I’d assume that’s either due to bias in the training set, or poor design choices. The former is already a big problem in facial recognition, and can’t really be fixed unless we update datasets. With the latter, this could be using things like visible light for classification, where the contrast between target and background won’t necessarily be the same for all skin tones and times os day. Cars aren’t limited by DNA to only grow a specific type of eye, and you can still create training data from things like infrared or LIDAR. In either case though, it goes to show how important it is to test for bias in datasets and deal with it before actually deploying anything…

Surely that depends on where in Asia you’re looking at as well? On average, the number of languages people speak is quite different between, say, India and Japan. Or Switzerland vs Romania in Europe.

I do understand the curiosity though, just seeing what malware is trying to do can be quite interesting. Maybe someone should tell that person about VMs though lol

Not sure if this counts since most of them have recently closed down, but for a while there was a large number of American candy stores popping up all over town. Many cash only, same products and same branding across many different stores. Hardly anyone was ever shopping there, and yet they could somehow always afford to pay rent for prime locations. Eventually, several journalists picked up on the topic and found evidence many of them were fronts for money laundering and were tied to organised crime. Not sure if it was directly connected to that increased awareness, but shortly after more of these articles were published, most of the local stores closed.

We definitely share basic values including political views, but there’s also many things where our opinions differ. For example, we both strongly believe in supporting human rights for everyone, but we have different views on local planning reforms or diets. Some hobbies but not all hobbies and interests are shared, we listen to different music, etc. Overall, I really like it this way - we’re different enough so I can always learn something new from her, but not so different we’d have arguments about basic values.

See username :) So much variety even though it’s all the same tea plant

Disappointing but not surprising. The world is full of racial bias, and people don’t do a good job at all addressing this in their training data. If bias is what you’re showing the model, that’s exactly what it’ll learn, too.