BMW and IBM Team Up to Bring Watson’s Capabilities to the Automotive World
This December marks exciting times ahead for both BMW and IBM as an agreement is unveiled between the two that looks to being the likes of cognitive computing to all the car giant’s motors.
This December marks exciting times ahead for both BMW and IBM as an agreement is unveiled between the two that looks to being the likes of cognitive computing to all the car giant’s motors. The prototype vehicle that’s being worked on at the moment is the classy BMW i8, but once the technology has been perfected, it will eventually be installed in all the German carmaker’s models.
With IBM’s Watson being at the core of the project, what one would how to soon see is a very advanced looking interface that will have the ability to assist the driver in interacting with the vehicles to the point of having an almost human conversation with them. Drivers will be able to ask questions about the car, while it uses machine learning to familiarize with your driving habits. It will also provide helpful suggestions as to how you could save fuel or take on a safer driving approach. As well as providing driving advice, the system will be able to give updates on the weather or traffic conditions too.
The team isn’t looking to replace the driver as many autonomous projects are heading towards, but instead to make use of the data that’s to be collected from connected cars and provide the ultimate vehicle communication system. Using Watson to decipher, categorize, and best use this information is what will set this partnership apart from the rest, so watch this space for what’s to come.
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Daniel Okafor
Daniel Okafor is the Senior AI Reporter at TrendinTech, where he covers large language models, machine learning research and the practical use of artificial intelligence across business and government. He previously reported on artificial intelligence for MIT Technology Review, covering the labs behind the current generation of frontier models and the policy debates in Washington and Brussels. Daniel holds a Master of Science in Machine Learning from Carnegie Mellon University and follows the research community closely, attending NeurIPS and ICML each year to speak with the people behind the papers. He has a particular interest in evaluation: how models are benchmarked, where those benchmarks fail and what that means for the companies betting on them.
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