Many important questions can be answered by just asking someone. But the biggest questions often require asking many people. Sometimes multiple times over a period of time. And asking good questions can be hard.
We have all seen surveys which were too long, or too confusing, or too repetitive, or where we couldn’t quite give the answer we wanted to. Or we have struggled to create questionnaires ourselves and in the end we still wished we had asked a specific question we left out.
Asking good questions would be easier if the questions themselves could help.
If they could be smart.
If they could only show up when they are needed. If they could change and rearrange themselves based on the situation. If they could remember your previous responses and ask you follow-up questions which make sense. Months later, if necessary. If, while creating a survey, they could recommend themselves to you based on your intent and point out or even correct mistakes.
Our roots are in AI research, so we are used to asking questions and dealing with data. We also have the expertise to infuse intelligence into the questions themselves. And for years, we have worked closely together with partners who have been asking some of the biggest questions for decades.
The foundation for truly smart questions, surveys, and studies must be a flexible digital model which serves as the core of a powerful survey engine where every aspect can be dynamically updated, where complex logical relationships can be defined, and where every imaginable possibility can be created.
This is what we have already built. coneno’s AI Survey Engine (CASE) serves as the basis upon which we will build the systems that will enable the truly smart questions of the future. But even on its own, it is already powering some of the most important questions which can be asked today.
CASE has been expanded into an open and flexible platform which includes a configurable web-client with a component-based UI layout and a server-backend that protects participants’ privacy while keeping their data secure. This platform is now powering the next generation of instances.
The National Institute for Public Health and the Environment (RIVM) in the Netherlands uses CASE to monitor symptoms of infectious influenza-like illnesses, including coronavirus infections. Data from Infectieradar.nl works as an addition to the figures from test streets, hospital admissions and other RIVM studies.
Some people suffer long-term health issues after being sick with COVID-19. To understand these effects, the National Institute for Public Health and the Environment (RIVM) in the Netherlands conducts studies with their LongCOVID platform based on CASE.
Infectieradar.be is a Citizen Science Platform for Infectious Disease Surveillance operated by the University of Hasselt and University of Antwerpen that aims to collect valuable information on the circulation of infectious diseases in Belgium.
Influweb.org is a voluntary participatory system for monitoring influenza and COVID-19 in Italy. It is based on an online platform where people from the general population are the protagonists: Anybody can actively participate, providing weekly updates on their health status, even if no symptoms are experienced.
Tekenradar.nl focuses on Citizen-Science-based research and education about ticks and Lyme disease in the Netherlands. The platform is coordinated by the National Institute for Public Health and the Environment (RIVM).
Grippenet is a participatory system for detecting and monitoring influenza and COVID-19 in Switzerland. The platform aims to collect data on flu-like symptoms, allowing real-time surveillance and monitoring.
We are currently working together with partners in multiple countries to deploy additional platforms.
We continue to work on bringing CASE to more questions, while increasingly focusing on further research to make questions smarter in more ways and even in ways which were never possible before.
This work is not only driven by the belief in the power of surveys when it comes to answering questions, be they related to the largest topics such as the COVID crisis, or to smaller topics such as understanding the needs of customers. But we also firmly believe that the world will be a better place if more people are empowered to ask good questions. Because asking good questions and receiving good answers enables all of us to make more informed decisions and to benefit from better decisions made by others.
Volume 04/2021 - UNISPECTRUM
With its technologies for participative systems, coneno supported the live experiment of a group of researchers from the University of Landau and TU Kaiserslautern. As part of the "TV-Trielle" for the 2021 Bundestag elections, the participants in the experiment were able to rate the chancellor candidates during the three TV debates based on their statements and impact via an app. The article on the project can be found in the current issue (4/21) of Unispectrum on page 46.
26.10.2021 - DFKI GmbH
COVID 19 hat gezeigt, wie wichtig umfangreiche Basis-Informationen zu Ansteckungswegen, Krankheitsverläufen und Symptomen sowohl für die Abschätzung des Pandemieverlaufes als auch für die langfristige Forschung sind. Die Plattform CASE des 2016 gegründeten DFKI-Spin-offs coneno unterstützt das Monitoring von infektiösen Krankheiten durch direkte Beteiligung der Bevölkerung.
21.09.2021 - University of Koblenz-Landau
Wahrnehmung und Wirkung der dritten TV-Debatte 2021 zwischen den Kanzlerkandidaten Armin Laschet (CDU), Olaf Scholz (SPD) und Annalena Baerbock (Bündnis 90/Die Grünen) haben die Universität Koblenz-Landau, das Deutsche Forschungszentrum für Künstliche Intelligenz (DFKI) und die Technische Universität Kaiserslautern im Rahmen eines Live-Experiments untersucht. Die Hauptergebnisse des Live-Experiments mit 114 Teilnehmern lauten: Annalena Baerbock hat auch das letzte Triell für sich entschieden. Armin Laschet agierte so zurückhaltend wie noch in keiner der TV-Debatten. Olaf Scholz gelang es erneut, für seine Politik zu werben und sich Kontroversen zu entziehen. Die Debatte hatte erneut einen erheblichen Einfluss auf die Kanzlerpräferenz.
14.09.2021 - University of Koblenz-Landau
Die Universität Koblenz-Landau, das Deutsche Forschungszentrum für Künstliche Intelligenz (DFKI) und die Technische Universität Kaiserslautern haben Wahrnehmung und Wirkung der zweiten TV-Debatte 2021 – dem Triell zwischen den Kanzlerkandidaten Armin Laschet (CDU), Olaf Scholz (SPD) und Annalena Baerbock (Bündnis 90/Die Grünen) - untersucht. Die Hauptergebnisse des Live-Experiments mit mehr als 400 Teilnehmern: Annalena Baerbock hat das Triell klar für sich entschieden. Armin Laschet wurde noch angriffslustiger als in der ersten Debatte eingestuft. Olaf Scholz musste sich häufig verteidigen. Die Debatte hatte Einfluss auf die Kanzlerpräferenz.
09.09.2021 - DFKI GmbH
Die Universität Koblenz-Landau, das Deutsche Forschungszentrum für Künstliche Intelligenz (DFKI), das DFKI-Spin-off coneno und die Technische Universität Kaiserslautern untersuchen in einem Live-Experiment die Wahrnehmung und die Wirkung der TV-Debatten 2021 („Triell“) zwischen den Kanzlerkandidaten Armin Laschet (CDU), Olaf Scholz (SPD) und Annalena Baerbock (Bündnis 90/Die Grünen).
06.09.2021 - University of Koblenz-Landau
Die Universität Koblenz-Landau, das Deutsche Forschungszentrum für Künstliche Intelligenz (DFKI) und die Technische Universität Kaiserslautern haben im Rahmen eines Live-Experiments Wahrnehmung und Wirkung der ersten TV-Debatte 2021 („Triell“) zwischen den Kanzlerkandidaten Armin Laschet (CDU), Olaf Scholz (SPD) und Annalena Baerbock (Bündnis 90/Die Grünen) untersucht. Die Hauptergebnisse des Live-Experiments mit rund 110 Teilnehmerinnen und Teilnehmern: Olaf Scholz und Annalena Baerbock profitieren deutlich mehr als Armin Laschet von der Debatte. Zudem hat die Debatte einen erheblichen Einfluss auf Kanzlerpräferenz und Wahlabsicht.
Whether you want to fund our research and want to ask us questions or whether you want to ask your own questions in a smarter way, we want to hear from you!
info@coneno.com
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