"ALGORITHM: Intel to release first 'causal' learning algorithms"
Intel Corp. unveils a do-everything machine learning package this week capable of fusing separate streams from real-time sensors as easily as spotting and identifying objects and conditions. Released as "open source" software, the downloadable machine learning suite will be described at the Neural Information Processing Systems (NIPS) conference this week in Vancouver, B.C. For the first time ever, according to Intel, its probabalistic network library will enable causal relations to be easily cast into control programs that monitor sensor networks. Prior to probabalistic networks, statistical methods could only categorize correlations, which could relate, for example, a wet lawn to rain, but not tell which caused which. By adding directed graphs, which show the direction of causality, large numbers of incoming data streams can be tamed down to the conclusions that should be drawn from them by merely "following the graph."
Audio Interviews / Text: http://www.eetimes.com/story/OEG20031208S0008