IMO, this article is too intriguing to just post a few teaser paragraphs and a link:
HAL 9000-Style Machines, Kubrick's Fantasy, Outwit Traders
By Jason Kelly
May 3 (Bloomberg) -- Way up in a New York skyscraper, inside the headquarters of Lehman Brothers Holdings Inc., Michael Kearns is trying to teach a computer to do something other machines can't: think like a Wall Street trader.
In his cubicle overlooking the trading floor, Kearns, 44, consults with Lehman Brothers traders as Ph.D.s tap away at secret software. The programs they're writing are designed to sift through billions of trades and spot subtle patterns in world markets.
Kearns, a computer scientist who has a doctorate from Harvard University, says the code is part of a dream he's been chasing for more than two decades: to imbue computers with artificial intelligence, or AI.
His vision of Wall Street conjures up science fiction fantasies of HAL 9000, the sentient computer in ``2001: A Space Odyssey.'' Instead of mindlessly crunching numbers, AI-powered circuitry one day will mimic our brains and understand our emotions -- and outsmart human stock pickers, he says.
``This is going to change the world, and it's going to change Wall Street,'' says Kearns, who spent the 1990s researching AI at Murray Hill, New Jersey-based Bell Laboratories, birthplace of the laser and the transistor.
As finance Ph.D.s, mathematicians and other computer-loving disciples of quantitative analysis challenge traditional traders and money managers, Kearns and a small band of AI scientists have set out to build the ultimate money machine.
For decades, investment banks and hedge fund firms have employed quants and their computers to uncover relationships in the markets and exploit them with rapid-fire trades.
Hyperquants
Quants seek to strip human emotions such as fear and greed out of investing. Today, their brand of computer-guided trading has reached levels undreamed of a decade ago. A third of all U.S. stock trades in 2006 were driven by automatic programs, or algorithms, according to Boston-based consulting firm Aite Group LLC. By 2010, that figure will reach 50 percent, according to Aite.
AI proponents say their time is at hand. Vasant Dhar, a former Morgan Stanley quant who teaches at New York University's Stern School of Business in Manhattan's Greenwich Village, is trying to program a computer to predict the ways in which unexpected events, such as the sudden death of an executive, might affect a company's stock price.
Uptown, at Columbia University, computer science professor Kathleen McKeown says she imagines building an electronic Warren Buffett that would be able to answer just about any kind of investing question.
``We want to be able to ask a computer, `Tell me about the merger of corporation A and corporation B,' or `Tell me about the impact on the markets of sending more troops to Iraq,''' McKeown, 52, says.
Kubrick's Dream
Some executives and scientists would rather not talk about AI. It recalls dashed hopes of artificially intelligent machines that would build cities in space and mind the kids at home. In ``2001,'' the novel written by Arthur C. Clarke and made into a movie directed by Stanley Kubrick in 1968, HAL, a computer that can think, talk and see, is invented in the distant future -- 1997.
Things didn't turn out as '60s cyberneticians predicted. Somewhere between sci-fi and sci-fact, the dream fell apart. People began joking that AI stood for ``Almost Implemented.''
``The promise has always been more than the delivery,'' says Brian Hamilton, chief executive officer of Raleigh, North Carolina-based software maker Sageworks Inc., which uses computer formulas to automatically read stock prices, company earnings and other data and spit out reports for investors.
Hamilton, 43, says today's AI-style programs can solve specific problems within a given set of parameters.
Chess vs Markets
Take chess. Deep Blue, a chess-playing supercomputer developed by International Business Machines Corp., defeated world champion Garry Kasparov in 1997. The rules of chess never change, however. Players have one goal: to capture the opponent's king. There are only so many moves a player can make, and Deep Blue could evaluate 200 million such positions a second.
Financial markets, on the other hand, can be influenced by just about anything, from skirmishes in the Middle East to hurricanes in the Gulf of Mexico. In computerspeak, chess is a closed system and the market is an open one.
