In 1999, IBM developed the Deep Computing Institute built on its experience with Deep Blue. The institute aimed to explore and solve large, complex technological problems through deep computing—using large-scale advanced computational methods applied to large data sets. Deep computing provides business decision makers with the ability to analyze and develop solutions to these very difficult problems.
Deep computing has been applied to many areas, including data mining. Most business enterprises around the world have accumulated massive databases containing valuable information about their customers, sales, competitors and products. Deep computing can be used to help discover hidden relationships and patterns in large databases using the tremendous speed and capacity of massively parallel supercomputers. This can give business leaders information to help them make the right decisions to improve efficiencies, increase revenue and build customer loyalty.
Financial risk assessment is another area that can be assisted by deep computing. A wide variety of statistical methods can be used to help assess portfolio risk and predict future equity values. Typically, these modeling applications require a rapid assessment of value and risk associated with a large number of stocks and portfolios—similar to the assessments which must be made in a chess game. Because the evaluations are largely independent of each other, parallel processing is uniquely qualified for the job. Deep computing can enable users to visualize and capitalize on trends to help offer a significant advantage in the marketplace.
Molecular dynamics is used within the pharmaceutical industry to discover and develop new drugs. Analyzing compound interactions on a molecular level requires massive amounts of processing and computational power. By creating a special purpose chip that can specifically address the complexities of molecular dynamics, a system can be designed that can quickly and efficiently analyze the interactions between atoms and molecules pertinent to the design of pharmaceutical compounds. Deep computing can help cut the development time for drugs dramatically and reduce costs substantially.