Want to stay ahead of machines? Think like a four-year old.

I was surprised, and disappointed, that the 2016 presidential debates never addressed the explosive growth of technology. Nothing. National borders, the economy, the environment, ethical behavior, etc. Same deck of cards. All important; none as imminently disruptive as the proliferation of technology. It would have comforted millions to hear candidates say, “Here’s what I will do to protect your job from technology.” But it wasn’t mentioned. Technology was summarily avoided like a port-o-pot with a moist seat; you just don’t go there.

I believe the candidates and networks/discussants worked out a deal to keep the topic out of bounds. Why?

Because technology is rapidly becoming more ubiquitous, unpredictable and disruptive than we thought. Restated in candidate speak, “It’s about the technology, Stupids.”

It absolutely amazes me to understand how one can run a campaign on job growth without addressing technology? News break: Undocumented immigrants, offshoring production and bad international trade agreements aren’t taking jobs. Technology is.

Culture may eat strategy for breakfast. Technology eats whatever it wants.

And one of its favorite appetizers is your job.Tech is a BIG deal. Bigger than jobs. But today everything’s connected, and jobs are a highly sensitive, personal and imminently affected, topic. A place to start.

As icecaps melt and wildfires roar, we can still deny human-influenced global warming. And you may still hear from a few pundits likening current technology-driven change to historical shifts in the world of work. “We’ve been here before. It’s just like the iron age, or industrialization.”

No. It isn’t.

If you think your job is out of reach, you’re out of touch.

This is like nothing we’ve seen before. For the first time, change is happening faster than our ability to change. We’re not driving change, change is driving us. If you think your job is out of reach, you’re out of touch. You won’t even hear it coming.

What does this mean for me?

Ever since the first humans sharpened sticks to jab them into a wooly mammoth, our work has been a huge part of our identity. “Damn, that Neanderthal can really drill that spear!” Not long ago, people took their name based on their work. Take mine, for example. “Steilberg,” translates into “steep mountain” in German. Obviously, I come from a family that used to climb steep mountains. That’s probably why there are so few of us left.

So, what happens when one unwillingly loses their job?

They grieve.

“How will I take care of my family?” “What will others think about me?” “What will I do?”

Job loss can literally create a state of depression.

If you’ve been in the workforce for at least seven years, you know exactly what I’m talking about. Job loss can literally cause a state of depression. In fact, it almost always does. What’s worse about technology-driven job loss is that technology not only takes your job, it takes all jobs. That really sucks.

But, hold on. Grief is a state, not a characteristic. You weren’t born to grieve. You can survive this – and come out even better off.

Grief has been a topic of study among psychologists for some time. One of the earlier models of grief was developed by Elisabeth Kubler-Ross. Although her research began by studying the loss of a loved one, her model generalizes to other grief-related scenes. Here I annotate her 5-stage model with hypothetical reactions following machine-driven job loss:

  1. Denial – “I can’t believe that machine took my job. It’s bound to break down.”
  2. Anger – “That damn piece of tin! Let’s see what happens when I throw my lunch pail in its hopper.”
  3. Bargaining – “Why don’t you fire Rusty instead? I can do his job better.”
  4. Depression – “I can’t do anything right.”
  5. Acceptance – “Bygones. Now I have time to do what I’ve always wanted to do.”

Like other stage-based models, Kubler-Ross’s model has drawn scientific criticism. For instance, not everyone experiences every stage, nor proceeds through the stages in standard sequence. Kubler-Ross has admitted that the neither the boundaries nor the sequence of stages are fixed.

But not all theoretical value is empirically derived or supported. (I could get, … de-somethinged, for this) Let’s take a look at a couple paths grief may take.

Here I share diagram I pulled from Wikipedia:

Notice the split and divergence of the line of attitude into two different outcomes of grief. On the “happy” trail, grief settles down and begins to fade in the rear view mirror as we accept the new reality and move on. But on the “sad” path, the individual is unable to advance past depression to the final stage, Acceptance. As depicted in this illustration, this ultimately lands the crestfallen individual in crisis mode.

So how do you best avoid the path of desperation?