``AI is very effective when there's a specific solution,'' Hamilton says. ``The real challenge is where judgment is required, and that's where AI has largely failed.''
AI researchers have made progress over the years. Peek inside your Web browser or your car's cruise control, and you'll probably find AI at work. Meanwhile, computer chips keep getting more powerful. In February, Santa Clara, California-based Intel Corp. said it had devised a chip the size of a thumbnail that could perform a trillion calculations a second.
AI Believers
Ten years ago, such a computational feat would have required 10,000 processors.
To believers such as Dhar, Kearns and McKeown, all of this is only the beginning. One day, a subfield of AI known as machine learning, Kearns's specialty, may give computers the ability to develop their own smarts and extract rules from massive data sets. Another branch, called natural language processing, or NLP, holds out the prospect of software that can understand human language, read up on companies, listen to executives and distill what it learns into trading programs.
Collective Intellect Inc., a Boulder, Colorado-based startup, already employs basic NLP programs to comb through 55 million Web logs and turn up information that might make money for hedge funds.
``There's some nuggets of wisdom in the sea,'' says Collective Intellect Chief Technology Officer Tim Wolters.
Another AI area, neural networking, involves building silicon versions of the cerebral cortex, the part of our brain that governs reason.
`It's Here'
The hope is that these systems will ape living neurons, think like people and, like traders, understand that some things are neither black nor white but rather in varying shades of gray.
Stock analyst Ralph Acampora, who caused a stir in 1999 by correctly predicting that the Dow Jones Industrial Average would top 10,000, says investment banks are racing to profit from advanced computing such as AI.
``It's here, and it's growing,'' says Acampora, 65, chief technical analyst at Knight Capital Group Inc. in Jersey City, New Jersey. ``Everybody's trying to outdo everyone else.''
The computers have done well. A November 2005 study by Darien, Connecticut-based Casey, Quirk & Associates, an investment management consulting firm, says that from 2001 to '05, big-cap U.S. stock funds run by quants beat those run by nonquants.
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HAL 9000-Style Machines, Kubrick's Fantasy, Outwit Traders
By Jason Kelly
May 3 (Bloomberg) -- Way up in a New York skyscraper, inside the headquarters of Lehman Brothers Holdings Inc., Michael Kearns is trying to teach a computer to do something other machines can't: think like a Wall Street trader.
In his cubicle overlooking the trading floor, Kearns, 44, consults with Lehman Brothers traders as Ph.D.s tap away at secret software. The programs they're writing are designed to sift through billions of trades and spot subtle patterns in world markets.
Kearns, a computer scientist who has a doctorate from Harvard University, says the code is part of a dream he's been chasing for more than two decades: to imbue computers with artificial intelligence, or AI.
His vision of Wall Street conjures up science fiction fantasies of HAL 9000, the sentient computer in ``2001: A Space Odyssey.'' Instead of mindlessly crunching numbers, AI-powered circuitry one day will mimic our brains and understand our emotions -- and outsmart human stock pickers, he says.
``This is going to change the world, and it's going to change Wall Street,'' says Kearns, who spent the 1990s researching AI at Murray Hill, New Jersey-based Bell Laboratories, birthplace of the laser and the transistor.
As finance Ph.D.s, mathematicians and other computer-loving disciples of quantitative analysis challenge traditional traders and money managers, Kearns and a small band of AI scientists have set out to build the ultimate money machine.
For decades, investment banks and hedge fund firms have employed quants and their computers to uncover relationships in the markets and exploit them with rapid-fire trades.
Hyperquants
Quants seek to strip human emotions such as fear and greed out of investing. Today, their brand of computer-guided trading has reached levels undreamed of a decade ago. A third of all U.S. stock trades in 2006 were driven by automatic programs, or algorithms, according to Boston-based consulting firm Aite Group LLC. By 2010, that figure will reach 50 percent, according to Aite.
