<html><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><div class="MsoNormal"><div class="MsoNormal"><font class="Apple-style-span" face="Geneva">FOR IMMEDIATE RELEASE</font></div><div class="MsoNormal"><font class="Apple-style-span" face="Arial">Contact: Teresa Herbert, MIT News Office</font></div><div class="MsoNormal"><font class="Apple-style-span" face="Geneva"><span class="Apple-style-span" style="font-family: Arial; ">T. 617-258-5403, E. <a href="mailto:therber@mit.edu" style="color: blue; text-decoration: underline; ">therbert@mit.edu</a> </span></font></div><div><font class="Apple-style-span" face="Arial"><br></font></div><div>============================================</div></div><div class="MsoNormal">MIT model helps computers sort data more like humans</div><div class="MsoNormal">--Advance could impact artificial intelligence field</div><div class="MsoNormal">============================================</div><div class="MsoNormal"> </div><div class="MsoNormal"><o:p></o:p></div><div class="MsoNormal">CAMBRIDGE, Mass. -- Humans have a natural tendency to find order in sets of information, a skill that has proven difficult to replicate in computers. Faced with a large set of data, computers don’t know where to begin — unless they’re programmed to look for a specific structure, such as a hierarchy, linear order, or a set of clusters.</div><div class="MsoNormal"> <o:p></o:p></div><div class="MsoNormal">Now, in an advance that may impact the field of artificial intelligence, a new model developed at MIT can help computers recognize patterns the same way that humans do. The model, reported earlier this month in the Proceedings of the National Academy of Science, can analyze a set of data and figure out which type of organizational structure best fits it.<o:p></o:p></div><div class="MsoNormal"> <o:p></o:p></div><div class="MsoNormal">“Instead of looking for a particular kind of structure, we came up with a broader algorithm that is able to look for all of these structures and weigh them against each other,” said Josh Tenenbaum, an associate professor of brain and cognitive sciences at MIT and senior author of the paper.</div><div class="MsoNormal"> <o:p></o:p></div><div class="MsoNormal">The model could help scientists in many fields analyze large amounts of data, and could also shed light on how the human brain discovers patterns.</div><div class="MsoNormal"> <o:p></o:p></div><div class="MsoNormal">The computer algorithm was developed by recent MIT PhD recipient Charles Kemp, now an assistant professor of psychology at Carnegie Mellon University, along with Tenenbaum.</div><div class="MsoNormal"> <o:p></o:p></div><div class="MsoNormal">The model considers a range of possible data structures, such as trees, linear orders, rings, dominance hierarchies, clusters, etc. It finds the best-fitting structure of each type for a given data set and then picks the type of structure that best represents the data.</div><div class="MsoNormal"> <o:p></o:p></div><div class="MsoNormal">Humans perform the same feat in everyday life, often unconsciously. Several scientific milestones have resulted from the human skill of finding patterns in data — for example, the development of the periodic table of the chemical elements or the organization of biological species into a tree-structured system of classification.</div><div class="MsoNormal"> <o:p></o:p></div><div class="MsoNormal">Children exhibit this data organization skill at a young age, when they learn that social networks can be organized into cliques, and that words can fit into overlapping categories (for example, dog, mammal, animal).</div><div class="MsoNormal"> <o:p></o:p></div><div class="MsoNormal">“We think of children as taking in data, forming theories, and testing those theories with experiments. They’re like little scientists,” Tenenbaum said. “Until now there’s been no good computational model for how children can, like scientists, grasp the underlying global structure of a set of data.”</div><div class="MsoNormal"> <o:p></o:p></div><div class="MsoNormal">The research was funded by the James S. McDonnell Foundation Causal Learning Research Collaborative, the Air Force Office of Scientific Research, and the NTT Communication Sciences Laboratory.</div><div class="MsoNormal"><br></div><div class="MsoNormal">By Anne Trafton, MIT News Office</div><div class="MsoNormal"># # #</div><div><br></div><div apple-content-edited="true"> <span class="Apple-style-span" style="border-collapse: separate; color: rgb(0, 0, 0); font-family: Helvetica; font-size: 14px; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: 2; text-align: auto; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-border-horizontal-spacing: 0px; -webkit-border-vertical-spacing: 0px; -webkit-text-decorations-in-effect: none; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0; "><div style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><span class="Apple-style-span" style="border-collapse: separate; color: rgb(0, 0, 0); font-family: Helvetica; font-size: 18px; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: 2; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-border-horizontal-spacing: 0px; -webkit-border-vertical-spacing: 0px; -webkit-text-decorations-in-effect: none; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; "><div style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><div><font class="Apple-style-span" size="4"><span class="Apple-style-span" style="font-size: 14px; ">Teresa Herbert<br>Media Specialist<br>Massachusetts Institute of Technology<br>News Office, Room 11-400<br>Cambridge, MA 02139-4307<br><br>Phone: 617-258-5403<br>Fax: 617-258-8762<br><br><a href="mailto:therbert@mit.edu">therbert@mit.edu</a></span></font></div><div><br></div></div></span><br class="Apple-interchange-newline"></div></span><br class="Apple-interchange-newline"> </div><br></body></html>