It's because this article is garbage: of you watch the original German video what he says is
Yuki is ein junger, aufstrebender, vor allem der beste Japaner.
Which translates to
Yuki is a young rising star and the best Japanese driver.
Which reads more like referring to iwasa who is also in the RB juniors program.
The car is the same as last week.
You have to remember that this is a track that verstappen really doesn't like: last year's race at Singapore was also his worst.
Usually verstappen drives ~3 tenths faster than Perez, which, if he did that this week, would also put him up there....
IMO this is less of a case that the car is worse and more that verstappen isn't able to get 100% from his car.
24, always driven manual, EU.
From my experience most people in the EU can or at least could: This is because many (if not all, not sure) countries make a distinction between manual and automatic licenses (see e.g. https://www.learn-automatic.com/qualified/automatic-driving-licence/).
I.e. if you want to drive manual, you have to take the test manual, but if you take the test on manual transmission, you are allowed to drive automatics as well.
No, it's built into the protocol: think of it like as if every http request forces you to attach some tiny additional box containing the solution to a math puzzle.
The twist is that you want the math puzzle to be easy to create and verify, but hard to compute. The harder the puzzle you solve, the more you get prioritized by the service that sent you the puzzle.
If your puzzle is cheaper to create than hosting your service is, then it's much harder to ddos you since attackers get stuck at the puzzle, rather than getting to your expensive service
Standard lossless compression (without further assumptions) is already very close to being as optimal as it can get: At some point the pure entropy of these huge datasets just is not containable anymore.
The most likely savior in this case would be procedural rendering (i.e. instead of storing textures and meshes, you store a function that deterministically generates the meshes and textures). These already are starting to become popular due to better engine support, but pose a huge challenge from a design POV (the nice e.g. blender-esque interfaces don't really translate well to this kind of process).
Zeiss is German, they also produce substantially more than just the optics https://en.m.wikipedia.org/wiki/Carl_Zeiss_SMT
It's $\mathbb{X}$ or unicode 𝕏 (U+1D54F)
Maybe he really likes metric spaces??
They will make it open source, just tremendously complicated and expensive to comply with.
In general, if you see a group proposing regulations, it's usually to cement their own positions: e.g. openai is a frontrunner in ML for the masses, but doesn't really have a technical edge against anyone else, therefore they run to congress to "please regulate us".
Regulatory compliance is always expensive and difficult, which means it favors people that already have money and systems running right now.
There are so many ways this can be broken in intentional or unintentional ways. It's also a great way to detect possible e.g. government critics to shut them down (e.g. if you are Chinese and everything is uniquely tagged to you: would you write about Tiananmen square?), or to get monopolies on (dis)information.
This is not literally trying to force everyone to get a license for producing creative or factual work but it's very close since you can easily discriminate against any creative or factual sources you find unwanted.
In short, even if this is an absolutely flawless, perfect implementation of what they want to do, it will have catastrophic consequences.
Everything using the activityPub standard has open likes (see https://www.w3.org/TR/2018/REC-activitypub-20180123/ for the standard), and logically it makes sense to do this to allow for verification of "likes":
If you did not do that, a malicious instance could much more easily just shove a bunch of likes onto another instance's post, while, if you have "like authors" it's much easier to do like moderation.
Effectively ActivityPub treats all interactions like comments, where you have a "from" and "to" field just like email does (just imagine you could send messages without having an originator: email would have unusable levels of spam and harassment).
Specfically, here is an example of a simple activity:
POST /outbox/ HTTP/1.1
Host: dustycloud.org
Authorization: Bearer XXXXXXXXXXX
Content-Type: application/ld+json; profile="https://www.w3.org/ns/activitystreams"
{
"@context": ["https://www.w3.org/ns/activitystreams",
{"@language": "en"}],
"type": "Like",
"actor": "https://dustycloud.org/chris/",
"name": "Chris liked 'Minimal ActivityPub update client'",
"object": "https://rhiaro.co.uk/2016/05/minimal-activitypub",
"to": ["https://rhiaro.co.uk/#amy",
"https://dustycloud.org/followers",
"https://rhiaro.co.uk/followers/"],
"cc": "https://e14n.com/evan"
}
As you can see this has a very "email like" structure with a sender, receiver, and content. The difference is mostly that you can also publish a "type" that allows for more complex interactions (e.g. if type is comment, then lemmy knows to put it into the comments, if type is like it knows to put it to the likes, etc...).
