and chaotic with a virus on the loose. With rigid social distancing protocols established to combat the pandemic, the meaning of home ownership is transcending connotations of just ‘financial affluence’. For most, mortgages are now the most vital pillar of finance.

The mortgage market in the UK strives to continuously reinvent itself to meet the changing demands of its growing and evolving aspirational customer base. You may be privy to the pre-screening methods to assess the viability of a loan that have been in vogue for quite a while. Alternatively, you might even be using credit scoring vendors to check your general eligibility for getting a mortgage (or any loan for that matter) or interacting with a smart conversational AI-driven chatbot like Alexa in mortgages to get more information on product types or payment holidays. Predictive techniques powered by artificial intelligence (AI) are responsible for such optimizations. While these are some examples of the traditional and mainstream uses of AI in the loan space, this chapter attempts to describe the challenges plaguing the mortgage industry, bolstered heavily by an intermediary-led business model, and some of the latest applications of AI in the mortgage space, especially in mortgage origination, and then explain how AI can further help customers, intermediaries and lenders make more informed decisions to reduce costs, increase revenue and improve overall satisfaction especially when lenders are swamped with phone calls brimming with anxiety. We also attempt to highlight some of the challenges that organisations are facing with specifics on roadblocks to implementation of machine learning in the mortgage industry.

The Slow and the Furious

We are all aware of the impact of digitisation and other advancements in technology (Briggs 2017). Digitisation has dramatically changed business landscapes across many industries, yielding itself particularly useful in the home buying and owning experience. With a radical shift in favour of digital experiences, consumers are now expecting more from their choicest lenders. Digital transformation is becoming one of the top three strategic priorities for leaders at the helm of these mortgage businesses (Bookallil and Birkby 2017).

What was once a figment of imagination in the minds of science fiction writers is now a ubiquitous reality in the United States of America. There are already various AI-endowed products in the market including: Garmin’s “auto-land” which can manage aircraft speed and engine performance or even descend towards the nearest airport and land a plane in case of a medical emergency (Pasztor 2020), and Clean Air, a technology that monitors and fixes air quality through fresh air, tempered, filtered and treated with ultraviolet light (McLaughlin 2020). In addition, primary healthcare is moving to a team of health-care professionals whose direct compensation is linked to keeping patients healthy by uploading data from home-monitoring equipment (Landro 2020). There are also trends for the future that are more indicative of science fiction: building a better athlete through tweaked brain circuits, culturing performance-boosting bacteria, and enhancing strength, speed and endurance by altering genes (Hotz and Hand 2020); seeking romance and friendship from artificial intelligence (AI) in the form of a chatbot for conversation during times of quarantine (Olson 2020); and producing meat in bioreactor tanks from animal cells rather than raising and slaughtering chickens, cattle and hogs (Bunge 2020).

As AI continues to develop, governments and practitioners must ensure that AI-enabled systems can work effectively with people and hold ideals that remain consistent with human values and aspirations; but this will not be easy. Increased attention has been drawn to these challenges and many believe that AI will create a better and wiser path forward for humankind. However, realistically one of the greatest challenges will be the risks involved for all citizens as AI continues to evolve and increase its impact on the workforce and society. The world is ever more competitive, and the underlying premise is that governments, industries and educational institutions that are ahead of the AI curve will reap the benefits of technological breakthroughs. The United States, in addition to many other advanced nations, aim to be at the forefront of these breakthroughs. This chapter will explore the good, the bad and the ugly, and then perhaps the beautiful of AI in the United States.

Introduction

Over the past 30 years, Canada has been able to support the deployment of the world's largest and most powerful artificial intelligence (AI) science community by investing rapidly in machine learning and deep learning research. Today, Canada's knowledge of AI has spread around the world.

Canada: A Global Artificial Intelligence Hub

Since the proliferation of modern AI in the 1950s, Canada has maintained its status as a global innovation hub in AI by implementing decisive national programs with long-term impacts. Canada continues to provide critical support to AI development and has quickly become a host nation for some of the most brilliant scientific minds in machine learning and deep learning. However, without a robust business-driven ecosystem taking those research discoveries into a commercial phase, Canada faces the challenge of not effectively benefitting from the industrial implementation of AI. As policymakers continue to support nationwide research programs, they remain committed to crystallizing the development of an AI-driven ecosystem.

Understanding Canada's Research-Led AI Model

Building the Foundations: Creation of CIFAR

In the early 1980s, Japan launched an innovative research program on “fifth-generation” computing systems, which integrated AI. This initiative spurred the launch of other research programs elsewhere in the world as part of an important step in technological advancement. In 1982, Canada created the Canadian Institute for Advanced Research (CIFAR) with the purpose of promoting knowledge breakthroughs, innovation and notable advances within the Canadian research community. In fact, in 1983, CIFAR launched the AI & Society Program, a national program, which was one of the world's first research programs specialized in AI. Decades later, it remains a foundational program for Canada's current status as both an AI hub and research leader.

Attracting Global Talent: The Godfathers of Artificial Intelligence

In the late 1980s, funding for research projects in AI was dramatically cut worldwide. Regarded as the “AI Winter,” CIFAR provided critical support to scientists in the field of AI. As a result, Canada retained its scientific talent, while attracting international brilliant minds such as Dr. Geoffrey Hinton from the United Kingdom, a recognized pioneer in deep learning.

Artificial intelligence (AI), or the foundation for the enhanced cognitive ability of machines, has grown by leaps and bounds over the years. It has refined and redefined business frameworks and has taken the notion of operational efficiency to an entirely new level.

