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 do not know where to begin.
Now, in an advance that may impact the field of artificial intelligence, an algorithm developed at MIT can analyse a set of data and decide which type of organisational structure best fits it.
Josh Tenenbaum, an associate professor of brain and cognitive sciences at MIT, said: ‘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.’
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.
The model considers a range of possible data structures, such as trees, linear orders, rings, dominance hierarchies, and clusters. 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.
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.