Cloud computing, a tad too literally

I couldn’t help but chuckle when this Slashdot post popped up:

The Register carries the funniest, most topical IT story of the year: ‘Facebook’s first data center ran into problems of a distinctly ironic nature when a literal cloud formed in the IT room and started to rain on servers. Though Facebook has previously hinted at this via references to a ‘humidity event’ within its first data center in Prineville, Oregon, the social network’s infrastructure king Jay Parikh told The Reg on Thursday that, for a few minutes in Summer, 2011, Facebook’s data center contained two clouds: one powered the social network, the other poured water on it.

Someone had better explain to Facebook that having an actual cloud in the data centre isn’t really what cloud computing is all about!

That said, this has happened before.  Boeing’s Everett factory (initially constructed to assemble the 747, and now assembles all of their widebody jets) used to have clouds forming near the ceiling, before the installation of a specialised air circulation system sorted that little problem out.  Evidently, some are destined to repeat history’s mistakes…

The only reason to attend a Justin Bieber concert

In case you were unaware, we had Justin Bieber performing at the Cape Town Stadium last night.

Given the average age of his audience, I thought you guys would appreciate this image of the beer queue that 2oceansvibe put up earlier today:

Justin Bieber beer queue

What’s the bet that these were the parents dragged along to the concert and waiting for it to end?

UPDATE: A friend of mine who was volunteering at the concert last night informed me that all of the cooldrink was sold out before he got on stage. Plenty of beer available, though.

It’s beer o’clock!

Those who know me know that I enjoy a good drink every now and then.  Those who have the misfortune of living with me experience this all the time (and then, they decide to bring out the Penalty Shot at a whim, but that’s going off on a bit of a tangent for this post).  I blame my Rhodesian heritage: back in that long-lost culture, having a daily glass of wine, typically when returning from a hard day’s work, was considered the norm.  (My mother frequently cites this as the reason for Rhodesia having far less of a drinking problem than South Africa, because people there were “taught to drink responsibly”.  Or at least, it was that way in the 1970s… well before my time though.)

That kind of still lives on: some office environments find that the occasional drink at work stimulates social interaction, and can actually result in some decent ideas and brainstorming being thrown around.  (It does depend on the working environment: informal ones, such as your typical IT company, would do just fine with this, but don’t try this in the manufacturing sector, the petrochemical sector, or — worst of all — in government.)  This is actually one reason why there’s usually beer supplied at our company-wide meetings and catch-up sessions (the other reason is that it’s the only way to entice the Development Department to actually attend these).  In fact, some places are taking this a step further and are offering free wine to customers on Fridays, though, as much as I would like to take them up on their offer, boutique stores are not my kind of thing — and I will reach though my laptop and slap the first person who suggests that “that’s how guys roll in the Cape”!  (The other reason involves someone who I sadly parted ways with somewhat acrimoniously, but of that, I will say no more.)

But that’s not what prompted this blog post.  What did prompt it was this morning’s run to the Vida in the office park for my morning cappuccino.   For the first time after their recent renovations, I noticed this:

Beer on tap!

Yes, that is indeed beer on tap.

And no, absolutely no idea why I didn’t notice it earlier — I guess I’m still half-asleep whenever I wander into there.  (No, you may NOT ask me how I negotiate the R27 every morning, because apart from heading through there after the majority of the traffic, I honestly can’t give you an answer to that!)

Perfect for your typical developer.  Now, the challenge: how to keep this particular one functioning at the Ballmer Peak

(Mandatory disclaimer: there is a difference between drinking responsibly and drinking irresponsibly, and the latter is definitely not condoned within this context.  If you don’t know said difference, rather don’t try to find it in the first place.)

When the sun sets…

Yes, I took this three weeks ago.  Yes, I’m only putting this up now.  Yes, you may draw and quarter me for slacking again…

Taken from Big Bay, shortly after sunset.

Taken from Big Bay, shortly after sunset.

One day, just one day, I’ll get a proper camera (the camera quality on mobile phones leaves plenty to be desired), then get someone really good at photography to teach me to take photos like these

Jozi Shore

This video has been doing the rounds this week, but as per usual, it takes me the whole week to get around to putting it up here:

Though, as 2oceansvibe says: “… we didn’t see the joke.  This looks pretty normal to us.  I mean, this is exactly what they’re like.  Right?”

(If anyone is interested in background information, the UCT Film Society has it.)

When is that extra 0.9% important?

