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25th Anniversary series: Machine Learning and Professional Work – A Lookahead To 2040

October 7, 2015

Machine learning will change most managerial and professional work beyond recognition over the next 25 years.  In 2040, a person without a reasonable level of understanding and appreciation of machine learning will be disadvantaged for almost every management or professional role, much as a deficiency in  numeracy, literacy or computer skills proves disadvantageous today.

The Machine Learning Breakthrough

Machine learning is the use of algorithms to recognise patterns and learn from data.  The results are classifications, or predictions from the data which can make or support sophisticated automated and human decision-making.  Bill Gates is on record saying that, “a breakthrough in machine learning would be worth ten Microsofts”.  Tony Tether, the Director of DARPA for most of the last decade, describes machine learning as, “the next internet”.

In my view, the machine learning breakthrough has already come.  We at Z/Yen Group have worked with emerging machine learning tools and techniques, primarily support vector machines (SVMs), for two decadess.   “Back in the day”, it was seriously nerdy, mainframe computer stuff; early this century, we needed to write our own algorithms to run on organisational machines such as PCs and workstations.  The breakthrough since 2010 has been the ubiquity of high quality, often free, machine learning algorithms.  The R statistical enviromnment, i.e. open source software, now has a cracking good SVM.  R software is free.  Really, really, free.  If you want to run a big data set, you can simply hire the processing power you need for a few quid and run your big model.

But machine learning and big data are not synonymous.  Big data might need machine learning in order to make sense, but machine learning can do plenty of fascinating things with relatively small sets of data.  The cost is not money, but time.  You need time to gain an understanding of how to use the techniques, coupled with the imagination to ask the right questions and harness the potential of machine learning.

25 years ago, I was considered to be a young professional with rare, valuable skills, as I had acquired understanding and extensive experience of spreadsheet modeling.  I could even word-process my own reports.  Such skills are now deemed to be basic essentials for most management and professional workers.  Today, machine learning skills are the rare and valuable ones.  By 2040, machine learning will be as ubiquitous in our orgnaisational toolkits as the office software suite is today.

Machine Learning in Professional and Managerial Work

The media has already latched on to subjects such as self-drive automobiles, robotic carers and nanobots as game changers for our working and social lives within the next 25 years.  Such technologies rely in part on machine learning, combined with many other exciting-looking innovations in technology and engineering.  Machine learning is less tangible  (you cannot drive a prototype of it, have your daughter cuddle a working model of it, or draw a picture of it whizzing around your body).  Yet, machine learning is the key to the profound changes to knowledge workers’ daily lives; it will soon underpin almost everything that organisational managers and professional workers do.

I envisage that, in the year 2040, it would be more or less unthinkable for a doctor to provide a diagnosis or prognosis opinion without a machine learning-based instrument having first provided a probabilistic assessment of the patient’s condition.  Indeed, for most common health matters, even the most serious ones, the patient would not visit a doctor unless or until a personal device had opined that there was a risk and a need for some additional tests or intervention.  A lawyer would not give an opinion on anything without a machine-based assessment of the legal position and chances of success; indeed savvy commercial folk would very rarely need the human lawyer as they would be trained and equipped to ask the machine themselves.  Almost all audit work (as we currently know it) will be done algorithmically; indeed most current activities of audit and accountancy will require very little human intervention.

There is a pessimistic take on the future of work in the next 25 years; “there’ll be no jobs other than bullshit jobs”, “the information society will herald the end of capitalism” or even “the whole of society will break down”.  I disagree with that.  Granted there will be painful change for many, but people and society are extraordinarily adaptable.  I am optimistic about the future for knowledge workers.  For a start, analytic skills and techniques in themselves (including analytic specialisms within professional disciplines) will become increasingly valuable and important; evidence suggests that this change has already started. The future is extremely bright for those who appreciate and understand how to harness machine learning.  The future is also brighter for those in creative and caring occupations, in particular for people who do hands-on work that requires personal and social interaction.

