Month: April 2013

A neuroscientist and developer entered a hacking contest and created a “a mentally activated ‘Do Not Disturb’ sign.”

His idea was sparked by a gift to participants: a cat-ear headset built by Neuro-wear with sensors that track the wearer’s brain waves and perk up fluffy motorized ears during periods of high brain activity. Scorcioni, who’d just finished a fellowship at the Neurosciences Institute in La Jolla, Calif., decided to hack the headset to create an app that blocks incoming calls when the receiver is concentrating. With 26 hours to complete the hackathon, he worked until the last minute, pausing only for two hours of sleep and a shower. That labor produced a working prototype of Good Times, which analyzes real-time brain wave data from the headset, then sends commands to AT&T’s telephone network to either permit or block incoming calls. Blocked callers are redirected to an automated message asking them to try again later.

The now ubiquitous emoticon was coined by Carnegie Mellon students to convery sarcasm on a message board in the ’80s:

Someone proposed an asterisk. Someone else, an ampersand (on the grounds that “&” resembles “a jolly fat man in convulsions of laughter”). And then a computer scientist named Scott Fahlman chimed in with the compound punctuation mark that would live on in chat windows and e‑mail inboxes the Internet over: “ :-) ”

“We were just nerds, goofing around,” Fahlman, now a research professor at Carnegie Mellon, told me. “This was not meant to be a serious invention.” But the smiley and its cousins succeeded where generations of misunderstood sarcasts had failed. In the late 1800s, the poet Alcanter de Brahm proposed a point d’ironie resembling a backward question mark—a suggestion echoed, a century later, by the novelist Hervé Bazin. Nabokov wanted “a special typographical sign for a smile—some sort of concave mark, a supine round bracket.” Ambrose Bierce offered the “snigger point” (a horizontal parenthesis, or “”) to punctuate “every jocular or ironical sentence.”

Bomb-sniffing dogs have their limits:

Dogs get tired, and less reliable, as the day goes on, just as we might. As for conditions, odors change in the weather, and dogs do, too. When they’re panting, dogs don’t sniff as readily as they normally might; an overly warm dog, forced to pant to cool himself, has a less reliable nose. And while we don’t think of odors as seasonal (until, upon reflection, we remember: there is a smell of spring, of summer; the odors of winter are fewer), smells are more volatile in warm weather, and travel and disperse differently than in the cold. This too affects detection rates.

Combine all of these factors, and a scenario like the marathon bombing presents ample challenges for a detection team: a densely peopled environment full of movement and scents, dogs that have been working for hours, and explosives that weren’t stationary until almost the last possible moment.

Peter Swire worries about the escalating battle over online privacy:

[P]rivacy groups, government regulators, and others have argued that users should have a choice. If people don’t want to be tracked across multiple sites, then they should be able to indicate that choice through their browser settings: “Do Not Track.” Sounds simple enough.

But it’s not. The devil lies in the details of implementation, for there are many stakeholders besides individual users in the online data collection and advertising ecosystem. Without effective targeting and tracking, advertisers argue, ad revenue could plummet and lead to the shuttering of many popular websites that rely on third party ads as their primary source of revenue. Those who buy and sell behavioral advertising and retargeting point out that advertising revenue supports the diverse array of free content available on the internet. From this perspective, online data collection enables innovative business models, and supports the long tail of smaller websites that get revenue from targeted advertisements.

Zach Braff is following the Veronica Mars model:

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Alyssa worries about this trend:

My concern is that we’ll hit a point where studios and creators with even larger fanbases turn to those fans to get financing for films that could have been funded for conventional means solely as a way to boost their own profit margins.

I totally understand that people are excited to pony up for projects that they’re tremendously excited to see go from fantasies to reality. But I hope that as much as fan bases are advocates for creators and series that they’re invested in, that fans remember to advocate for themselves in this process too. Just because you’re willing to give someone a ton of money, or even a little money, to make their movie doesn’t mean that what you get in return is actually a fair trade. It’s terrific to win creative freedom for talented people, but if fans want to upset the corporate business model that drives the film industry, they should recognize that this is still a business, and someone is still making money off of their investments. And if fans goal is to buy that creative freedom for their favorite artists over and over again, it’ll be much more sustainable for them to do that if they’re getting some return on their crowdfunding investments.

Gabe Delahaye is most unpleased:

OH COOL STRAW MAN ARGUMENTS, ZACH BRAFF.

