We’re talking about the same event, aren’t we?

Big university in Germany that’s well-known for their computer science department. Started in 2008 and took way longer than planned. As stated in my other comment, being openly trans was rare when I started but had become more common by the time I got my degree, especially among new students who took the chance to make new friends who never knew their pre-outing personas.

So they see you as a human. Not a gay human.

On the Internet, nobody knows you’re a dog

From personal experience, I would say it’s a phenomenon of the last… maybe 10 years, at least in Germany. When a friend of mine started university in about 2010, I think she was the only openly trans person out of 300 first semester computer science students. These days, when you go to a Chaos Computer Club event, it’s full of pride flags and queer people dressed in skirts, striped programmer socks and cat ear headbands. In the opposite direction, free tampons for trans-masc people in the men’s bathrooms are just… normal.

For a while this caused a bit of friction, not because people were outright anti-queer or anti-trans but because they felt it had gotten so extreme that their queer-welcoming computer nerd event had turned into a pride event which just happened to include a few people with laptops. Now everyone seems to get along though.

That being said, there have always been gender-nonconforming people in IT and gaming. As an arbitrary example, Rebecca Heineman is a trans-woman who taught herself how to pirate and reverse-engineer Atari 2600 games in the 1970s, became the first (US) national video gaming champion in 1980, worked at a gaming magazine, co-founded Interplay Productions, worked on many well-known games. It’s just that being trans wasn’t as accepted back then so a lot of them chose not to out themselves, which honestly can’t be good for one’s mental health.

As we said back in university: “If we have too few women in our field, we will make our own.”

Telegram on desktop shows all participants on the left if the window is over a certain width so you don’t have to move your eyes left and right over the whole screen.

Only rich studio bosses and investors are allowed to exploit Hollywood.

As said: it’s not just the current government. As soon as the data is on a government server, it’s every single government for the rest of my life. And that’s a gamble I wouldn’t be willing to take.

And there’s a big difference between a police agency spending lots and lots of time and money to get to the people they’re interested in (gestapo, stasi, whatever) and them already having the data and being able to filter by whatever criteria they want at zero extra cost within seconds.

When it comes to my data, I treat everyone like they’re my enemy. Some of those enemies I do have to trust with parts of my data, otherwise I couldn’t live a normal life but I still would want to avoid giving a single entity (especially one that literally has power over people) too much at once.

Also, I do live in a country with plenty of public services and a more or less functioning government. Still, 20.8% voted for literal Nazis in February and no matter how often I vote for someone more sensible and how many protests I join, that probably won’t make those people less hateful.

Explain to me how running the registry office gives them nearly as much info about people’s preferences as a dating app does. They may know who people are married to and if they have children. From that they might have a rough idea if someone is straight or not and that’s about it. They don’t know if some who’s in a heterosexual marriage is actually bisexual or even uses the marriage as a socially accepted front to hide being gay from their family. The state has no idea where an umarried person lies on the spectrum from aromantic-asexual to bouncing from orgy to orgy on a daily basis. They don’t know if someone is into BDSM, roleplay, doing it outdoors or threesomes. They also rarely know much about non-sexual hobbies.

All those things may show up in dating site profiles or if not there, in the private messages sent between users. And this is not even about a government not being trustworthy now. Anything that gets put in such a site will stay there for the foreseeable time and even the most stable democracy might be just one freak election away from having a weirdo in power who thinks that people who like sex with their socks on don’t deserve health insurance.

Of course that’s also a risk with private dating platforms but at least for those the government would have to subpoena this kind of data from them instead of having it always available without the public even knowing if and what they’re analyzing.

I would rather not have the state sit on a huge database of people’s sexual orientation and other preferences, thank you very much.

My old manager sent out invitations to the bride‘s family before telling me I was the groom.

(he publicly announced the new product‘s price and release date before telling the dev team that there will be a new product)

So do bananas

Important note: this is about quantum teleportation. They transferred data between two quantum computers without a cable or wifi. Teleporting matter, let alone matter in useful quantities is far off.

With the trend to make ever larger but thinner phones, we’ll eventually end up with an A3 sheet of paper. I just want the 5/5S/SE1 or Mini form factor back. I don’t care about thin phones, only about being able to use the whole screen with one hand.

