The perfect composition for the real-time virtual space in which distance creates slight delays of an unknowable degree. The canvas for the work is real-time and yet slightly displaced at each endpoint of the network. Like real life, except moreso.
“In C”
by Terry Riley
Instruction for beginners
1 Any number of people can play this piece on any instrument or instruments (including voice).
2 The piece consists of 53 melodic patterns to be repeated any amount of times. You can choose to start a new pattern at any point. The choice is up to the individual performer! We suggest beginners are very familiar with patterns 1-12.
3 Performers move through the melodic patterns in order and cannot go back to an earlier pattern. Players should try to stay within 2-3 patterns of each other.
4 If any pattern is too technically difficult, feel free to move to the next one.
5 The eighth note pulse is constant. Always listen for this pulse. The pulse for our experience will be piano and Orff instruments being played on the stage.
6 The piece works best when all the players are listening very carefully. Sometimes it is better to just listen and not play. It is important to fit into the group sound and understand how what you decide to play affects everybody around you. If you play softly, other players might follow you and play soft. If you play loud, you might influence other players to play loud.
7 The piece ends when the group decides it ends. When you reach the final pattern, repeat it until the entire group arrives on this figure. Once everyone has arrived, let the music slowly die away.
San Francisco State University School of Music presents “In C” by Terry Riley
I stumbled across some near-term plans for the Amazon Corporation. It’s funny to think that they started as an online bookstore. Now it’s hard to say exactly what they are. They’ve purchased other companies, branched off into space exploration and are even pioneering delivery by drone.
I think everyone will agree that the new set of services they’re working on will move online commerce to a whole new level. The last mile problem has been there since the beginning. An order can be placed at the speed of light and the large national distribution networks get the goods quite close to where the customer lives very quickly. But getting packages from a local distribution warehouse to a specific residence ends up being the most expensive part of the distribution process.
Even if the packages arrive on schedule and are placed on the doorstep, they are often stolen by criminals cruising neighborhoods. These crooks trail delivery vans and pick off packages that look like they might have resale value on the black market. Customers are always complaining about stolen packages.
This is why Amazon bought Ring, the home security company. Ring puts video cameras on your front door and around your property. If a thief approaches, intending to steal a package, the video cameras capture an image of the person’s face. Recently Ring customers within particular neighborhoods have started sharing these photos. “Watch out for this guy, he’s stealing packages.” Often the photos are also shared with the local police.
Here’s where Amazon can really add value through its network of companies and infrastructure services. Imagine a future with even faster delivery and free of package thieves. By combining drone delivery and Ring’s home surveillance technology, you’ll never lose another package.
Here’s a typical scenario. The customer places an order. The item is picked and packed, and moved into the distribution chain. The package arrives at a local distribution center and is assigned to a drone for home delivery. The drone races to your house and places the package on the designated receiving location. What this? A thief sees the delivery, waits until the drone is out of sight, then moves in to steal the package. Here’s where Ring’s network-connected cameras kick in. The cameras are watching the receiving area—having been notified by the drone that a delivery was imminent. The images of the thief are sent to the Amazon Cloud for processing. The photo of the thief is compared to the family of consumers occupying the house. If there’s no match, the algorithm goes through the extended family, work colleagues and friends. It looks through address books and photo albums to see if there’s any possible match. Given what’s coming next, Amazon doesn’t want to make a mistake.
It looks like there’s no match. This person is stealing your package. The image is now compared to outstanding arrest warrants and neighborhood watch photos. Based on several year’s worth of video footage, the algorithm produces a list of people who have no regular pattern of activity in your neighborhood to determine if this person has been casing the neighborhood. All the while, Amazon’s facial recognition systems are attempting to identify the individual. As a courtesy, Amazon shares the information with the Immigration and Customs Enforcement agency (ICE) to determine whether enforcement action should be coordinated.
At the same time, Amazon’s drones working in the area are alerted to the theft and they begin to gather into a swarm. The swarm tracks the thief as he tries to make his escape. Since Ring cameras are installed in almost every home in the neighborhood, it’s straightforward to track his route. The thief’s location is transmitted to the drone swarm and the cameras on the drones make an identification and lock in and begin tracking the thief. Amazon echo nodes in neighborhood homes notify residents via Alexa to shelter in place while the action is executed.
We’re Watching.
