German engineering giant Bosch has partnered with researchers at the University of Amsterdam (UvA) to develop deep learning technologies for application in intelligent cameras, self-driving cars, homecare and beyond.
The joint initiative will see teams from the university’s Delta Lab (Deep Learning Technologies Amsterdam) and the Bosch Center for Artificial Intelligence design models and algorithms specifically for the processing of huge amounts of data collected from sensors and vehicles.
Based at the Amsterdam Science Park, the collaborative project aims to promote stronger knowledge sharing between the academic and business worlds to help accelerate innovation around AI and deep learning analytics.
The research programme will be led by UvA professors Max Welling and Arnold Smeulders. Bosch has agreed to fund the project for the next four years, to the tune of €3 million (approx. £2.6 million).
‘Deep learning allows you to discover hidden structure in data and make predictions based on data,’ commented Welling. ‘The applications are numerous. For instance, think of fully autonomous cars, robots, smart homes and the internet of things (IoT).’
One learning phase, he said, would involve programming an automated vehicle to distinguish a playing child from a ball rolling across the road and make the decision whether or not to brake.
Smeulders added that computer vision technologies are expected to generate the largest amount of sensory data. ‘[The] sub-field of artificial intelligence is one of the most exciting goals for deep learning and why the intersection of deep learning and computer vision is so important. It presents specific challenges to deep learning to learn from the enormous amount of data,’ he said.
Bosch noted that the research findings will be incorporated directly into its applications and products.
Last year, Bosch announced a computer vision research project with the Fraunhofer Institute, alongside Volkswagen and Visteon, named Intelligent Car Interior. The study is focusing on ways to monitor the interior of a vehicle, including the number of riders, their size and posture, as well as the movement of components such as sun visors and glove compartments.