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#201 - Sarah Tatsis - SVP, IVY Platform Development at BlackBerry

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Manage episode 350395803 series 2793161
Contenu fourni par Jayesh Jagasia. Tout le contenu du podcast, y compris les épisodes, les graphiques et les descriptions de podcast, est téléchargé et fourni directement par Jayesh Jagasia ou son partenaire de plateforme de podcast. Si vous pensez que quelqu'un utilise votre œuvre protégée sans votre autorisation, vous pouvez suivre le processus décrit ici https://fr.player.fm/legal.

Over ten years ago, an idea that captured our imagination was that software is eating the world. Well, software may not have eaten the automobile, but it is certainly transforming it in very profound ways.

The modern car is incredibly complex - not just from a hardware perspective, but also from a software point of view. Today’s software-defined vehicle is made up of numerous subsystems, each run by its own code; hundreds of sensors generating tonnes of data every second; and a number of vehicle functions now automated to a large degree. So how does this all work together?

We invited Sarah Tatsis, SVP of IVY Platform Development at BlackBerry to help us understand exactly how. In this episode of the AI in Automotive Podcast, Sarah unpacks the automotive software stack for us - from the foundational operating system software that helps various subsystems communicate with each other, to middleware that uses machine learning models to turn sensor data into actions, to application software on top that will help your car do amazing things like pay for your coffee in the future.

I thought my cutting-edge new car was smart, but what stuck out most for me through this conversation is the huge amount of headroom available yet for our cars to get smarter and more connected to the world around us.

I hope you enjoy my conversation with Sarah. If you do, please do share the AI in Automotive Podcast with your colleagues who might enjoy it as well.
https://www.ai-in-automotive.com/aiia/201/sarahtatsis

AI in Automotive Podcast

  continue reading

40 episodes

Artwork
iconPartager
 
Manage episode 350395803 series 2793161
Contenu fourni par Jayesh Jagasia. Tout le contenu du podcast, y compris les épisodes, les graphiques et les descriptions de podcast, est téléchargé et fourni directement par Jayesh Jagasia ou son partenaire de plateforme de podcast. Si vous pensez que quelqu'un utilise votre œuvre protégée sans votre autorisation, vous pouvez suivre le processus décrit ici https://fr.player.fm/legal.

Over ten years ago, an idea that captured our imagination was that software is eating the world. Well, software may not have eaten the automobile, but it is certainly transforming it in very profound ways.

The modern car is incredibly complex - not just from a hardware perspective, but also from a software point of view. Today’s software-defined vehicle is made up of numerous subsystems, each run by its own code; hundreds of sensors generating tonnes of data every second; and a number of vehicle functions now automated to a large degree. So how does this all work together?

We invited Sarah Tatsis, SVP of IVY Platform Development at BlackBerry to help us understand exactly how. In this episode of the AI in Automotive Podcast, Sarah unpacks the automotive software stack for us - from the foundational operating system software that helps various subsystems communicate with each other, to middleware that uses machine learning models to turn sensor data into actions, to application software on top that will help your car do amazing things like pay for your coffee in the future.

I thought my cutting-edge new car was smart, but what stuck out most for me through this conversation is the huge amount of headroom available yet for our cars to get smarter and more connected to the world around us.

I hope you enjoy my conversation with Sarah. If you do, please do share the AI in Automotive Podcast with your colleagues who might enjoy it as well.
https://www.ai-in-automotive.com/aiia/201/sarahtatsis

AI in Automotive Podcast

  continue reading

40 episodes

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