The actual protocol is a little more complex, but if you replace "ActivityPub" with "typed email" you are correct 99% of the time.
The different services, like lemmy, kbin, mastodon, or peertube are now just specific instantiations of this standard. E.g. a "like" might have slightly different effects on different services (hence also the confusion with "boosting" vs "liking" on kbin)
They choose to do this. Delicious has historically been a point and click developer, but they wanted to diversify, especially since their previous title "pillars of the earth" flopped. They first tried their have at rts with "a year of rain" which is simply not that good, and then looked into Gollum.
You also can't raid make the argument that the project was rushed out the door, considering the game was supposed to release in 2021 (two years ago).
They tried something they had no experience in, not through coercion but because they wanted to, and produced a game of shockingly low quality. Since this wasn't the first flop, but just the latest in a huge series of flops, (though it was the most expensive and high profile one) the studio closed.
While the inability to source is a huge problem, but you also have to keep in mind that complaining about AI has other objective beyond the obvious "AI bad".
Complaining about AI is as much about the AI as it is about the economical incentives behind AI.
That's not what lossless data compression schemes do:
In lossless compression the general idea is to create a codebook of commonly occuring patterns and use those as shorthand.
For example, one of the simplest and now ancient algorithms LZW does the following:
Basically, instead of rewriting long sequences, it just writes down the index into an existing dictionary of already seen sequences.
However, once this is done, you now need to find an encoding that takes your characterset (the original characters+the new dictionary references) and turns it into bits.
It turns out that we can do this optimally: Using an algorithm called Arithmetic coding we can align the length of a bitstring to the amount of information it contains.
"Information" here meaning the statistical concept of information, which depends on the inverse likelihood a certain character is observed.
Logically this makes sense:
Let's say you have a system that measures earthquakes. As one would expect, most of the time, let's say 99% of the time, you will see "no earthquake", while in 1% of the cases you will observe "earthquake".
Since "no earthquake" is a lot more common, the information gain is relatively small (if I told you "the system said no earthquake", you could have guessed that with 99% confidence: not very surprising).
However if I tell you "there is an earthquake" this is much more important and therefore is worth more information.
From information theory (a branch of mathematics), we know that if we want to maximize the efficiency of our codec, we have to match the length of every character to its information content. Arithmetic coding now gives us a general way of doing this.
However, we can do even better:
Instead of just considering individual characters, we can also add in character pairs!
Of course, it doesn't make sense to add in every possible character pair, but for some of them it makes a ton of sense:
For example, if we want to compress english text, we could give a separate codebook entry to the entire sequence "the" and save a ton of bits!
To do this for pairs of characters in the english alphabet, we have to consider
26*26=676combinations.We can still do that: just scan the text 600 times.
With 3 character combinations it becomes a lot harder
26*26*26=17576combinations.But with 4 characters its impossible: you already have half a million combinations!
In reality, this is even worse, since you have way more than 26 characters: you have things like
", . ? !and your codebook ids which blow up the size even more!So, how are we supposed to figure out which character pairs to combine and how many bits we should give them?
We can try to predict it!
This technique, called [PPM](Prediction by partial matching) is already very old (~1980s), but still used in many compression algorithms.
The important trick is now that with deep learning, we can train even more efficient estimators, without loosing the lossless property:
Remember, we only predict what things we want to combine, and how many bits we want to assign to them!
The worst-case scenario is that your compression gets worse because the model predicts nonsensical character-combinations to store, but that never changes the actual information you store, just how close you can get to the optimal compression.
The state-of-the-art in text compression already uses this for a long time (see Hutter Prize) it's just now getting to a stage where systems become fast and accurate enough to also make the compression useful for other domains/general purpose compression.