Assuming you have read the book is the issue. Is it going to he Marx? Or maybe Judith Butler? What about other thinkers that write about race? What about anarchism, is Kropotkin out of scope? Should the quiz also cover market economies? What about Thomas Sowell?
My point is while I have barely scratched the surface, I have already mentioned a wide variety of authors which most people haven't read (some of them for good reasons).
Here is a list of note-taking apps:
https://github.com/tehtbl/awesome-note-taking
By the way, I am building my own Journaling system, it's still early stages and I am looking for ideas!
IMO Lemmy is a social media, it allows people to socialize over shared interests. It doesn't need to facilitate IRL connections, even though they are likely to happen.
I don't know if this has been used before, but there is a good reason not to:
The concentration of power would be a huge problem for such a system. If a single person gets the majority of votes, then they get to make the decisions. That's a system with a single point of failure, if corruption is bad right now, imagine what it would be then...
Keep in mind that voters tend to focus on a few key individuals. In a system which you don't need more seats if you have the votes, the concentration of votes to a few individuals would be taken to new extremes.
One could make the counterargument that if the voters want to be represented by a single person, then it should be their right to get that. However, it's more likely that such a result would be driven by the choice for the lesser evil.
Maybe a completely different electoral system, (a) without a fixed number of seats (aka a single vote is enough to be part of the decision making body) and (b) really frequent elections (6 months or even less), would work in the favor of the people, but there a tonne of practical issues with both requirements.
PS A single person is the extreme but not unlikely case, instead it's more likely a dozen or two candidates will gather that decision making majority, but the corruption argument is still the same.
wow buddy that shrooms are really hitting you
(tried to delete this comment but somehow didn't work, I re-read the article and have a different outlook, sorry for the low effort comment)
I doubt number verification is going to change, they say they use it to filter out spam/bot accounts.
We need a reliable way to tell humans from bots without using government records.
To be honest, setting such a goal is scary to me, I have been working on my self-confidence, and I wouldn't have been able to do it a few years ago. I try to remind myself that I am an autistic with ADHD: if I had food, meds, a bed, a place to study, and the frequent affirmation of a similarly interested peer group I would be happily studying 12 hours a day.
Super Crunchers seems like a good place to start, it's a book about how quantitiative analysis can be used for social sciences.
I am a software engineer, I have literally forked tensorflow and modified the executor, and I have created neural networks for predicting aquaculture KPIs that have been deployed with great success.
I stopped looking for a year, and now I feel AI illiterate. (insert "too afraid to ask" meme)
My experience suggests it's too early to start teaching people. Let the technology do its loop and settle down.
As always I would say there is a huge "it depends".
For context, I am part of a small team of engineers, working on a relatively new product, we have continuous deployment setup for our release branches. We prefer many small PRs, think at least a PR a day per engineer.
I am responsible for setting up a new e2e test suite right now, so it's possible I reconsider later on. But, there are a couple lessons learned from our previous iteration.
My fucking drunk self, highly agrees with you.
While the consumption for AI train can be large, there are arguments to be made for its net effect in the long run.
The article's last section gives a few examples that are interesting to me from an environmental perspective. Using smaller problem-specific models can have a large effect in reducing AI emissions, since their relation to model size is not linear. AI assistance can indeed increase worker productivity, which does not necessarily decrease emissions but we have to keep in mind that our bodies are pretty inefficient meat bags. Last but not least, AI literacy can lead to better legislation and regulation.