As a result of AI, countless breakthroughs have taken place in many industries. Examples of industries that have benefited from AI include: healthcare (data-based diagnostic support), automotive (autonomous fleets for ride sharing), financial services (personalized financial planning), retail and consumer (personalized design and production), technology, communication and entertainment (media archiving and search), manufacturing (enhanced monitoring and auto correction), energy (smart meters), transport and logistics (autonomous trucking) and many more (PwC 2016).

AI has seeped into the day-to-day life of an individual as well, affecting the way they work, live and entertain themselves. Voice-based assistance systems like Siri and Alexa are common example of AI. Online shopping, web search, digital personal assistance, machine translation, smart homes, smart cities, infrastructure, logistics, education, driverless vehicles, and so forth are just few of the examples of integration of AI in commercial and personal applications. All the chapters in this book, contributed by authors from different parts of the world, provide an insight into the use of AI in all walks of life. AI is finding its way in each and every application at a pace one cannot imagine. By the time this book reaches the hands of the reader, there will be far more applications of AI than have been mentioned in this book.

AI can be characterized in at least five ways:

Introduction

Artificial intelligence (AI) is a new digital frontier, and it represents uncharted territory that will be a profound driving force on the global economy, social affairs, and will transform the way we live and work. Over the past decade, AI has matured considerably and, as a fundamental innovation, is becoming the driver of digitalization and autonomous systems in all areas of life. As AI moves from the theoretical concept to the global marketplace, its growth is energized by a great quantity of digitized data and rapidly advancing computational processing power. AI can (1) improve weather forecasting, advanced manufacturing processes, speech recognition, industrial productivity, (2) improve and enhance cybersecurity defenses, (3) boost agricultural productivity, (4) enhance detection of cancer in the medical field, (5) predict an epidemic, and (6) help in development of autonomous vehicles and autonomous weapons systems. For these reasons Germany has initiated a holistic approach to their AI strategy within the European Union. Germany is already extremely well situated in many areas of AI. In the 2019 federal budget, the German Government Federation has taken the first step, allotting a total of €500 million to reinforce the AI strategy for 2019. In the following years, up to and including 2025, the German Government Federation intends to provide around €3 billion for the implementation of the strategy. This chapter seeks to discuss the framework for a holistic strategy and the policy for future development and application of AI in Germany. This chapter also shows that AI holds enormous potential and has a positive impact on German economy due to the technical capabilities of German companies, skilled workers, and a demand in the market for AI services.

Businesses and business processes are continuously evolving due to technological advancement. The need for competitive advantages in businesses has historically been the engine for development of advanced and cost-effective new mechanisms. In this effort, the third industrial revolution emerged in the IT environment, which in turn gave rise to widespread digitalization and moved to the fourth industrial revolution. In Germany, the term “Industry 4.0,” has been embraced by the German industry (Hannover fair 2011), which is one of the main indications of this new technological shift and is part of the High-Tech Strategy 2020.

The chapters in this book highlight the fact that AI is alive and well in many corners of the world. It is evident that in diverse shapes and forms, AI is having an impact on businesses, governments, economies and lives of people worldwide.

From the chapters, five key themes are notable with regard to the international practice of AI:

These themes suggest the following:

In light of the above, the authors suggest the following courses of actions for governments, corporations, and executives and entrepreneurs.

Governments

Table 14.1 outlines important strategic considerations for governments.

These strategies underscore the fact that gaining advantages in AI does not happen by accident. For governments to optimize the economic benefits relating to AI, careful planning and well-conceived supporting investments are necessary.

Corporations

Table 14.2 outlines important strategic considerations for corporations.

These strategies suggest that corporations need to think in new ways. They need to aspire to be ‘cognitive leaders’ in their field.

Introduction

For the term ‘artificial intelligence (AI)’ Google searched 797 million results in 0.49 seconds. There were 2,590,000 hits in 0.14 seconds in the ‘Scientific paper’ category. The increases in scientific publications can be seen in Figure 7.1. The numbers prove the popularity of the topic.

Issues related to AI are addressed at different levels, but with many different approaches. Popularity of AI is not accidental as it is embedded in our daily lives and shapes our thinking and decisions in private and at work. But it poses a serious challenge to an organization's management systems as it is the driving force behind the Fourth Industrial Revolution (Brynjolfsson et al., 2018). International Data Corporation (IDC 2019) analyses that the global value of developments can reach $90 billion by 2023 (Chernov and Chernova 2019).

The contents of publications, studies, blogs and web pages about AI are almost untraceable and the possibilities of understanding and incorporating into an organization's everyday life depend on several factors. The most important of these is knowledge, coupled with trust and/or mistrust in technology and its safety. Another important factor is technical readiness (development), which determines the integration of AI into everyday practice in a broad range/gamut. The third, also influencing the former two, is financial conditions. Other factors can be listed, but the above are closely related, and they distinguish the situation of the countries presented in this study fundamentally from the practices of Western European, American and Asian countries. In the following, the study describes the situation and practice of the group formed in the heart of Central and Eastern Europe, the Visegrad Four.

AI and the Visegrad Four

Within the European Union (EU), the Visegrad Group (V4) is a regional organisation of four Central European member states – the Czech Republic, Poland, Hungary and Slovakia. Their aim is to jointly represent the economic, diplomatic and political interests of these countries. The V4 was founded in Visegrad (Hungary) in 1991 to promote the development of the region. In 2004, they all joined the EU. These four countries account for more than a tenth of the EU's territory and population, contributing almost 6 per cent of the EU's economic performance in terms of GDP, around 8 per cent of car production, and almost 20 per cent of major crops.