6 Standardizations that are just too damn hard to get
We are caught in sub-optimal situations
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In Machine Learning, you describe a problem and you try to find a model which solves that problem well. It’s very common that the model is stuck in a locally optimal solution — meaning tiny changes make the model worse… but big changes make it better.
It’s pretty common to get into that situation in life. Once too many people and systems depend upon one established system, we will not change it. For example, where you live. Even if another city would be much nicer, you have your job, your family, and your friends in your current city. You optimize your location within that city, but moving to another city more than 100km away is a big jump.
Let’s see where we have this issue in our world 😃
№1: The Metric System — US, England, Canada
The metric system measures mass in kilograms, distance in meters, areas in square meters, and volumes in cubic meters. The US customary measurement system uses ounces (oz), pints (pt), gallons (gal), inches, yards, foot, miles, pounds, square chains, acres, cubic inches, cubic foot, cubic yards, …
This difference caused the Mars Climate Orbiter to crash in 1999. Just like that NASA lost $327.6 million.
Besides such obvious issues, the metric units are just simpler. Everything is divisible by 10 with prefixes for powers of 1000.
№2: Calendar System 📅
The number of days in a year is determined by the time the earth takes to make a full rotation around the sun: 365 days, 5 hours, 59 minutes, and 16 seconds.
Those approximately 6 hours cause a year to have 365.2495 days. This means we need roughly every 4 years a leap day.
The status quo: The Gregorian Calendar has chosen to split those 365 days into 12 months…
