IBM will be challenging Jeopardy champions Ken Jennings and Brad Rutter by introducing Watson, the  super-computer. The competition between the three players will be broadcast in February over three nights. The prizes are $1 million, $300,000 and respectively $200,000, half of which Jennings and Rutter said would be donated to charities. IBM officials stated they would donate the entire sum.

The IBM Jeopardy Challenge is a momentous for progression in the artificial intelligence domain given past performances of this amazing computer created by 25 IBM scientists in the last four years. The system comprises about 200 million pages of content, Watson being capable of scanning 2 million pages of content in less than three seconds.

“We are at a very special moment in time,” said Dr. John E. Kelly III, IBM Senior Vice President and Director of IBM Research. “We are at a moment where computers and computer technology now have approached humans. We have created a computer system that has the ability to understand natural human language, which is a very difficult thing for computers to do.”

Watson is the improved version of Deep Blue, the IBM super-computer that ultimately defeated chess grandmaster Garry Kasparov in 1997. The Watson project is of utmost importance for the humankind considering that it can be applied to law, medicine, business and several other domains.

During the press conference held today at IBM Research headquarters in Yorktown Heights, NY, a Jeopardy practice round was held between Watson, Jennings and Rutter. The round was led by Watson with $4,400, followed by Jennings with $3,400 and Rutter with $1,200.

IBM has recently announced another breakthrough in the domain, namely ‘racetrack memory’, a stunning technology that has the potential to exponentially increase the computing performance and can facilitate storage of information, and is 100x bigger than any of the existing ones. This  new technology is of great interest to many players in the cloud industry, especially Fusion-io, which has also been working on its own version, seeing early results with in-memory caching.