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.