Research & Development - Industrial AI
We actively contribute to the continuous development of Industrial AI!
What we do
Developing Industrial AI solutions can be extremely difficult. Limited availability of labels, high variance and constantly changing conditions pose great challenges. We are continuously expanding the limits of what is possible through cutting-edge research. For this purpose, we work with the world's leading academic institutions and companies in order to continuously develop and differentiate ourselves from the competition.
It is all about learning
Meta Learning is a subfield of machine learning and is often also referred to as “learning to learn”. In Meta Learning, machine learning algorithms are trained to make predictions based on metadata, such as network architecture and data set characteristics, instead of raw data. Therefore, Meta Learning allows for generalization of learning problems and may lead to drastically increased efficiency when substituting otherwise expensive experimentation procedures. At craftworks, we research and use Meta Learning techniques for exactly that.
The field of Representation Learning engages with the automated discovery of optimum representations of data given a specific learning problem. Learning the representation instead of spending hours or even days crafting it manually using traditional feature engineering techniques is time saving, often leads to significantly superior outcomes and frequently exposes previously unknown structures in the data. At craftworks, we use Representation Learning techniques to solve challenges ranging from Natural Language Processing to Computer Vision.
Applying machine learning to real world problems is not only time consuming and resource intensive, but also challenging. Automated Machine Learning (AutoML) takes care of large parts of the machine learning process, such as feature engineering, model selection and hyperparameter tuning in an autonomous fashion. This frees up time of data scientists and, additionally, enables people not familiar with machine learning to leverage its power. Therefore, a team of our engineers is working on developing cutting-edge AutoML software.
Reinforcement Learning is an iterative optimization approach that is motivated by the way humans learn: trial-and-error. In combination with deep learning, Reinforcement Learning is used to teach machines highly complex games, make robots learn arbitrary tasks, optimize control systems of power plants and many more. At craftworks, we recognize the wide area of application and the huge potential of Reinforcement Learning. Therefore, we develop novel approaches for solving multi-agent problems with incomplete information.
Using Text Embedding Algorithms in Recomm. Systems
WeAreDevelopers AI Congress
Augmented Intelligence - A Marriage between Machine and Human
SUSPICION Final Video (Phase I)
13.04.2021 - joanneum.at
SUSPICON: Prediction of robot-related failures in spot welding applications with industrial AI methods
In the manufacturing industry, different production steps follow each other fluently and in close sequence. Any delay of a machine...
11.03.2019 - dispo.cc
Machine Learning & Optimization make logistics more efficient at RailCargo Austria [GER]
Die Struktur der transportierten Güter verändert sich. Gleichzeitig wirft das Physical Internet immer deutlichere Schatten – für die Güterbahnen sind das keine guten Nachrichten...
15.01.2019 - medium.com
Augmented Intelligence - A Human-Machine Marriage on the Way to Complete Intelligent Automation
Nowadays, everybody is talking about Artificial Intelligence (AI). It has become a major buzzword of the yet young 21st century...
20.02.2018 - crate.io
Machine Learning, and Hydroelectric Power: Part One
Crate.io partnered with craftworks to build a proof-of-concept for Illwerke VKW that uses CrateDB and machine learning...