Human-Machine Alliances: My Kind of Romance
I grew up in a loving but very modest household. If I wanted anything beyond basic necessities, I had to find a way to get it myself. As a result, I began working at a young age and have accumulated quite the weird and wide-ranging work history 20 years later.
As you can imagine, many of these jobs I’ve had — especially in the early years — were comprised of uninspired, repetitive, low-value tasks. The biggest offenders on the list being:
Paper route
“Big Box” store retail agent
Telemarketing agent
Automotive parts factory worker
Automotive parts warehouse workerNail polish factory worker
I am pretty good at making the most of any situation but I’d be lying if I said I enjoyed these jobs. Beyond these least-favourite jobs, I’ve known for a long time (like many of my fellow millennials) that beyond meeting my basic needs, I need something more to work for than money. Perhaps this feeling was exaggerated by the fact that in addition to being painfully dull, these jobs didn’t offer me that much money either!
Ultimately, I cannot justify spending half the waking hours of my life NOT enjoying and/or challenging myself.
Frankly, beyond absolute necessity, I don’t see the point in any human spending living life this way. I do not understand why we, as a society, would ever want to protect or encourage that kind of existence now that we have totally acceptable alternatives.
This is why I’m glad “machines” are gradually taking over our highly repetitive, predictable, low-skill jobs.
To illustrate, when I read about people protesting the replacement of grocery store check-out cashiers (humans) — who have performed the same simple actions over and over again so many times that they have the four digit code for every single fruit and vegetable memorized — with self-checkout machines I can’t help but think “why on earth would we want humans to keep doing this job?!”
I mean, I understand the motivation at its basic level: most people don’t like it when big business cut costs (thereby potentially increasing profit) by putting a not-insignificant number of low-skilled and possibly at-risk workers out of a job. (Even if it means economic benefits in the way of shorter wait times and possibly even lower-priced goods for them.)
And I don’t disagree that the business has a responsibility — whether it’s ethical or fiscal or both — to support those workers in finding new employment through promotion, re-skilling or other job assistance. Any organization that operates in a competitive market within a democratic (read: people-empowered) environment have a legitimate obligation to take into account the ripple effects of their decisions on the community they serve and operate within. Regardless of whether you look through a business-centric lens or social-centric lens the obligation stands.
But if we can shelve our skepticism and pessimistic feelings toward “business” for a moment, here are two things to consider:
- Protecting low-skill, low-efficiency jobs is NOT a sustainable strategy for society from both a financial and societal perspective.
Imagine how expensive and hard to come by books would be if people still wrote them by hand if we had successfully protested the use of printing machines.
To get an idea, guesstimate how many hours it would take you to write Harry Potter and the Philosopher’s Stone by hand and then multiply those hours by minimum wage. That’s JUST the labour portion of the price and is already a huge number. You have to add the cost of the actual materials, the storage costs, the marketing and sales costs, the retailer’s margin, the sales tax and the shipping-and-handling on top of that to get the final price to the end user!!!!
This enormous expense would have resulted in many fewer people being educated, inspired and entertained every year for the last few centuries. Millions and millions fewer. The thought saddens and scares me.
2. The nature of low-skill, low-efficiency jobs do not tend to be a great source of health, happiness and self-worth for the people performing them, and I have a hard time thinking of more important determinants of society’s well-being than those.
Of course, there are ALWAYS opportunities to find and create meaning in whatever work you’re doing. Perhaps some grocery store cashiers really enjoying chatting with the people that flow through their check-out lane. I would love to think that they all did. And I am a big proponent of the positive-frame, optimistic attitude. (When I briefly worked at that nail polish factory, I would come up with little mental games or challenges that would spur funny commentary or interesting conversations to help pass the two-hour intervals we would stand at each “station” either sticking little brushes into bottles of nail polish or twisting caps onto bottles of nail polish or sticking stickers onto bottles of nail polish. Over and over again.)
The opportunity to apply that positive attitude is certainly not limited to low-skill, low-efficiency jobs. Heck, I’d argue there is less opportunity for social engagement like chatting with customers in low-skill work than in high-skill work — there are also a lot of people who dread waiting in long check-out lines. More so, I’d argue there is a much greater return on that positive attitude when it’s applied to high-value activities.
For these reasons, I believe offloading uninspiring, repetitive work to Artificial Intelligence (herein referred to as “AI”), the most recent evolution of “machines”, we humans can spend our lives performing more meaningful work and realize magnitudes more socio-economic benefits as a result.
I am not an expert on AI nor the impact it’s poised to have on the collective “job market”. So when I bought the book Prediction Machines I wasn’t just interested in beefing up my understanding — I was selfishly searching for supporting evidence for my argument. Which I totally found.
Here’s a summary of what authors Joshua Avi and Ajay’s had to say:
As a result of computers being much faster and smarter, the cost of arithmetic has dropped significantly.
“Where others see transformational new innovation, [economists] see a simple change in price…computers do arithmetic and nothing more.”
Artificial Intelligence is just advanced arithmetic that will eventually make performing tasks of prediction — using the information you do have to generate the information you don’t have — so cheap that we will use dramatically more of it.
“Light is so cheap that you use it with abandon. But as the economist William Nordhaus meticulously explored, in the early 1800s it would have cost you 400 times what you are paying now for the same amount of light.”
In other words, eventually it will be so cheap for computers to perform prediction tasks that paying a human to do them will be equivalent to using candles to light your house — romantic but wasteful (and risky!)
But, as it turns out, when humans and machines work on a task together, they achieve more than what either can do alone.
Humans and machines have different yet complementary “talents” or strengths. The authors note that “reverse causality remains a challenge for prediction machines” compared to humans who “are sometimes extremely good at prediction with little data…[and] at analogy, taking new situations and identifying other circumstances that are similar enough to be useful in a new environment…”
Computers are great at prediction but to make a decision, judgement is required — and that’s where humans come in. (Especially in circumstances where data and therefore the use of prediction machines, is limited.) Because of this, decision making (and the social benefits that follow) can be greatly improved by delegating the prediction component to machines and allowing humans to spend more time on judgement.
Basically, if you want to get the job done right Human-Machine Alliances are the way to go.
Of course, change is not without its challenges, and being optimistic (as I am) isn’t about ignoring them — it’s about approaching challenges genuinely believing there is a solution for them. To get it right, we need open-minded commitment from all stakeholders (i.e. anyone who is a human being.)
For now, the three conclusions I take from the book are:
1. Machines need us just as much as we need them. (If not more.)
2. The greatest possible social outcomes will come from a strategic division of labour and, effectively, partnership between humans and machines.
3. The human role in such Human-Machine Alliances involves the kind of work that humans are uniquely good at (understanding the WHY behind the WHAT) and, I’d argue, that humans get more satisfaction from.
To me the advent of Artificial Intelligence means better decisions will be made by more people more often because by taking over the data-crunching, humans gain opportunity to double-down on what we’re uniquely good at: the intuitive, emotional and what I’ll call “expansive” side of life (and work.)
