One, Two, Many – Why You May Not Be Replaced By A Robot

Some aboriginal tribes in Australia see little value in counting and are believed to discriminate only between “one”, “two” and “many”. This is not through lack of intelligence; beyond two they simply lose interest in the details. We can smile and feel superior but I suspect we are not much better when it comes to predicting our technology future. We understand “one” (what we have right now) and technical experts reasonably understand “two” (modest future enhancements on what we have right now), but I respectfully submit that, given our lack of attention to detail, our best guesses when we look just a little further out are no better than “many”.

Not that this stops us couching forward-looking views in the appearance of considered wisdom. A current example can be found in popular coverage of Artificial Intelligence (AI) and robots. There’s no question that impressive advances have been made on both fronts. Vision systems now recognize targeted classes (such as road signs or dog breeds) with higher accuracy than we are able and others have bested human competitors in chess, Go and game-shows. And it is common for factories to use robots rather than human workers because they are faster, more accurate and cheaper to operate and maintain.

So naturally we jump to our “many” predictor (while skipping the boring details) – if these things are already possible, surely it is only a matter of time before all human tasks are performed by artificially intelligent robots and then what will we do? Depending on the writer, the end-game is either a utopia where we all do whatever we please – learn, indulge our artistic passions and play games – or a dystopia where the machines rule and we humans become at best slaves to serve the machines, or worse yet a virus to be eliminated.

This is all very well for science fiction novels, but if we’re aiming for an informed opinion on future trends, I think we can and should do better than casual extrapolation from a few points (something none of us would do to a graph). Especially we should realize that where machines do well, they do so in performing bounded, repetitive and (in manufacturing applications) high-volume tasks in a stable and well-characterized environment. Some tasks may seem particularly impressive (winning at Jeopardy or Go) but they still fall within this description. Remove any of these constraints and the machines become expensive doorstops. By way of example consider three human tasks where the popular view falls short on closer examination.

Think about drywall installation – not a skilled task by most measures. It doesn’t require a lot of training, it’s repetitive and it certainly doesn’t require advanced education, but think about the constraints. The environment is not well characterized – houses have different and often custom shapes and the area around the house (though which you must carry the drywall) may be a sea of mud. The installer must work in confined spaces running over floors designed only to hold people and furniture. Homes under construction are scattered all over the place and the drywall machine has to be moved to each house for each installation – not exactly a high-throughput application. Maybe you fix this by only manufacturing pre-built homes on an assembly line. But a home is very important part of our identity. I doubt that anytime soon we will all submit to living in mass-manufactured boxes.

Next take teaching. Teachers do many things; think about just one – getting students to understand subject matter. Teachers are required to have degrees and additional qualifications – if you like they need a substantial database of information – but a good teacher needs more than that. They need to be able to see which students are stuck, understand why they are stuck and help them get past those problems, perhaps by presenting the material in a different way. They may also need to encourage or motivate a student. These are skills that require understanding how we learn in general and how a particular student deviates from that norm, something we barely understand ourselves, much less know how to code into a machine.

Let’s get away from mechanical (and perhaps interpersonal skills) needs and consider a software programmer as another example. Educational skills are arguably comparable to those for a teacher but the application is different. A programmer must map problem requirements into code or search existing code for a bug (not so different from a teacher, where the code is the student’s understanding). We can automate through higher programming abstractions but unless the requirement is a minor increment on an existing code base, the programmer must creatively select a method from a potentially infinite set of possibilities. This requires judgment as much as experience, which is as difficult to teach as it would be to codify for a machine.

These are just three of a wide range of tasks that we humans perform. At the end of it all, people are very adaptable and machines are much less so. Could machines eventually become more adaptable? Perhaps, but would it be worth the effort? I doubt it – replacing you with a robot would not be anywhere near as cost-effective as extending your abilities though AI and automation (with the expectation that you will also adapt). This will certainly change the nature of human-powered work. It just won’t change the need for human-powered work. Which, I respectfully submit, is how our technology future will really evolve.

More articles by Bernard…