There are certain situations where GOTO makes sense. I've had to use once or twice to do thins like restart a loop arbitraily where you have go back to a parent loop that may be 3 or 4 times removed. Its not ideal, and in nearly every case there are better alternatives, but GOTO is still valid sometimes

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Not necessarily. A summons can be synonymous with an invite although it is often more strongly worded. You can summon someone for tea at which point it would be a bit rude if they decline. So when the foreign office is summoning the Chinese ambassador they tell him the time and place of the meeting and expect him to be there. The content of the meeting or how it is performed is a separate issue. But typically when an ambassador is summoned in this way it is because the government have a few important and urgent questions or statements for the ambassador. Demanding that he stand like a school boy for the duration of the meeting would be an insult and imply that he is subordinate to the British government, which he is not. But you can discuss these things in a more civilized matter like equals, each a diplomat of their own country.

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Water conducts electricity fairly well (due to the presence of dissolved ions in it). So from the point of view of electric current water is something it can run through. And since water tends to leak everywhere it is likely to be in contact with further conductors.

>Does the ambassador have to stand in an office while getting lectured by a minister? What’s the purpose of this?

No. The ambassador has no obligation to go anywhere. When a country summons an ambassador, it's a request, not an order, and the means is the message. In other words, the act of summoning the ambassador itself conveys that the host country is unhappy with something that the country represented by the ambassador has done. It's diplomatic theater meant to sent a message to the ambassador's country. Whether the ambassador actually attends any meetings is up to them, although not going is frowned upon and could lead to further diplomatic steps.

Yes, more or less that’s what is happening. The purpose is the formal declaration that the British government didn’t like something about what the Chinese government did, and they want the Chinese government to be fully aware of it. Other, more material steps might follow, or they might not. It depends.

Basically there is what is known as a frank exchange of views, if the ambassador doesn't respond to the summons they can be expelled from the country or diplomatic relations could be broken off so they always go since it is their job to have diplomatic relations with the host country.

GOTO is one of a handful of really fundamental, core concepts in programming, you basically can’t achieve anything useful without it. The flip side to that is that, because you need GOTO for everything, you need to read a lot of the surrounding code to understand what, exactly, you’re trying to achieve.

As it turns out, there’s a small handful of uses (if/else, while, for, try/catch, function calls) that completely dominate compared to everything else. Instead of a goto that can mean anything, you can use one of those language constructs instead and say exactly what you mean (this is also why most languages are slowly adopting for x in list, because it’s the most common usage pattern for for loops). This is easier to read, it’s less error prone (because the compiler/interpreter can handle all the setup and cleanup chores for you).

People do forget that there is a long tail of legitimate use cases for goto, though, which is a bit of a shame.

Can a Stallone sit all day? No hell have a heart attack but a Mike Myers can. How fat are you to worry about such an issue?

I think they just used the wrong ><><><><><><

For those wondering, most camera can record video in a much higher fps than their burst photo rate. This is usually because the video is lower resolution and has a lot more compression, this leads to the processor not needing to work as hard/long to take each frame.

neural network*

Because download size does not equal installed size. A bunch of files can be compressed to be much smaller in order to make downloading faster.

to find out what's taking up your space, I recommend WizTree

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You might be more or less asking how a neural network works. That's what a lot of people will be thinking about when they think of the phrase machine learning.

A neural network is only a category of machine learning. It's not the whole picture.

Machine learning can be something complex like a neural network. But it can also be something very simple, like Menace. A simple to understand process, popularly known for learning tic-tac-toe.

Machine learning is all based on a goal, score, and reward/punishment system. It's a program that has a goal, gets somehow scored based on the results it gives, and receives a change relative to the difference.

The difference between the results and the goal is often called error. And the error is used to create the change. This change can be seen as a punishment or reward.

A* pathfinding isn't exactly machine learning. But I like to include this here too. Because it also uses some goal, score, and punishment/reward techniques. It can help get the right idea about how to compute to solve a problem.

