Automation consultant. I build AI-powered workflows using Claude, n8n, and open-source tools. Sharing practical guides on AI agents, no-code automation, and cost optimization.
This is a brilliant analogy. The 'garbage in, garbage out' principle maps perfectly from geophysics to ML — your model can have perfect math internally, but if your training assumptions don't match reality, the outputs are confidently wrong. I see the same pattern in automation work: a workflow that's technically flawless but built on an assumption about how the business process works. The first time an edge case hits (an invoice in a different format, a customer who replies in a language you didn't expect), the whole thing breaks. The fix isn't better algorithms — it's better assumption auditing before you write a single line of code.
"assumption auditing before you write a single line of code" is doing a lot of work there and it's the right instinct but the harder problem is that most assumptions aren't visible until the edge case surfaces them. The invoice in a different format wasn't in the spec because nobody knew it existed. That's the part the geology framing adds that GIGO doesn't: in geophysics, you learn to map the terrain before you run the method, not because you know all the edge cases but because you know the terrain will surprise you. The discipline is in the pre-survey, not the post-mortem.
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This is a brilliant analogy. The 'garbage in, garbage out' principle maps perfectly from geophysics to ML — your model can have perfect math internally, but if your training assumptions don't match reality, the outputs are confidently wrong. I see the same pattern in automation work: a workflow that's technically flawless but built on an assumption about how the business process works. The first time an edge case hits (an invoice in a different format, a customer who replies in a language you didn't expect), the whole thing breaks. The fix isn't better algorithms — it's better assumption auditing before you write a single line of code.
"assumption auditing before you write a single line of code" is doing a lot of work there and it's the right instinct but the harder problem is that most assumptions aren't visible until the edge case surfaces them. The invoice in a different format wasn't in the spec because nobody knew it existed. That's the part the geology framing adds that GIGO doesn't: in geophysics, you learn to map the terrain before you run the method, not because you know all the edge cases but because you know the terrain will surprise you. The discipline is in the pre-survey, not the post-mortem.