Ideally, the drone swarm will want to take action before the thief enters a vehicle. Even if full identification hasn’t been completed, the drone swarm will move in to herd the thief toward a designated location that has been communicated to local police. Since the police can’t always immediately respond to this kind of incident, the drone swarm is equipped to keep the “suspect” in the designated location for up to 12 hours.
If the thief has abandoned the package, and it appears undamaged, a drone will break off from the swarm and re-deliver it to your home. Damaged packages are taken by drone back to the local distribution warehouse and a request for a replacement item is automatically generated.
Once the police arrive on the scene, all video and audio evidence, along with any background profile data, is transmitted. Generally this results in an open-and-shut case when delivered to the District Attorney’s office. A permanent record is created in Amazon’s central data warehouse to make sure once this person has served their time in prison they receive heightened surveillance on release and for the rest of their lives.
Recidivism is the tendency of a convicted criminal to reoffend. Using the vast resources of the Amazon family of companies, we can often deter a person from reoffending by foregrounding the surveillance apparatus at a key moment prior to a criminal act. Sometimes all it takes is a reminder that someone is watching, and that any criminal act will be swiftly and surely punished.
That’s the future, but here’s some things Amazon is working on today…
Just a quick note to help people, particularly the Media in the United States, to better understand what it means when language is weaponized—and especially what it means in the context of connected digital communication networks. While these techniques have been refined over many years in Russia and the former Soviet Union, they are somewhat new to mainstream American politics. This is not to say that these techniques haven’t been used over the years, but generally they’re employed around the edges. It hasn’t been possible, until recently, to move them to the center of a political communication strategy.
Certain tools are designed as weapons, for example: guns, knives, clubs, brass knuckles, bombs, poison gas. Each of these tools is specially constructed to inflict a certain kind of harm on its target. Now here’s another list of tools: a cast-iron frying pan, a fireplace poker, a baseball bat, a car, a brick, and an electrical current. Each of these tools has a proper use—a set of uses that humans understand through habit. All of these non-weapons have been used to commit murder in some mystery novel. Part of solving the mystery involves a detective envisioning an ordinary tool expressing its potential as a deadly weapon.
When language is used as a weapon—it’s deployed to inflict the maximum possible damage. The usual response to language used in this way is to say that it is neither true nor proper. While this may be a reasonable approach to language used to communicate, it has no effect on language when used as a weapon. It’s the equivalent of saying that the blow inflicted by a frying pan to the head of the victim was not a proper use of frying pans.
Much of the effectiveness of advertising is due to the frequency with which it is broadcast. If you see or hear an ad ten times a day for three months, it’s likely you will remember it for the rest of your life. Most of us can recite ad copy we heard in our childhood even when the product has long since disappeared from the shelves. To maximize the effectiveness of weaponized language, it must have high frequency. In political campaigns this is usually accomplished through producing negative attack ads and buying lots of radio and television time.
In the age of cable news and connected digital social networks, another strategy is possible. An attack is constructed that will harm the target using metaphors, statements and images. In itself, this isn’t enough to assure the attack will be retweeted frequently and universally (by all sides) throughout cable news, newspapers and social media networks. Two elements must be added to the attack, the first is that it should be demonstrably false. This will cause many media outlets to rebroadcast the attack, and then explain why it is false. If the attack is on the veracity of the media itself, many will discount the explanation. The second added ingredient is that the attack must break with ordinary decorum, it must cross a moral line. This causes many media outlets to rebroadcast the attack and explain why it is immoral. Each of these media responses is the equivalent of decrying the improper use of a frying pan in committing an assault.
The media becomes complicit in the attack because it serves as the force multiplier that maximizes the harm. That’s how “playing the media” works. And as the media chases its tail on obviously false sensational headlines, it loses its credibility on the serious investigations it’s doing. When a President has immunity through Republican majority in Congress, a free press is needed more than ever. The attacks on the media have escalated and the media seems blind to its own role in amplifying the harm that is done.
Why doesn't Twitter have something like WordPress's Akismet? Akismet is a plugin that filters spam by combining information about spam captured on all participating sites. It uses that information to generate rules to block future spam. I know that bad actors can easily create new Twitter accounts, but should also be easy for a large group of people to tag them in real time.
And I'd imagine if you can create an algorithm that can predict what you'd like to buy, surely an algorithm could be created to identify both hate speech and the speaker based on a few online real-time gestures. Identifying these storms of attacks, like the ones against Leslie Jones, is not too different from identifying the events that Twitter wants to sell advertising against.
Twitter valued being unfiltered at a certain point, but now the stream is quite polluted.