One important addendum: complexity classes always consider how hard a problem is depending on the input size. Sorting is in P (usually O(n*log(n)), so one of the easiest problems overall) but given a few trillion inputs, it would be pretty much impossible to solve on consumer hardware. On the other hand, problems like 3-sat, the knapsack problem or travelling salesman are all NP-hard but with small enough inputs (up to a few dozen or so), they are easy to solve, even with pen and paper and are even regularly included in puzzle books.

The diagram is pretty good but your interpretation is not quite right, especially for NP-complete and NP-hard.

NP-hard means “at least as hard as all problems in NP”, proven by the fact that any single NP-hard problem can be used to solve the entire class of all NP problems.

NP-complete means “at least as hard as all problems in NP and itself also in NP”, so the intersection between NP and NP-hard.

The thing about P = NP or P != NP is something different. We don’t know if P and NP are the same thing or not, we don’t have a proof in either direction. We only know that P is at least a subset of NP. If we could find a P solution for any NP-hard problem, we would know that P = NP. That would have massive consequences for cryptography and cyber-security because modern encryption relies on the assumption that encrypting something with a key (P) is easier than guessing the key (NP).

On the other hand, at some point we might find a mathematical proof that we can never find a P solution to an NP-hard problem which would make P != NP. Proving that something doesn’t exist is usually extremely hard and there is the option that even though P != NP we will never be able to prove it and are left to wonder for all eternity.

Done.

Alright, part 2, let’s get to NP.

Knowing that P means “in polynomial time”, you might be tempted to think that NP means “in non-polynomial time” and while that kind of goes in the right direction, it means “in non-deterministic polynomial time”. Explaining what non-deterministic calculations are would be a bit too complicated for an ELI5, so let’s simplify a bit. A regular computer must make all decisions (for example which way to turn when calculating a shortest route between two points) based on the problem input alone. A non-deterministic computer can randomly guess. For judging complexity, we look at the case where it just happens to always guess right. Even when guessing right, such a computer doesn’t solve a problem immediately because it needs to make a number of guesses that depends on the input (for example the number of road junctions between our points). NP is the class of problems that a non-deterministic computer can solve in polynomial Time (O(n^a) for any a).

Obviously, we don’t really have computers that always guess right, though quantum computers can get us a bit closer. But there are three important properties that let us understand NP problems in terms of regular computers:

1. a non-deterministic computer can do everything a regular computer can do (and more), so every problem that’s part of P is also part of NP.
2. every problem that takes n guesses with x options for each guess can be simulated on a regular computer in O(x^n) steps by just trying all combinations of options and picking the best one. With some math, we can show that this is also true if we don’t have n but O(n^a) guesses. Our base x might be different, but we can always find something with n in the exponent.
3. While finding a solution on a regular computer may need exponential time, we can always check if a solution is correct in polynomial time.

One important example for a problem in NP is finding the prime factors of a number which is why that is an important basic operation in cryptography. It’s also an intuitive example for checking the result being easy. To check the result, we just need to multiply the factors together and see if we get our original number. Okay, technically we also need to check if each of the factors we get is really prime but as mentioned above, that’s also doable in polynomial time.

Now for the important thing: we don’t know if there is some shortcut that lets us simulate NP problems on a regular computer in polynomial time (even with a very high exponent) which would make NP equal to P.

What we do know is that there are some special problems (either from NP or even more complex) where every single problem from NP can be rephrased as a combination of that special problem (let’s call it L) plus some extra work that’s in P (for example converting our inputs and outputs to/from a format that fits L). Doing this rephrasing is absolutely mind-bending but there are clever computer scientists who have found a whole group of such problems. We call them NP-hard.

Why does this help us? Because finding a polynomial-time solution for just a single NP-hard problem would mean that by definition we can solve every single problem from NP by solving this polynomial-time NP-hard problem plus some polynomial-time extra work, so polynomial-time work overall. This would instantly make NP equal to P.

This leaves us with the definition of NP-complete. This is simply the class of problems that are both NP-hard and themselves in NP. This definition is useful for finding out if a problem is NP-hard but I think I’ve done enough damage to your 5-year-old brain.

Yes, it’s pretty intuitive. A formal proof is still a bit more work than what I can fit in an ELI5 but at the same time simple enough that it can be given to a 2nd semester computer science student as an exercise.