Evolution of Digitalization in Industry 4.0 – Part I
(In this part we go back into the past to look at the evolution of Industry 4.0)
Historians and economists have often opined, civilizations undergo social, business and political revolutions every 250 years. It is worthwhile to explore whether such a phenomena can be observed in Science and Technology by taking a historical odyssey going back 500 years to the 1500s. Such historical observations do reflect a thread of scientific and technological innovations leading to Industry 4.0 and Digitalization.
In the 1500s Europe came out of despair and darkness from the Bubonic plague. During that time there were silver linings with the spread of knowledge being revolutionized with the invention of the Gutenberg printing press. This was an epochal invention comparable to the impact of the modern day internet. Some of the other inventions from the Middle Ages were telescope and microscope. During that time there were many scientific discoveries coming from Copernicus, Galileo, John Napier, Fermat, Robert Boyle, Sir Isaac Newton. There was an intellectual innovation giving birth to modern anatomy, astronomy, biology, chemistry, mathematics and physics.
Societies were also undergoing revolutionary changes. In Europe, with evolution of the Protestant Movement many people dispersed to settle across geographies as immigrants. This was the time when Christopher Columbus discovered Americas, leading to the birth of United States.
Cognitive psychologists have observed that learning in an individual happens in four stages. These are the four stages how our brain comprehends. The first step is perceptual, when we first sense by listening or reading. The second phase is conceptualization when we observe and rationalize applying the laws of science. The third phase is visualizing and the fourth phase is application. So perceptual – conceptual – visual – application.
My historical odyssey, made me to observe that knowledge of science till around the 1500s was primarily perceptual with subjects like Philosophy taking the lead. From 1500s to the mid 1700s primarily conceptual.
Starting around mid 1700s we could observe the brewing of a new revolution. This revolution, from a learning perspective was primarily visualization and application. This was the Industrial Revolution. Application of knowledge was the genesis of our modern day Engineering. Engineers build by applying the concepts of science.
The Industrial Revolution starting from around 1750s can be divided into four phases of developments. The first phase of Industrial Revolution till around second half of 1800s was around mechanical machines for example the spinning jenny by James Hargreaves , steam engine by James Watt and sewing machine by I M Singer. The second phase was around electro mechanical machines for example the electric motor, light bulb, telephone. The Internal Combustion engines were also developed during the second phase. The third Industrial Revolution started around the 1960s with relays, transistors and semiconductors leading to the era of computers.
From the first decade of 2000s we are in the fourth phase of Industrial Revolution. We refer to this as Industry 4.0. Let us try to understand what is happening in Industry 4.0.
In the first three Industrial Revolutions we were only building machines which were tools to make us more productive and efficient. An automobile is a tool to drive from Point A to Point B. An aircraft is a tool to fly from City A to City B. All such tools are controlled by human beings in operations.
In Industry 4.0 we are building machines which functionally represent how a human brain works. What is unique about the human brain. The human brain understands patterns, has the power of reasoning and has command over language. This means we are building machines which can perceive, reason and communicate or act. They are at times autonomous from human controls. Examples are driverless cars, pilotless planes and drones. And this is the reason why we refer to them as smart machines or intelligent machines. We also communicate with these machines. Many of the smart cars today have pre-crash warnings. The cars alert us by tightening of the seat belt and awakening us if we are asleep. Innovators are building machines which can communicate with us reading our emotions. For example visual sensors which operate by reading our emotions by the dilation of our pupils.
Well renowned inventor and futurist, Ray Kurzweil in his book ‘The Singularity is Near’ highlighted the merging of technology with human intelligence with numerous examples. This merging is happening by our increasing understanding of how the human brain functions. Efforts are to reverse engineer the functions of the brain. Ray Kurzweil gave the example of the retina in the eye which is 2 centimeters wide and a half millimeter thick primarily functioning for image capturing. The retina captures 10 million image detections every second. The estimate is that 100 computer instructions are required to recreate each such detection at human levels of performance. This means to replicate this function of the retina we require 1000 MIPS (Million Instructions Per Second). Similarly the number of computations that the human brain does is 10¹⁶. With such computational estimations, an analogy can be made to the computation power in computers to animal brains. A supercomputer 50 years back could do 0.25 MIPS equivalent to the intelligence of a bacteria or a worm. The IBM Blue Gene computer in 2007 could do Million Giga Flops per second i.e. 10¹⁵ instructions per second which is estimated to be equivalent to the brain power of a mouse. By early part of next decade processor technologies will be developed which can do 10¹⁶ instructions per second. And towards 2030s it is estimated that machines for example a robot, will be developed which can pass the ‘Turing Test’. Such a machine will be indistinguishable from a human being.
Increasingly skills will be determined by the ability to work with smart and intelligent machines. Post the ubiquity of Turing Test certified computers or machines, human skills will be determined by its ability to complement the machine cognition. Humans failing to complement or supersede the abilities of the machines will be below average. Well known economist, Tyler Cowen refers to this scenario as “Average is Over”.
Developments in processor technologies to support such massive levels of computations are being contributed by 3D molecular computing, DNA computing, quantum computing and spintronics.Many of the traditional transistor technologies are being disrupted as logic gates are being defined at atomic dimensions.Innovators are working by adopting such technologies on nano robots or nanobots which can travel through human blood vessels and will be connected through communication protocols with other nanobots.
These disruptive digital developments have forced organizations across industries to innovate and leverage digitalization technologies. For many organizations, failing to do so, it will be a question of survival.
Author – Mr. Abhijit A Barua, Director – at Siemens Industry Software ( PLM ) India
Mr. Abhijit Barua – profile - Linkedin - https://www.linkedin.com/in/abhijit-barua-b357682/
Note- The ideas expressed in Part I and Part III are personal. Ideas expressed in Part II are based on the digitalization initiatives and the resources thereof available in Siemens Industry Software.