The unprecedented scale of Aadhaar’s data will make managing it extraordinarily difficult. One of Nadhamuni’s most important tasks is de-duplication, ensuring that each record in the database is matched to one and only one person. That’s crucial to keep scammers from enrolling multiple times under different names to double-dip on their benefits. To guard against that, the agency needs to check all 10 fingers and both irises of each person against those of everyone else. In a few years, when the database contains 600 million people and is taking in 1 million more per day, Nadhamuni says, they’ll need to run about 14 billion matches per second. “That’s enormous,” he says.
Coping with that load takes more than just adding extra servers. Even Nadhamuni isn’t sure how big the ultimate server farm will be. He isn’t even totally sure how to work it yet. “Technology doesn’t scale that elegantly,” he says. “The problems you have at 100 million are different from problems you have at 500 million.” And Aadhaar won’t know what those problems are until they show up. As the system grows, different components slow down in different ways. There might be programming flaws that delay each request by an amount too tiny to notice when you’re running a small number of queries—but when you get into the millions, those tiny delays add up to a major issue. When the system was first activated, Nadhamuni says, he and his team were querying their database, created with the ubiquitous software MySQL, about 5,000 times a day and getting answers back in a fraction of a second. But when they leaped up to 20,000 queries, the lag time rose dramatically. The engineers eventually figured out that they needed to run more copies of MySQL in parallel; software, not hardware, was the bottleneck. “It’s like you’ve got a car with a Hyundai engine, and up to 30 miles per hour it does fine,” Nadhamuni says. “But when you go faster, the nuts and bolts fall off and you go, whoa, I need a Ferrari engine. But for us, it’s not like there are a dozen engines and we can just pick the fastest one. We are building these engines as we go along.”
Using both fingerprints and irises, of course, makes the task tremendously more complex. But irises are useful to identify the millions of adult Indians whose finger pads have been worn smooth by years of manual labor, and for children under 16, whose fingerprints are still developing. Identifying someone by their fingerprints works only about 95 percent of the time, says R. S. Sharma, the agency’s director general. Using prints plus irises boosts the rate to 99 percent.
That 1 percent error rate sounds pretty good until you consider that in India it means 12 million people could end up with faulty records. And given the fallibility of little-educated technicians in a poor country, the number could be even higher. A small MIT study of data entry on electronic forms by Indian health care workers found an error rate of 4.2 percent. In fact, at one point during my visit to Gagenahalli, Nadhamuni shows me the receipt given to a woman after her enrollment; I point out that it lists her as a man. A tad flustered, Nadhamuni assures me that there are procedures for people to get their records corrected. “Perfect solutions don’t exist,” Nilekani says, “but this is a substantial improvement over the way things are now.”
William Feller was a probability theorist at Princeton University. One day he and his wife wanted to move a large table from one room of their large house to another, but, try as they might, they couldn’t get it though the door. They pushed and pulled and tipped the table on its side and generally tried everything they could, but it just wouldn’t go.-Professor Stewart’s Hoard of Mathematical Treasures
Eventually, Feller went back to his desk and worked out a mathematical proof that the table would never be able to pass through the door.
While he was doing this, his wife got the table through the door
His lectures were loud and entertaining. He wrote very large on the blackboard, in a beautiful Italianate handwriting with lots of whirls. Sometimes only one huge formula appeared on the blackboard during the entire period; the rest was handwaving. His proof—insofar as one can speak of proofs—were often deficient. Nonethless, they were convincing, and the results became unforgettably clear after he had explained them. The main idea was never wrong.
He took umbrage when someone interrupted his lecturing by pointing out some glaring mistake. He became red in the face and raised his voice, often to full shouting range. It was reported that on occasion he had asked the objector to leave the classroom. The expression "proof by intimidation" was coined after Feller's lectures (by Mark Kac). During a Feller lecture, the hearer was made to feel privy to some wondrous secret, one that often vanished by magic as he walked out of the classroom at the end of the period. Like many great teachers, Feller was a bit of a con man.
I learned more from his rambling lectures than from those of anyone else at Princeton. I remember the first lecture of his I ever attended. It was also the first mathematics course I took at Princeton (a course in sophomore differential equations). The first impression he gave was one of exuberance, of great zest for living, as he rapidly wrote one formula after another on the blackboard while his white mane floated in the air. After the first lecture, I had learned two words which I had not previously heard: "lousy" and "nasty."
