XAAI

- faster machine learning in interaction with humans

Introduction

One challenge with machine learning is the extensive data required to train new models. It requires experience with a wide variety of errors for the AI to learn what is right and thereby optimize its ability. The learning process can take a long time and impair companies' ability to respond quickly to changing market conditions, with the risk of losing competitiveness.

Purpose

The XAAI research project aims to improve and simplify the use of AI in production and products, and is carried out in close collaboration with industry. The project focuses on machine learning and how computers can more quickly gain sufficient knowledge to make more accurate predictions and take better decisions on their own.

Expected results

In the XAAI research project, we are trying to shorten the learning time of computers by developing new so-called reinforcement learning algorithms. Simply put, we help AI understand faster by demonstrating what we want it to do. By combining technology and human expertise, we speed up learning and thus optimize the computational resources required.
The work in XAAI should also be able to provide valuable insights into neural networks and how they structure their knowledge.

In addition to faster learning and more adaptable systems, the XAAI project aims to improve control over the process and make it easier to interpret.
Companies will be able to quickly implement new models tailored to their needs, monitor their performance and thus strengthen their competitiveness on the market.

The project name XAAI is an acronym for Explainable and Adaptable Artificial Intelligence

Want to know more about the project?

Are you interested in a future collaboration?