How Machine Learning is turning the Automotive Industry upside down | Jan Zawadzki

The automotive industry has mobilized the global economy for decades. German automobile manufacturers (OEMs) alone employ more than 1 million people worldwide and generate sales of more than USD 500 billion. Since a Google + Stanford team won the Darpa Self-Driving Vehicles Challenge 2006 with the help of machine learning, the industry has been undergoing rapid change. Machine learning opens up brand-new business models, from autonomous driving to smart production to personal assistance in the car. However, the use of machine learning requires a different infrastructure than that found in traditional OEMs. Technology-first companies like Waymo or Tesla threaten to overtake established OEMs with billion-dollar market capitalization. Autonomous vehicles produce terabytes of data every day. This data can be immensely valuable in developing machine learning-driven functions. However, substantial challenges remain in the way of using this data. Visit this talk to hear about these challenges to help turn the automotive industry from a mechanical engineering to a software industry.
Jan Zawadzki, Project Lead Data Science, Carmeq GmbH

[@tobiaszelger891]

This one was the best talk at the Data Festival 2019.

[@user-sv9hb1cy2g]

Hey brother I want to start data science course so please suggest me what the best course for me because I am mechanical engineer

[@mohammaddanish9713]

Either go for Applied AI or iNeuron

[@bmitrek6589]

Huge markets, highly profitable, big-data, AI companies (such as Alphabet and Amazon), unicorns (Tesla) - profitability is driving a cycle of R&D and increased profitability - accelerating change. Silicon Valley talent and R&D predominantly goes into civilian applications, though military applications and specialized R&D can't be ignored. WWI used masses of HORSES. Innovate and learn to always innovate faster.