“math”
24 posts under this tag.
Overview and Detail. The pair keeps coming up whenever you start pondering on interfaces, interface patterns, interface & information design, and well (why won’t be grand?) space, time,ELZR and thought itself. Achieving both—the ancient dream of simultaneity—is one of the deep purposes of any media creator, from writersEE to interface designers, and though it may be a humble example, The Secret Lives of Numbers—an interface to the results of a crazy study of the search-engine popularity of every integer between 0 and 1 million1—is a superb one.
The resulting information exhibits an extraordinary variety of patterns which reflect and refract our culture, our minds, and our bodies.. We surmise that our dataset is a numeric snapshot of the collective consciousness. Herein we return our analyses to the public in the form of an interactive visualization, whose aim is to provoke awareness of one’s own numeric manifestations.
The denizens of the number line are not the mere automatons or corporate tools we have made them out to be: each has a personality, talents, communities, and sometimes a little je ne sais quois. They reflect us. This unusual reflection is the focus of this project.
As for the credits:
Concept, direction, interface design & programming: Golan Levin
Interface and information design: Martin Wattenberg
Database & CGI programming (2002): Jonathan Feinberg
CGI programming (1997): David Becker
Statistics consulting: David Elashoff
Essay and research: Shelly Wynecoop
If you believe in geniuses you’re in for a treat checking out the three URLs above—each of them’s one. Martin Watenberg in particular, has some of the most intriguing information visualizations I’ve ever seen.
1 Though owing to limitations of internet bandwidth only data for the first 100,000 are provided online.
Artificial Intelligence is 50 years old this summer, to celebrate here’s an interesting New York Times article on computer models: Maybe We Should Leave That Up to the Computer.
Here some highlights:
“As long as you have some history and some quantifiable data from past experiences,” Mr. Snijders claims, a simple formula will soon outperform a professional’s decision-making skills.
Something researchers have known for decades: that mathematical models generally make more accurate predictions than humans do. Studies have shown that models can better predict, for example, the success or failure of a business start-up, the likelihood of recidivism and parole violation, and future performance in graduate school.
They also trump humans at making various medical diagnoses, picking the winning dogs at the racetrack and competing in online auctions. Computer-based decision-making has also grown increasingly popular in credit scoring, the insurance industry and some corners of Wall Street.
The algorithms behind so-called quant funds, he said, act with ” much greater depth of data than the human mind can. They can encapsulate experience that managers may not have.”
Other cherished decision aids, like meeting in person and poring over dossiers, are of equally dubious value when it comes to making more accurate choices, some studies have found, with face-to-face interviews actually degrading the quality of an eventual decision.
“People’s overconfidence in their ability to read someone in a half-an-hour interview is quite astounding,” said Michael A. Bishop, an associate professor of philosophy at Northern Illinois University who studies the social implications of these models.
Max H. Bazerman, a professor at Harvard Business School, wonders how many managerial decisions can actually be modeled. “The vast majority of decisions that we make in professional life don’t have this quality,” he said.
He agrees that models can make better decisions about credit card applications and college admissions, he said, “but there are many decisions that are much more unique, where that database doesn’t exist. I’m as skeptical about human intuition as these folks, but it’s not only a computer model that we replace it with. Sometimes it’s thinking more clearly.”
Many in the field of computer-assisted decision-making still refer to the debacle of Long Term Capital Management, a highflying hedge fund that counted several Nobel laureates among its founders. Its algorithms initially mastered the obscure worlds of arbitrage and derivatives with remarkable skill, until the devaluation of the Russian ruble in 1998 sent the fund into a tailspin.
Mark E. Nissen, a professor at the Naval Postgraduate School in Monterey, Calif., who has been studying computer-vs.-human procurement, sees a fundamental shift under way, with humans becoming increasingly peripheral in making routine decisions, concentrating instead on designing ever-better models.
By making smart use of computer models’ advantages, ” you’ll become like the crafty A student who doesn’t work that hard but gets mostly right answers, rather than the really hard-working student who gets lots of wrong answers and as a result gets C’s.”
“Quant fund” is a keeper word, remember it.
As for the eeriest applied A.I. example I’ve heard lately:
A French company, Poseidon Technologies, sells underwater vision systems for swimming pools that function as lifeguard assistants, issuing alerts when people are drowning, and the system has saved lives in Europe.
There are also 7 interesting eemadges on the topic.
to blackbox could be to reify thru interface. To suggest or implement a conceptualization thru interface. A basic strategy for synthetizing reality, it stems from an active rewriting of the famous duck test: “If I make this look like a duck, and quack like a duck, I may as well be able to conceptualize it as a duck”. The conscious, deliberate, “I make” part is crucial; to blackbox is not just to simply conceptualize, is to wilfully conceptualize something by painting an interface on it.
(Contrived) Usage Examples:
- “In modern programming, we blackbox our way out of complexity thru functions, objects, aspects, macros, and the like.”
- “Money is our society’s blackboxing of wealth, that is, of ‘what people want.’ We ought to remember it when trying to ‘make’ money.”
- “With the magic of silicone, you too can blackbox yourself a pair of massive pointy hooters!”
- “At this point, perhaps a better title for this essay is probably ‘An easy way to blackbox your own file-extension.”
- “The Kuratowski definition of an ordered pair as {{a},{a,b}} is pure blackboxing.”
- “In defining the class PlanePoint, from the stored attributes xPos and yPos you can (and probably should) blackbox Distance from them thru the distance formula.”
- “Let’s wrap these almost-expired candies with this cute cellopane bag and this lace bow, and blackbox them into a ‘Super Saving Kit’.”
- “I’m dying for someone to blackbox reputation, population, authority—the whole memetic shebang—thru some kind of social software.”
- “Don’t you find it amazing how blackboxing lanes and pedestrian crossings on the street thru mere painting can be so useful?”
- “Let’s blackbox operating systems away thru browsers!”
The word comes, of course, from the technical meaning of blackbox: “a device or system or object when it is viewed primarily in terms of its input and output characteristics.”
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