The question in the post title was asked on Quora recently (in the form of “when is the difference between 99% accuracy and 99.9% accuracy important”), and while it mainly attracted some stock-standard responses, such as service level agreements, Alex Suchman told us when it’s really important: when it can stop a zombie apocalypse.

It’s 2020, and every movie buff and video gamer’s worst fear has become reality. A zombie outbreak, originating in the depths of the Amazon but quickly spreading to the rest of the world (thanks a lot, globalization) threatens the continued existence of the human race. The epidemic has become so widespread that population experts estimate one in every five hundred humans has been zombified.

The zombie infection (dubbed “Mad Human Disease” by the media) spreads through the air, meaning that anyone could succumb to it at any moment. The good news is that there’s a three day asymptomatic incubation period before the host becomes a zombie. A special task force made of the best doctors from around the world has developed a drug that cures Mad Human, but it must be administered in the 72-hour window. Giving the cure to a healthy human causes a number of harmful side effects and can even result in death. No test currently exists to determine whether a person has the infection. Without this, the cure is useless.

As a scientist hoping to do good for the world, you decide to tackle this problem. After two weeks of non-stop lab work, you stumble upon a promising discovery that might become the test the world needs.

Scenario One: The End of Mankind

Clinical trials indicate that your test is 99% accurate (for both true positives and true negatives). Remembering your college statistics course, you run the numbers and determine that someone testing positively will have Mad Human only 16.6% of the time [1]. Curse you, Thomas Bayes! You can’t justify subjecting 5 people to the negative effects of the cure in order to save one zombie, so your discovery is completely useless.

With its spread left unchecked, Mad Human claims more and more victims. The zombies have started taking entire cities, and the infection finally reaches Britain, the world’s last uncontaminated region. Small tribal groups survive by leaving civilization altogether, but it becomes clear that thousands of years of progress are coming undone. After the rest of your family succumbs to Mad Human, you try living in isolation in the hope that you can avoid the epidemic. But by this point, nowhere is safe, and a few months later you join the ranks of the undead. In 2023, the last human (who was mysteriously immune to Mad Human) dies of starvation.

Scenario Two: The Savior

Clinical trials indicate that your test is 99.9% accurate. Remembering Bayes’ Theorem from your college statistics course, you run the numbers and determine that someone testing positively will have Mad Human 66.7% of the time [2]. This isn’t ideal, but it’s workable and can help slow the zombies’ spread.

Pharmaceutical companies around the world dedicate all of their resources to producing your test and the accompanying cure. This buys world leaders precious time to develop a way to fight back against the zombies. Four months after the release of your test, the U.S. military announces the development of a new chemical weapon that decomposes zombies without harming living beings. They fill Earth’s atmosphere with the special gas for a tense 24-hour period remembered as The Extermination. The operation is successful, and the human race has been saved!

Following the War of the Dead, you gain recognition as one of the greatest scientific heroes in history. You go on to win a double Nobel Prize in Medicine and Peace. Morgan Freeman narrates a documentary about your heroics called 99.9, which sweeps the Academy Awards. Your TED Talk becomes the most-watched video ever (yeah, even more than Gangnam Style). You transition into a role as a thought leader, and every great innovator of the next century cites you as an influence.

Life is good.

That is when the difference 99% and 99.9% matters.

[1] A 99% accurate test doesn’t mean that someone who tests positive has a 99% chance of actually being positive. Because the event of having the infection is so relatively rare (only 1 in 500) and the event of not having the disease is so common (499 in 500), even though the test is rarely wrong, it turns out to be more likely that a positive test comes from a healthy person than a sick one. To compute this we use Bayes’ Theorem, which states that

P(A|B) = \frac{P(B|A)P(A)}{P(B)}

We let A be the event that the person is sick and B be the event that the person tests positive, so we have

P(sick|+ test) = \frac{P(+ test|sick)P(sick)}{P(+ test)}
In this situation,

P(+ test|sick) = .99

and

P(sick) = .002 (that’s 1 in 500)

To compute P(+ test) we have to condition on whether the person is sick or not. So

P(+ test)

= P(+ |sick)P(sick) + P(+|not)P(not)

= (.99)(.002) + (.01)(.998) = 0.01196

Plug everything in and we get

P(infected|+ test) = \frac{.99*.002}{0.01196} = 0.16555

[2] This time,

P(+ test|sick) = .999

and

P(+ test)

 = (.999)(.002) + (.001)(.998)

 = 0.002996

so

P(sick|+ test) = \frac{.999*.002}{0.002996} = 0.66689