CHAPTER 1
Introduction

“Artificial intelligence will reach human levels by around 2029. Follow that out further to, say, 2045, we will have multiplied the intelligence, the human biological machine intelligence of our civilization a billion-fold.”

—Ray Kurzweil

Evolution is the story of tension and mutation. A difference between aspirations and available resources creates tension while changing needs and advancing technology drive mutations. Successful mutations thrive until eventually they, too, are replaced by more successful variants. By its very definition and design, evolution is a slow process. However, every once in a while, evolution is known to change pace, resulting in rapid strides that suddenly leave the standard way of life redundant and usher in a new era.

We live in an increasingly digital world – one in which we are inundated by IoT devices, connectivity, data, applications, and new experiences every moment. Our economy and our way of life are rapidly transforming into a digital one for most parts. This change is requiring businesses to rapidly reinvent themselves in this new world order, not only to thrive but sometimes just to survive.

In very recent times, Artificial Intelligence (AI) has taken center stage in the digital space. AI has existed as a discipline for more than 60 years now; its recent rejuvenation is driven by the advances in digital capabilities around IoT, big data management, cloud computing, and communication technologies. As per a recent McKinsey study,(1) by the end of 2030, the impact of AI is expected to be about $13 trillion with over 70% of companies impacted by AI. An Accenture study on the subject talks about a similar impact and interestingly identifies innovation diffusion as a key benefit in addition to productivity-related benefits. A Forbes study quoting a similar influence cites AI as the “new electricity” already driving a $2 trillion impact on the economy. Whichever study we refer to, the effect of AI on the economy and our lives is undeniable. While these numbers are pre-COVID-19 crisis, they still hold. While the timing may shift as the world starts returning to normalcy, there is no doubt about the extent of the impact. As we will see in several later chapters, COVID-19 has increased the need for AI in every aspect of our lives.

The promise of AI was rooted in the desire to simulate human cognition in the machines as they started becoming more pervasive in the first half of the 20th century. While the academic community and selected industry researchers were successful in developing the mathematical and statistical foundation to solve several AI problems, lack of data and computational power limited the practical implementation of AI on a large scale. These recent digital technological advances allow us to now connect to a very large population of people and things to collect huge volumes and varieties of data, manage and process that data inexpensively and effectively, and apply the AI-based algorithmic analytics to create new insights and drive new experiences and outcomes.

Let us first understand why AI even matters. In the last couple of years, we have already moved from an information age to an algorithmic age.(2) A few factors have driven this transition:

• Human sensory functions and the human brain have significantly more things to process today. This means that our attention spans are reducing and we also need capabilities to react fast to new inputs and events. We become more effective if along with information we are given more actionable insights. Consequently, we prefer to engage with different types of businesses and services that give us more actionable insights along with the information.
• Similarly, businesses are also flooded with significantly more information than they were ever exposed to and ready to deal with. Information is now coming from the connectivity of their devices and processes, inputs from users, and other types of transactional data that is getting generated every second. On top of that, they are in a complicated competitive environment that is rapidly transforming as technology is reducing the barriers of historical knowledge, access, and infrastructure required by businesses to be successful. Businesses now have to compete for users' attention every second and use that opportunity to demonstrate better value than others. Businesses also need to quickly innovate around their business models and services to avoid being disrupted.

Traditional business practices and underlying technology infrastructure that were based on predefined rules do not allow one to respond to these types of rapid synthesis and reaction scenarios. It is not possible to know every possible scenario and plan for it, so we need more machine-driven cognitive capabilities that require AI to now become more pervasive, hence the transition from the information age to algorithmic age. In his Future of Life Institute article, AI researcher at the University of Louisville Roman Yampolskiy said, “AI makes over 85% of all stock trades, controls operation of power plants, nuclear reactors, electric grid, traffic light coordination and … military nuclear response…. Complexity and speed required to meaningfully control those sophisticated processes prevent meaningful human control. We are simply not quick enough to respond to ultrafast events such as those in algorithmic trading and … in military drones.”

While AI brings a lot of new possibilities, it also brings new problems because businesses have to now reinvent themselves in this new world order. Traditional economic models and management practices are being disrupted. Companies that have considered AI as only a technology enabler for incremental improvements have missed the mark and there is increasing realization among business leaders about that. Those who have taken a more holistic transformative approach are emerging as the leaders in their space in this new world order. The largest technology companies of the world like Microsoft and Google are pivoting with more AI centricity; financial services companies like JPMC are using AI to renovate their client engagement; a traditional industrial giant like Schindler is driving rapid growth using digital and AI; a new age environment management company like Bee'ah is basing its future on AI; and small progressive nations like UAE have made AI a national agenda with possibly being the only country with a separate minister of AI.

The recent COVID-19 crisis has amplified the need for us to focus on digitization and AI. Our world rapidly changed in the first three months of 2020. From being highly integrated and interdependent, we moved into an era of isolation, containment, fear, and remote working in no time. A previously vibrant global economy is seeing one of its worst crises ever; many companies will not make it through this crisis. Organizational resilience in being able to meet the challenges of an unpredictable future is key to survival in this environment. Analytics today is a core capability for achieving such resilience. AI has also been incredibly useful in working through the crisis, forecasting the rate and direction of infection spread, and helping with decision support for containment strategy effectiveness and even with research around the vaccine.

While AI did see many springs and winters over the past 70 years of its existence, it is now firmly intertwined with everything we do. Let us begin our journey into the exciting world of AI.

EVOLUTION OF AI

This book is about the future, but we will spend a little time discussing the past because understanding evolution gives us context and helps us appreciate the path to the future. In this section, we have divided the evolution into multiple eras; these are our choices, they are not industry standards.

Since the beginning of civilization, humans have been ruminating about recreating human-like capabilities in machines. Starting with Greek mythology, there are references to machine-men. Fiction literature is littered with examples of artificial intelligence for a couple of hundred years now. Mary Shelley's Frankenstein (1818), Samuel Butler's Darwin Among the Machines (1863), and Karel Čapek's R.U.R. Rossum's Universal Robots (1920) are some examples of publications where concepts of intelligent devices and robots were discussed. Beyond literature, these ideas also showed up in motion pictures and television from the early days. Arguably, the 1927 German science-fiction film Metropolis is the first movie to depict a robot. A very popular show from the 1960s in the United States, The Jetsons, also very accurately depicted a future world. While the show was set in 2062, a number of the cool technologies shown in that are already part of our daily lives; these include video calls, robotic vacuum cleaners, robotic assistants, tablet computers, smartwatches, drones, holograms, flying cars, flat televisions, jetpacks, and many more. The trend of depicting a technology-enriched future has continued to the present day. We often take inspiration in life from literature and fiction. That can be said to be true for AI as well.

The 1950s – The Nativity Era

The birth of AI as a formal disciple of study and practice happened in the 1950s. Alan Turing, John McCarthy, Marvin Minsky, Allen Newell, Nathaniel Rochester, Claude Shannon, and Herbert A. Simon are considered as the founding fathers of AI. Norbert Wiener laid the foundation for cybernetics,...