I am almost certain my projector actually outputs 1080p. I can tell the difference between 1080p movies and 720p movies, I just don't care too much.
I think I might be weird because I also notice, but don't care about the difference between 60Hz and 90/120/240Hz for my desktop monitors or my phone screen. I just leave everything at 60Hz which allegedly saves a bit of power. Other people tell me I'm insane for doing that and that they'd never come back to a 60Hz monitor.
I'm watching on a 2m wide projector screen, and honestly for most movies high-bitrate 720p is still acceptable. Heck, if the movie is good enough I'd even watch it in DVD quality (which is 480p). High-bitrate 1080p is nicer of course, but usually not enough to waste storage space on it. I'd much rather see 3-4 movies in 720p than 1 movie in 1080p (which is about the size difference you get).
I guess different people have different preferences, but for me movies are not about examining the pimples on character's faces. It's about temporarily transporting me to a different reality, starting some in-depth discussion with whoever I'm watching it with, or just making me think about some area of life. Don't need 1080p for any of that, my brain is more than good enough at suspending disbelief with a million pixels at any one time.
Heck, some of my favorite movies have been shot on consumer-grade cameras and film. The medium doesn't matter as much as the direction, acting, composition and cinematography in general.
Are there any actual dwellings around the road? If so, those speed limits are quite reasonable or even on the high end. In Europe it is 50 km/h (30 mph) if there are houses around a road, and 30 km/h (18 mph) for actual town streets. The streets are also specifically designed to be narrower and there are a lot of traffic calming measures to make drivers naturally want to drive the speed limit. This way it is safe & pleasant for townsfolk to cross the street, the town doesn't end up split in two by the highway, and if you're a sane driver you won't get fined even if you accidentally miss a speed limit sign.
Perhaps the best solution is to make the highways go around towns completely to avoid all of this. (and then we can also work towards banning cars in populated areas)
Actually you don't really have to "flatten" anything, the parking lots are already flat enough. Remove asphalt and build mixed-use housing there. Take away a couple lanes from cars and build a tram line or at least a dedicated bus lane. It's not that difficult if there's political willpower for it.
Don't know about Farsi, but in Russian you would have to declare your knowledge and specificity of the subject. You can transmit some of that information by changing word emphasis, but it's not very clear.
As for your sentences I would translate them as:
The first one puts more emphasis on the "girl". The second one puts more emphasis on "downstairs", almost suggesting that the identity of the girl doesn't matter, kinda "some girl who happened to be downstairs". But it is not as explicit as a definite/indefinite article for sure.
If you want to make it certain that the identity of the girl doesn't matter, you could use an adjective to that extent: Внизу [downstairs] какая-то девушка [some girl] играла [was playing] на скрипке [violin].
If you want to make it certain that both you and the person you're talking to know about the girl, you can use a determiner: Та девушка [that girl] внизу [downstairs] играла [was playing] на скрипке [violin].
Computers could make cars a lot more reliable and moddable. It's proprietary/locked-down software and hardware that's the problem.
I would hope that the Switch falls under "Tablets". Because that's exactly what it is.
Video consoles are either desktop computers with proprietary parts, or tablets with proprietary parts. There is nothing especially difficult to repair in them compared to the rest of their categories.
In that case, what are they doing to college for?
No, it is actually statistically impossible for a human to replicate this on sufficiently long runs of text.
This is not about replicating writing like a model. This is basically about guessing which words to pick from the list of suitable words based on a rule that you don't know (because the key is secret).
To reduce this to the simplest possible example, imagine you are writing a "text" from just two letters: "a" and "b". Let's say for convenience that the text is supposed to be random. So the text would look something like "ababaaabbababbbabababaabbbabaababbaaabbabaabbaaaaabaaabbbaabaabababbabbbbbbbbabbabaabbbbbbbaabbabaab"
(generated with
'''.join(random.choice(['a', 'b']) for i in range(0, 50)))The watermarking works as follows: the model owner holds a key, and then uses that key to influence the random choices between "a" and "b" somehow, in a context-dependent way. The actual algorithm is quite complicated, but for simplicity let's just say we have a secret pattern which biases the random choice towards it. In order to see the exaggerated results, let's say the secret key is "aaaabbbb" (of course this is a bad secret key, once again just an example), and that the bias is strong (let's say 80%). So this would mean that the first four letters in our text are more likely to be "a", the next four letters are more likely to be "b", then the next four letters are more likely to be "a", and so on.
Then the text would look something like "aaaabaabaabaabbbabaaaabbaaaababbbaaaabbbabaabbbbaaabaabbaaaaabbbabababbaaaaabbbbaaabbbbbaaaaababaaba".
(generated with
''.join(random.choice(['a', 'b'] + ([key[i % len(key)]] * 3)) for i in range(0, 100)))You can see visually that the secret key has affected the text. Of course in this example even if you didn't know the secret key you could probably figure it out, in reality the algorithm is way more complicated than that, relying on cryptography, so you wouldn't be able to know the secret key or see that the string has been biased at all.
If the text is long enough, and you know the secret key, you can guarantee that the text was generated with it. In our examples, the letters in the text match our key 77% of the time. The probability of an actual random algorithm generating a text like that is already very low, despite the base entropy being only 100 bits. If my math is correct, for our example the p-value is 2.7 * 10⁻⁸, or about 0.00000027%. I would bet a hungy that the text was generated by our watermarking algorithm, with odds like these!
Of course we did exaggerate the bias and our base algorithm was random. In reality the bias is smaller, the algorithm for determining the likelihoods of possible next tokens is very complicated (it's the LLM itself), and the algorithm for determining which token to bias is also way more complicated (involving cryptography and real secret keys). That said, hopefully it should help you understand why, for sufficiently long texts, this fingerprinting is just not possible to be replicated by humans.