RESEARCH
Our technical expertise is enriched with a deep understanding of how people interact with technology.
Some of the big cross-disciplinary challenges we address:
Representation learning: turning all data entities (text, images, videos, web pages, emails, …) into semantically-rich representations to that can be efficiently processed, mined and related to each other.
Interactive learning: including the human in the learning loop to benefit from the complementary capabilities of humans and machines
Lifelong learning: exploring how to learn and manage prediction systems, policies or processes in a sustainable manner, i.e. in a manner that makes maximum reuse of previous experiences.
Machine reasoning: connecting existing pieces of knowledge to create new knowledge, e.g. being able to answer complex questions that require multiple supporting facts.
Decision making: learning how to make actionable decisions in uncertainty, especially in ever-changing environments
Agility: we live in an ever-changing world which means the technology we use must evolve in synergy with the people and business processes that touch it.
User centric: an important challenge in any technology is to the adoption by people. Only then does science become innovation. Our solutions are grounded in a deep understanding of how people work and how technology can help them every day.
The AI for Robotics theme comprises work from multiple research groups.
Collaboration
Our scientists collaborate with national and international partners, academics and businesses to solve problems and invent technology and services that will have impact in the real world.
Discover how our research is recognized.
