Find Video Clips By Quotes

I had a few good professors in my painting and drawing classes, but all my graphic design classes tried to teach us how to use Photoshop and Illistrator by showing the class demonstration video clips. You know, exactly like the kind you can watch for free on Youtube, except these video clips cost me thousands of dollars to watch. I felt like I paid a lot of money to learn martial arts, only to show up to find the instructor is fat, sluggish, and cowardly, and he tries to overcome that by trying to teach us how to fight by showing us Chuck Norris movies. (Fact: Chuck Norris could teach me how to fight without even bothering to show up to class).

He’d had to watch a video on YouTube to figure out how to tie the damn thing—which he was never telling anyone, ever. Except maybe Lane. Lane would either know how to tie a perfect Windsor knot or would own a clip-on tie. The thought made Jared smile. But he also mentally told himself to find out which it was. He couldn’t let Lane go to a similar meeting in a clip-on tie.

His eyes went back to the screen. I felt him sink a little further back into the bed. We were both stroking in earnest now. His breathing became shallower, and the sound of it sent another shot of lust up my spine. I wanted to be the one making him pant like that. But then his pace faltered, and I looked up to find out why. The video had ended. I’d chosen a clip that was only a few minutes long. And

games. A summary: Exposing children to a violent TV or film clip increases their odds of aggression soon after.41 Interestingly, the effect is stronger in girls (amid their having lower overall levels of aggression). Effects are stronger when kids are younger or when the violence is more realistic and/or is presented as heroic. Such exposure can make kids more accepting of aggression—in one study, watching violent music videos increased adolescent girls’ acceptance of dating violence. The violence is key—aggression isn’t boosted by material that’s merely exciting, arousing, or frustrating. Heavy childhood exposure to media violence predicts higher levels of aggression in young adults of both sexes (“aggression” ranging from behavior in an experimental setting to violent criminality). The effect typically remains after controlling for total media-watching time, maltreatment or neglect, socioeconomic status, levels of neighborhood violence, parental education, psychiatric illness, and IQ. This is a reliable finding of large magnitude. The

Often interfaces are assumed to be synonymous with media itself. But what would it mean to say that “interface” and “media” are two names for the same thing? The answer is found in the remediation or layer model of media, broached already in the introduction, wherein media are essentially nothing but formal containers housing other pieces of media. This is a claim most clearly elaborated on the opening pages of Marshall McLuhan’s Understanding Media. McLuhan liked to articulate this claim in terms of media history: a new medium is invented, and as such its role is as a container for a previous media format. So, film is invented at the tail end of the nineteenth century as a container for photography, music, and various theatrical formats like vaudeville. What is video but a container for film. What is the Web but a container for text, image, video clips, and so on. Like the layers of an onion, one format encircles another, and it is media all the way down. This definition is well-established today, and it is a very short leap from there to the idea of interface, for the interface becomes the point of transition between different mediatic layers within any nested system. The interface is an “agitation” or generative friction between different formats. In computer science, this happens very literally; an “interface” is the name given to the way in which one glob of code can interact with another. Since any given format finds its identity merely in the fact that it is a container for another format, the concept of interface and medium quickly collapse into one and the same thing.

As the subject watches the movies, the MRI machine creates a 3-D image of the blood flow within the brain. The MRI image looks like a vast collection of thirty thousand dots, or voxels. Each voxel represents a pinpoint of neural energy, and the color of the dot corresponds to the intensity of the signal and blood flow. Red dots represent points of large neural activity, while blue dots represent points of less activity. (The final image looks very much like thousands of Christmas lights in the shape of the brain. Immediately you can see that the brain is concentrating most of its mental energy in the visual cortex, which is located at the back of the brain, while watching these videos.) Gallant’s MRI machine is so powerful it can identify two to three hundred distinct regions of the brain and, on average, can take snapshots that have one hundred dots per region of the brain. (One goal for future generations of MRI technology is to provide an even sharper resolution by increasing the number of dots per region of the brain.) At first, this 3-D collection of colored dots looks like gibberish. But after years of research, Dr. Gallant and his colleagues have developed a mathematical formula that begins to find relationships between certain features of a picture (edges, textures, intensity, etc.) and the MRI voxels. For example, if you look at a boundary, you’ll notice it’s a region separating lighter and darker areas, and hence the edge generates a certain pattern of voxels. By having subject after subject view such a large library of movie clips, this mathematical formula is refined, allowing the computer to analyze how all sorts of images are converted into MRI voxels. Eventually the scientists were able to ascertain a direct correlation between certain MRI patterns of voxels and features within each picture. At this point, the subject is then shown another movie trailer. The computer analyzes the voxels generated during this viewing and re-creates a rough approximation of the original image. (The computer selects images from one hundred movie clips that most closely resemble the one that the subject just saw and then merges images to create a close approximation.) In this way, the computer is able to create a fuzzy video of the visual imagery going through your mind. Dr. Gallant’s mathematical formula is so versatile that it can take a collection of MRI voxels and convert it into a picture, or it can do the reverse, taking a picture and then converting it to MRI voxels. I had a chance to view the video created by Dr. Gallant’s group, and it was very impressive. Watching it was like viewing a movie with faces, animals, street scenes, and buildings through dark glasses. Although you could not see the details within each face or animal, you could clearly identify the kind of object you were seeing. Not only can this program decode what you are looking at, it can also decode imaginary images circulating in your head.

