A friend of mine, Glen Chiacchieri, has created a Chrome extension to help solve this problem: Dictionary of Numbers. It searches the text in your browser for quantities it understands and inserts contextual statements in brackets. It might turn the phrase “315 million people” into “315 million people [≈ the population of the United States]“.

As Glen explains, he once read an article about US wildfires which mentioned that the largest fire of the year had burned “300,000 acres.” This didn’t mean much to Glen:

I have no idea how much 300,000 acres is [...] But we need to understand this number to answer the obvious question: how much of the United States was on fire? This is why I made Dictionary of Numbers.

Dictionary of Numbers helpfully informs me that 300,000 acres is about the area of LA or Hong Kong.

Wolfram|Alpha provides a lookup service like this, but you have to load the site and type in the quantity you’re curious about, which I never remember to do. (It’s also often short on good points of comparison.)

Dictionary of Numbers is a new project, so it’s got its share of glitches and rendering hiccups; it’s very much a work in progress. You can submit bug reports, feedback, and suggestions for data sources via a link on the project’s website.

I think these kinds of tools are a great idea, and I want to encourage them. Intelligence is all about context, and when computers get better at providing it, they make us smarter.

The extension can even be surprisingly funny, like when it seems to be making an oblique suggestion for how to solve a problem—e.g. “The telescope has been criticized for its budget of $200 million [≈ Mitt Romney net worth].” It can also come across as unexpectedly judgmental. Glen told me about complaint he got from a user: “I installed your extension and then forgot about it … until I logged into my bank account. Apparently my total balance is equal to the cost of a low-end bicycle. Thanks.”

You can get Dictionary of Numbers here.

One evening, when I was feeling morbid, I wrote a Python script to calculate death probabilities for any collection of people: actuary.py (.txt). It takes a list of ages and genders and produces various statistics. Here’s the report for the nine living people who have walked on the moon:

~$ python actuary.py 81m 82m 80m 81m 80m 81m 76m 78m 77m
There is a 5% chance of someone dying within 0.08 years (by 2012).
There is a 50% chance of someone dying within 1.1 years (by 2013).
There is a 95% chance of someone dying within 4.08 years (by 2016).

There is a 5% chance of everyone dying within 10.78 years (by 2023).
There is a 50% chance of everyone dying within 16.12 years (by 2028).
There is a 95% chance of everyone dying within 22.57 years (by 2035).

Probability of all dying in 1.0 year: <0.001%
Probability of a death within 1.0 year: 46.32%

And here’s the table for four of the main stars of the original Star Wars (Harrison Ford, Carrie Fisher, Mark Hammill, James Earl Jones):

~$ python actuary.py 69m 55f 60m 81m 10
There is a 5%  chance of someone dying within 0.42 years (by 2012).
There is a 50% chance of someone dying within 4.74 years (by 2017).
There is a 95% chance of someone dying within 12.83 years (by 2025).

There is a 5%  chance of everyone dying within 18.17 years (by 2030).
There is a 50% chance of everyone dying within 31.28 years (by 2043).
There is a 95% chance of everyone dying within 42.62 years (by 2055).

Probability of all dying in 10.0 years:   0.272%
Probability of a death within 10.0 years: 85.94%

Of course, these are based on average death rates based only on age and gender. Adding more specific information about the people in question will refine the calculation. For example, I’d guess former astronauts are more likely to be in good health—and have longer life expectancies—than the rest of us.

Groundhog Day is, like Office Space, a comedy containing a gimmick that really sticks with you, even as the rest of the story fades. Or, at least, it did with me—I’ve probably seen the movie a couple of times, but I think I’ve spent a lot more time dwelling on the time loop scenario it describes. Now that people have raised the question, I’m not even sure that I interpreted the scene this way when I was watching it.

From a sci-fi point of view, the whole idea that the time loop was broken by emotional/personal development seemed kind of cheesy, but I just chalked that up to one of those things movies do because that’s how we like stories to work. Nobody wants a movie where the climax consists of an hour of excitedly inferring and testing revisions to the standard model of physics. (Or, at least, there aren’t enough of us to support a big-budget movie.) So while drawing my comic, I remembered that the time loop ended after he took Rita back to his room, and I filled in the typical romanticized Sleeping Beauty idea that I assumed had gone with it.

I appreciate the corrections—in addition to being a reminder to double-check pop culture references, it’s driven home for me what a neat, original movie Groundhog Day really is.

And now I wonder what kind of misconceptions I have about Ghostbusters.

I’m really looking forward to it! The chaos of the past year and a half didn’t leave me with much time or energy for travel or events, so it’ll be fun to get out and meet people again. I’m also looking forward to seeing the campus, which I hear has changed substantially since I left.

The talk isn’t limited to CNU students, so if you live nearby, you’re welcome to come! Admission is free, but since space is limited, you’ll need to reserve tickets here.

Hope to see you there!

I wanted to make an algorithm that anyone could implement, which didn’t rely on a central authority or ongoing support from any one maintainer. I also wanted to make it impossible to know the locations more than a day or so in advance, so that if geohashing became popular in an area, no one could publish a list of future locations that woud give property owners, park rangers, or local police time to prepare. So each day’s coordinates are randomized using the most recent Dow Jones opening price, which isn’t known until the morning of that day—or, in the case of weekends, a day or two in advance, which helps with planning larger weekend trips.

