Growing Pains

We've all heard the saying, "Make learning fun." That’s because learning isn’t inherently fun. Here's why we should do it anyway.

Learning is an essential part of life—one which can’t be avoided. Acquiring information is not always easy; in fact, it can be quite painful at times. You might not think of it this way, but pain plays a significant role in acquiring life experiences—aka learning.

Comedian Jerry Seinfeld once said, "Pain is information rushing in to fill a void of knowledge." If you bump your toe on a piece of furniture in the middle of the night, it's because you didn’t know it was there. You needed to learn that, and you did—through pain. Of course, not every piece of information has to be acquired by banging your head against door frames, like in a bad slapstick comedy scene. The notion of pain is subtle and takes many forms—one of them being discomfort.

Discomfort, even if just occasional, is something we experience throughout most of our lives and it starts from day 0. When I was a kid, I don’t remember anyone saying they loved school. We all hated it because it was a deeply uncomfortable experience. But pushing through the discomfort, overcoming obstacles, and achieving milestones allowed us to learn. That brought us satisfaction, motivating us to keep going. Repeating this process over many years is what shaped us into the confident adults we are today. None of this would have been possible without effort and the ability to endure the pain that comes with it. There is simply no way around it.

When we teach our children, we’re not just teaching them facts—we’re also teaching them how to learn so they can do it for themselves. We've been doing this since the inception of mankind. Now, we are doing the same with machines.

We’ve created machines capable of reasoning, and we are teaching them how to learn. Machines learn in much the same way as humans. If you observe a simple robot navigating an obstacle course, you'll notice that it repeatedly collides with obstacles until it eventually learns how to overcome them. It almost seems painful—and, in a way, it is. Its machine learning algorithm is designed to reward success and penalize failure. The penalty for failure is the equivalent to pain in the AI realm.

If we want robots to perform advanced tasks, implementing learning capabilities is crucial. The main reason we want machines to learn on their own is because otherwise, even for the simplest movements—like navigating a room—hundreds of thousands of lines of code would need to be manually programmed. Thousands of potential scenarios must be anticipated and accounted for. Otherwise, if an unforeseen situation arises, the robot wouldn’t know how to handle it. Imagine an autonomous car encountering a pedestrian crossing the street outside of a designated crosswalk. The car might simply continue on its path—with potentially deadly consequences. But machine learning allows the algorithm to learn and adapt to new scenarios, granting the robot a higher degree of autonomy and freeing us from the trouble of excessive coding.

We are approaching a time when most of what we do today will be done better and faster by AI. As a result, the workplace will fundamentally change. Instead of managing people, we will transition to managing algorithms bundled into agents with various roles that were previously human jobs.

AI is quickly becoming that employee who is always one step ahead—anticipating your needs and, more often than not, performing tasks better than you. Soon, it may no longer make sense for humans to carry out many of these tasks at all. It might even feel unnecessary to learn certain skills when machines can already do them more efficiently. As a result, we delegate more and more, creating an increasing reliance on technology.

However, we've always relied on technology, so how can over-reliance be defined? Historically, we've rarely returned to an older technology—and if we did, it was only temporary and driven by extreme circumstances (for example, through the dark times following the fall of the Roman Empire). These days, it's almost impossible to imagine a scenario where you would put away your PC to calculate on an abacus, discard email in favor of pen and paper, or leave your car to ride a horse to work.

Over-reliance is when we no longer have a choice but to use the current tools—It is when we loose the knowledge of what was before.

To avoid this fate, we must refrain from skipping stages in the learning process and never cease to learn for ourselves. There is a natural progression from pen and paper to digital, just as there is from walking to running. No matter what technological advancements the future may bring, we must never forget the steps we took to get to where we are—you never know when you might need to go back a step or two.

As technology makes things more and more comfortable, we might be tempted to avoid experiencing the pain of learning. But to maintain our sovereignty in the face of AI advancements, we need to hold on to our greatest power: choice. That is, the choice to use technology to accomplish our objectives while remaining fully in control of and understanding the processes that lead to those outcomes. If we fully succumb to the wonders of technology without understanding it, we risk labeling it as magic—and that is the moment when we are no longer in control, but rather controlled by our reliance on technology.

That's when we no longer have a choice.