Algorithms permeate our existence. With my deep dive into computer science, I came to realize that our lives themselves operate much like algorithms. Today, I wish to introduce a concept fundamental to this understanding, namely the objective function, or loss function. Here's a concise summary:

<aside> 💭 A objective function is used in machine learning to guide the learning process. It quantifies how well a machine learning model is performing by computing the difference between the predicted output and the actual output. During the training process, the goal is to minimize the value of the objective function by adjusting the model's parameters. The lower the value of the objective function, the better the performance of the model.

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Consider the Mean Squared Error (MSE), a metric frequently utilized in regression problems:

$$ MSE = \frac{1}{n} \displaystyle\sum_{i=1}^n(y_i-\hat{y_i})^2 $$

where y is obsered value and y hat being the predicted values

Here's something to think about:

Our lives inherently mirror a learning process - a dynamic journey of acquiring, acknowledging, and assimilating knowledge. This is equivalent of constantly oberving y in this case. Refusing to accept this ever-changing scenario is akin to denying life itself.

Suppose we were to quantify life. In that case, we might gain a deeper understanding of ourselves. Consider Bryan Johnson, who quantified the rate of aging using numerous biometrics, leading him to a novel way of living and perceiving himself. His intriguing project is worth exploring in a separate article.

The concept of MSE hints at the existence of a "ground truth". The idea of comparing our current state with this "truth" is daunting. It requires us to identify the truth, and then to acknowledge that our perception of reality might not align with it. It's a stark contrast to the familiar narrative of gaining a "new perspective" that we often hear in team-building sessions. Hence, this is symbolize in the action of y minus y hat.

The objective function strives to be as small as possible, indicating a clear direction. However, a lower value doesn't necessarily translate to a "better life" for everyone, but to a better version of your own life. Using someone else's objective function as a benchmark is not beneficial. The focus should be on self-improvement rather than comparisons with others. This emaphsize on the simplicity yet unsophisciated nature of the MSE. It might not be applicable for everyone.

The term '1/n' suggests averaging all sample points, advocating for consistency in our way of living. We should not judge our life based on a single bad day but should instead consider longer time periods for meaningful insights. Its alll about gaining more training examples, thus living a longer life and richer experience.