Climate change is affecting extreme weather – we identify how and when society is at risk
Seasonal to sub-seasonal (S2S) predictability could provide societies with valuable information on weather-related risk, allowing decision-makers to initiate early warning action plans and to optimize resource management. Using Artificial Intelligence (AI), we are developing a rigorous data-driven framework that enables automatic detection of interpretable physical drivers on S2S timescales. Combining expert knowledge, causal inference and a variety of machine learning techniques, our tool has worked for science. Now we aim for societal impact by making predictions for stakeholders (e.g. NGO’s).
Eye-opening insights Consumers make about 95% of their decisions without conscious awareness. Attention Check objectively maps out their behavior by using biometrics such as eye tracking, skin conductance, and brain activity. In this way we tap into the non-conscious processes that truly underlie human behavior. In addition we run large N (e.g. 1000) online psychological […]
Conscious Consumption Company What to do with imperfect food products that retailers won’t buy? COCO Comp identifies overproduced, misprinted, and soon-to-expire items from various producers and manufacturers and connects them directly with offices, clubs, and associations who can acquire these goods for a discounted price. We partner with local and smaller producers to enhance social […]
AI-based, wearable visual aid technology 1 in 30 Europeans suffers from visual impairment, and 75% of them are unemployed. Taking the form of a pair of glasses, Sonoptic is an AI based solution helps the visually impaired at work, at home and in education by providing three key features: Object identification – to help find […]