Investment firms have increasingly begun exploring Mathematics to it fullest, as arbitrage opportunities disappear so quickly now, neural networks have emerged that can consider thousands of scenarios at once.

The Apama Algorithmic Trading Platform has made it possible for day traders to build complicated trading algorithms almost as easily as they drag an icon across a digital desktop. Studies estimate that a third of all stock trades in the United States were driven by automatic algorithms last year, contributing to an explosion in stock market activity. Between 1995 and 2005, the average daily volume of shares traded on the New York Stock Exchange increased to 1.6 billion from 346 million.

Ray Kurzweil, an inventor and new hedge fund manager, said "Artificial intelligence is becoming so deeply integrated into our economic ecostructure that some day computers will exceed human intelligence......Machines can observe billions of market transactions to see patterns we could never see."

Microsoft executive and chairman of the Nasdaq stock market, Michael Brown, is an investor in Kurzweil's new hedge fund, FatKat, and Bill Gates once described him as "the best person I know at predicting the future of artificial intelligence."

"Five years ago it would have taken $500,000 and 12 people to do what today takes a few computers and co-workers," said Louis Morgan, managing director of HG Trading, a three-person hedge fund in Wisconsin. "I'm executing 1,500 to 2,000 trades a day and monitoring 1,500 pairs of stocks. My software can automatically execute a trade within 20 milliseconds - five times faster than it would take for my finger to hit the buy button."

Orhan Karaali, a computer scientist and director at the $1.7 billion hedge fund Advanced Investment Partners said "A machine that can generate complicated rules a person would never have thought of, and that can learn from past mistakes is a powerful tool."

Complicated stock-picking methods are nothing new. For decades, Wall Street firms and hedge funds like D.E. Shaw have snapped up people with math and engineering doctorates, the so-called quants, and assigned them to find hidden market patterns. When these analysts discover subtle relationships, like similarities in the price movements of Microsoft and IBM, investors seek profits by buying one stock and selling the other when their prices diverge, betting that historical patterns will eventually push them back into synchronicity. Whew!