Here’s my $.02:*

  1. Keep moving. This is my “take 2 aspirin and call me in the morning” go to advice for people who tell me they’re stuck (how novel). Motion and depression don’t hang out together. If you get moving, you’ve a better chance of getting somewhere better.
  2. Take stock. Audit your job to identify what aspects are most easily done by machines and note things machines don’t do well. Use to your advantage. (More on this, below)
  3. Learn how to use the new technology. Experts agree that we will work with machines for some time before machines will work in place of people. Besides, it shows management you support their ridiculous decision.
  4. Change your mindset. Now, THIS isn’t easy. In fact, it’s nearly impossible. (OK, minus 2 points for recommending something “nearly impossible.”) Changing your mindset regarding adding value involves unlearning just about everything you’ve been taught since Barney the dinosaur was your mentor.

Points 1-3 are common advice, but mere patches. Point 4 is your salvation.

Now, I’ve mentioned that it’s very difficult to change one’s mindset. That’s why it’s called a mind, set. To wit – What’s your record for keeping new year resolutions? How effective have you been convincing your friend that their political beliefs, if enacted, would set us back 1,000 years? Why do we say there are no dumb questions when we really mean, “Are you serious? That’s the stupidest question I’ve ever heard.”

Fact is: we’re programmed a lot like we’ve programmed machines. Why not? “Let a machine do the work and I’ll play Candy Crush.” {Watch how this logic suddenly shifts when management uses it to their advantage.}

Now machines can do just about everything we do that involves intentional activity. Moving mountains? easy as Sunday morning; solving differential equations? pass the jam, please; managing call centers, routing traffic, flying planes, Easy Peasy Lemon Squeezy. And the list goes on.

But there’s a little secret that computer geeks keep. They won’t acknowledge that their machines don’t know, Why. Why? Because they’re programed according to conventional, association-oriented, curve-fitting mathematics; precisely the opposite of those stupid questions that kid asks just before the bell rings.

Opinion. Implications. What if scenarios. Counterintuitive doubt. Machines can’t contemplate or expound. They simply solve problems – equations, really – basically coming up with the best fitting answer based on the data available.

Machines amaze us by always providing the right answer, but they don’t know why. This is where theories like Kubler-Ross’s come into play. (This isn’t a great example, but it’s all I got time for here.) Computers solve problems by association via mathematical models aimed at “fitting the data” with the most likely (probability-wise) solution. But they got NO THEORY within which to manipulate and ruminate on broader implications. Machines can tell you what will happen after you strike the cue ball in pool, but they don’t “call their shots” in a cause-and-effect manner according to a theoretical model of pool.

I’m not saying they’re lucky – they’re just ignorant. They produce answers without context or contingency. They don’t ask counterintuitive questions. Yes, those “dumb” questions.

In fact, a four-year old is better at ideation than the strongest computer. Put them side-by-side and provide these instructions:

“When I say, ‘go’ – no, not yet, — when I say it again; tell me as many things as you can that you can do with a brick.” Stop them at 30 seconds and add up their answers without judgment. Simply count the number of responses.

Think about this for yourself. What would you say? If you’re like most, you’d probably come up with between 8 – 12 uses. And they’d all seem reasonable, e.g., paper weight, door stop, nail driver, etc.

Make brick soup

The 4-year old will likely come up with 15-25 answers. And NONE of them will seem plausible. “Make brick soup” “Catch sunshine” “Go potty” I’m not making this shit up. Four-year old kids have literally provided these, and other, similarly extraordinary answers. Perfectly legitimate for the task. Adults, stuck in right/wrong mindsets, and computers, beholden to mathematical (Bayesian) probabilities, lose to 4-year old kids.

To be great, is to be misunderstood. R. W. Emerson

What does this tell us?

  1. Stop answering kids’ questions. Encourage them to further develop or generate even more ideas.
  2. Get off your “high horse” of knowledge. My watch knows more than you ever will.
  3. Do something new. Get out of your “comfort zone.” Be amazed. Ask questions without expecting answers. Euphemistically, think outside of the box. {I can’t believe I just used the most overused metaphor in training.}
  4. Unbind your thinking. Break the mold. Go crazy. Talk to yourself (and listen, too).

The prominent philosopher/poet, Ralph Waldo Emerson, claimed, “To be great, is to be misunderstood.” Prophetic, in many ways.

We’ve become obsessed with reliable, repeatable, verifiable problem solving. And we’re not even close to how well machines do the same thing.

Being right isn’t being predictable. Neither is it understanding. “Right,” as most know it, and all computers do, is either convention (people) or just collapsing data on a curve (machines). Understanding isn’t limited to a solution. It’s considering the “big picture,” being misunderstood, and dreaming up possibilities.

Make brick soup.

  • I’ve provided insights on keeping your jobs from machines in a previous post
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