Technology
Which posts fit here?
Anything that is at least tangentially connected to the technology, social media platforms, informational technologies and tech policy.
Post guidelines
[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. Use original link
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.
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
!globalnews@lemmy.zip
!interestingshare@lemmy.zip
Icon attribution | Banner attribution
If someone is interested in moderating this community, message @brikox@lemmy.zip.
view the rest of the comments
And the AIs themselves can generate data. There have been a few recent news stories about AIs doing novel research, that will only become more prevalent over time.
Though, a big catch is that whatever is generated needs to be verified. The most recent story I've seen was the AI proposing the hypothesis of a particular drug increasing antigen presentation, which could turn cold tumors (those the immune system does not attack) into hot tumors (those the immune system does attack). The key news here is that this hypothesis was found to be correct, as an experiment has shown that said drug does have this effect. (link to Google's press release)
The catch here is that I have not seen any info on how many hypotheses were generated to find this correct hypothesis. It doesn't have to be perfect: research often causes a hypothesis to be rejected, even if proposed by a person rather than AI. However signal-to-noise is still important for how game changing it will be. Like in this blogpost it can fail to identify a solution at all, or even return incorrect hypotheses. You can't simply use this data for further training the LLM, as it would only degrade the performance.
There needs to be a verification and filtering first. Wikipedia has played such a role for a very long time, where editors reference sources, and verify the trustworthiness of these sources. If Wikipedia goes under because of this, either due to a lack of funding or due to a lack of editors, a very important source will be lost.