Toyota drives innovation & enhances operational efficiency with gen AI (AUT201)

Generative AI in Automotive: Driving Innovation and Enhancing Operational Efficiency

Introduction

  • The automotive industry spends over $1 billion on new product development, but not all investments pay off.
  • Generative AI can help reduce design and development time by several weeks to several months.

Generative AI Use Cases in Automotive

Product Design and Quality

  • Generative AI can be used to generate innovative design and styling recommendations, and evaluate them for vehicle aerodynamics.

In-Vehicle Experience

  • Generative AI can enable real-time conversational assistants to guide drivers, provide route guidance, and offer touring recommendations.
  • It can also provide guided troubleshooting steps for service technicians to simplify the diagnosis and repair process.

Autonomous Driving

  • Generative AI can be used to generate synthetic data based on real-world edge cases to improve autonomous driving models.

Other Domains

  • Generative AI can also be applied to manufacturing, supply chain, sustainability, electric vehicles, and battery optimization.

AWS and Generative AI

AWS Offerings

  • AWS meets customers wherever they are on their generative AI journey, offering different levels of control and abstraction.
  • AWS Bedrock provides access to leading foundation models through a single API, with the ability to customize some models.
  • Amazon Q enables business users and developers to leverage generative AI for a variety of tasks, from content creation to code generation.

Toyota's Generative AI Journey

Mobility Vision and Challenges

  • Toyota is transitioning from a car manufacturing company to a mobility company, which brings both opportunities and challenges.
  • The Enterprise AI team was established to help guide the organization through this transformation, leveraging partnerships and creating frameworks for experimentation.

Knowledge Retention Use Case

  • Toyota identified a gap in knowledge transfer between team members with different levels of experience as the company transitions to a mobility focus.
  • The Knowledge Retention use case leverages generative AI to create a platform that allows subject matter experts to share their knowledge, which is then made available to other team members in a structured and accessible way.
  • The system uses Retrieval Augmented Generation (RAG) to fill in contextual gaps and cater to different audience levels.

GearPal and Battery Brain Use Cases

  • GearPal is a chatbot application that helps manufacturing line team members quickly diagnose and fix issues with production machines, reducing downtime and costs.
  • It uses a RAG-based framework to ingest and index a variety of technical documents, allowing users to ask questions and get guided troubleshooting steps.
  • Battery Brain is a similar application focused on the battery manufacturing process, helping team members triage and optimize battery production.
  • These use cases demonstrate the reusability of the RAG-based architecture and the importance of data-agnostic ingestion pipelines.

Code Modernization and Mainframe Modernization

  • The mainframe modernization project at Toyota showcases the collaboration between Toyota and AWS, leveraging generative AI to analyze and convert legacy COBOL code to modern Java.
  • The process involved using Amazon Q's capabilities to understand the business logic and data flows within the legacy code, with human feedback loops to ensure accuracy.
  • This hybrid approach of human-AI collaboration is essential for complex tasks like code modernization, where generative AI is an assistive technology rather than a replacement for human expertise.

The Future of Generative AI in Automotive

  • Toyota is investing in generative AI for both customer-facing and enterprise applications, with the goal of continuously improving the mobility experience for all.

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