Scanning and monitoring relevant information (tech trends, market/financial analysis, political instability, natural disasters, insider info…).
Competitive intelligence; supply chain risk analysis; availability/cost risk prediction.
Detection and prediction of changes in the driver’s (or passenger’s) cognitive and physical state due to driving events and changes in vehicle configuration.
Multi-party computation allows multiple participants to perform a computation securely, while keeping each party’s input secret. In this model, cryptography protects the privacy of the participants from each other.
Recommender Systems, Dynamic Pricing & Personalized Search help you to strengthen customer loyalty and generate more revenue.
Federated learning allows an AI model to be trained and evaluated in a decentralized manner on different devices or clients without the training or evaluation data ever leaving the local storage location. Only the model parameters are exchanged over the network, the AI model is executed locally on each individual client.
Self-explanatory AI can name those features in the data under consideration that were decisive for your decision, so that human experts can understand them. This makes it possible to use algorithms in the automotive supply chain as well, where trustworthiness and the possibility of auditing play a central role.
Robust models provide reliable results even under changing conditions; expert knowledge helps to make the models plausible.
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