ICAI Amsterdam (UvA) and Bosch extend collaboration with new research lab
The UvA and technology company Bosch have agreed to extend their established collaboration with the launch of a new public-private research lab. Delta Lab 2 – the follow-up to the successful collaboration Delta Lab 1 – will focus on the use of Artificial Intelligence and Machine Learning for applications in computer vision, generative models and causal learning.
Delta Lab 2 will form part of ICAI, the national Innovation Center for AI. The lab will be headed by the UvA’s Jan-Willem van de Meent and Theo Gevers. Eric Nalisnick will be the daily lab manager.
‘For Bosch, collaboration and close exchange with academic institutions is an essential component of our efforts in the development of safe, robust, and explainable AI. By expanding and realigning the previously successful collaboration in the UvA-Bosch Delta Lab, we are realising our ambition of combining cutting-edge research with high application potential,’ says Michael Fausten, Senior Vice President and Head of the Bosch Center for Artificial Intelligence (BCAI).
In the Delta 2 lab, ten PhD students, one postdoc and one lab manager will work on projects over the next five years with a total budget of €5.2 million. The aim is new research on deep (causal and partial differential equation-based) generative models; certainty and causality in machine learning; and 3D computer vision.
Building on success
Gevers: ‘The collaboration between UvA and Bosch in Delta Lab 1 has been a great success. We want to build on that success with new fundamental research into machine learning and computer vision technologies. We are grateful to Bosch for continuing to invest in fundamental research. We also thank the previous directors and researchers for all their efforts, and we will continue to work enthusiastically on unexplored areas of AI.’
Van de Meent: ‘We are excited to continue our productive collaboration. Working with Bosch is a win win. It not only facilitates uptake of AI innovations in industry, but also provides a wealth of use cases that can inspire new innovations, such as incorporating physical knowledge into models, reasoning about their causal structure, and evaluating the level of confidence that can be attributed to predictions.’
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