I can’t stop thinking and I can’t sleep. So I take a walk. I don’t know where I am headed, so I leave it to the traffic lights to decide. It is snowing, and it is beautiful. I walk the Boston Common. I see the Brewer Fountain. I stop and watch the footsteps on the avenue, as they emerge into something bigger. Something meaningful.
I see a guy. Walking, stepping. He is going somewhere. It is 1 am on a Thursday night. He has his own destination, his own path, his own experience, and his own reasons. I try to see his path, but his path blends into others. His footsteps around the fountain are now vague; I can’t really see.
Even if I see it, I can’t make sense of it. However, the footsteps, all of them collectively, are something that I can make sense of. It looks like a circle.
“Patterns emerge, leading to something humans can ultimately understand. But in the process, they stay out of reach—something humans can not make sense of.”
I thought.
I hate not understanding. Letting systems run on their own, giving results without knowing the “How” or “Why,” makes me restless.
My mind always jumps to the same thought: “There has to be a reason. Everything has a reason.” And most of the time, there is.
But this time, there wasn’t. And when there isn’t, it doesn’t just bother me—it makes me feel sick. A deep, twisting kind of sickness, like something important has been taken away and I don’t know how to get it back.
I remember feeling this way not too long ago. It hit me when I was looking into machine learning.
And that’s when it all came together. It wasn’t just a realization—it was a “wow” moment that shook me and left me uneasy at the same time.
The connection between the footsteps in the snow and the inner workings of machine learning feels inevitable once noticed, as if the two were always meant to echo each other. Snow-covered paths, like the middle layers of neural networks, tell a story that is both ephemeral and profound. At first glance, both appear chaotic, incomprehensible—a random scattering of steps or values. But through repetition and reinforcement, they coalesce into something purposeful, something that can be understood, if only in its final form.
A single step in the snow is meaningless, just as a single weight adjustment in a neural network seems arbitrary. But neither exists in isolation. As more steps fall in line with the first, a path begins to emerge, just as data flowing through layers of a neural network transforms raw inputs into recognizable patterns. Both systems thrive on feedback, evolving iteratively—steps inviting more steps, weights adjusting to minimize error. The randomness does not vanish; it is merely channeled into something greater.
And yet, the process remains inscrutable. Like the multidimensional projections within a neural network, the logic of these snow paths—the choices of hundreds, perhaps thousands, of individuals—exists in a realm we cannot directly access. We see only the shadow of this multidimensional dance, projected onto a two-dimensional canvas. In the snow, this canvas takes the form of visible trails; in machine learning, it is a prediction, an output.
The true beauty of these systems lies in their emergent intelligence. The paths in the snow are not dictated by any one individual, just as no single neuron in a neural network holds the key to its output. Both systems operate as collective intelligences, decentralized and self-organizing. And both reveal a profound truth: meaning is not imposed from above but arises naturally from the interplay of countless small, independent decisions.
But there is a melancholy in this realization, too. Humans, bound by their limited perception, cannot grasp the full complexity of these systems. The middle layers of a neural network, with their projections and transformations, might as well be alien terrain to us. And the footsteps around the fountain, for all their beauty, are the product of choices we will never fully know. We understand the whole, but the pieces remain a mystery.
This is the paradox that haunts me. In the snow and in the machine, I see a reflection of ourselves—creatures of chaos, shaping and being shaped, carving paths we cannot fully comprehend. Yet these paths, born of randomness, lead somewhere. They lead to meaning.
As we explore the parallels between desire paths and neural networks, we confront a fundamental question: where does randomness end, and structure begin? How do we navigate the fine line between pattern formation and chaotic collapse? Just as neural networks may overfit to data, losing their trajectory in the face of complexity, human behavior—shaped by subconscious motivations, societal feedback, and error—can also spiral into patterns that no longer serve us.
But what if randomness isn’t truly random? What if it’s simply something we cannot comprehend—a projection of multidimensional processes that are beyond our current understanding, yet ultimately guide the formation of meaning? If we could somehow observe the mid-process of snow path formation—before the chaos resolves into recognizable structure—would we see the raw building blocks of human behavior in the same way? Could we understand the forces driving us, just as we analyze data in machine learning models?
If there is no true randomness, could it be that we simply lack the ability to comprehend it?
What about other forms of “randomness” in our four-dimensional existence?
Is there a similarity between these phenomenons and the numbers π or e?
Or even quantum fluctuations?
Ultimately, entropy.
I feel so.
Let’s think of entropy: the ultimate disorder.
A ‘cold’ object and a ‘hot’ object, placed side by side.
Heat should flow from hot to cold—
The second law of thermodynamics, right?
Not necessarily.
God “rolls the dice,”
assigning energy to particles in every fleeting moment.
The chance exists: cold transferring heat to hot.
But it’s not the mere existence of this chance that matters—
it’s how probability shapes the rules we know.
The number of combinations where heat flows hot to cold
vastly outweighs the alternatives.
Cumulative direction, shaped by this imbalance,
repeated billions of times,
becomes the law we experience.
Heat flows hot to cold,
steps form a circle,
a handwritten “9” transforms into ASCII “111000.”
Randomness, tempered by probability,
gives the universe its direction.
And yet,
in fleeting moments—
cold may grow colder,
hot may burn brighter.
A step might carve an irregular octagon,
a neuron might fire in chaos.
These moments, lost to us in their singularity,
leave behind forms we understand.
Hot to cold.
Steps to circle.
Handwritten “9” to ASCII “111000.”
So, randomness—is it truly random?
Or is it simply what we call
the dimensions we cannot see,
the rules we do not yet grasp?
Do we have the right to call it random?
And this god, this force rolling the dice—
is it truly God?
Or is it just our word for the unknown,
the unfathomable formula?
Do we have the right to call it god?
If we could understand—
or even understand why we cannot understand—
the hidden layers of a neural network,
the individual steps etched into the snow,
could we finally understand the divine?
Would we find randomness?
Or something far greater?
yusuf.direct 
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