Joseph Barkai

Connected & autonomous vehicles will generate zettabytes of data and collect just as much from their surrounding environment. In fact, some of the data that should arguably be publicly accessible can help create products and services such as improved brakes that may eventually become standard. Creating cost-effective ecosystems is key to achieving the highest ROI. In this session, we will explore what data should be stored in the vehicle, the cloud, the edge, or at a data center; how data is collected from other vehicles, infrastructure, and the environment through sensors; and how to set up data management systems to collect, index, and tag the data to aid analysis and support AI workloads. We will consider different scenarios, such as using black box systems vs. allowing small data sets that can be translated to stakeholders such as governments and other organizations.

AI is powering the breakneck transition from the Information Age to the Age of Intelligent Machines and creating profound changes in business and society. Autonomous cars, personalized medicine, chatbots and co-bots, and other types of intelligent machines promise to automate processes, boost efficiencies, and create new disruptive products and services. But these intelligent machines will also track our every move, create and expose societal biases, eliminate jobs, and, some fear, will one day develop a will of their own and go rogue. In his talk, Joe will offer a perspective on the future of AI and machine-learning technologies. He will explain the principles of AI and machine learning technologies, what they can and cannot do (at least not in the foreseeable future), and discuss the opportunities and threats posed by autonomous machines in a future dominated by AI.