Our Research

We are specialists in cutting-edge mathematical and artificial intelligence research. Our team of highly-skilled scientists is on a mission to relentlessly push back the boundaries of these domains. We actively engage with the research community through publications, collaborations with academia and participation in major conferences. In order to build intelligent technology, we combine state-of-the-art modeling and data analysis with next generation infrastructure to efficiently transition from theory to practice. We rely on the skill of expert, passionate and creative developers to invent the future of technology.

We are developing new deep learning methods to make sense of the world’s textual information. We focus on finding new ways to automatically extract relevant information from a large and diverse range of sources, and on building smarter systems for human-machine interactions.

We are creating new reinforcement learning algorithms that learn to make optimal decisions in complex environments. Our research allows us to deliver new and better solutions for a wide range of industries such as finance, manufacturing, and logistics.

Introduction to Reinforcement Learning

Reinforcement learning (RL) is a branch of AI in which an agent learns how to interact with its environment in order to maximize a reward signal. RL has successfully been used to reach superhuman performance at Go, chess, and a wide range of video games, and UncharTECH is on a mission to untap its potential for real world applications. We are pleased to share these learning notes that describe some key concepts in the field and some applications.

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Mathematics and Reinforcement Learning

We shared the stage with Facebook and Google at the "Maths de l'IA" event at the Laboratoire de Mathématiques d'Orsay on the 13th of February 2019, attended by a large number of students and researchers in mathematics. Dr. William Clements, senior researcher at Unchartech, described some ways in which mathematics research can contribute to overcome the challenges faced by the reinforcement learning community.

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