In the days after I posted the comic, there was a flurry of geohashing activity, which quieted down as the initial novelty wore off. But it didn’t die, and for the past several years there’s been a small but vibrant community building around the Geohashing Wiki. There are numerous daily expeditions, and they’ve taken some beautiful pictures and come up with a lot of neat tools, games, and achievements.

One of the many things they did was use a tweaked version of the algorithm to come up with a globalhash, a single coordinate for the day somewhere on the planet (biased toward the areas near the poles). They’re usually over the ocean, but a few of the land ones have been reached.

Yesterday’s globalhash fell less than a kilometer from the South Pole. User Carl-Johan got in touch with the Scott-Amundsen research station, and later that day, the hash was reached by Katie Hess, Dale Mole, and Joselyn Fenstermacher of the US, Robert Schwarz of Germany, and Sven Lidström of Sweden.

Wow. Just wow. Congratulations.

If you want to look up today’s geohash for your location, you can use this online tool, or one of the others listed on the Coordinate Calculators page.

When I was a kid, I was had an irrationally powerful fear of tsunamis (Etymology-Man would suggest “cymophobia”). I swam in the ocean a lot when I was very young, so waves were a big part of my world.  I would fret about tsunamis whenever I was near the coast, and to this day I have occasional nightmares about a wave coming out of nowhere and sweeping me away.

Looking back, part of what made tsunamis frightening was was that I didn’t know what they looked like, and my imagination ran wild filling in the gaps. I read what I could find about them. In particular, I remember being just old enough to work my way through this book, and carrying it around with me so I could read the tsunami section over and over. It included a grainy photo of a ship in a Japanese harbor plowing through an unimpressive-looking line of breakers. I think that’s also where I found a photo of some people running away from something (it was this photo, but the reproduction in my book was too grainy to see what they were running from).

Years later, after the rise of the web, I realized maybe I could now find a video of a tsunami, and finally see the thing that had so captivated me as a child. But my searches for videos didn’t turn up much of anything.

Then the 2004 tsunami happened. Shortly after, as YouTube and its various clones proliferated, there was more horrifying footage available than I could handle.

A year or two ago, I read an article somewhere (I have tried to find it again with no luck) which mentioned that before 2004, there hadn’t been much in the way of photographic or video records of tsunamis, and that this had contributed to a lack of understanding of their form. My childhood impression seemingly confirmed, I worked this into a comic.

It turns out I was mistaken. There are several photographs, some of which can be seen here, here, and here. There’s also a video here (sent in by Phil Plait).

I think what confused me as a child was that none of the photos showed the wave I expected—just debris, and occasionally some visible water. Now that I’ve seen horrifying videos like this, I’ve gone back to some of those old photos and realized that they did show a tsunami. It was just so unlike what I was expecting that I didn’t recognize it.

So thank you to everyone who sent in information. It’s really fascinating stuff. Oh, and anyone interested in the history of tsunamis might want to check out a Google Books advanced search for material published before 1850 containing phrases like earthquake wave, earthquake tide, or earthquake water feet. There are some gripping historical accounts buried there, along with some really interesting speculation by 19th-century scientists about the mechanisms behind earthquakes and their associated waves (the consensus seemed to be hot gas moving between subterranean chambers).

I’ve always been a little uneasy about having all my domains with them, since they’ve got a long history of screwing over domain owners, but never got around to doing anything about it. A little while back, as the SOPA thing blew up, I poked davean, the xkcd sysadmin, about whether it was time to make switching to someone more geek-friendly a priority.

He’s also wanted to switch away from GoDaddy for years (and recently met with the reddit folks to chat about SOPA stuff). He’s periodically done surveys of the alternatives, but—strange as it sounds—he’s actually had trouble finding an affordable registrar with the feature set we needed. In particular, he said he had trouble finding any that support IPv6 Glue and DNSSEC via a control system that doesn’t rely on filing and waiting on support tickets, which he says (and I quote) “freaks me out” as a means of handling registrar stuff (he’s very much an xkcd.com/705 style of administrator). The ones that did offer those features tended to be a little too high-priced for our large number of domains.

We’ve had a number of alternatives recommended in the past week or two, but none have quite satisfied davean’s criteria. If you know of any registrars that might work for us, you can email us at contact@xkcd.com and he’ll take a look.

We’re being cautious about how we handle this switch, since GoDaddy has seemingly been obstructing transfers in a way that can leave the sites trapped in limbo. But don’t worry—it’s in the works!

I’ve corrected a bunch of typos and other minor errors, most of which crept into the project during the final 36-hour no-sleep marathon to integrate all the pieces together. (Those corrections, plus a few more that will be added over the next few days, will all be integrated into the printed version).

Now it’s time for me to to spend some time relaxing with family, and trying to break the habit—trained into me by the research I did for the chart—of thinking of everything in economic terms. To those of you in the US (GDP: $15.18 trillion^[1], total net worth: $58.73 trillion^[2]), have a happy Thanksgiving!

We got married!