We link your experience with artificial intelligence
We are a spin-off from the Chair for Artificial Intelligence at Technical University (TU) Berlin. By participating in a research project on industrial quality control with partners from science and industry, we became aware of the great potential of automated solutions and decided to develop a QC system, combining cutting edge technology from machine learning, computer vision as well as deep learning.
Team
Sebastian Thiel
Head of Strategy, Managing Director
- MSc (Computer Science) Technical University of Berlin
- Computer Vision: Object-Recognition,- detection and -tracking / Pattern Recognition
- Research and implementation of Machine Learning methods on image and video data (2D and 3D)
- 5 Years experience as Research Assistant at GfAI Berlin, developing and implementing solutions for various industry partners
- Research and Teaching Assistant at TU Berlin (Methods of Artificial Intelligence, Prof. Opper), assisting Machine Learning/AI lectures and supervising a Master thesis
eMail: sebastian.thiel(at)alm.gmbh
Philipp Batz
Head of Machine Learning, Managing Director
- MSc (Statistics) Humboldt University of Berlin; MSc (Computer Science) Siegen University
- Machine Learning / Fast approximate algorithms for Big Data
- Research and implementation on time series algorithms / Reinforcement Learning and Control on mechanical systems (with focus on robotics)
- Research Assistant at TU Berlin (Methods of Artificial Intelligence, Prof Opper), member of EU-funded Project CompLACS
eMail: philipp.batz(at)alm.gmbh
Scientific Publications
P.Batz, T.Will, S.Thiel, T.M.Ziesche, C.Joachim (2023)
From identification to forecasting: the potential of image recognition and artificial intelligence for aphid pest monitoring,
Frontiers in Plant Science, Section: Sustainable and Intelligent Phytoprotection, Volume 14
P.Batz, A.Ruttor, S.Thiel, J.Wegener, F.Zautke, C.Schwekendiek, K.Bienefeld (2022)
Semi-automatic detection of honeybee brood hygiene—an example of artificial learning to facilitate ethological studies on social insects,
Biology Methods and Protocols, Volume 7, Issue 1, Oxford University Press OUP
S.Thiel, A.Ruttor, P.Batz (2018)
Beehaviour – Computerunterstützte Erkennung von Verhaltensmustern in Bienenvideos,
65. Jahrestagung der Institute für Bienenforschung e.V., Koblenz
P.Batz, A.Ruttor, M.Opper (2018)
Approximate Bayes learning of stochastic differential equations,
Physical Review E, 98/2, American Physical Society APS
P.Batz, A.Ruttor, M.Opper (2016)
Variational estimation of the drift for stochastic differential equations from the empirical density,
Journal of Statistical Mechanics: Theory and Experiment, 2016/8, Institute of Physics Press IOP
A.Iwainsky, S.Thiel (2016)
A Mixed Reality Environment for Educating Nurses (Best Paper Award),
Future Education and Training in Computing (FETCH), BMBF-Verbundprojekt AKOLEP
A.Ruttor, P.Batz, M.Opper(2013)
Approximate Gaussian process inference for the drift function in stochastic differential equations,
Advances in Neural Information Processing Systems 26 (NIPS), p.2040-2048