U.S. Intelligence Group Seeks Machine Learning Breakthroughs
Network World (12/02/11) Michael Cooney
The U.S. Intelligence Advanced Research Projects Activity (IARPA) announced that it is looking for new ideas that may become the basis of cutting-edge machine-learning projects. "In many application areas, the amount of data to be analyzed has been increasing exponentially [sensors, audio and video, social network data, Web information], stressing even the most efficient procedures and most powerful processors," according to IARPA. "Most of these data are unorganized and unlabeled and human effort is needed for annotation and to focus attention on those data that are significant." IARPA's request for information asks about proposed methods for the automation of architecture and algorithm selection and combination, feature engineering, and training data scheduling, as well as compelling reasons to use such approaches in a scalable multi-modal analytic system and whether supporting technologies are readily available. IARPA says that innovations in hierarchical architectures such as Deep Belief Nets and hierarchical clustering will be needed for useful automatic machine-learning systems. It wants to identify promising areas for investment and plans to hold a machine learning workshop in March 2012.
Network World (12/02/11) Michael Cooney
The U.S. Intelligence Advanced Research Projects Activity (IARPA) announced that it is looking for new ideas that may become the basis of cutting-edge machine-learning projects. "In many application areas, the amount of data to be analyzed has been increasing exponentially [sensors, audio and video, social network data, Web information], stressing even the most efficient procedures and most powerful processors," according to IARPA. "Most of these data are unorganized and unlabeled and human effort is needed for annotation and to focus attention on those data that are significant." IARPA's request for information asks about proposed methods for the automation of architecture and algorithm selection and combination, feature engineering, and training data scheduling, as well as compelling reasons to use such approaches in a scalable multi-modal analytic system and whether supporting technologies are readily available. IARPA says that innovations in hierarchical architectures such as Deep Belief Nets and hierarchical clustering will be needed for useful automatic machine-learning systems. It wants to identify promising areas for investment and plans to hold a machine learning workshop in March 2012.
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