There’s a saying you’ve probably heard: “You are the average of the five people you spend the most time with.”
It’s a timeless piece of wisdom. But if you look at it through the lens of artificial intelligence, it takes on a fascinating new meaning. Because in many ways, you are just like a neural network — and your surroundings are the data that train you.
The Brain as a Neural Network
Artificial neural networks don’t start out smart. They begin as blank slates — layers of connected nodes waiting for input. It’s not their structure that makes them intelligent, but the data they’re trained on. Garbage in, garbage out.
Humans are no different. At birth, our brains come with architecture — vision centers, language circuits, and memory systems. But what shapes who we become isn’t just the wiring. It’s the quality of the input we get from our environment: the conversations we hear, the stories we absorb, the mentors we meet, and the communities we belong to.
Sociology as Training Data
In sociology, your environment — family, peers, institutions, communities — plays a decisive role in shaping your beliefs and behaviors. In machine learning, this is exactly what training data does.
- Biased or noisy data → distorted models
- Toxic surroundings → distorted worldviews
- Clean, diverse data → robust models
- Supportive, growth-oriented surroundings → healthy mindsets
In both worlds, quality matters more than quantity.
Senses as Sensors
But there’s a second layer we often overlook: our senses.
Think of them as the input sensors for our personal neural network. Just as an AI relies on cameras, microphones, or sensors to capture the world, we rely on sight, hearing, touch, taste, and smell.
The environment could be rich and supportive, but if your perception is narrow or biased, you won’t capture that richness. Two people can live in the same city — one sees opportunity, the other only chaos.
The lesson: The quality of your surroundings matters, but so does the clarity of your perception.
In AI terms: Surroundings = Dataset, Senses = Sensors, Brain = Model.
The Bengaluru Example: Retraining the Model
When I moved to Bengaluru, I experienced this firsthand. My upbringing — my “pretrained weights” — sometimes conflicted with the new environment I was exposed to. Conversations, pace of life, culture — it all felt different.
At first, there was resistance. But over time, like a neural network adjusting to new data, I adapted. My goals, aspirations, and even worldview shifted. The environment reshaped me.
This is sociology in action: environments don’t just influence us, they retrain us.
Communities as Institutions of Learning
Just as tech companies carefully curate datasets to train AI models, societies curate individuals through communities and institutions:
- A university is more than classes — it’s a dataset of peers, mentors, and opportunities.
- A workplace is more than tasks — it’s a collaborative training ground.
- A city is more than infrastructure — it’s an ecosystem that conditions habits and aspirations.
Your communities are your training pipelines.
The Takeaway: Be Wise With Your Data
If neural networks are only as good as their training data, so are we. And while AI models can’t choose their data, humans can choose their environment.
Be deliberate about the people you surround yourself with, the communities you join, and the institutions you align with. Because they aren’t just influences — they’re training data for your future self.
Final Thought
In AI, we obsess over data quality because it determines the model’s performance. In life, sociology teaches us the same lesson: the quality of your surroundings, filtered through your senses, determines the strength of your mindset.
So the next time you think about personal growth, don’t just focus on yourself. Look at your environment, your peers, your mentors. Because like any good neural network, you are a reflection of your training data.