Our Fields of Research
Primarily, neuralfinity is a customer driven AI research lab. Discover our areas of research and help us on our journey to better solving problems through applied artificial intelligence.
Applied Artificial Intelligence
Through combining different agents in subsystems, with a focus on solving real world problems. Our aim is to push the boundaries on applied artificial intelligence, helping to move computer science out of the laboratories into complex challenges that are worth solving.
In the field of machine vision, we are trying to find better, more efficient ways for computers to analyse and understand moving or static images. Like this, computers can tell the difference between good and bad production batches or analyse the quality of a machine part automatically with no human action required.
Automated Decision Making
Machines can react faster and control systems more accurately than humans can. With our work, we focus on finding better ways to tackle challenges like energy consumption, resource efficiency and personalised services.
Whilst we may not yet have thought about the right questions, the answers often already exist, hidden in large amounts of data. At neuralfinity, we are researching how this data can be accessed, utilised and how it can help us ask the right questions.
Deep Reinforcement Learning
Creating agents that interact with their environment, actively controlling parameters and pursuing a goal is one of the central elements of our work. We are working on enhancing frameworks and finding new ways to apply algorithms like SARSA in deep Q-nets.
Deep Learning Frameworks
Currently there are already many deep learning frameworks. We work on ways to reduce compute requirements and training times, increase accuracy and create easier to train models that help developers to apply deep learning to more challenges with less effort.
Working with AI agents is not always welcomed by employees or customers. We research ways to increase acceptance, further the understanding of how AI actually works and find limitations where its use is limited by society and not technology.
Sometimes a scientific breakthrough can enable new fields of applications, but what often is missing is scalability to go from research project to deployment with millions of users. We dissect new algorithms, frameworks and approaches and develop new ways to make them scalable.