Noetic Future Shock!
In an insightful talk, historian Yuval Noah Hariri asks us to think about how we are preparing the next generation for the future. That will be based on how we imagine the future, and that is where, he feels, the problem arises. We simply have no idea of what the shape of this unknown future is going to be like, and hence how the lives of all of us are going to be impacted. If that is the case, then what are we diligently going about doing every day, in the education field? Are we providing various teaching inputs to learners in the hope that they will learn something of value that will help them in their lives? Have we incorporated any changes into the content and pedagogy that is different from what was happening a generation ago? Or, are we making incremental changes, and hoping for the best?
While Hariri was probably referring to the shape of society and the economy in the unknown future that is emerging, he was also specifically referring to jobs and productive work and how these will be affected by important changes in technology that are happening today. These include Artificial Intelligence (AI), Machine Learning (ML), Data Analytics and related technologies that enable the creation of algorithms that are capable of doing a great deal of what we imagine today only humans can do. Already, since the beginning of history, machines were employed by man to reduce the effort required by humans to carry out tasks.
Influence of Industrial Age
In the industrial revolution, about 300 years ago, the factory system using power came into prominence, and that fundamentally affected the way society was organized. People moved from rural areas and farms to urban areas, in order to work in factories, where the demand for workers was huge and growing. This large cohort of workers and the support staff needed to be literate and trainable, and hence there was a corresponding need for an educational system also organized along the same lines. The drivers of this education system were mass production, standardization, measurement, and above all, predictable outcomes.
The modern school system was created to meet this need, and that was soon followed by the modern university system, quite different from the older western model based on the great centers of learning like the universities of Bologna, Padua, Siena, and others. They were student-centric and a great deal of attention was given to the rights of and interests of students. That is in marked contrast to the university that evolved in response to the industrial revolution, with the focus changing to mass production, measurement, greater importance given to science and technology, and especially to their application to industry and agriculture.
No Intellectual Freedom
Up to the present day, while much is spoken about intellectual freedom, open learning, flexibility, creativity, and so on, most educational institutions still are measured, and more importantly, measure themselves, by the number of graduates who were successfully ‘placed’ in well-paying jobs in the economic system they are meant to serve. The track record of educational institutions is not measured by the knowledge produced within their walls, but by the career progression of their graduates, in the organizations they serve. While a few institutions do talk about the research programs that they encourage and promote, it is increasingly true that such research too is discussed in terms of how it can be monetized and made to yield a return, of the way in which it is ‘useful’.
While educational institutions have been slow to change, their graduates have found that the organizations they serve are being forced to change, as technology changes. These changes have not been driven by changes in habits and tastes and demand, as is the common misconception. On the other hand, as Marx had foretold, changes in the relations of production are driven by changes in the means of production ie technology. Through the 20th century, we have seen the rise and decline of mass production based on the notion of economies of scale, and driven by integrating processes under one roof. By the last decades of the 20th century, that notion was already being challenged, and smaller flexible manufacturing units were found to be better suited to the changing market demand, which was for more customized products, made in a wide range of models. The old large integrated manufacturing enterprises were not suited for this changing customer preference, and have started to change into smaller and leaner organizations.
The advent of robotics and AI and ML has accelerated this trend, in two distinct ways. First, entire factories have been built that did not require humans to operate the machines. In fact, these factories worked even in the dark, 24 hours a day, every day. Giant warehouses of Amazon and Alibaba are today entirely operated by AI-driven robots, and they work round the clock, sorting, packing, and stacking hundreds of thousands of packages, and later retrieving them, and delivering them to truck bays for onward shipment to destinations. These warehouses and factories would have ordinarily provided thousands of jobs for people.
As AI and ML became more sophisticated, the number and variety of jobs that could be better done by machines or algorithms also grew. We are today under the mistaken impression that there are jobs that can be performed only by humans, jobs that require design and creativity, and hence that machines and robots will never make man redundant. That is already being proved to be wrong, as the dramatic example of an AI algorithm defeating the world GO champ demonstrated. GO is a game that involves simpler rules than chess, but more complex in the role played by strategy. As recently as 2014, it was thought that a computer will never defeat a human at the game. Recently, that too was proved wrong.
It is now thought that ‘high touch’ jobs requiring empathy and emotional inputs cannot be performed by machines driven by AI. As thinkers like Hariri have shown, there are developments happening in the development of algorithms intended for driverless vehicles that are capable of being programmed to take ‘ethical’ decisions in difficult situations. In short, there is no scientific and technical reason to imagine that there is any task today performed by humans that cannot be done by AI. Already, algorithms are seen to be very good at matching jobs to job-seekers, at diagnosing diseases and medical conditions, at examining legal options and comparing precedents, at choosing between career options, and so many other tasks once thought to be beyond the capabilities of AI.
These changes are happening today, and the pace of their adoption is going to get faster as the advantages become apparent. The mistake we make is to compare how things are being done today, with what can possibly happen tomorrow. We are attempting to project from a known and defined present into an unknowable and uncertain future, at a time when rapid technological change is happening in almost every sector. Every discussion on the subject of making education future-ready is evidence of the old saying: man is a reasonable creature; he does the reasonable thing when he has no other choice!
‘Sunk Cost’ Fallacy
I am not an educator and claim no expertise in the subject. However, I know that we are not doing any of the things we should be doing to prepare the young students for a world that is still about to emerge. We are watching the struggles of the organism as it undergoes the transformation from a parva to a pupa to the imago. We are seeing something writhe and struggle inside a cocoon, but we have no idea of what is it that will emerge. Today, we continue to act as if what we have been doing so far will continue to work in the future, probably with a few little changes. We are held back by the ‘sunk cost’ fallacy, by the fact that we have all these huge educational investments in premises, facilities, teaching staff, and pedagogy. What will we do with all that, if we have to change? That inertia is what is preventing us from thinking beyond the ‘latest’ advances offered by the large tech companies, or their smaller variants. They too are merely interested for the same reasons in amortizing their heavy development costs, through the maximum sales they can manage to achieve.
Change the System
In my view, education will be able to respond well to these challenges, only if the system changes. The large, centralized, capital intensive model of schools and colleges cannot survive, as they are not resilient. Just as manufacturing is being forced to change through the adoption of additive manufacturing, robotics, and DIY models of kits, education too will need to change. I foresee smaller local schools and colleges, drawing students from local communities, with very few specialized institutions serving a larger hinterland. These schools and colleges will address skills and knowledge required b y local businesses and services. There will be many jobs created to care for elders, those chronically ill, those who are handicapped. More care will be carried on at home, and less at hospitals. That will require a cohort of trained staff to be available in the local community. Most work will become gig work, with local aggregators or platforms serving as the clearinghouses of such information. Skill development will become a lifelong process, so the formal schooling process could possibly be shortened with no loss of depth or breadth of learning.
Seeking a job a thousand miles away, and finding a place to rent and schools for the children, which is the norm these days, will become the exception in the future. The local community will become the fulcrum of work and life. That will also be a result of the metamorphosis of capitalism into post-capitalism, where the driver is no longer the unending quest for ever-increasing surplus value. It will instead lead to a realignment of our needs with the needs of the community, and those needs with those of the environment without which we cannot survive. This will be the long-awaited correction needed if we are to survive as a species.