Knowledge does not arise from the simple accumulation of facts. Rather, it is a complex, dynamic system, and its emergent outcomes - including scientific consensus - are unpredictable. The complexity of knowledge creation has exploded with the growing number of participating scientists and citizens. If human knowledge is to grow efficiently, we need a deeper understanding of the processes by which knowledge is conceived, validated, shared and reinforced. We need to understand the limits of knowledge in relation to these processes. In short, we need knowledge about knowledge.
The Knowledge Lab seeks to understand how knowledge is created, certified, used and forgotten in order to improve the process of discovery. The explosion of digital information, including articles, preprints, software, patient records, patents, videos, and sensor data, offers an unprecedented opportunity to study the dynamics that shape research. Knowledge Lab researchers develop new big data, machine learning and crowd-sourcing approaches and techniques to comprehend the current shape and limits of human understanding. These projects will catalyze a new field devoted to representing, recombining and generating knowledge in powerful ways across all fields.
The Knowledge Lab includes researchers from the University of Chicago, Argonne National Laboratory, Stanford University, Northwestern University, the University of California, Los Angeles, University of Washington, University of Wisconsin, Princeton University and Harvard University.