In the Metal Recycling Business, It’s Loud, Dirty and Suddenly Lucrative

The company also attracts smaller-scale customers, like Johnny Slavos, 23, whose ponytail dripped with sweat the other day as he unloaded a 100-pound Cadillac engine that he said he had picked up at a junkyard. He would not say anything more about where he collects scrap metal. “I can’t tell you my secrets,” he said, explaining that he worried that others might elbow in on his turf. “It’s like the old gold rush.”

Nobody at the yard knows what happens to any of the scrap metal after it leaves the site. “Metal has no memory,” Mr. Monteleone said, looking down at the pen in his hand. “It could be made into this pen tip.”

The New York Times – June 27, 2008

The company also attracts smaller-scale customers, like Johnny Slavos, 23, whose ponytail dripped with sweat the other day as he unloaded a 100-pound Cadillac engine that he said he had picked up at a junkyard. He would not say anything more about where he collects scrap metal. “I can’t tell you my secrets,” he said, explaining that he worried that others might elbow in on his turf. “It’s like the old gold rush.”

Nobody at the yard knows what happens to any of the scrap metal after it leaves the site. “Metal has no memory,” Mr. Monteleone said, looking down at the pen in his hand. “It could be made into this pen tip.”

The New York Times – June 27, 2008

Chunking is not a city in China

**On the automatic generation of case libraries by chunking chess games **

Stephen Flinter1 and Mark T. Keane1

(1) Trinity College, Dublin

Abstract

As a research topic computer game playing has contributed problems to AI that manifest exponential growth in the problem space. For the most part, in games such as chess and checkers these problems have been surmounted with enormous computing power on brute-force search methods using massive databases. It remains to be seen whether such techniques will extend to other games such as go and shogi. One suggestion is that these games and even chess might benefit from a knowledge-based treatment but such approaches have met with limited success. The problem, as ever from such approaches, is the characterisation of the knowledge to be used by the system. This paper deals with the Tal system, which employs case-based reasoning techniques for chess playing. In the paper, rather than focus on playing, we concentrate on the automatic generation of suitable case knowledge using a chunking technique on a corpus of grandmaster games.

**On the automatic generation of case libraries by chunking chess games **

Stephen Flinter1 and Mark T. Keane1

(1) Trinity College, Dublin

Abstract

As a research topic computer game playing has contributed problems to AI that manifest exponential growth in the problem space. For the most part, in games such as chess and checkers these problems have been surmounted with enormous computing power on brute-force search methods using massive databases. It remains to be seen whether such techniques will extend to other games such as go and shogi. One suggestion is that these games and even chess might benefit from a knowledge-based treatment but such approaches have met with limited success. The problem, as ever from such approaches, is the characterisation of the knowledge to be used by the system. This paper deals with the Tal system, which employs case-based reasoning techniques for chess playing. In the paper, rather than focus on playing, we concentrate on the automatic generation of suitable case knowledge using a chunking technique on a corpus of grandmaster games.

Fwd: Phase space mapping

Begin forwarded message: > From: Jeffrey Warren > Date: June 26, 2008 9:25:08 PM EDT > To: David Pitman > Subject: Phase space mapping > > What if we took any dataset, found the range for each column, > determined a scale and linear/log distribution, and produced a > matrix of graphs of every possible pairing of columns plotted on > x,y… Weren’t you suggesting something like that for our visual > language concept? Wanna try a quick proof of concept?

Begin forwarded message: > From: Jeffrey Warren > Date: June 26, 2008 9:25:08 PM EDT > To: David Pitman > Subject: Phase space mapping > > What if we took any dataset, found the range for each column, > determined a scale and linear/log distribution, and produced a > matrix of graphs of every possible pairing of columns plotted on > x,y… Weren’t you suggesting something like that for our visual > language concept? Wanna try a quick proof of concept?