When we think about work we usually think about people – their mental or physical effort – either alone, with other people or with technology. However, the characteristics of both technology and people are changing and so will the future of work.
Technology has always been an important factor in work – from the earliest of times people have developed and used tools to compliment, enhance and amplify what they can do. Where work and the actions of a tool are repetitive and predictable then it becomes possible to automate the tool to create a machine.
Tools compliment people in work whereas machines replace them in work and change the nature of work at the same time. While people use a tool to do work, with a machine its different – the machine does the work and people’s work becomes the machine – operating and attending to the machine.
Machines have only been able to go so far economically (compared with the cost of people to do the same task) and to where and how they can be applied. However, all this is changing – changing economics and technology suggest that we are entering a new machine age and this has radical consequences for the future of people in work.
Technology developments are starting to radically reduce the cost of robots and machines while at the same time the cost of people continues to increase – making machines more economical than ever before. Computer developments, machine learning and AI are radically changing how and where machines are applied. The predictability required by machines once meant that they were applied only in controlled environments (a typical factory installation for example) but now we are starting to see more machines operating in the real world – Google’s driverless car is an important precursor of this development. The same trend has already happened in IT – where once computers were large, expensive and used in special conditions (think of an office and a desktop PC) .. we now find them out in the real world with us (think smartphones and wearable tech). In the years ahead we should expect to see more and more machines and robots leaving their factories for our world.
Once something becomes digital change and impact becomes rapid (if not exponential) – we are starting to see “Moore’s law” in the digital aspect of machines – if this is the case then we should expect to see radical advances in the application of machines and robots in the 21st century. In 1997 IBMs Deep Blue became the first machine to beat a human world champion at chess – 14 years later IBM returned with Watson to become the first machine win the TV trivia game Jeopardy in 2011. Deep blue was very much a traditional machine – it did one thing .. a special purpose computer to play chess by “brute force analysis” to work out chess moves to greater depth than any human player ever could. Watson however represented something different – IBM describes it as a “smart machine” able to answer questions in natural language. Since winning Jeopardy IBM has developed Watson and what it calls cognitive technology – Watson is now 24 times faster, 90 times smaller and described as performance improved by 2,400%. IBM have made Watson available on the web as a cloud product and developer “ecosystem” to support the development of what IBM describe as “cognitive apps” – today – you can carry Watson in your pocket!
On the 7th June 2014 computer program Eugene Goostman simulated a 13 year old boy from Odessa in unrestricted conversations – a machine passed the Turing test for the first time
On December 7th 2014 IPsoft launched Amelia – described as “the first cognitive agent who understands like a human … our cognitive knowledge worker, interfaces on human terms. She is a virtual agent who understands what people ask – even what they feel – when they call for service. Amelia can be deployed straight from the cloud in a fraction of the time. She learns as she works and provides high-quality responses consistently, every day of the year, in every language your customers speak” IPsoft sees Amelia supplementing, or directly replacing, virtually all ‘non-expert, repetitive’ job functions from customer support to expert assistance and back office roles.
Management consultancy Accenture is using Amelia in its cognitive services saying “The cognitive and learning capabilities of the Amelia platform allow it to easily absorb routine processes as well as learn from natural language interactions in order to solve customer problems and respond successfully to a wide range of queries”. Accenture is helping Shell deploy Amelia in its internal training programme, answering queries from learning advisors – “she will observe how advisors interact with staff until she is ready to automate the processes herself.” Baker Hughes is testing Amelia in its financial department on its Accounts Payable helpdesk to address queries from vendors around invoices and payments.
Sean Ammirati writes that Any office job that involves drudgery is a candidate for automation. One way to think about occupations ripe for robots is to look at different professional tasks with a knowable problem and solution – even if it’s really complex to figure out that solution.
Research from the Oxford Martin School at Oxford university suggests that nearly half of all jobs in the US are likely to be automated in the coming decades. The research concludes that “While computerisation has been historically confined to routine tasks involving explicit rule-based activities algorithms for big data are now rapidly entering domains reliant upon pattern recognition and can readily substitute for labour in a wide range of non-routine cognitive tasks . In addition, advanced robots are gaining enhanced senses and dexterity, allowing them to perform a broader scope of manual tasks. For workers to win the race, they will have to acquire creative and social skills.
A recent report from Deloitte suggests that Computers and robots are set to replace more than a third of UK jobs in the next twenty years. Work in repetitive processing, office administration, clerical and support service jobs, sales and transportation are most at risk. The report says that “Although the replacement of people by machines is well understood, the scale and scope of changes yet to come may not be … Unless these changes coming in the next two decades are fully understood and anticipated by businesses, policy makers and educators, there will be a risk of avoidable unemployment and under-employment”
But wait .. there’s more. Brian Arthur writes about the Second Economy – the computer-intensive portion of the economy where machines transact with other machines without humans in a “vast, automatic, and invisible economy without workers thereby bringing the biggest change since the Industrial Revolution”
At the very extreme pessimistic end of the spectrum Stephen Hawking thinks that “Artificial Intelligence Could End Human Race”, Nick Bostrom warns that AI could be more dangerous than nuclear weapons and that “artificial intelligence may doom the human race within a century” while Elon Musk hopes “we’re not just the biological boot loader for digital superintelligence” that “With artificial intelligence, we are summoning the demon” and “worries Skynet is only five years off”
The changes in technology mentioned above suggest radical and pessimistic negative impacts for work and for people but this assumes nothing else changes. However, people are incredibly resourceful and other views are more optimistic.
