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Which statement do you believe? Robots will wipe out our jobs. AI and robotics will make everything free. These extreme viewpoints are both vying for our attention.
Singularity University, which aims to solve our global grand challenges through exponential technologies, widely reports that AI is the world’s cure. Peter Diamandis, co-founder and chairman of the university, says “rapid demonetisation of the cost of living” is earning his attention. Powered by technologies, he says, the cost of housing, healthcare, education and more will fall, “eventually approaching, believe it or not, zero”.
It’s easy to sensationalise superintelligence when you mix lofty institutional beliefs such as Singularity with fear from workers about the impact machines will have on their jobs.
Government ministers are right to show concern about the rise of unemployment and how the hollowing out of work might later affect skilled workers. But much talk embellishes and focuses on a future we can’t yet imagine. This is very early days – to use a cricket analogy, we're at lunchtime on the first day of a five-day match and we have a long way to go.
There are many examples of firms chasing the next big idea. The latest is that machines can learn our most human traits. But it’s important to consider the counterargument.
Cogito is one such envelope-pushing firm. It’s an MIT spinoff that’s getting close to being able to detect your emotional state just by listening to your voice. Cogito is testing an app, Companion, which picks up on vocal cues that signal changes in mood and uses your phone to detect how active and happy you are.
Widespread use of Amazon’s voice assistant, Alexa, is proof that we’re comfortable integrating robo-voices into our lives, but are we ready for humanised technology? Let’s examine New Zealand-based start-up, Soul Machines, which develops emotionally responsive avatars. The “digital humans” are designed around the physiological model of a person’s face and have personalities to match the task they’ve been given. The inventors bring the avatars to life with neural networks that map the human brain.
Here’s the sticking point: toddlers are smarter than many of these computers. Gary Marcus, a professor of psychology at New York University and the founder of Geometric Intelligence, offers an insightful view.
He reminds us that, among other things, machines need thousands of examples to learn. Computers are great with big data, but they lack common sense. They make statistical observations about the way the world works. They fall down because they don’t ask “Why?” and have no underlying causal model – the way we, as humans, do things.
Researchers attempting to develop machines capable of engaging in natural conversation have more work to do. Some machines are already augmenting our work – for instance, sales call centres with bot advisors helping sales people land deals – but these outliers rely on tons of data from countless transcripts of real-life conversations. When it comes to cognitive science, that’s not quite how the human mind works.
Toddlers learn by extrapolating information. Their brains can recognise patterns in big data, but they can also use tiny pieces of information to make smart assumptions. For instance, if a child hurt their hand on an oven just once, they wouldn’t do it again. A self-driving car, on the other hand, needs thousands of miles of new road conditions to learn how to travel along it.
Intelligence involves perception, planning, analogy, reasoning, common sense and language. AI has made progress in the area of perception but it can’t beat us on empathy, creativity and relationships between things.
Much talk has centred on machines destroying work, but for many, machines will help knowledge workers do better.
There is no work left that is routine in its approach and narrow in its scope that can’t be automated in the developed world.
The important qualification here is that routine and narrow work is different from jobs. Humans have jobs, machines fulfil repetitive tasks. We’ve only scratched the surface of the work that can be automated. Corporate titans such as Facebook and Google have made rapid progress, but then it tails off – there are thousands of small and medium businesses that could benefit now.
To build a sense of urgency, just look at your customers’ traits. Thanks largely to the smartphone, they’re more demanding. By the power of the scrolling thumb they are more informed and therefore pickier. This is forcing us to reimagine the way we work.
Take the mightiest bank in the US, JP Morgan Chase & Co. With the help of machine learning it cut 360,000 hours of mundane finance work to a matter of seconds. The machine also helped the bank eradicate 12,000 mistakes made by human error every year.
Automating tasks frees up time for executives to deal with delicate customer cases that require acute problem-solving skills, empathy and creativity. Customers receive special attention instead of chat bots at the end of a phone line.
The impact of automation in professional services, where high-touch consultancy work is critical to quality service, is clear: automate the drudgery and reallocate time to doing more thoughtful work. It serves your customers better.
Once narrow routine work is automated, the working world will begin to look like the tapestry Rob Goffee and Gareth Jones weaved in their book, Clever: a handful of smart people creating enormous value. Problem is, clever people don’t like to be led. So expect to see more people working flexibly on their own terms, dipping in and out of meaningful gigs and enjoying a portfolio career.
This is the first time tech has changed so fast it’s within the span of a manager’s career.
The rules of the game have been upended. Managers in their 50s are the only generation to have junior employees arguably better equipped to do their jobs.
So, if you're a senior manager and you're not making decisions where you engage fiercely with digital natives, you're likely to be making a whole bunch of mistakes.
There’s a huge disconnect between what managers know they should be doing and how effectively they’re doing it. We expect CIOs to lead that technological charge, but most aren’t. Fewer than 40% of those surveyed for Gartner’s 2016 CIO Agenda said they’re overseeing their company’s digital transformation efforts despite it being their biggest priority.
There are many barriers to managers using AI right now – human imagination, rules, regulations, time to think – but biggest of all is the ability to make sense of everything. When there’s a shift like we’ve seen with tech disruption today, it’s really hard to figure out what you need to do.
For some, AI is an outside-context problem – a curve ball with no point of reference. It’s like a remote islander spotting an aeroplane for the first time and thinking, “What is it? How should I feel about it?” Scottish writer Iain Banks sums up these problems as “the sort of thing most civilisations encountered just once, and which they tended to encounter rather in the same way a sentence encountered a full stop.”
It’s worth experimenting with technology right now because change is happening at the speed of light. You need to take personal responsibility for learning new skills. People have already been left behind by the digital revolution, but when it comes to AI we haven’t seen anything yet. This is not about educating people to deal with technology, this is about a seismic shift towards reskilling and lifelong learning. No repetitively routine and narrow work is safe. As you keep learning, businesses will keep automating – so get used to mastering new skills over and over again.
You have to experiment with AI and technology right now to free people up and face the skills challenge head-on, but how?
With ambidexterity: the ability to exploit your existing business while you explore new opportunities. And with acuity, the knack of spotting and trying what’s relevant.
In other words, you need to give yourself real options. Get smart, go see it, try it, use it and run small-scale pilots. If you're well informed, when the time comes, you’ll have chips that you can play.
If General Motors (GM), one of the oldest legacy brands, is experimenting, shouldn’t you? In 2016, GM launched Maven, a car-sharing service with millennials in mind. Currently, 80% of Maven's users are aged between 18 and 34. GM doesn’t know if it will work in the long-run, but top management are gaining wisdom from trying something new.
Another way to create real options is to revisit what your company is creating. To make any innovation worth your time, it has to be valuable to customers and also impossible to replicate. You see it with Apple and Samsung. Apple is great at figuring out how to take something complex and make it easy. Samsung is great at packaging what it offers in a unique way. At any one time, Apple and Samsung have 10 billion iPhone components – camera modules, batteries, cases – in flight through the US market.
Yes, that’s 10 billion real options. Nowadays almost everyone is innovating and driving the next big idea. But few places have made rapid progress. It’s time we side-stepped the AI sci-fi hype and started experimenting with real options.
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