Pipoca
Assume the dataset will grow and eventually mimic reality.
How would that happen, exactly?
Stereotypes themselves and historical bias can bias data. And AI trained on biased data will just learn those biases.
For example, in surveys, white people and black people self-report similar levels of drug use. However, for a number of reasons, poor black drug users are caught at a much higher rate than rich white drug users. If you train a model on arrest data, it’ll learn that rich white people don’t use drugs much but poor black people do tons of drugs. But that simply isn’t true.
It’s not necessarily a matter of fact checking, but of correcting for systemic biases in the data. That’s often not the easiest thing to do. Systems run by humans often have outcomes that reflect the biases of the people involved.
The power of suggestion runs fairly deep with people. You can change a hiring manager’s opinion of a resume by only changing the name at the top of it. You can change the terms a college kid enrolled in a winemaking program uses to describe a white wine using a bit of red food coloring. Blind auditions for orchestras result in significantly more women being picked than unblinded auditions.
Correcting for biases is difficult, and it’s especially difficult on very large data sets like the ones you’d use to train chatgpt. I’m really not very hopeful that chatgpt will ever reflect only justified biases, rather than the biases of the broader culture.
It’s a simple idea, but it’s not quite that simple.
When it gets to be around freezing outside, you have to deal with frost buildup on the outdoor unit.
And as temperatures fall, output and efficiency generally falls. So you need an oversized unit to heat your house on the coldest days, but an oversized unit isn’t great the rest of the year.
Historically, heat pumps were only good if it never got down below freezing. Now, modern cold- climate heat pumps are efficient well below freezing and Mitsubishi’s models advertise that they deliver 100% of their output down to -23F/-30C. Between adding variable inverters, better defrosting, etc they’ve come a really long way in the past decade.
For what it’s worth, a chain is a literal standardized metal chain that surveyors used when physically staking out parcels. It’s not a unit normal people have ever used.
An acre is a chain by a furlong because a furlong is the distance you’d plow with an ox, and an acre is about the area you’d plow in a day. They derived the standard chain from that, much as metric chains are 20 meters or 30 meters. France used to use 10 meter chains, with 20cm links.
Normal people don’t measure things in chains, whether metric chains or imperial.
80% of the US lives in metropolitan and micropolitan areas. Most people work in the same metro or micropolitan area that they live in.
Biking will never work for every trip for everyone. Public transit will never work for every trip. But it’s something that can work for many more trips, given better zoning & infrastructure. If you’re in a town, city or suburb, you shouldn’t need a car to buy milk or have a pint with friends.
Getting most people out of cars is good for everyone. Cars lanes have much, much lower throughput than bus lanes or bike lanes do. Think about how much nicer driving is if you’re not stuck in traffic.
Do you live in a census block with an average of 200 houses per square mile? Then you’re outside the urban area. 200 houses evenly spaced is 3.2 acre lots on average.
https://www2.census.gov/geo/pdfs/reference/ua/Census_UA_2020FAQs_Feb2023.pdf