Gerd Leonhard suggests that the concept of work as we know it is toast but that many new areas will open up in new or unpredictable niches, with titles we can only guess at at present and that there are all those areas where human soft skills are essential. Many lower-paid but intricate jobs (think electricians or plumbers) with too many variables may be too expensive to automate. And there will surely always be a premium for the human touch in some areas that could be automated – cooking or teaching, for example
Greg Satell gives some useful advice on How to Avoid Being Replaced By A Robot – learn To Ask Questions, Improve your social skills and go beyond the routine. “the division is no longer between manual and cognitive tasks as much as it is between routine and non-routine work.” Anything that is standardised and routine is at risk of being automated. Greg leaves us with the optimistic message that by “automating tasks, we are liberating human imagination and the human spirit. The more we unlock the secrets of technology, the more we find ourselves.”
Andrew McAfee compares the information revolution with the industrial revolution and takes a very optimistic view – “what we’re in the middle of now is overcoming the limitations of our individual brains and infinitely multiplying our mental power. How can this not be as big a deal as overcoming the limitations of our muscles?” … we ain’t seen nothing yet. The best days are really ahead”. Andrew makes the point that “Economies run on ideas. So the work of innovation, the work of coming up with new ideas, is some of the most powerful, some of the most fundamental work that we can do in an economy. In the technology-facilitated world .. the work of innovation is becoming more open, more inclusive, more transparent, and more merit-based.
As automation looks set to impact traditional notions of work and how we work technology changes and a new generation of people emerge that can make the most of the new conditions and potentially reimagine work as we know it. In 2014 Internet traffic from mobile use exceeded PC use for the first time – signalling the start of a new era of anytime, anywhere IT and the potential for anytime, anywhere work. Rather than us having to come to work – work can come to us. Mobile IT combined with social media, cloud and web access are powerful tools in the right hands. New cultural movements like the Maker Movement combined with new technologies like 3D printing, Internet of Things and cheaper more accessible “maker” electronics like Raspberry PI, Adruino and Intel’s Edison suggest potential future artisan economies of scope, creativity and imagination while machines replace more routine and standardised work.
The generation who have “grown up digital” in the 21st century have grown up with the tools we shaped for them – the Net, the Web, mobile phones, smartphones, social networks and social media. Generation Z have grown up with information and communication at their fingertips. Those born in the 21st century will be able to “race with the machines” – and as Greg Satell says “our value will be determined not by how much we know or even how hard we work, but how well we collaborate with machines and with each other”. Research by Sparks & Honey’describes Generation Z as developing their personalities and life skills in a socio-economic environment marked by chaos, uncertainty, volatility and complexity. They have learned that traditional choices don’t guarantee success. They “Intend to change the world. That entrepreneurship and social entrepreneurship is one of their most popular career choices – 72% want to start a business and 61% want to be an entrepreneur rather than an employee.
While it seems that a new generation are ready to “race with the machines” John Hagel suggests that our institutions and their organisation are the main problem. He says that “at its core, this isn’t a technological challenge, but an institutional challenge. We’re dealing with a set of institutions that are increasingly inappropriate for the mounting pressure we face. The root cause is how we’ve defined work in companies … one of the issues is this formula for how work is conducted was developed in the last century, and it was based on a set of infrastructures and assumption of a stable environment that made it easy to define standardized highly-scripted work. Now we’re in a world that’s more rapidly changing, more uncertainty, more of those extreme events that Taleb calls the “black swans” that make it really critical for us as individuals in the workplace to take much more initiative, to be constantly exercising creativity and imagination to respond to the unexpected events. That’s a very different model of work. It requires a very different way of organizing our institutions and a different set of work practices that are much harder to automate. Rather than pursuing scalable efficiency, perhaps we need a new set of institutions that can drive scalable learning, helping participants to learn faster by working together.
“We have stone-age emotions, medieval institutions and godlike technology.”— E.O. Wilson
Hagel says that “Until we can develop an alternative institutional model, one that can scale as effectively as the scalable efficiency model, we will face mounting pressure from machines and remain locked in a race against the machine without the ability to finally race with the machine. The problem is how do we innovate our institutions and our work practices so that we, in fact, can start “racing with the machine.”
Ultimately technology may provide a platform to race with machines – a new generation of developers like Vitalik Buterin working with open, autonomous, decentralised technologies suggest could Bootstrap decentralized autonomous corporations where we can work together with other agents on the network … not necessarily knowing whether they are human or not.