Digitalisierung: Wie Porsche Artifical Intelligence nutzt

Extrem spannender Artikel über den Einsatz von Articifial Intelligence bei Porsche. Must read!

Improving Personalization and Data Security by DesignIn the past years, we could observe that data privacy and security is advancing to a luxury good: Possibly everyone of us trades very personal data against the offering of highly personalized services. At Porsche however, we want to improve privacy and data security by design. The big challenge we are tackling is: What if everybody could still benefit from the knowledge of the masses, without the need to reveal too much information about ourselves?An example could be, the usage of personalized speech and haptic data to teach and improve a personal assist in our cars. Conceptually, when the personal assist shows a suggestion query, the car locally stores information about the current context and whether the customer clicked or accepted the suggestion. This data can be used to apply machine learning techniques to improve the recommendations of the personal assist. But how can every customer benefits from such knowledge, without violating the data privacy of our customers?To solve this problem, we are actively investigating use-cases that employ an emerging technique in Machine Learning called Federated Learning [1]. With this technology, we can maximize data security and privacy, with the additional upside of significantly reducing data transfer between our cars and the backend infrastructure. The technique enables us to still contribute to a global set of knowledge for the benefit of other Porsche customers.In contrast to standard machine learning techniques, Federated Learning represents a fully decentralized learning approach, with training being carried out on many distributed machines, in our case our customers’ cars. The magic sauce of Federated Learning is a method for merging thousands of local models into a global model by a negotiation protocol. This form of learning helps us to solve issues that are related to data which is massively scattered among a lot of individual clients as well as issues that are caused by data not being uniformly distributed. For example, there will be significantly more data available on wonderful Porsche-routes like the scenic Highway 1, compared to less frequently visited places.Between Personalization and Swarm IntelligenceToday and in the future, we want to offer our customers a unique and personalized Porsche experience based on their personal preferences. It is part of our principles that data collection should never influence a user‘s privacy negatively. To achieve this, we have outlined how we aim to solve the contradiction between personalization, swarm intelligence and privacy by the use of Federated Learning. However, there is an important drawback because some use-cases will still require us to store bits of personal information in the cloud, instead of just within the car.For example, revisiting the personal assist use case from before. Every customer helps by their locally stored and protected data to improve the so-called predictive models to improve their suggestion accuracy. Nevertheless, each client interacts differently with the system and there will be probably some suggestion queries which suits not everyone preferences. Considering the case when a customer says to the system, “I feel cold” — is it right to heat up by one, two or three degrees celcius? Who knows? This depends on the individuum, hence, on top of the shared knowledge by Federated Learning some personalization character is required.Therefore, we are exploring new developments, one the so called multi-task Federated Learning, which has the advantage that each customer can access the collaboratively trained models, but influence part of this knowledge by their personal preferences locally. The main and important differences is basically how the model is trained and inferred in the car and how those locally personalized models are merged in the cloud. Each data source can be seen as one task, but there could be similar tasks, hence clients with same preferences. Thinking of the “I feel cold” use case from before — there will be many people which are happy with heating up by 2 degree celsius. Technically speaking, a wrapper model, which is stored in the cloud and accessible, like the collaborative models, by each customer, admits to identify relations between the locally stored data, without exchanging those. This wrapper model can then be used to parametrize the suggestion queries by each customer in a personalized way, since the output of this wrapper model depends on the locally stored data. Summarizing, multi-task Federated Learning is based on a collaborative trained knowledge source where each customer pick parts of a model by their own preferences automatically.In regard of this novel and promising tool kit one can also think of a lot of use cases in the field of automated driving. A few example are corner case detection in driving maneuvers or video and image segmentation, where large data streams are needed to train machine learning models on the new data. On one hand, Federated Learning shares this knowledge by its distributed character, on the other hand data transfer is reduced, since models are much smaller compare to data.

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