A recent bank of England Study has said that 15 million of the UKs 30 million jobs could be at risk from automation over the coming years. Meanwhile, a US PEW study found that two-thirds of Americans believe that in 50 years time, robots and computers will “probably” or “definitely” be performing most of the work currently done by humans. However, 80 per cent also believe that their own profession will “definitely” or “probably” be safe.
Putting to one side the obvious inconsistency, how might we ensure that our jobs, and those of our children, are safe in the future? Are there any particular professions, skills or behaviours that will ensure that we remain gainfully employed until we decide that we no longer want to be? Indeed, is there anything of significance that we do that artificial intelligence can’t? And how might education be upgraded to ensure peak employability in the future? Finally, how realistic are doomsday forecast that robotics and automation will destroy countless millions of jobs?
To answer all of these questions we should first delve into what it is that robots, artificial intelligence, automated systems and computers generally do today and then speculate in an informed manner about what they might be capable of in distant tomorrows.
A robot is generally defined as an artificial or automated agent that’s programmed to complete a series of rule-based tasks. Originally robots were used for dangerous or unskilled jobs, but they are increasingly used to replace people when people are absent, expensive or in short supply. This is broadly true with automated systems as well. They are programmed to acquire data, make decisions, complete tasks or solve problems based upon pre-set rules. Not surprisingly, machines such as these are tailor made for repetitive tasks such as industrial manufacturing or for beating humans at rule-based games such as chess or Go. They can be taught to drive cars, which is another rule-based activity, although self-driving cars do run into one big problem, which is humans that don’t follow the same logical rules.
The key phrase here is rule-based. Robots, computers and automated systems have to be programmed by humans with certain endpoints or outcomes in mind. At least that’s been true historically. Machine learning now means that machines can be left to invent their own rules based on observed patterns. In other words, machines can now learn from their own experience much like humans do. In the future it’s even possible that robots, AIs and other technologies will be released into ‘the wild’ and begin to learn for themselves through human interaction and direct experience of their environment, much in the same way that we do.
Having said this, at the moment it’s tricky for a robot to make you cup of tea let alone bring it upstairs and persuade you to drink it if you’re not thirsty. Specialist (niche) robots are one thing, but a universally useful (general) robot is that has an engaging personality, which humans feel happy to converse with, is something else.
And let’s not forget that much of this ‘future’ technology, especially automation, is already here but that we choose not to use it. For example, airplanes don’t need pilots. They already fly themselves, but would you get on a crewless airplane? Or how about robotic surgery? This too exists and it’s very good. But how would you feel about being put to sleep and then being operated on with no human oversight whatsoever? It’s not going to happen. We have a strong psychological need to deal with humans in some situations and technology should always be considered alongside psychology.
That’s the good news. No robot is about to steal your job, especially if it’s a skilled or professional job that’s people-centric. Much the same can be said of AI and automated systems generally. Despite what you might read in the newspapers (still largely written by humans despite the appearance of story-writing robots) many jobs are highly nuanced and intuitive, which makes coding difficult. Many if not most jobs also involve dealing with people on some level and people can be illogical, emotional and just plain stupid.
This means that a great many jobs are hard to fully automate or digitalise. Any job that can be subtly different each time it’s done (e.g. plumbing) or requires a certain level of aesthetic appreciation or lateral thought is hard to automate too.Robots, AIs and automated systems can’t invent and don’t empathise either. This probably means that most nurses, doctors, teachers, lawyers and detectives are not about to be made redundant. You can program machines to diagnose illness or conduct surgery. You can get robots to teach kids maths. You can even create algorithms to review case law or predict crime. But it’s far more difficult for machines to persuade people that certain decisions need to be made or that certain courses of action should be followed.
Being rule-based, pattern-based or at least logic-based, machines can copy but they aren’t capable of original thought, especially thoughts that speak to what it means to be human. We’ve already had a computer that’s studied Rembrandt and created a ‘new’ Rembrandt painting that could fool an expert. But that’s not the same as studying art history and subsequently inventing cubism. This requires rule breaking not pattern recognition.
Similarly, it’s one thing to teach a machine to write poetry or compose music, but it’s entirely another thing to write poetry or music that connects with people on an emotional level and speaks to their experience of being human. Machines can know nothing about the human experience and while they can be taught to know what something is they can’t be taught to know what something feels like from a human perspective.
