keegomatic
Personally, I’ve found that LLMs are best as discussion partners, to put it in the broadest terms possible. They do well for things you would use a human discussion partner for IRL.
- “I’ve written this thing. Criticize it as if you were the recipient/judge of that thing. How could it be improved?” (Then address its criticisms in your thing… it’s surprisingly good at revealing ways to make your “thing” better, in my experience)
- “I have this personal problem.” (Tell it to keep responses short. Have a natural conversation with it. This is best done spoken out loud if you are using ChatGPT; prevents you from overthinking responses, and forces you to keep the conversation moving. Takes fifteen minutes or more but you will end up with some good advice related to your situation nearly every time. I’ve used this to work out several things internally much better than just thinking on my own. A therapist would be better, but this is surprisingly good.)
- I’ve also had it be useful for various reasons to tell it to play a character as I describe, and then speak to the character in a pretend scenario to work out something related. Use your imagination for how this might be helpful to you. In this case, tell it to not ask you so many questions, and to only ask questions when the character would truly want to ask a question. Helps keep it more normal; otherwise (in the case of ChatGPT which I’m most familiar with) it will always end every response with a question. Often that’s useful, like in the previous example, but in this case it is not.
- etc.
For anything but criticism of something written, I find that the “spoken conversation” features are most useful. I use it a lot in the car during my commute.
For what it’s worth, in case this makes it sound like I’m a writer and my examples are only writing-related, I’m actually not a writer. I’m a software engineer. The first example can apply to writing an application or a proposal or whatever. Second is basically just therapy. Third is more abstract, and often about indirect self-improvement. There are plenty more things that are good for discussion partners, though. I’m sure anyone reading can come up with a few themselves.
Put simply, some states get more electors than other states to account for greater population, and each state decides how their electors are supposed to vote according to their statewide popular vote. Most states apply all of their electors to the winner of the popular vote in their state, while some apply them proportionally. Most do the former (“winner takes all”).
This leads to a discrepancy between the popular vote and the electoral vote, and it’s mathematically biased against states with higher populations. So, votes in the more populous states (which tend to vote Democrat) are worth “less” in the electoral college than those in less populous states, leading to Democrats winning the popular vote yet losing the actual election… which has happened in every election they’ve lost since Bush v. Gore, if I’m not mistaken. I’ll double check that and edit if I’m wrong.
Edit: Sorry, it did not happen for Bush v. Kerry, Bush won the popular vote in that one by less than 1%. However, in the other two (Bush v. Gore and Trump v. Clinton) the popular votes were actually won handily by Gore and Clinton, not by Bush or Trump.
Edit 2: This is also notably NOT made worse by gerrymandering, because the number of electors you get is equal to the combined number of senators and congressmen your state gets. Since all states apply their electors based on the popular vote result, it doesn’t matter what party alignments your congresspeople have, so gerrymandering plays no role here.
The mathematical bias comes from the fact that every state gets two senators no matter what the population is, and only your congressperson count is proportional to population, but both count toward your number of electors. So, less populous states have proportionally somewhat more “electors per capita” than states with higher populations.
I never said I thought training AI with the copyrighted work of others causes harm to others. If anything, I think training is analogous enough to human learning that it’s a gray area. However, I think there are different ethical concerns with AI training data than there are with piracy, and those concerns mostly arise from the profit being made from the models.
That’s not quite true, though, is it?
$50 earned is yours to spend on anything. A $50 discount is offered by a vendor to entice you to spend enough of your money on them to make the discount worthwhile.
Pirates don’t pirate because they’re trying to save money on something they would have bought otherwise… typically they pirate because the amount they consume would bankrupt them if they purchased it through legitimate means, so they would never have been a paying customer in the first place.
So, if they wouldn’t have bought it anyway, and they’re not reselling it, did they really harm the vendor? Whether they pirated it or not, it wouldn’t affect the vendor either way.
That’s not really the same thing, in my opinion.
If you were able to pay for everything handily but pirated anyway, or if you resold pirated content, then yeah you have something similar to theft going on. But that’s not really the norm; those people are doing something bad irrespective of the piracy itself, aren’t they?