SATW: DATA & ARTIFICIAL INTELLIGENCE

DATA & ARTIFICIAL INTELLIGENCE

Thanks to its wide portfolio of technologies spanning many markets, CSEM has access to a unique mix of heterogeneous data, including medical sensing data, large image and video databases and various industrial sensor data. This coupled with CSEM’s privileged position with SMEs have highlighted the following challenges to exploiting the data driven techniques: (1) Methods to facilitate AI training and reduce the effort required to analyze data, train and deploy a model, as the cost of collecting and labelling data often hampers the deployment of machine learning. (2) Enabling distributed processing, ultra-low power and hierarchical computing, with the goal to balance the computation needs throughout a network. (3) Assuring security & privacy, to deal with the mechanisms for data to be safely collected, transmitted, and stored.

Last updated: 08.04.2022

written text, speech recognition, speech generation, recognition, image reconstruction, supervised learning, unsupervised learning, reinforcement learning, self-learning, sparse (dictionary & data set) learning, anomaly detection, reliability, explainability

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CSEM