Becoming a Number in Machine Learning
A short reflection on the statistical inference in machine learning modelling
Have you ever thought about yourself as a number.
A 1 or a 0. Perhaps you would rather be a 1 than a 0.
If you are lucky you might be a long sequence of numbers.
As the complexity arises maybe a part of you can be captured.
Like a picture in hexadecimals. The 1s and 0s of the small pixels.
You might have never thought of yourself as square yet you are constructed with squares on a screen.
Increasingly talking to a family member in quarantine amidst the pandemic.
Becoming a number in machine learning.
Imaginary beings have been created acting like you.
By people not like you. Bots or fake accounts.
Can likability be programmed?
Can you be programmed?
If — you are becoming a number.
What is inference if the evidence is false and reasoning not as reasonable.
Vitruvian, economic, natural or computational as you might be.
Accessing memory.
As if you were computational.
Yet you are not, although it can be computed.
Equation as a statement of values, a boolean false or true — you.
User interface for your face and traces of 1s and 0s.
Becoming a number.
One.
This is #500daysofAI and you are reading article 339. I am writing one new article about or related to artificial intelligence every day for 500 days. My focus for day 300–400 is about AI, hardware and the climate crisis.
A few days of these have been poetry, and I think today is one of those days. Usually I do articles on the topic of AI.
