Machine Learning

It’s some garbage neo-rock track belonging to a Cyberpunk-style video game. Any listener might be forgiven for believing the track had no purpose. It’s background music – and repetitive background music at that. Siev can transpose this in her sleep. What is there to interpret? Interpretation of media utterly bores her.

The song is called Machine Learning. It starts, sounding as though it’s underwater. Bright, in a sombre way, swimming through claustrophobic, dark depths as bubbles rise to the surface.


Every note is distorted – Siev can draw the connection between the instrumentation and the track’s title, a rheumatic pulsing to each step. Perhaps it’s not swimming, but a hot construction being pulled out of coolant to the surface?

Siev believes the verse is an assembly line. It marches on proudly, mass-producing an awarded creation for an important figure.

The triple morse code-like eighth notes playing every four bars suggests it is communicating. Perhaps to its creator. Bright and lively staccato pulse outward, waiting for an answer…

The bass drum counts each beat. Does the machine recognize its existence? Does it know it’s been mass-produced? As it tampers into the bridge, it realizes there has been no response. The enthusiasm despairs. While there is still momentum plunging it forward, the driving force behind the track seems to fall away.

It returns to the assembly line; is it now creating itself, in lieu of a creator? It’s not as momentous as the first verse. It lacks… engagement, as though the machine had let go of its agency.

Finally, it all draws back, tones distorting as they move farther away. The track concludes.

As Siev starts writing down ideas behind each bar, she sees the timestamp. Somehow, a track just four minutes long had told its own story.

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