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Life lessons from Artificial Intelligence

The advancement in the field of artificial intelligence with developments like ChatGPT has taken the world by storm. And it's the trending field that everyone is talking about. The concept of the neural network is not just a powerful model to predict future happenings but also can be used to understand more about ourselves.

Why Neural Networks?

While getting started on data science, I learned about various core ML stuff, but the one thing that really caught my attention apart from the deep-rooted foundation in mathematics was when I learned about the Artificial Neural Network. Developing an algorithm that mimics the human brain to finally try to create machines that can think and do cognitive analysis like humans, is something that I found worth exploring.

I always feel more drawn to a field of study when I could always relate it to daily life, or when I’m able to pick up some life skills and understand more about myself from the topic under study no matter how scientific or technical the topic is. So writing on this neural network and life analogy.

This concept of neural networks teaches us a lot about how our brains work because this is a brain-inspired algorithm. And when we understand how our brains work, we get better at living off our biases and develop hacks to navigate through life problems better.

Neurons and Weights

In neural networks, there is an input layer and multiple interconnected hidden layers that finally produce an output. This exactly happens in our brain, at any given point in time, there are millions of neurons always firing up giving us multiple thoughts and ideas. In neural networks, different nodes (neurons) interact with each other with some weights assigned to them, to form a complex equation that finally leads to an outcome.


Similarly in the human brain, every decision we make is a product of some interconnected thoughts (activated neurons) with weights assigned to them. So ultimately it comes down to weights that help activate any particular neuron that contributes to the hidden network that works up to activate a final thought that drives action.

Life Learning:

The weights assignment of neurons brings up the importance of “focus”. Focus is what drives our dreams and ambitions and helps us to achieve them.

Almost every trigger/thought is always not simple but can be rooted back to some past, recent happening, or future apprehension. The type of trigger/thought we put more weight on, the brain activates similar thoughts and tries to form multiple hidden layers between them, assigning weights, and thus bringing our entire attention to that particular thought, which finally leads to action.

So we can just have one thought, and the brain will build an entire kingdom on it. Amazing right?

Back-propagating Errors

In machine learning, we build a model by training it with a good set of input from users and with a lot of data, so that the artificial model can learn very well, and be able to predict the future outcome when presented with unseen data.

Backpropagation is a process involved while training a neural network that involves taking the error rate of a forward propagation neural network and feeding this loss due to error backward through the neural network layers to fine-tune the weights.

The neural network learns and gets better by back-propagating the error toward the contributing neurons and hence modifying the original weight.



Life Learning:

We, humans, are creatures of habits and training. Like any machine learning model that gets better when trained with good input features, we humans too are products of training on any particular skill till we get better at it. And any training process is not always a linear path but full of trial and error till we master that skill.

This brings us to the fact that life should be lived forward while looking backward, or as precisely described by Steve Jobs's famous quote: “you can’t connect the dots looking forward; you can only connect them looking backward”.

We get better at looking back at our mistakes and learning from them by adjusting our beliefs and future actions for upcoming events until we become our best version. We ourselves are like a machine learning model with training in progress, and the more we leverage this back-propagation of errors and learnings the better we become at what we train.


Healing and realizing dreams

These brain-inspired algorithms can provide a lot of life learnings if we just pause and reflect. The concept of assigning weights gives us a superpower in itself, which is the power of attention. If we consciously don’t give much weight(importance) to some neurons(thoughts) that are triggered, they never contribute to the final equation of action.

This can be a great tool for coping, or for healing some past trauma, because if we give minimal attention to those painful memories, they cease to exist, it’s like they almost never even happened or existed. So attention is everything.

Our brains are mostly projecting devices spotlighting thoughts based on some backend equation like in neural networks, but unlike artificial neural networks, we humans have the power of discrimination, where we can think about our triggering thoughts, and re-focus our attention.

The most amazing part of humans is that we’ve got this creative energy that is lacking in these artificial neural network models or any other species. We have the power to imagine, introspect and develop the world of our dreams and not just survive and perish.


And we understand this beauty of our brains so much that we are trying to develop one, cause sure as hell, it’s an engineering marvel in action and deserves a celebration, and what better way to rejoice than devoting an entire field of study towards its replication?

Well, the concept of artificial intelligence only teaches us one thing i.e. we are so much more than we think we are. If our complex brain can weave waves of depression and negativity then what wonders can it do if focused on healing and progression? We really have the super-power, it all just depends on where we're giving the attention to.




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不明なメンバー
6月05日

In neural networks, we start with an input layer that receives data, which is then processed through multiple interconnected wordle hidden layers before producing an output. This mimics our brain's operation, where millions of neurons constantly fire, generating a multitude of thoughts and ideas.

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