AI proponents say their time is at hand. Vasant Dhar, a former Morgan Stanley quant who teaches at New York University's Stern School of Business in Manhattan's Greenwich Village, is trying to program a computer to predict the ways in which unexpected events, such as the sudden death of an executive, might affect a company's stock price.
Uptown, at Columbia University, computer science professor Kathleen McKeown says she imagines building an electronic Warren Buffett that would be able to answer just about any kind of investing question.
``We want to be able to ask a computer, `Tell me about the merger of corporation A and corporation B,' or `Tell me about the impact on the markets of sending more troops to Iraq,''' McKeown, 52, says.
Kubrick's Dream
Some executives and scientists would rather not talk about AI. It recalls dashed hopes of artificially intelligent machines that would build cities in space and mind the kids at home. In ``2001,'' the novel written by Arthur C. Clarke and made into a movie directed by Stanley Kubrick in 1968, HAL, a computer that can think, talk and see, is invented in the distant future -- 1997.
Things didn't turn out as '60s cyberneticians predicted. Somewhere between sci-fi and sci-fact, the dream fell apart. People began joking that AI stood for ``Almost Implemented.''
``The promise has always been more than the delivery,'' says Brian Hamilton, chief executive officer of Raleigh, North Carolina-based software maker Sageworks Inc., which uses computer formulas to automatically read stock prices, company earnings and other data and spit out reports for investors.
Hamilton, 43, says today's AI-style programs can solve specific problems within a given set of parameters.
Chess vs Markets
Take chess. Deep Blue, a chess-playing supercomputer developed by International Business Machines Corp., defeated world champion Garry Kasparov in 1997. The rules of chess never change, however. Players have one goal: to capture the opponent's king. There are only so many moves a player can make, and Deep Blue could evaluate 200 million such positions a second.
Financial markets, on the other hand, can be influenced by just about anything, from skirmishes in the Middle East to hurricanes in the Gulf of Mexico. In computerspeak, chess is a closed system and the market is an open one.
``AI is very effective when there's a specific solution,'' Hamilton says. ``The real challenge is where judgment is required, and that's where AI has largely failed.''
AI researchers have made progress over the years. Peek inside your Web browser or your car's cruise control, and you'll probably find AI at work. Meanwhile, computer chips keep getting more powerful. In February, Santa Clara, California-based Intel Corp. said it had devised a chip the size of a thumbnail that could perform a trillion calculations a second.
AI Believers
Ten years ago, such a computational feat would have required 10,000 processors.
To believers such as Dhar, Kearns and McKeown, all of this is only the beginning. One day, a subfield of AI known as machine learning, Kearns's specialty, may give computers the ability to develop their own smarts and extract rules from massive data sets. Another branch, called natural language processing, or NLP, holds out the prospect of software that can understand human language, read up on companies, listen to executives and distill what it learns into trading programs.
Collective Intellect Inc., a Boulder, Colorado-based startup, already employs basic NLP programs to comb through 55 million Web logs and turn up information that might make money for hedge funds.
``There's some nuggets of wisdom in the sea,'' says Collective Intellect Chief Technology Officer Tim Wolters.
Another AI area, neural networking, involves building silicon versions of the cerebral cortex, the part of our brain that governs reason.
`It's Here'
The hope is that these systems will ape living neurons, think like people and, like traders, understand that some things are neither black nor white but rather in varying shades of gray.
Stock analyst Ralph Acampora, who caused a stir in 1999 by correctly predicting that the Dow Jones Industrial Average would top 10,000, says investment banks are racing to profit from advanced computing such as AI.
``It's here, and it's growing,'' says Acampora, 65, chief technical analyst at Knight Capital Group Inc. in Jersey City, New Jersey. ``Everybody's trying to outdo everyone else.''
The computers have done well. A November 2005 study by Darien, Connecticut-based Casey, Quirk & Associates, an investment management consulting firm, says that from 2001 to '05, big-cap U.S. stock funds run by quants beat those run by nonquants.
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