A neural network is extremely difficult to wrap your head around. Particularly for obscure tasks like driving a car or creating images. They can be extraordinarily complex. It's a line formula (oftentimes multidimensional) that approximates a line you want given a set of point values as a goal. There's a lot of calculus and angry math involved.

Imagine trying to figure out a line formula that draws the path of a roller coaster. Then imagine a formula with variables that can be adjusted to draw the path of any roller coaster.

The Taylor Series is again, not machine learning. But can give you a little taste of what it's like behind the scenes of a neural network. Some of the math is kinda similar.

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My cousin bought a kidney from a man sentenced to life in prison. The surgery was done in a hospital but the convict was intoduced as family. This happened in the Philippines.

In addition what made the while thing worse is that the financial system partly fulfills a critical role for the rest of society (like water, electricity, Internet). As a result when the rot was spread it became everyone's problem. Sadly nothing fundamental got done to isolate the infrastructural role of the financial sector from the high risk greedyness part, so a future case, maybe with a different financial invention can lead to the same fallout.

> All I thought at that time was "Who the fuck is Bear Stearns and Lehman brothers???"

They weren't really the "cause" of the crisis - they were just two companies that were especially badly hit by it. They were both "investment banks", which are essentially banks that provide services to large companies, super-wealthy individuals, governments, etc.

The primary cause of the crisis was a reshaping of the US mortgage sector which increased the risks of people defaulting on their mortgages and which most of the global finance industry failed to see coming. Essentially, mortgage providers started packaging up the mortgages they held into financial products, which they sold off to investors. For example, they might sell you an agreement for, say, $1 million dollars, which gives you the rights to 1% of the repayments on 1000 specific mortgages. Traditionally, mortgages are seen as safe - people rarely default on them because banks are unwilling to hand them out to people who aren't financially secure. And by packaging up a large number of mortgages and selling off small fractions of the whole thing, it seemingly becomes even safer - 1 or 2 of the mortgages you own a share in might default, but not all 1000 of them.

These pacakged-up mortgages were seen by the wider financial industry as extremely safe investments, and so many businesses started buying them up and using them as the core of their investment portfolio, complementing investments that were deemed more risky. However, this led to a problem. The banks that were handing out mortgages no longer cared if people were able to pay them back, because they were selling off the rights to all the repayments anyway. And these packaged-up mortgages were so lucrative that they were under a lot of pressure to create more of them. So, in the US, it became very easy to get a mortgage. Lots of people ended up with mortgages that they couldn't really afford - they were often given a period of cheaper repayments at the start of the mortgage to entice them into it.

As the economy started slowing down and house prices started falling, lots of people in the US started defaulting on their mortgages. The global financial industry suddenly discovered that many of the "safe" investments that formed the bedrock of its portfolios were plummeting in value. There was an expectation that many businesses that were especially exposed to these investments were going to fail, but at first nobody really knew which businesses were heavily exposed. This meant that financial institutions became very wary of lending to other businesses, making the problem worse - businesses that could have weathered the storm by taking on more debt found that they were unable to. And it also started to emerge that many financial institutions had been cutting corners during the good times, lying to their investors and regulators about how much risk they were exposing themselves to.

that works with human too

circulation....

VENTILATION (are you breathing aptly?)

hmmmm

, depending on your points of squashed fleshy parts & prominences, veins suffering and VERICOSE VEIN RISKS ARE UPPED!!!!!OH NOOOOOO.

thanks for letting me contribute!-c

Machine learning, as people have pointed out, is broad. However, I think that understanding gradient descent in general really gets to the heart of most new applications (especially neural networks).

Gradient Descent is kind of like a game of hotter/colder. You start by walking in a completely random direction, and then someone tells you you're either getting warmer or getting colder.
A neural network starts similarly, taking it's input and doing a bunch of random multiplications and getting random output. Then you tell it what the answers should have been and it knows how far off it was. Then it goes back to all those random variables (parameters) and calculates how much each one contributed to it being wrong, and adjusts them ever so slightly so that they would have produced a better result.