Predict This

Here are a few of my predictions on the ways I think organisational management and the professions might change by 2040:

  • Professions restructuring: Commercial professions such as accountants, actuaries and lawyers will no longer operate in practices dedicated to those specific disciplines. Instead, there will be multidisciplinary professional practices of perhaps three main types:
    • advisory,
    • regulatory and standards,
    • outsourced information systems and information management services.
  • The return to prominance of the general practitioner in professions: The last 25 years has seen increasing specialisation in the professions, in large part due to the increasing complexity of technical, regulatory and standards regimes. The general practitioner has largely been relegated to a small bit part, referring all but the smallest and/or most minor matters to specialists.  Those referral and routine intervention roles are unlikely to survive the next 25 years, but nor will many of the more detailed, yet rule-based specialist roles.  The surviving professional roles will be those that require hands-on work with clients and/or colleagues, not least professional-pastoral care and social interaction with that community.   Imagine a cross between the old-fashioned bank manager’s role as general advisor and a cerebral personal trainer.
  • The value of para-professionals will increase: Many routine, reactive ancillary back-office roles will bite the dust, but there will be increasing roles for hands-on para-professionals. Training and education is likely to prove a good example of this – while delivery of educational content and marking might be increasingly automated through machine learning, there will be expanded roles for paraprofressional educators (or Teaching Assistants), whose value will consequently increase.  Similarly in medicine and care, while routine elements of the work might be automated, the value of hands-on health and care professionals for non-routine work will be demonstrable and commensurate rewards available for those who adapt.
  • Professional and management roles will change more dramatically and rapidly than those of, e.g. drivers and carers: While it is easier to envisage driverless vehicles replacing almost all driving jobs and robotic carers replacing much routine care work for the young, old and frail, it is also easier to rail against such changes on ethical or moral grounds. Personally, I see little or no difference between the ethical issues in replacing drivers with driverless cars, replacing medical orderlies with robotic orderlies and replacing a professional person with an expert system that can support important decisions without the need for the expert person.  Yet, the court of moral public opinion seems more queasy about the first two than it does about the third example.  That moral queasiness might slow down progress in those areas.  The computer scientist Jaron Lanier explains why we tend to feel queasy about these elements of the future.  “…this generation is creating the computer network and the infrastructure of computer software that will be running for a thousand years. I call it the Karma Vertigo Effect because when you realize how much karma we have in this generation, you get vertigo!” Further, I suspect that economic and commercial drivers will come into play more readily in the transformation of professions and management than in the other examples, where public policy and indeed the public purse have a greater say.

In short, I anticipate that the world of work will eventually be transformed, within, say, 40 to 50 years for most current jobs.  But my 25 year lookahead to 2040 sees the working world of the professions and management already transformed by machine learning, faster than the rest.  Be ready.

Written by Ian Harris of Z/Yen.

The views expressed are those of the author and not necessarily of SAMI Consulting.

Further Reading

John Brockman, Digerati: Encounters with the Cyber Elite, HardWired Books, 1996, http://www.edge.org/documents/digerati/Lanier.html

Carl Benedikt Frey and Michael A. Osborne, The Future Of Employment: How Susceptible Are Jobs To Computerisation?, September 17, 2013, http://www.oxfordmartin.ox.ac.uk/downloads/academic/The_Future_of_Employment.pdf

David Graeber, The Utopia of Rules: On Technology, Stupidity and the Secret Joys of Bureaucracy, Melville House (2015) ISBN: 1612193749.  See also: On The Phenomenon Of Bullshit Jobs, Strike Magazine, 17 August 2013, http://strikemag.org/bullshit-jobs/

Michael Mainelli and Ian Harris, The Price of Fish: A New Approach to Wicked Economics and Better Decisions, Nicholas Brealey Publishing (2011) ISBN: 1857885716.

Paul Mason, PostCapitalism: A Guide to Our Future, Allen Lane (2015) ISBN: 1846147387.

Audrey Watters, Teaching Machines and Turing Machines: The History of the Future of Labor and Learning, 10 August 2015, http://hackeducation.com/2015/08/10/digpedlab/

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One Comment leave one →
  1. October 29, 2015 8:22 am

    Reblogged this on radicalscribe.

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