AI to run the Real World

Data is the key to intelligence. In the cloud, where enterprise applications run, AI has easy access to data and plenty of time to process that data. But smart machines in the real world need to access and process data much faster. So, in real-world systems, the key to intelligent reaction is data flow: getting the right data to the right place at the right time. This is one of the most complex and demanding challenges of the future. It requires an entirely different architecture.

This talk will explore the technology challenges in AI, and the foundational architecture that is enabling intelligence across industries. It will also discuss real-world examples ranging from healthcare to transportation, and explain why AI is so important to the future of digital transformation in almost every industry.

Software-defined vehicles can offer increasingly intelligent capabilities enabled by continuous data flow between components that perform various functions including sensing, situational awareness, navigation, planning, control, and actuation.

This talk will highlight the key requirements, standards, and techniques for building trustworthy software defined vehicles, using a layered data-centric architecture that protects the data in motion between software components.

We will define the core pillars of trustworthiness, and describe how the principles of data-oriented software system design are being applied to implementing intelligent driving tasks. The approach provides a framework for ensuring the desired degree of trustworthiness in the system while also reducing the overall software development costs.