Twillight Eclipse opens on the big screen today, but if you want to save yourself a few hours of time and the price of a movie ticket, check out this interactive 8-bit style version instead.
[Via Kotaku]
Comedy Duo: Gates, Jobs and the new iPhone
[Via Reddit]
Street Fighter Cats + Epic Team Fortress II Mod: Laser Cat
This one is for all you cat-loving geeks and gamers out there. The first video takes something we’ve all seen, a fight between two cats, and adds the music from Street Fighter on top. Be on the lookout for Guile’s signature move! The second video shows a TF II mod which includes Laser cat as one of the main opponents. Enjoy!
Street Fighter Cats
Awesome Team Fortress II Mod: Laser Cat
WARNING: You Are Being Monitored [PIC]
Killer Instincts: How One Neuroscientist Discovered He Had the Mind of a Psychopath
Part of being a geek is an unending quest for information. While not all of us are scientists, even in our daily lives we seek answers. We apply logic and reason to even the most simple tasks. Part of it, I think, is that we believe information can help us better understand our world. And there is no such thing as too much when it comes to data. We thrive off of it.
But what happens when the evidence staring you in the face is a kind of living horror?
Well, that’s just what happened to a neuroscientist named Jim Fallon. According to a report in Discover magazine, based on an episode of Morning Edition, Fallon was doing research on his families “psychological and neurological quirks.” On the suggestion of his mother, he decided to delve into his own brain ancestry. Considering that his family was related to Lizzy Borden, well, I’m sure he imagined there’d be some surprises. But what he didn’t expect to find was that his brain scan’s neurological patterns matched a genetic variant that showed a high aptitude for violent behavior and a tendency toward becoming a psychopath. He had the brain of a killer.
Lazy or Awesome? [PIC]
[Via BuzzFeed]
Monkey Pirate Robot Ninja Zombie [Most Badass Headline Ever]
We’re pretty sure most of you have played rock paper scissors at least once in your lives, and maybe a few have even tried rock paper scissors lizard spock, but what about monkey pirate robot ninja zombie?
Here’s how the game goes:
Monkey
* Monkey fools Ninja
* Monkey unplugs Robot
Suggested noise: ee-ee-eek!
Robot
* Robot chokes Ninja
* Robot crushes Zombie
Suggested noise: ex-ter-min-ate!
Pirate
* Pirate drowns Robot
* Pirate skewers Monkey
Suggested noise: arrrrr!
Ninja
* Ninja karate chops Pirate
* Ninja decapitates Zombie
Suggested noise: keeee-ah!
Zombie
* Zombie eats Pirate
* Zombie savages Monkey
Suggested noise: braaaaaaaaaainsss!
[Via BoingBoing]
The Circle of No Life – Blogger Edition
So you’ve seen the Circle of No Life, which may apply to a few of you, but for us professional bloggers, the circle is a little different. Check it out:
For a few people, the items on the circle may be positioned a little differently, and there may even be one or two things missing, but for the most part, that’s the life of a pro blogger. It may look a bit depressing at first glance, but you get used to it. I wouldn’t trade my job for anything else in the world.
Twitter Sentiment Analysis: How Do We Feel About the iPhone?
Sentiment analysis is tricky anyway, even with thousands of words to mine for “positive” or “negative” indicators and top-notch machine learning and natural language researchers on the case. Of course, distilling a sample down into 140 characters or less suddenly makes that tricky problem much trickier – which is why sentiment analysis for Twitter is really kind of hit-or-miss.
Considering how divided opinion seems to be on the iPhone in the past few days since the launch, I thought this would be an interesting topic to use to try out two Twitter sentiment analysis systems I recently came across. The first is Twitter Sentiment, which rather than a commercial product is a research project (for a machine learning class) out of Stanford. If you look on their about page, you’ll see a link to a paper written about the algorithms they used to put this together. The tool provides overall sentiment as well as a graph over time – like this one, that shows when there have been spikes in tweets about the iPhone in the past six months:
Putting in a date range of just 6/25/2010 to 6/28/2010, it tells me that sentiment around the keyword “iPhone” is 64% negative. Here are some examples of the classified tweets:
NEGATIVE: iPhone 4 network is horrible – keeps dropping off. Thank goodness that I am still on my 14d cooling off period. #iphone4 #o2 #apple
NEGATIVE: Broke the screen on another IPHONE sheesh I need to upgrade anyway … are the stores all sold out
POSITIVE: Gave into temptation of buying the iPhone 4 Bumper. So far, so good. But $30 …. seriously Apple?
