Interesting Global News
What is global news?
Something that happened or was uncovered recently anywhere in the world. It doesn't have to have global implications. Just has to be informative in some way.
Post guidelines
Title format
Post title should mirror the news source title.
URL format
Post URL should be the original link to the article (even if paywalled) and archived copies left in the body. It allows avoiding duplicate posts when cross-posting.
[Opinion] prefix
Opinion (op-ed) articles must use [Opinion] prefix before the title.
Rules
1. English only
Title and associated content has to be in English.
2. No social media posts
Avoid all social media posts. Try searching for a source that has a written article or transcription on the subject.
3. Respectful communication
All communication has to be respectful of differing opinions, viewpoints, and experiences.
4. Inclusivity
Everyone is welcome here regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender identity and expression, education, socio-economic status, nationality, personal appearance, race, caste, color, religion, or sexual identity and orientation.
5. Ad hominem attacks
Any kind of personal attacks are expressly forbidden. If you can't argue your position without attacking a person's character, you already lost the argument.
6. Off-topic tangents
Stay on topic. Keep it relevant.
7. Instance rules may apply
If something is not covered by community rules, but are against lemmy.zip instance rules, they will be enforced.
Companion communities
- [email protected] - International and local legal news.
- [email protected] - Technology, social media platforms, informational technologies and tech policy.
- [email protected] - Interesting articles, projects, and research that doesn't fit the definition of news.
- [email protected] - News and information from Europe.
Icon attribution | Banner attribution
view the rest of the comments
I read about an early study into AI where they were using it to predict whether the pictured animal was a dog or a wolf. It got really good at detecting wolves and when they analyzed how it was determining whether it was a wolf or not, they found that it wasn't looking at the animal at all but instead checking if there was a lot of snow on the ground. If there was, it would say it was a wolf, if there wasn't it would say dog.
The problem was with the data set used to train the AI. It was doing exactly what it was told. That's the big problem with AI is that it does exactly what we tell it to do, but people are hilariously bad at describing exactly the result they want down to the absolute finest level of detail.
I would describe it more as giving the results we're asking for rather than doing what we tell it to, but that's a little bit of too much semantics probably. We mostly don't tell it what to do. We just give it data with some labels and it tries to generate reasons for those labels basically. It's essentially the issue humans have of "correlation does not equal causation" except with no awareness of this and significantly worse.