Academy of Finland offers wide support for AI research – Several Aalto projects funded
Just over 6 million euros was granted to AI-related research projects under the ICT 2023 programme and 7 million euros to research projects under the Academy Programme AIPSE (Novel Applications of Artificial Intelligence in Physical Sciences and Engineering Research).
The research, development and innovation programme ICT 2023 is jointly coordinated by the Academy of Finland and Business Finland (formerly Tekes). The aim of the programme is to further improve Finland’s scientific expertise in computer science and to promote the extensive application of ICT. The AIPSE Academy Programme, in turn, promotes the utilisation of AI in physical sciences and engineering research.
Different types of data-driven methods are continuing to gain in importance in research, administration and industry. The rapid development of various methods that utilise AI owes much to the advances that have been made in machine learning, pattern recognition, statistics, data mining and computational and software-based database technology. Other factors contributing to the rise of AI include the exponential growth of computing power. These new methods have broad application prospects in scientific research, too.
Funded Aalto projects:
ICT 2023: Computation, Machine Learning and Artificial Intelligence
Gionis Aristides, Active knowledge discovery in graphs, 235 203e
Kaski Samuel, White-boxed artificial intelligence / Consortium: WAI, 153 084e
Kyrki Ville, Deep reinforcement learning for physical agents / Consortium: DEEPEN, 225 141e
Laaksonen Jorma, Deep neural networks in scene graph generation for perception of visual multimedia semantics, 273 884e
Lähdesmäki Harri, ensor Learning for Biomedicine / Consortium: TensorBiomed, 113 602e
Oulasvirta Antti, White-boxed artificial intelligence / Consortium: WAI, 155 003e
Puolamäki Kai, Structure from randomization, 313 142e
Rousu Juho, Tensor Learning for Biomedicine / Consortium: TensorBiomed, 167 084e
Vehtari Aki, Reliable Automated Bayesian Machine Learning / Consortium: RAB-ML, 191 081e
AIPSE 2017
Foster Adam, Computational tomographic atomic force microscopy / Consortium: CATAFM, 305 072e
Gionis Aristides, Adaptive and intelligent data / Consortium: AIDA, 418 395e
Kannala Juho, Computational tomographic atomic force microscopy / Consortium: CATAFM, 305 994e
Kyrki Ville, AI spider silk threading / Consortium: ASSET, 280 360e
Laaksonen Jorma, Structure / Consortium: AIROBEST, 293 240e
Liljeroth Peter, Computational tomographic atomic force microscopy / Consortium: CATAFM, 305 078e
Linder Markus, AI spider silk threading / Consortium: ASSET, 325 794e
Mamitsuka Hiroshi, Intelligent Crop Production: Data-integrative, Multi-task Learning Meets Crop Simulator / Consortium: AI-CropPro, 640 328e
Rinke Patrick, Artificial Intelligence for Microscopic Structure Search / Consortium: AIMSS, 326 813e
Zhou Quan, AI spider silk threading / Consortium: ASSET, 326 269e
Source and further information: (> aka.fi )
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