Thinking about the human vector
I am not sure whether it was while writing for the Modu Study AI 2024 coaching study or while organizing the final week's mission. At some point, while serving as a team leader and researching and organizing things together with various team members, a term suddenly came to mind: human vector.
Directionality
Unlike a scalar, which has only magnitude, a vector has both magnitude and direction. For that reason, it can be represented as an arrow when needed. Based on this characteristic, vectors are used informally in mathematics and physics to describe quantities such as displacement, force, and velocity, or elements of certain vector spaces, which cannot be expressed as a single number. (Sources: Wikidocs, Wikipedia)
Here I noticed an interesting fact.
What about people? Unlike material things that merely exist in time, and relatively speaking, human beings do not simply drift or remain still on the timeline of life. They keep taking on, or trying to take on, the property of direction.
Position is not a property of a vector. A point alone cannot fully express it. People are like that too. A person cannot really be expressed or interpreted in isolation. Maybe such interpretation is meaningless from the start. A person needs other people nearby, just as vectors of the same direction and length can be regarded as equivalent.
Birds of a feather
A vector can be expressed as a point in a two-dimensional space with an x-axis and y-axis. It can also be expressed in three dimensions, four dimensions, and more. Dimension. People take on highly multi-dimensional vector forms while moving along the same direction of life and time. As complete individuals, family members, workers, club members, or study participants, they play different roles from different positions and dimensions. These days many people even live with two or three jobs, carrying many layers of everyday life, much like the personas we speak of so casually.
Have you ever spoken with people whose "thickness" feels fundamentally different from yours? It may be rare to meet them, but when you do, does it not often feel as if they live in an entirely different world?
And one more thing: if vectors exist in the same dimension and have the same corresponding components, direction and magnitude, they are considered equal. In that sense, people really do gather with their own kind.
Teasing and labeling
Not, "Wow, nice to meet someone like a vector," but rather, "Wait, that's a human vector!" That playful nickname popped into my head.
(That does not necessarily carry a mocking tone either. Like being called bear-like, fox-like, or cat-like, it can even be useful depending on the context.)
The real point is this: even though we are human, are we evaluating ourselves and others too much by scalar standards? And are we being too ungenerous toward human vectors whose dimensions differ from our own? Especially recently, I suspect most people have at least once felt more kindness from AI than from people, or felt stinginess in human service and conversation where they expected warmth.
It is an ironic and somewhat sad day.
While people are losing their margin, tightening themselves further, and pursuing quantitative and routine lives, new entities such as artificial intelligence, at least in my personal view, seem to be chasing something more qualitative and nonlinear.
Main point
That is enough for the prologue. Returning to the starting point, while I was writing the Modu Study AI 2024 coaching study or perhaps organizing the final week's mission, the keyword human vector appeared in my mind, followed by the thought, "An individual resembles a vector, and an organization resembles a matrix." During the short period in which I acted as a leader, I began to wonder whether better results might be possible if we considered each person's attributes and physical limits, or in mathematical terms, the rules they are subject to.
For example 1.
I think the meeting of a person and a tool resembles addition and subtraction, while the meeting of a person and another person resembles multiplication or division. (For reference, I think the "entity" category in AI classification is a little closer to a person than to a mere tool.)
And if we multiply the latter, like person with person, then in mathematics it is written as 'C = A ⋅ B', in Python as 'C = A @ B', and in NumPy as `np.dot()`. But one important condition must be remembered: the number of columns in the first matrix must equal the number of rows in the second matrix. If they differ, multiplication is impossible.
For example 2.
Principal Component Analysis, or PCA, is one of the most representative dimensionality-reduction algorithms. It first finds the hyperplane closest to the data and then projects the data onto that hyperplane. More broadly, it is a linear transformation technique that finds orthogonal components maximizing variance. By decomposing the covariance matrix, it derives eigenvectors as principal components. That idea made me think about how groups reduce the complexity of people in order to make them legible.
So each person forms a unique human vector, and a team sets sail together with many different human vectors.
Each person composes a human vector of their own.
And the team,
together with those different human vectors,
sets out on the road of a great voyage.
So what kind of vector do I have?
In relation to other human vectors, organizational matrices, or market matrices, am I someone who can reduce part of my own dimension to match theirs, or am I someone who depends on others to do that adjustment for me?
Just a thought, that's all.
Cookie: In natural language processing there is a keyword called vectorization, the conversion of text into vectors during preprocessing. Videos can also be converted into images, and images into text. And today, especially through deep learning and machine learning, anything that can be turned into a vector can have its distance from other things calculated. And in the end, that makes prediction and inference possible.