There is some bad news though. Quite a bit in fact. An enormous number of current jobs, especially low-skilled jobs, require none of these things. If your job is rigidly rule based, repetitive or depends upon the application of knowledge based upon fixed conventions then it’s ripe for digital disruption. So is any job that consists of inputting data into a computer. This probably sounds like low-level data entry jobs, which it is, but it’s also potentially vast numbers of administration, clerical and production jobs.
Whether you should be optimistic or pessimistic about all this really depends upon two things. Firstly, do you like dealing with people and secondly, do you believe that people should be in charge. Last time I looked humans were still designing these systems and were still in charge. So it’s still our decision whether or not technology will displace millions of people or not.
Some further bad news though is that while machines, and especially robots, avatars and chatbots, are currently not especially empathetic or personable right now they could become much more so in the future. Such empathy would, of course, be simulated, but perhaps this won’t matter to people. Paro, a furry seal cub that’s actually a robot used in place of people with dementia in aged-care homes, appears to work rather well, as does PaPeRo, a childcare and early learning robot used to teach language in kindergartens. You might argue that elderly people with dementia and kindergarten kids aren’t the most discerning of users, but maybe not. Maybe humans really will prefer the company of machines to other people in the not too distant future.
Of course, this is all a little bit linear. Yes, robots are reliable and relatively inexpensive compared to people, but people can go on strike and governments can intervene to ensure that certain industries or professions are protected. An idle population, especially an educated one, can cause trouble too and no government would surely allow such large-scale disruption unless they knew that new jobs (hopefully less boring jobs) would be created.
Another point that’s widely overlooked is that demographic trends suggest that workforces around the world will shrink, due to declining fertility. So unless there is a significant degree of workplace automation the biggest problem we might face in the future is finding and retaining enough talented people, not worrying about mass unemployment. In this scenario robots won’t be replacing or competing with anyone directly, but will simply be taking up the slack where humans are absent or otherwise unobtainable.
But back to the exam question. Let’s assume, for a moment, that AI and robotics really do disrupt employment on a vast scale and vast numbers of people are thrown out of work. In this case, which professions are the safest and how might you ensure that it’s someone else’s job that’s eaten by robots and not yours?
The science fiction writer Isaac Asimov said that: “The lucky few who can be involved in creative work of any sort will be the true elite of mankind, for they alone will do more than serve a machine.” This sounds like poets, painters and musicians, but it’s also scientists, engineers, lawyers, doctors, architects, designers and anyone else that works with fluid problems and seeks original solutions. Equally safe should be anyone working with ideas that need to be sold to other people. People that are personable and persuasive will remain highly sought as will those will the ability to motivate people using narratives instead of numbers. This means managers, but also dreamers, makers and inventors.
In terms of professions, this is a much harder question to answer, not least because some of the safest jobs could be the new ones yet to be thrown up by developments in computing, robotics, digitalisation and virtualization. Nevertheless, it’s highly unlikely that humans will stop having interpersonal and social needs, and even more unlikely that the delivery of all these needs will be within the reach of even the most flexible robots or autonomous systems. Obviously the designers of robots, computers and other machines should be safe, although it’s not impossible that these machines will one day start to design themselves. The most desirable outcome, and it’s also the most likely in my view, is that we will learn to work alongside these machines, not against them. We will design machines that find it easy to do the things we find tiresome, hard or repetitive and they will use us to invent the questions that we want our machines to answer.
As for how people can simultaneously believe that robots and computers will “probably” or “definitely” be performing most of the jobs in the future, while believing that their own jobs will remain safe, this is probably because humans have two other things that machines do not. Humans have hope and they can hustle too. Most importantly though, at the moment we humans are in still charge and it is up to us what happens next.
One of the key drivers of global change is the convergence of technologies. This is, in turn, driving the convergence of products and ultimately services. Meanwhile, over in business (and especially retail), we are experiencing a blurring of industries and sectors. Hence we’ve got bookshops selling coffee, coffee shops selling music, supermarkets selling loans, Ralph Lauren selling white paint and water companies selling gas. Just how far can you stretch a brand these days before it snaps?