Jim Parsons is an Emmy winning star of the most successful show on television. I don’t think the “money people” would be upset that Zach Braff wanted him to play his “friend.” No one is making Zach Braff rewrite his movie to star Justin Bieber, and Comi-Con has been a prominent feature of multiple movies and TV shows, so please do not try and appeal to the “nerds” as if they are underserved in our culture. If anything, nerds need to take a step back because seriously enough with the nerds already. But, like, DO NOT LIE TO EVERYONE, ZACH BRAFF! And especially don’t lie to everyone UNDER THE GUISE OF BEING THE MOST HONEST. Whatever the complications and restrictions involved in signing financing deals by traditional methods may be, the worst case scenario would by some slight fine-tuning of your project to broaden its commercial appeal, not a wholesale Mad Magazine parody of what people in the 1980s thought Hollywood was about.

In an interview, Braff says that he “would love, more than anything, to have it be you get an equity stake”:

You have 10 bucks, you make your 10 bucks back with the percentage of profit, like a stock. But that’s not legal yet. I think it’s an exciting idea, that you can go, “Oh, I like x, y, and z, I want to buy a piece of that potential film project.” I think that that’s coming. But we’re not there yet legally.

So what do you do in the meantime? You offer them any and every incentive you can think of. But at the very least, if you pay 10 bucks, you’re joining what I like to think of as this club. You see how active I am on social media. I drive my family, friends, and girlfriend crazy. I get a lot of joy out of it. So turning that into an online behind-the-scenes filmmaking magazine, where there will be videos and content and people who are interested in the behind-the-scenes of the making of a movie will go on this ride alongside me — I think that’s cool for 10 bucks.

The Dish’s coverage of the Veronica Mars experiment here.

It turns out that the drop in the Dow caused by the fake AP tweet was probably not carried out by human traders, but rather robot ones, specifically high-frequency trading (HFT) algorithms, which parse and automatically act on key words contained in raw news feeds (in this case, “explosions” and “White House” in the same headline). Matthew Phillips explains more:

Though it’ll be months before we know exactly what happened, the consensus is that a handful of trading algorithms responded to the fake tweet by selling a broad range of stocks, bonds, and commodities. As message traffic spiked and prices started declining, HFT firms started backing out of the market, just as they did during the May 2010 Flash Crash [which was caused by a rogue algorithm]. As a result, liquidity dried up, as you can see here in this chart from Nanex. Since there were suddenly relatively few buy orders to match against all those sell orders flooding the market, the dip picked up speed. “When the amount of bids and offers thins out like that, it takes very little volume to move the market in a big way,” says Manoj Narang, chief executive officer and founder of Tradeworx, a Red Bank (N.J.)-based HFT firm.

Phillips goes on to detail how many firms employ aggregation firms to collect and analyze potentially important news for their trading models. But it now seems clear that some firms are also “mainlining Twitter’s ‘firehose’ feed directly into [their] trading algorithm, allowing the model to place trades instantly off the information it’s gleaning through some sort of text-analysis program.” That’s probably not a good idea:

As Narang points out, the vast amount of trading done by computers is statistical in nature, meaning algorithms look for historical patterns in the volumes of market data they sort through every day. The problem with tweets is two-fold. One, they’re super noisy, so gleaning a decent signal out of them takes a lot of analysis and is still pretty hairy. But also, even though some 400 million tweets get sent every day, there’s still not enough historical data out there. They’re still too new.

However, Steven Gandel reports that while the tweet-reading algorithms surely played their part in Tuesday’s flash-crash, it’s unlikely they actually started it:

The fake tweet went out 1:07 PM and 50 seconds. According to Eric Hunsader, who runs market research firm Nanex, the first high-frequency trading firms didn’t react to the fake tweet until 15 seconds after that. Most algorithms are written to respond to data within milliseconds. What’s more, most only respond to specific market-moving news events, like the monthly jobs number.

So Hunsader thinks humans made the first trades based on the fake tweet. Nonetheless, computer trading algorithms, trained to follow the market, likely piled into the selling once it got started. Hunsader says that computer trading systems may have incorporated the potentially bad news into their algorithms. When actual selling began to occur, those systems were primed and headed for the exits faster than usual.

Zooming out, Nick Baumann recently explored the rise and potential dangers of high frequency trading:

This rapid churn has reduced the average holding period of a stock: Half a century ago it was eight years; today it is around five days. Most experts agree that high-speed trading algorithms are now responsible for more than half of US trading. Computer programs send and cancel orders tirelessly in a never-ending campaign to deceive and outrace each other, or sometimes just to slow each other down. They might also flood the market with bogus trade orders to throw off competitors, or stealthily liquidate a large stock position in a manner that doesn’t provoke a price swing. It’s a world where investing—if that’s what you call buying and selling a company’s stock within a matter of seconds—often comes down to how fast you can purchase or offload it, not how much the company is actually worth.

As technology has ushered in a brave new world on Wall Street, the nation’s watchdogs remain behind the curve, unable to effectively monitor, much less regulate, today’s markets. As in 2008, when regulators only seemed to realize after the fact the threat posed by the toxic stew of securitization, the financial whiz kids are again one step—or leap—ahead.