18 DECEMBER 2009 VOL 326 SCIENCE www.sciencemag.org 1644 PERSPECTIVES been demonstrated on a small UAV ( 15), and there is ongoing research in control strategies to improve cruise performance of small and mini-UAVs by gust energy harvesting. The improved perception provided by the sensing and processing systems, coupled with the improved persistence provided by atmo- spheric energy harvesting, should enable long-endurance missions that are far beyond the capabilities of current robotic aircraft. Eventually a soaring-capable, autonomous, mini-UAV that is equipped with a sophisti- cated sensing system will be able to follow a migrating bird and provide close-up in-flight video, as well as in situ atmospheric mea- surements. Successful completion of such a challenging mission would demonstrate that flight by human-built robotic aircraft could rival that of birds. References and Notes 1. The Aerosonde UAV has flown meteorology missions into hurricanes (www.aerosonde.com/), but it is still too large for many military and scientific missions. 2. The Defense Advanced Research Projects Agency (DARPA) definition of MAV specifies a wingspan of 15 cm or less. Here we use the term more loosely to include all vehicles that are hand-launchable and can be operated by a single person. An example is the Aerovironment Wasp MAV (www.avinc.com/uas/small_uas/wasp/). 3. S. Griffiths et al., in Advances in Unmanned Aerial Vehicles: State of the Art and the Road to Autonomy, K. P. Valavanis, Ed. (Springer-Verlag, Dordrecht, Netherlands, 2007), pp. 213–244. 4. J. J. Leonard et al., Dynamic Map Building for an Autono- mous Mobile Robot. Int. J. Robot. Res. 11, 286 (1992). 5. S. Thrun, Learning metric-topological maps for indoor mobile robot navigation. Artif. Intell. 99, 21 (1998). 6. M. Achtelik, A. Bachrach, R. He, S. Prentice, N. Roy, Proc. SPIE 7332, 733219 (2009). 7. A. Torralba, K. P. Murphy, W. T. Freeman, M. A. Rubin, in Proceedings of the Ninth IEEE International Conference on Computer Vision (IEEE, Piscatuway, NJ, 2003), pp. 273–280. 8. K. Lai, D. Fox, Proceedings of Robotics: Science and Sys- tems (MIT Press, Cambridge, MA, 2009). 9. Y. Wei, E. Brunskill, T. Kollar, N. Roy, in Proceedings of the IEEE International Conference on Robotics and Auto- mation (IEEE, Piscatuway, NJ, 2009), pp. 3761–3767. 10. J. W. S. Rayleigh, Nature 27, 534 (1883). 11. M. J. Allen, V. Lin, AIAA Aerospace Sciences Meeting and Exhibit (AIAA Paper 2007-867, American Institute of Aeronautics and Astronautics, Reno, NV, 2007). 12. A. Chakrabarty, J. W. Langelaan, AIAA Guidance, Naviga- tion and Control Conference, AIAA Paper 2009-6113 (American Institute of Aeronautics and Astronautics, Chicago, IL, 2009). 13. G. Sachs, Ibis 147, 1 (2005). 14. M. Deittert, A. Richards, C. A. Toomer, A. Pipe, J. Guid. Control Dyn. 32, 1446 (2009). 15. C. K. Patel, Energy Extraction from Atmospheric Turbu- lence to Improve Aircraft Performance (VDM, Saarbrüken, Germany, 2008). Supporting Online Material www.sciencemag.org/cgi/content/full/326/5960/1642/DC1 Videos: S1 and S2 Mining Our Reality COMPUTER SCIENCE Tom M. Mitchell Real-time data on the whereabouts and behaviors of much of humanity advance behavioral science and offer practical benefits, but also raise privacy concerns. S omething important is changing in how we as a society use computers to mine data. In the past decade, machine- learning algorithms have helped to analyze historical data, often revealing trends and pat- terns too subtle for humans to detect. Exam- ples include mining credit card data to dis- cover activity patterns that suggest fraud, and mining scientific data to discover new empiri- cal laws ( 1, 2). Researchers are beginning to apply these algorithms to real-time data that record personal activities, conversations, and movements ( 3– 8) in an attempt to improve human health, guide traffic, and advance the scientific understanding of human behavior. Meanwhile, new algorithms aim to address privacy concerns arising from data sharing and aggregation ( 9, 10). To appreciate both the power and the pri- vacy implications of real-time data mining, consider the data available just to your phone company, based on your phone records and those of millions of other individuals who are going about their daily lives carrying a smart phone—a device that contains a Global Posi- tioning System (GPS) sensor locating you to within a few meters, an accelerometer that detects when you are walking versus stationary, a microphone that detects both conversations and background noises, a camera that records where each picture was taken, and an interface that observes every incoming and outgoing e-mail and text message. The potential bene- fits of mining such data are various; examples include reducing traffic congestion and pollu- tion, limiting the spread of disease, and better using public resources such as parks, buses, and ambulance services. But risks to privacy from aggregating these data are on a scale that humans have never before faced. One line of research is based on watch- ing where people are, where they are head- ing, and when. Anonymous real-time loca- tion data from smart phones are already being used to provide up-to-the-minute reports of traffic congestion in many urban regions through services such as Google Maps ( 11). Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA. E-mail: tom.mitchell@cs.cmu. edu 10.1126/science.1182497 5 4 3 2 1 0 60 50 40 30 20 10 0 0 10 20 30 40 50 60 East (km) North (km) Altitude (km) Wind-assisted outdoor flight. The potential for extracting energy for flight from natural winds created by mountain “wave”—long-period oscillations of the atmosphere—over central Pennsylvania (Allegheny Plateau, Bald Eagle Ridge, and Tussey Ridge). The cyan isosurfaces bound the regions where soaring can occur—vertical wind velocity exceeds the sink rate of the vehicle. Nighttime wind-field changes are shown in video S2. CREDIT: (PLOT) A. CHAKRABARTY/DEPARTMENT OF AEROSPACE ENGINEERING, PENN STATE UNIVERSITY. (DATA) G. S. YOUNG, B. J. GAUDET, N. L. SEAMAN, D. R. STAUFFER/DEPARTMENT OF METEOROLOGY/PENN STATE UNIVERSITY Published by AAAS on December 18, 2009 www.sciencemag.org Downloaded from