I came in expecting a victory lap of ML breakthroughs; instead, I found a mirror for our habits. We compress messy lives into tidy numbers, then train machines to chase regularities. That's powerful and perilous. Numbers aren't neutral; they carry the values we encode and the blind spots we ignore. And when models find patterns, they can be compressing quirks as if they were truths.
A few anchors I'm keeping:
- Treat datasets as sketches, not mirrors.
- Optimize with context: ask which distances matter, to whom, and when.
- Celebrate generalization, not memorization.
- Use models as instruments, not oracles; always listen to the room you're playing in.
With that posture, ML isn't about proving the world is simple. It's about seeing it more clearly, one careful representation at a time.