If you had an Internet connection and lived in North America at the time, you may have seen it. Vasquez is the man behind the “Double Rainbow” video, which at last check had 38 million views. In the clip, Vasquez pans his camera back and forth to show twin rainbows he’d discovered outside his house, first whispering in awe, then escalating in volume and emotion as he’s swept away in the moment. He hoots with delight, monologues about the rainbows’ beauty, sobs, and eventually waxes existential. “What does it mean?” Vasquez crows into the camera toward the end of the clip, voice filled with tears of sheer joy, marveling at rainbows like no man ever has or probably ever will again. It’s hard to watch without cracking up. That same month, the viral blog BuzzFeed boosted a different YouTuber’s visibility. Michelle Phan, a 23-year-old Vietnamese American makeup artist, posted a home video tutorial about how to apply makeup to re-create music star Lady Gaga’s look from the recently popular music video “Bad Romance.” BuzzFeed gushed, its followers shared, and Lady Gaga’s massive fanbase caught wind of the young Asian girl who taught you how to transform into Gaga. Once again, the Internet took the video and ran with it. Phan’s clip eventually clocked in at roughly the same number of views as “Double Rainbow.” These two YouTube sensations shared a spotlight in the same summer. Tens of millions of people watched them, because of a couple of superconnectors. So where are Vasquez and Phan now? Bear Vasquez has posted more than 1,300 videos now, inspired by the runaway success of “Double Rainbow.” But most of them have been completely ignored. After Kimmel and the subsequent media flurry, Vasquez spent the next few years trying to recapture the magic—and inadvertent comedy—of that moment. But his monologues about wild turkeys or clips of himself swimming in lakes just don’t seem to find their way to the chuckling masses like “Double Rainbow” did. He sells “Double Rainbow” T-shirts. And wears them. Today, Michelle Phan is widely considered the cosmetic queen of the Internet, and is the second-most-watched female YouTuber in the world. Her videos have a collective 800 million views. She amassed 5 million YouTube subscribers, and became the official video makeup artist for Lancôme, one of the largest cosmetics brands in the world. Phan has since founded the beauty-sample delivery company Ipsy.com, which has more than 150,000 paying subscribers, and created her own line of Sephora cosmetics. She continues to run her video business—now a full-blown production company—which has brought in millions of dollars from advertising. She’s shot to the top of a hypercompetitive industry at an improbably young age. And she’s still climbing. Bear Vasquez is still cheerful. But he’s not been able to capitalize on his one-time success. Michelle Phan could be the next Estée Lauder. This chapter is about what she did differently.

I didn’t trust the film clips we’d been using to induce the emotions we wanted in infants (it takes a more developed comic sensibility to find bathing gorillas amusing, after all), so I decided to go with the basics: video clips of an actress laughing or crying.

Why didn’t you call?” Eli looks down sheepishly. “Um...my phone died from taking too many pictures and video clips,” he says. “And then we couldn’t get a signal on Tara’s, and without GPS, we got turned around. The maps don’t show anything out here unless you’ve got data. So we tried climbing high in a tree to get like a signal or figure out where we were, but...” He winces. “I fell, and my ankle’s twisted. But it’s not broken,” Tara finishes matter-of-factly. She seems to have a good, clear head on her shoulders. “It just hurts to walk.” “Yeah, so we looked for a place to wait where it wasn’t so wet and animals couldn’t find us,” Eli adds. “We were okay, Dad.