In age of AI, universities will need to rethink their purpose

Governments and world policy agencies now have technological unemployment and the future of work on their policy agendas. The rapid emergence of artificial intelligence (AI) and deep learning in the past decade has taken us by surprise, both in their development and the scope of their applications.

‘Convergent technologies’– nano-bio-info-cogno technologies – in the knowledge system, which enable each other and propel a vision of a science-based future, have an accelerating impact and exercise a determining direction on economic and cultural development.

There are multiple challenges facing education in a future digital society. The digital university will see the rise of artificial intelligence, deep learning (machine learning), robotisation, ‘intelligent systems’ in manufacturing and ‘Industry 4.0’ strategies, sometimes called the ‘fourth industrial revolution’, which presage what some critics have called the move to the ‘Bioinformational Society’.


In particular, a new technoscience synergy, ‘convergent technologies’, referred to as the ‘nano-bio-info-cogno’ paradigm, represents a big jump to a new stage of the knowledge economy.

It comprises:

• • Nano – The branch of technology that deals with dimensions and tolerances of less than 100 nanometres, especially the manipulation of individual atoms and molecules;
  
  
• • Bio – The exploitation of biological processes for industrial and other purposes, especially the genetic manipulation of micro-organisms for the production of antibiotics, hormones, etc;
  
  
• • Info – New information technologies with the development of quantum computing;
  
  
• • Cogno – Convergence of nano, bio and IT for remote brain sensing and mind control.

These convergent technologies are also referred to as NBIC technologies.

Convergence

What happens when 21st century technologies converge? The National Science Foundation in the United States has published many reports over the past decade exploring the convergence of NBIC technologies based on a new conception of unity of science at the nano-level that calls for unifying science and higher education.

Science based on the unified concepts of matter at the nanoscale provides a new foundation for knowledge creation, innovation and technology integration. The National Science Foundation advances an integrative approach for converging science and engineering at the nanoscale, information and system levels with a refocus on human needs and aspirations. Those needs and aspirations are identified in the development of biomedical and cognitive areas.

In education, it argues, we need a parallel ‘integrative approach’ for converging technologies and sciences. This would be a technology-led (innovation) process with a greater applied technology focus based on problem-solving for human needs.

Today governments are scrambling to think outside the box, realising that this is a moment unlike any other in history and that all signs indicate that the theoretical principle of the infinite substitution of capital for labour has arrived in applications of AI to labour processes in factories, offices and university research.

The huge gearing up for ‘intelligent capitalism’ in manufacturing and services promises the disappearance of labour as a factor in production, according to one scenario which argues jobs will disappear (‘joblessness’).

A second scenario (‘hybrid’) argues that we can change the future and we should go for augmented intelligence rather than autonomous learning systems. That will provide a hybrid model with human beings firmly in control.

A third scenario (‘normal’) states that it is business as usual and that AI and intelligent systems are just another tech-hype discourse that will erode, but also create, some jobs.

All three scenarios are based on models of change, but the first two recognise that there is something at work that is different from old linear industrial processes of scale and assembly.

They point to a dynamic model of change involving complex, non-linear, dynamic, system transformations. These were inherent in the promise of the quantum model as probabilistic in principle. This threatens the classical idea of causality and the notion of scientific realism. This probabilism makes the future very hard to predict and makes it very difficult to make public policy.

If either the first or second scenarios are more likely to be correct, then we face a bumpy future, especially in those Western societies that built themselves on the principles and institutions of the capital/labour duality: parliamentary democracy representing two dominant parties reflecting business and unions in a tripartite understanding with the government.

Rising inequalities

The G7 Future of Work Forum records some of these issues and also the anxiety of confronting a future where labour markets are changing, with jobs at risk from automation, with great labour market polarisation and rising inequalities.

The 2018 OECD Automation Policy Brief confirms that 14% of jobs are automatable and another 32% will face substantial change in how they are carried out; young people will find it harder to enter the labour market and, while jobs in manufacturing and agriculture face greater risk of automation, others are not immune to change. The greatest risk is to low-skill routine jobs, although education and training will not offset the risks of automation.

The ‘joblessness’ scenario is a frightening one, especially for young people who will increasingly find growing competition for a decreasing pool of available jobs with higher entry qualifications and conditions, and lower wages. The future of work in this scenario looks bleak even if we admit that the process is not one of the simple elimination of jobs through increasingly sophisticated automation and the application of intelligent systems to the world of work.

It is not clear what function education will serve in the era of final automation once the vocational justification for it is removed. Indeed, as a thought experiment it is useful to contemplate the question: what is the purpose and function of higher education in the age of final automation once labour as a set of processes and as a political category has disappeared?

Dr Michael A Peters is distinguished professor in the faculty of education at Beijing Normal University, China. He is author, with Petar Jandric, of The Digital University: A Dialogue and Manifesto, published by Peter Lang in 2018 and co-editor of Education and Technological Unemployment to be published by Springer later in 2019.