POSITIVE: New shoes, new dress, new top!!! New new new!!!!! And yes they all go with my new iPhone 4!!! Lol
Though my overall impression is that the sentiment classifications are pretty darn good, considering, there are some things that you can’t really blame it for missing. For example, this tweet is classified as negative: “i wish i got an iphone now so that i can play game. is very boring bored standing and the websites you can only go is fb and twitter.”
Another sentiment analyzer I tested out is TweetFeel, a commercial venture, and so unlike the previous one where you can read all about the nifty science behind it, their FAQ just assures you that they use “insanely complex algorithms” to deliver their results. The verdict? 54% positive for “iphone” after letting it run for about ten minutes.
Considering that Twitter Sentiment gives you a much, much larger corpus of data much, much faster, I’d be inclined to say it’s the better tool – though considerably less cute. Still, here’s some examples from TweetFeel:
NEGATIVE: Isaac & I are switching to T-Mobile android phones sooner then later. My iphone blows.
NEGATIVE: damn stupid iphone and stupid fat fingers! Yes I LOVE my job
POSITIVE: I’m tweeting while still listening to Pandora! Awesome iphone 4
POSITIVE: I love you iphone 4!
Again, kind of tough for it to catch nuances. For example, two tweets both classified as negative: “New iphone blows me away” and “Ugh new iPhone blows.”
So what’s my verdict for how Twitter is feeling about the new iPhone? Taking both of these together and reading through the tweets, I’d say… very mixed, verging on negative. Which is probably what you already knew just from the media coverage the past few days. But hey, here are some new toys to play with for those of you interested in analytics!
Once In A Red Moon: A Look at Lunar Colors
According to National Geographic, scientists suspect that volcanic ash from Iceland may have colored our moon a pale red this past Saturday during an eclipse. While not a total eclipse, the show in the sky began at dawn in North America, in the central and western part, that is. Those of us on the East Coast definitely missed out.
Astronomer Geza Gyuk at the Adler Planetarium in Chicago explained the science behind the phenomenon, “While I haven’t heard of reports of particularly fantastic sunsets occurring because of the Icelandic volcano, it might be quite pretty if the ash in the air causes an extra reddening of the light reaching the moon.”
Judging from the pictures, the red moon wasn’t quite as splendid as advertised, but still rather beautiful.
The red lunar eclipse, however, is just one among many intriguing phases of the moon. For those among you who are curious, here’s a few lunar terms explained:
Blue Moon – A common misconception is that a blue moon refers to color (which sometimes does occur; see the Tyndall Effect below). In actuality, a blue moon is simply a month wherein there are two full moons (more commonly understood these days) or, a season in which there is a fourth moon (rather than three).
Tyndall Effect – There have been occurrences of truly blue moons in the past, but the instances are rare. More often than not–just like the red moon–it’s because of particles in the air, most frequently due to volcanic events around the globe, or, in some cases, large forest fires. According to Wikipedia, the blue color we see is due to particles “slightly wider than the wavelength of red light (0.7 micrometre)… [with] no other sizes present.”
Wet Moon – I’d never heard of this term, but I’ve seen it many times. A Wet Moon is when the moon is, as I think of it anyway, in the Cheshire Cat position–both horns pointing up. The term comes from Hawaiian myth, which held that the moon looked like a bowl, ready to be filled with water, and portended rain to come.
Harvest Moon – The Harvest Moon is the closest full moon to the autumnal equinox, and often makes quite a statement in the sky. Due to the tilt of the earth at that time of year, it can appear significantly larger than normal, hanging low on the horizon, and can even be a deep orange color. As Wikipedia explains:
The warm color of the moon shortly after it rises is caused by light from the moon passing through a greater amount of atmospheric particles than when the moon is overhead. The atmosphere scatters the bluish component of moonlight (which is really reflected white light from the sun), but allows the reddish component of the light to travel a straighter path to one’s eyes.
Hunter’s Moon – The first full moon after the Harvest Moon. It’s called the Hunter’s Moon because it means bright nights, ideal for hunters looking for more time and better lighting during the hunt. Both Native Americans and Western Europeans are known to have held feasts celebrating the Hunter’s Moon, and with good reason. It was likely quite a cause to celebrate.
(via Wikipedia, National Geographic; image in the public domain from NASA)