The announcement was made at the opening of the Neural Information Processing Systems Conference (NIPS2003).

The software enables computers to estimate the likelihood that something will happen by calculating how often it occurred in the past. The software can be used to enhance a wide variety of interactive and industrial computer applications -- everything from culling through huge databases of gene studies to spot promising proteins for new drugs to email systems that create a model of a person's behavior to decide how best to manage newly arriving messages on its own. The software is available through Intel's Open Source Machine Learning Library (OpenML), a toolbox of functions that helps researchers develop machine learning applications.

"Intel wants computers to be more proactive," said David Tennenhouse, vice president in Intel's Corporate Technology Group and director of research. "To do this they need to be able to learn from their experiences with users and the world around them. Using new statistical methods to identify key patterns, these systems will start anticipating the needs of their users and pre-computing responses to the most likely questions so that the answers will be instantly available the moment they are needed. Combined with faster microprocessors, OpenML is certain to drive an explosion of machine learning-based applications such as toys that respond to a child's movements and networks of wireless sensors that will enhance our safety, productivity and stewardship of our environment."

OpenML is based on "Bayesian" mathematical principles which essentially are the idea that the probability of future events can be calculated by studying their prior frequency. Because Bayesian models are based on data collected from experience, the more data obtained the better the predictions, and if the data changes, the results correct themselves.

NIPS2003 is the premier scientific meeting on neural computation. Presentations cover a wide range of topics, including algorithms and architectures; applications; brain imaging; cognitive science and artificial intelligence; control and reinforcement learning; emerging technologies; learning theory; neuroscience; speech and signal processing; and visual processing.