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News Release 16-056

Crowd-augmented cognition

NSF early career awardee develops tools that combine human and machine intelligence to accelerate learning

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illustration showing a bubble filled with faces of people

Crowdsourcing enlists the services of many people, either paid or unpaid, typically via the Internet, to accomplish a task that might be difficult or not cost-effective with full-time or geographically limited staff.

Credit: Testbirds GmbH (Own work) [CC BY-SA 4.0] via Wikimedia Commons


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Aniket Kittur

Aniket Kittur is an associate Pprofessor and holds the Cooper-Siegel Chair in the Human-Computer Interaction Institute at Carnegie Mellon University. His research on crowd-augmented cognition looks at how we can augment the human intellect using crowds and computation.

Credit: Carnegie Mellon University


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a machine and text knowledge, prioritizing, clustering, aggregating, finding, judgin

The Knowledge Accelerator uses a machine-learning program to sort and organize information.

Credit: Carnegie Mellon University


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diagram showing the intersection of cognition, computation and crowds

Kittur's research focuses on augmenting human cognition using crowds and computation. They study and build social computing systems that combine the flexibility of many human minds working together with the raw power of computational systems to accelerate learning, innovation, knowledge production and scientific discovery.

Credit: Aniket Kittur, Carnegie Mellon University


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screenshot fo a wikipedia page about tomatoes

Crowdsourcing offers a powerful new paradigm for online work. The Knowledge Accelerator takes as input an arbitrary question (e.g., "how do I grow better tomatoes?") and uses human judgments and computation to output a synthesized digest which finds and vets relevant online sources, extracts relevant information segments, clusters them into appropriate topics, synthesizes knowledge within each topic, and maintains context within and between topics. Here, the final output of the Knowledge Accelerator system.

Credit: Aniket Kittur, Carnegie Mellon University


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a conceptual overview of how the Alloy system works between crown and machine

A conceptual overview of the Alloy system. In the first phase, crowd workers identify seed clips to train a machine learning model, which is used to classify the "head" of the distribution. In the second phase, crowd workers classify the more difficult items in the "tail." A machine learning backbone provides a consistent way to connect worker judgments in different phases.

Credit: Aniket Kittur, Carnegie Mellon University


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