Naomi Klein’s first two bestsellers are No Logo (1999) and The Shock Doctrine (2007). She wrote This Changes Evertything:Capitalism vs the Climate in 2014. Her documentary film The Take was made in 2004 in collaboration with her husband. Her latest book is No Is Not Enough. She feels the politicians are in awe of wealth, and feel that wealth can fix anything. They court the ‘Davos class’ and billionaires who are treated as Messiahs. The present US president’s sole qualification for the job was his wealth. His presidency is an extension of the Trump business brand. He brings his wealth power attitude to presidency. The merger of the Trump brand and the Trump presidency is so complete tht we fail to recognise where the one ends and the other begins. She invokes her shock docrine politics to explain this. Brutal tactics are deployed to exploit public disorientation in the wake of collective shock from a war or terror attack or a natural disaster. In the name of shock therapy, pro-corporate measures are pushed. There is a wider global phenomenon that has seen a surge of authoritarian, xenophobic, far right politics. Her tittle no is not enough presupposes active resistance to this phenomenon. However, she fails to spell out an alternative vision.
Governments fear that bitcoins may replace domestic currency and then they will lose their control over money supply. Some fear that bitcoin is a Ponzi scheme.Those who favour bitcoin claim that its mysterious originator has guaranteed that its quantity will never exceed a limit.
The creation of each Bitcoin token is an elabrate process based on blockchain technology which involves ‘miners’ who solve a mathematical puzzle each time.
We can treat bitcoin as a commodity rather than money. As a commodity, it is a store of value. Had there been no coin in the name bitcoin people would not have noticed.
Bitcoin are dressed in libertarian cloak, though it is a facade. Bitcoin is produced by humans and the operation is monopolistic limiting its supply. Though a private monopoly, it could be compared to government monopoly of the currency issue.
Since then monopoly issue gets modified, as a number of other cryplocurrencies too have emerged. They are oligopolies.
Because of its exchange value, it resembles fiat currency. It is not a means of payment and gets translated back into currency so as to spend it. It is however, a store of value. Its limited use as a means of payment and its slow mining assure that it will not substitute the national currencies. It could be compared to gold which is a store of value.
However, if these tokens command trust, they could become acceptable as means of payment. It presupposes unfettered entry and competition.
The first Industrial Revolution happened in the late 18th century and early 19th century. Its focus was on mechanization where it substituted human and animal labour by mechanical labour. It gave boost to manufacturing and urbanization. Nations used it as key to their development.
The second industrial Revolution in the late 19th centuary and early 20th century focused on mass production using the assembly lines. It gave boost to large scale production, specialization and interdependence in manufacturing. It also encouraged transportation to expand the market reach.
The third Industrial revolution happened in the later part of the 20th centuary. It relied upon automation, an alternative to mechanization. It encouraged globalisation, and manufacturing activities shifted from the developed to the developing economices.
The fourth Industrial Revolution is in the process of unfolding and depends on robotization and supporting IT infrastructure. It enables gteater freedom of choosing locations and manufacturing processes. There is flexibility of scale and customization. Here, robotization goes beyond just mechnization to undertake more complex tasks. Labour becomes ubiquitous because of robotization. Manufacturing and supply chain become closely embedded. In time to come, even distribution gets embedded to production.
India is formulating new Industrial Policy to replace the old policy that was last revised in 1991. It should meet the requirements of the 21st century’s digital world. There are four factors which would be affecting this policy.
Smart manufactring using IoT should be exploited by India. India missed the bus at the time of third industrial revolution.
Servitisation Servitisation is the trend for fourth Industrial Revolution. There should be seamlss data flow. Privacy laws must be in place. The labels of industry and services must be discarded. There are going to be changes in global supply chain. The boundaries between industry and services will blur — Google and Apple entering automobiles, Uber and Ola either as automotive value chains or the service sector, products being sold as services. All this calls for new laws for taxation. The new policy must address the challenges of moving to digitally-delivered solutions to both industrial and individual customers.
Micro-enterprises In the fourth industrial revolution, a new industrial amd work structure emerges. The way enterprises and enterpreneurs fuction would change. Digital technology enables us to overcome the barriers of scale. There is access to resources, customers, and suppliers. It is easy for an individual to be a micro-entreprenur. Servitisation facilitates this. Micro-entreprenurs are key players in the last mile eco-system.
Life-long learning system and labour laws for the 21st century must be designed.
While reaping the benefits of technology, the effects of environmental damage are minimised by using laws, social norms and international agreements.In this we are inspired by Nash equilibrium in game theory. It is named after John Nash, a mathematician and economist. In game theory, a Nash equilibrium is a set of strategies that, once discovered by a set of players, provides a stable fixed point at which no one has an incentive to depart from their current strategy.
Such an equilibrium is reached when the players understand the consequences of their own and others potential actions. For instance, in cold war, peace among nuclear powers depended on the understanding that any attack would ensure everyone’s destruction.
