It is early 1960s. Sales, customer success, and lead management teams are struggling to keep up with the incoming products and/or services queries. The first instance where Generative AI models took shape of robotic conversation assistance to tackle queries and suggest solutions based on algorithms. We’ve interacted with Generative AI models for decades. However, since 2014, we’ve witnessed it in action through Generative Adversarial Networks (GAN). The GAN could create images, videos, and audio. But, what’s the difference between Generative AI models of 2014 to 2024? Download our White Paper “Generative AI at the Edge” where we explore the opportunities and challenges of this technology specifically for the Automotive Industry.
What’s Inside this White Paper?
The whitepaper acts a comprehensive analysis of the implications and potential applications of deploying Gen AI models (edge/on-device) within the automotive industry. It explores the myriad opportunities that this technology presents, including enhanced customer experiences, efficient resource allocation, sales growth, and accelerated design iterations. It also addresses the data privacy concerns, computation constraints, and the need for robust infrastructure through relevant examples.
What to expect from this White Paper?
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Introduction to Generative AI and Edge Computing
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Historical Nuances of the Generative AI Application
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Generative AI applications for the Automotive industry
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Challenges of running Generative AI models at the Edge
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Well-structured approach to develop, deploy, and maintain Large Language Models (LLMs) and Language Vision Models (LVMs) utilizing an LLM-ops framework.
By
Debashis Panigrahi (Manager - Automotive Portfolio, Sasken Technologies Ltd.)
