News
Welcome to the News community!
Rules:
1. Be civil
Attack the argument, not the person. No racism/sexism/bigotry. Good faith argumentation only. This includes accusing another user of being a bot or paid actor. Trolling is uncivil and is grounds for removal and/or a community ban. Do not respond to rule-breaking content; report it and move on.
2. All posts should contain a source (url) that is as reliable and unbiased as possible and must only contain one link.
Obvious biased sources will be removed at the mods’ discretion. Supporting links can be added in comments or posted separately but not to the post body. Sources may be checked for reliability using Wikipedia, MBFC, AdFontes, GroundNews, etc.
3. No bots, spam or self-promotion.
Only approved bots, which follow the guidelines for bots set by the instance, are allowed.
4. Post titles should be the same as the article used as source. Clickbait titles may be removed.
Posts which titles don’t match the source may be removed. If the site changed their headline, we may ask you to update the post title. Clickbait titles use hyperbolic language and do not accurately describe the article content. When necessary, post titles may be edited, clearly marked with [brackets], but may never be used to editorialize or comment on the content.
5. Only recent news is allowed.
Posts must be news from the most recent 30 days.
6. All posts must be news articles.
No opinion pieces, Listicles, editorials, videos, blogs, press releases, or celebrity gossip will be allowed. All posts will be judged on a case-by-case basis. Mods may use discretion to pre-approve videos or press releases from highly credible sources that provide unique, newsworthy content not available or possible in another format.
7. No duplicate posts.
If an article has already been posted, it will be removed. Different articles reporting on the same subject are permitted. If the post that matches your post is very old, we refer you to rule 5.
8. Misinformation is prohibited.
Misinformation / propaganda is strictly prohibited. Any comment or post containing or linking to misinformation will be removed. If you feel that your post has been removed in error, credible sources must be provided.
9. No link shorteners or news aggregators.
All posts must link to original article sources. You may include archival links in the post description. News aggregators such as Yahoo, Google, Hacker News, etc. should be avoided in favor of the original source link. Newswire services such as AP, Reuters, or AFP, are frequently republished and may be shared from other credible sources.
10. Don't copy entire article in your post body
For copyright reasons, you are not allowed to copy an entire article into your post body. This is an instance wide rule, that is strictly enforced in this community.
view the rest of the comments
Given you're one of the more rational commenters on Lemmy I've seen, you might be interested in why this is such an issue.
Large language models are stochastic, where their output can vary randomly, but only for equally probable things to say. Like if you say "where are we going to go on this sunny day" it might answer "the beach" one time and "a park" another.
But when things are not equally probable in the training data, because they have no memory between invocations, they end up collapsing on the most likely answer - this is after all what they were trained to predict.
For example, if you ask Google's LLM to give you a random number between one and ten, you'll get the number seven every single time. This is because humans are more biased to the number 7 (followed by 3) over numbers like 4, and that pattern is picked up by the model, which doesn't have a memory between invocations so it goes with the most represented option and doesn't vary it at all over the initial requests (it will vary when there's a chat history though).
So what happens when you ask for a description of a doctor? By default, you get a white male every single time. This wouldn't be an issue if it varied biased probabilities in the training data stochastically, but it can't do this for demographics any better than it can for numbers between one and ten.
Obviously an intervention is needed, and various teams are all working on ways to do that. Google initially gave instructions to specifically add diversity to every prompt showing people, which was kind of like using a buzzsaw where a scalpel was needed. It will get better over time, but there's going to be edge cases that need addressing along the way.
In terms of the Holocaust query, that topic is often adjacent to conspiratorial denialism which is connected to a host of other opinions no one (other than Gab) wants in a LLM or voice assistant, so here too we're almost certainly looking at overly broad attempts to silence neo-Nazi denialism propaganda and not some sort of intended censorship of the actual history.
And that's probably what the NY Post is actually upset about.
Any idea why they don't just apply LLMs to natural language processing? "Turn the living room lights off and bedroom lights on" should be pretty simple to parse, yet my assistant has a breakdown any time I do anything more than one command at a time.
It's expensive and slow. Especially to do well and to connect to 3rd party system calls like "turn_off_lights(["living room"])".