I went to Sonoma for a NASCAR race and found out heat is the bad guy, fluids are the secret weapon, and Valvoline's engineers are basically mad scientists with pit passes. We may earn revenue from the products available on this page and participate in affiliate programs. A tire is making decent progress coming out of a turn at Sonoma Raceway --except for the fact it's no longer attached to Cody Ware's No. 51 Ford Mustang. Crowds gasp, cars swerve, and the wheel menacingly rolls off, then on, and then off the track again before it finally collapses. I've never related to a tire more.

Porsche uses artificial intelligence (AI) in vehicle development to make drive systems even more efficient and sustainable and to further optimize development processes. AI gives computers the ability to analyze problems and solve them automatically--much like a human being. This potential is being exploited in powertrain development, both in the field of e-mobility and in the internal combustion engine as a traditional domain of mechanical engineering. In the development of traditional combustion engines, for example, AI solves the problem of predicting the gas content in the engine oil. Since high gas content leads to oil foaming and thus to reduced lubricity, the oil system must be designed for the lowest possible gas content.

Porsche has outlined how it is using artificial intelligence (AI) in vehicle development to make drive systems even more efficient and sustainable and to further optimize development processes. In the development of traditional combustion engines, for example, AI is being used to solve the problem of predicting the gas content in the engine oil. Since high gas content leads to oil foaming and thus to reduced lubricity, the oil system must be designed for the lowest possible gas content. However, measurements can hardly be carried out in the vehicle with the engine running. Porsche states that a new AI process is able to provide reliable forecasts of the gas content in the engine oil.

As a reasonably new person to the field of Machine Learning and programming in general (working in Python for 6 months now), I wanted to go to this forum and ask for some feedback and suggestions for an assignment I just started. For this assignment I have to look into machine-learning possibilities that automatically classify incoming invoices to decide what kind of repair was done on a car. There are between 1030-1500 different outcomes. Some occur very frequently, some almost never. The product code and product description probably contain the most useful information.