However, of late technology has evolved so rapidly that it is difficult to understand the consequences of any new action. Below a certain level of complexity the Nash equilibrium is useful in describing the likely outcomes. Beyond that, there is chaotic zone where players never find stable and reliable strategies. Then they cope with it by shifting their behaviours in irregular way. The outcome is random and unpredictable.
The emerging technologies in computing, software and biotech such as gene editing are unstable, evolve faster than regulatory frameworks. We may be approaching a profound moment when the guiding idea of strategic equilibrium will run up against its limits.
Keith Weed, Unilever’s global chief marketing and communications officer, feels that the digital supply chain does need cleaning up. We move from issue to issue. There is talk about ad fraud, viewability, verification and measurement. However, all these are examined in isolation. They should be examined holistically.
There are three Vs : Viewability, Verification and Value.
In digital world, Viewability is treated in terms of the view of 50 per cent of pixels. This is the concept of a view. Can we extend this concept to TV? Can 50 percent of the TV count? There should be agreement that a view is 100 per cent.
As the digital market has extended, verification has become important. It creates accountability. There could be third party verification.
Brand safety ensures that the ads appear in a suitable environment. There should be high guard rails in place. There should be clarification regarding inventry being bought. Then there are tools which advertisers and media companies use. When you buy really cheap, there are chances that you are on a cheap site. As 60 per cent traffic on Internet is that of bots, ad fraud is very common.But if you buy good quality media, there are the lowest levels of fraud.
We require one measurement across media, as it is one consumer and one ad budget. This we have to unlock as an industry.
The home shopping channels started in the 1990s. Mostly the dubbed English slots were telecast which promised too much.Since then they have come a long way, innovating content and introducing new products. The video-based content explains the features of the product and assist customers in their purchases. Some popular channels are HomeShop18. Best Deal TV, DEN Snapdeal TV and Gemporia.These channels were challenged by e-commerce and online retailing. These have taken away many customers from home shopping. Many channels shut shop. It has become difficult for home shopping channels to attract new customers.
The TV shopping business requires high investments in quality content production. Majority of players advertise their products on standard TV channels after 11 pm. Many new categories such as travel and insurance have been advertised on home shopping channels.
Home shopping channels now focus on regional markets, particularly the southern states. These channels function on inventory based nodel and marketplace model. Their revenue comes from commission from sales, and they also charge a fixed fee to the brands. These channels exploit festivals and special occasions.
OTT players so far offered movies and original content. However, of late there is increase in local content and content in regional languages. India, being a multi-lingnal country, there are many opportunities here. By 2020, almost 75 percent internet users will be from the rural areas, and they will consume data in local languages. OTT players commenced givimg content in Hindi and English, but have expanded beyond these two languages. OTT players have started investing in regional content production. Even focus is shifting beyond metros and so the growth in regional content will be exponential. Such content will bring local advertisers. There are different business models. The most used/abused model is AVoD model, SVoD, TVoD, which also includes freemium.
There are concentric circles of Artificial Intelligence-Machine Learning-Deep Learning. AI is manifested through capabilities of machine learning. Computers get some facets of human intelligence and this has been made possible by machine learning. There is no coding for specific set of instructions for performing specific tasks. ML rather uses algorithms to parse data, learn from it, and make a determination or prediction about certain actions or events. A machine is trained by using a large amount of data and algorithms give it the ability to learn how to perform the task.
ML is further enhanced by deep learning which is made by artificial neural networks that have discrete layers, connections and directions of data propagation.
There is a rapid sweep of digital technologies, customer and supply-side analytics and new tools of augmented reality, robotics, 3D printing and Internet of Things ( IOT ) which enable seamless vertical connectivity through the customer and order fulfilment processes. There is also horizontal integration of value chain partners across the industry in which each firm operates.
Engineers of tomorrow must have the ability to think beyond the set of machines, or even blocks of data and move towards true systems thinking. Smart manufacturing is the unity of data, technology, environmental prospectives and people-leading manufacturing enterprises — all this happening in real time.
Workmen will have the capabilities to deal with robotic processes,self healing machinery. We will have to move from the traditional TPM, 5S and Six Sigma to newer digital capabilities. We will have to deal with sensor-generated IoT data from shop floor which will co-exist with information generated by ERP systems.
After all, Artificial Intelligence too is software that examins confusing situations to venture a guess, and learn from what happens when there is action. The line between ordinary software and AI software has blurred and cloud computing has made AI available to both big and small companies.
The earlyy progress in the 1950s was the invention of neural networks. These networks are the software which processes data with some pattern recognition capabilities of the human brains. A refined programme called a perception came later. It would create a talking, walking and thinking machine. In the early years of this millennium, research labs at companies such as Google, Microsoft and Facebook took root. They had large amount of data and computing power. They hired capable manpower to do speech recognition and image analysis.
Will AI benefit only the cabitalists? Anything that cuts labour costs does so. Another issue — if huge data and computing power drives AI, would future economy be controlled by a handful of companies?