In July the Santa Cruz Police Department began experimenting with an interesting bit of software developed by scientists at Santa Clara University. The researchers behind the software are like an intellectual “Oceans Eleven” team of specialists: two mathematicians, an anthropologist and a criminologist. They’ve combined their cerebral forces to come up with a mathematical model that takes crime data from the past to forecast crimes in the future. The basic math is similar to that used by seismologists to predict aftershocks following an earthquake (also a handy bit of software in southern California).-Pre-Cog Is Real – New Software Stops Crime Before It Happens
Large earthquakes are unpredictable, but the aftershocks that follow are not and their occurrence can be predicted with mathematical models. It occurred to Dr. George Mohler, one of the Santa Clara mathematicians, that criminal activity might not be random and that, similar to aftershocks, some crimes might be predicted by other crimes that precede them. The reasoning is based on the assumption that crimes are clustered – it’s what police call ‘hotspots.’ Burglaries will occur in the same area and at the same houses because the vulnerabilities of that area will be known to the burglars. Gang violence is also clustered. A gang shooting will often trigger retaliatory shootings.
Using the aftershocks-inspired algorithms Dr. Mohler and his team came up with a model, then sought to test it. In collaboration with the LAPD they plugged in data on 2,803 residential burglaries occurring within a block of the San Fernando valley 11 miles by 11 miles throughout 2004. For a given day the software calculated the top 5 percent of city blocks most likely to be burglarized. The results convinced the LAPD that, had they been using the program, they could have prevented a quarter of burglaries across the entire test region for that day.
The current, real world test of the software involves generating a map of the city areas most likely to be burglarized, the time of day they are most likely to get hit, and deploying personnel accordingly. The software is recalibrated every day when burglaries from the previous day are added to the dataset. They don’t actually expect to catch people in the act, but to deter more crimes with more effective patrolling. The test that is underway will be evaluated at six months, but already the data is encouraging. Zach Friend, crime analyst for Santa Cruz police, confirmed to the New York Times that the program led to five arrests in July. Even more impressive, compared to July 2010 burglaries, the number of July 2011 burglaries are down 27 percent. Whether or not that trend holds remains to be seen, but so far it appears that being in the wrong place at the right time works.
over a lifetime and assuming today’s mean wages, a handsome worker in America might on average make $230,000 more than a very plain one.
Chapter 8. Legal Protection for the Ugly
Fairness and Public Policy
What Kinds of Protection Are Possible?
How Have Existing Policies Been Used?
Is It Possible to Protect the Ugly?
What Justifies Protecting the Ugly?
What Justifies Not Protecting the Ugly?
What Is an Appropriate Policy?
Protecting the Ugly in the Near Future
I was surprised to learn that the majority work in geometry, supposedly the most ‘visual’ discipline, and fascinated to learn that they generally believe the experience of sight puts people at a disadvantage because it locks us into a perception-led view of space.
Silence
Help them to experience this possibly for the first time in their lives. Teach about it (take a look at my blog on silence for some ideas) and then work up from short shared silences - maybe one minute to start with - to longer ones. This will be very precious for them, but also very challenging. Ask them to write or share their experience of these silences, and what silence means in their lives.
Mixer
Take them to rich aural environments (start inside the school) and have them pair and log all the sound sources they hear. If you have the resources, let them experiment with multichannel sound.
Savouring
Give them a multi-day project to notice sounds and bring their three favourites in to class to share. If you have the resources (eg own a Zoom H2 digital recorder or similar) do this one small group at a time and have them record the sounds to play to all. You could do the same with sounds they dislike.
Listening positions
The most powerful of all. Pair them up and have A say what they had for breakfast while B listens from different positions (for example 1 I'm bored; 2 I want to be friends with this person; 3 I'm in a hurry; 4 what can I learn from this - please make up your own also). Have the As share their experiences at the end, then the Bs. Swap and repeat. If they get the principle that you can change reality by listening from a different place, that will be a great gift.
RASA (receive, appreciate, summarise, ask)
Practice each element by pairing up again and have listeners turn each element off and on while listening and then both people share their experience. Have them share about their general experience of being listened to at home, in school and elsewhere (especially by adults), and how it affects their own listening to others.
The reason, the centre's boss Ken Patchett explains, is the weather. At first blush, this seems odd. Temperatures in Prineville routinely drop to -5°C (22°F) in the winter and climb to 32°C (90°F) in the summer. And the desert clime means that drops of 28°C (50°F) between day and night are not unheard of around this time of year. That ought to make keeping the servers at a steady 20–25°C (68–77°F) and 40–55% relative humidity an arduous task.
In fact, the Prineville plant is a leading exponent of a new style of data-centre management. It does away with expensive air-conditioning "chillers". Instead, air is brought in from outside. For this approach to work, however, the desert is key. For much of the year outside air is actually cool enough to keep the servers from overheating. At the lowest temperatures, just the gentlest of breezes needs to be brought inside at all. And, this being the desert, nights are chilly irrespective of the season, so even in the summer additional cooling is only needed during the hottest times of day.
