093 | zach: machine learning
With each passing day, we humans cede more and more control of our lives over to the machines that we've built. Menial tasks, rote decision-making, and even basic social interaction have been outsourced to services that create intricate profiles of our habits and desires before selling them back to us as "recommendations". And of course, those recommendations are usually quite good (how could they not be?), so we keep buying and scrolling and watching and liking, endlessly producing more data for the harvest.
It's not exactly a galaxy brain revelation to say that this feedback loop has landed us in some seriously hot water, existentially speaking. Although this technology is ostensibly designed to make things easier for us, a lot of it just makes us dumber, angrier, and more anxious. Meanwhile, the technology itself is getting smarter and smarter as it comes to understand more about what motivates its creators.
As we continue to fumble the ball when it comes to COVID-19, political polarization, global warming, and pretty much every other nuanced issue facing society, a question popped into my head: should we just give the keys to the bots now? What do the recommendation engines have to say about reducing CO2 emissions by 2050?
In this mix, you'll hear from a few AIs who argue for exactly that solution. To accompany their words, I've put together a selection of records that straddle the increasingly blurred line between man and machine; it's something of an olive branch extended towards our eventual algorithmic overlords. Hopefully androids also dream of raving until 6AM.
Points for making it this far - if you're reading this text on this website, it means you haven't entirely given yourself over to the algorithm just yet. It's tempting though, isn't it?
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( techno, tribal house, electro, breaks, found sound )