The more globalisation takes hold the more powerful small local players become. Equally, the stronger the forces of globalisation, the more important provenance and personalisation become. There is even a school of thought that says globalisation is coming to an end although it’s rather unclear what will replace it.
In the IPO prospectus for Google, the company stated that it would invest in projects that had a 10% chance of success if the return could be a billion dollars plus. The company also said it would ‘place smaller bets in areas that seem speculative or even strange’. In other words, Google will invest in strategies and products that are more likely to fail than succeed.Failing often, failing fast and failing cheap (otherwise known as experimentation) will be a core competence in the future, but how do you create a culture that not only accepts and learns from its mistakes but does things that it knows will fail? The difference is important. Unlike scientists, managers do not deliberately try to prove themselves wrong. Instead, they use historical evidence to prove that something will work in the future. Scientists, work on a different basis – that you can never prove something is right, only that it is wrong. In other words, only through making mistakes can you disprove a hypothesis and the more mistakes you make the faster you will disprove it. So how can you harness the power of deliberate mistakes to improve your strategy or innovation process? Or to put it another way, how can you tell the difference between a smart mistake and a dumb one? To some extent the answer is not about how but when. Mistakes made on purpose are best used when the potential gain of being right far outweighs the cost of being wrong. Another reason to make deliberate errors and study the outcome is when rigid assumptions drive large numbers of small decisions. For example, it was once a core assumption that giving credit cards to students was a bad idea. However, Citibank decided to test that assumption and found that that the ‘worst possible risks’ were in fact nothing of the sort. Other instances when making deliberate mistakes can pay off include situations where the industry landscape is changing fast, where problems are complex and solutions are numerous or where experience with a situation is limited. In each of these cases deliberately making a mistake and learning from it can be a powerful strategy but it is rarely something that large organisations feel comfortable with. Programs like Six-Sigma have created measurement systems and cultures that aim to eliminate failure, not promote it. Employees feel insecure because of short-term employment contracts and evaluations so while they buy into ‘mistakes done right’ in theory, in practice they are loathe to put their own careers on the line. One solution to this dilemma is to evaluate employees on longer timeframes.This is something that IBM Research is trying. Bonus payments are linked to one-year performance, but salary and rank are linked to three-year timeframes. Maybe they’ve been listening to Thomas Watson, the founder of IBM, who once said ‘ If you want to succeed, double your failure rate’.
Markets used to consist of a broad range of offerings ranging from the cheap and basic to luxury and expensive. Not any more. As people have become richer and technology has become cheaper, markets have started to polarise between the top and bottom segments. In other words the middle market has all but disappeared, with people either trading up to quality or luxury products or happily accepting high quality basic (value driven) products. In other words, metaphorically speaking, everyone is either flying with a low-cost airline or upgrading to business class.
Companies are looking to reduce fixed costs while employees want flexibility in terms of hours and location of work. Result? — an increase in part time work, working from home, virtual offices, sabbaticals and ‘downshifting’ (25% of homes now contain some kind of home office and 25% of people claim to have ‘downshifted’ to improve the quality of their life in the past 18 months). However, most companies still don’t get it. But they will. Demographic trends will mean less people available for work, which means more competition for workers, which in turn will drive a more flexible approach by employers.
Thirty years ago most Western countries had between 20-40% of their workforces in manufacturing. Now the figure is closer to 2-4%. Have we seen a shift like this before? Yes. During the industrial revolution and, more recently at the end of the 1800s, most people in the US were employed in just three areas: agriculture, domestic service and horse transport. So what are the future implications of a shift from resource based manufacturing to service based and then idea based economies?
There is a lot of talk about the growing power and influence of Brazil, Russia, India and China (the so-called BRIC countries) and companies are falling over themselves to invest in China in particular. For example, according to McKinsey Asia (excluding Japan) will represent 30% of global GDP by the year 2025 (Asia is currently 13% and Western Europe currently represents 30% of global GDP). However, this all has a faint smell of history about it since other countries have been hot before (remember Japan, Germany and the so-called Tiger Economies?). To some observers, China and Russia are also built on foundations of sand. Ethnic violence, widespread corruption and speculative building all have the potential to upset this growth trend. Nevertheless, expect a major shift of economic power to the East.