// THE INEVITABLE AI DEEP DIVE //
The state of the art in machine learning and information extraction has advanced the detection and recognition of concepts and objects, like people, locations, and various other types of named entities. However, there are still various types of human knowledge that cannot yet be captured by machines, especially when dealing with wide ranges of real-world tasks and contexts. The key scientific challenge is to provide an approach to capturing human knowledge in a way that is scalable and adequate to real-world needs. Human Computation has begun to scientifically study how human intelligence at scale can be used to methodologically improve machine-based knowledge and data management. Lora Aroyo’s research focuses on understanding human computation for improving how machine-based systems can acquire, capture and harness human knowledge and thus become even more intelligent.
Lora will share use cases related to smart culture, e.g. enrichment of cultural heritage collections of artworks, videos, newspapers, etc. and how the CrowdTruth crowdsourcing framework (http://crowdtruth.org) facilitates data collection, processing and analytics of human computation knowledge. Processing real-world data with the crowd leaves one thing absolutely clear - there is no single notion of truth, but rather a spectrum that has to account for context, opinions, perspectives and shades of grey. CrowdTruth is a new framework for processing human semantics drawn more from the notion of consensus than from set theory.
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