Paths To Take Are Endless
What computer science can teach us about finding the right one.
Life is an outrageous cry for meaning.
We’re constantly seeking purpose in our actions. It’s biological. Modern society often makes us turn our back on our primal instincts, giving attention to expectations, status, and conformation to the rules.
But it’s not all lost.
The tech dystopia we’re living in can shed light on our existential crises.
We blame computers for disrupting much of our lives, but we can learn a thing from them.
Machine learning is grounded on the principle of finding the most efficient path; burrowing through data sets to identify how to do things faster, more efficiently, and cost-effectively.
Gradient descent is one of the most fundamental techniques in machine learning and unintendedly offers an outlook for us all to navigate our own life networks.
This algorithm is trained to find the steepest track (think of it as descending a hill; you want to take the most inclined option to get to the bottom line quicker) and constantly iterates to minimize its cost function (error).
It can’t see the whole path in advance, just evaluate the information available at the present moment and determine the most likely progression.
Just like us.
We can’t predict how things will turn out in the future, but we can test the direction of travel and follow the one that minimizes our cost function (a.k.a gets us closer to our purpose faster).
Our ultimate goal.
Maslow’s hierarchy of needs
Here’s the biology part.
Maslow's hierarchy of needs is often depicted as a pyramid with five levels:
Physiological needs (food, water, shelter).
Safety needs (security, stability).
Love and belongingness needs (relationships).
Esteem needs (self-esteem, recognition).
Self-actualization needs (achieving one's full potential, personal growth).
Once humans have the three first levels covered, the need for purpose and growth rises. This is rarely a linear path. The nature of self-discovery is that of exploration and experimentation. Trial and error.
We may embark on a promising track, believing the utmost prize is waiting for us at the finish line. Only to realize we were wrong.
Time for redirection
When the chosen direction starts to feel less favorable, it might be a sign to change it.
Unlike us, soft and vulnerable humans, a gradient descent algorithm doesn’t place emotion in its decisions. Simply (re)assesses the current situation and adapts its course to pursue the intended outcome.
This could be a simplistic, more useful way to view our need to withdraw from a misaligned path. Detract from a sense of failure and lostness and acknowledge it’s an important part of the journey.
Instead of crying over spilled milk, we can go “whoops, wrong way. Let’s try another one”.
Pivoting is an act of courage; protecting our values and goals and diverting from the actions that distance us from living by them.
If we have to place emotion on something, why not feel deep fear of staying on the wrong track?
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