Beyond citizen science: Hybrid intelligences
Name of applicant
Jacob Friis Sherson
Institution
Aarhus University
Amount
DKK 15,080,067
Year
2018
Type of grant
Semper Ardens: Advance
What?
This proposal aims to create an extensive, interdisciplinary effort combining natural, social, cognitive, and computer science to address three fundamental modern challenges: Increasing the understanding of human behavior. Developing tools for systematic mapping of cognitive and psychological demographics and individualized profiling as a step towards population-scale benchmarking and individualized mental health diagnostics. These will allow us to lay the foundations for future algorithms using machine learning to build upon uniquely human search characteristics.
Why?
The impressive advances within artificial intelligence (AI) and machine learning (ML) are largely due to two distinct novelties: increasing computational power and the increasing availability of massive labeled datasets. Despite their success in specialized applications, they also clearly demonstrate that future AI systems will continue to rely on human insight and intuition, or even interoperate with human intelligence. Humans sometimes exhibit the ability to solve high dimensional, complex problems relying on the ability to extrapolate from sparse data, by applying domain-specific heuristics in the form of ‘intuition’. Recognizing this, we explore the potential of citizen science to better understand human cognitive processes and design human-AI systems to tackle complex challenges.
How?
Our unique approach is to exploit three novel uses of the citizen science: We will setup a novel infrastructure allowing for simple and flexible initialization of online, large-scale social science experiments (social science supercollider). We will create a suite of simple online games, which can be used as an orthogonal basis from which individual player cognitive characteristics can be extracted. We will utilize the massive amounts of human player data generated in natural science research games to train machine learning algorithms to tackle various problems such as search and optimization.