Climbing Ladder For Generative AI (Artificial Intelligence) in Autonomous Vehicles

The generative content (GC) has taken popular forms such as text, image, audio and even videos. All these forms are primarily consumed by human organs such as eyes, ears and figures. Clearly beginning of generative AI has found its popularity on the foundation of human habit of consuming mobile content via eyes, ears and figures.

The cyber security standards (CSS) still revolve around consumption of internet by humans. The generative AI guardrails are combined with single sign on, identify management and security notions designed to eliminate fraudulent tendencies exhibited by human actors.

In autonomous vehicles, the vehicles become the Automated Actors (AA). The communication language is specified by the micro-services end points often consumed as REST API. Each end point can dynamically grow or shrink in characteristics by adding or removing attributes and parameters data values. The next phase of generative AI for autonomous vehicles will need to learn to consumer and generate content that the autonomous vehicle design dictates.

AI Infrastructure (AII)

First step in the climbing ladder includes compute platforms, networking, storage, energy efficiency, reference architecture, cloud, hybrid, on premise and on the edge energy consumption are higher level considerations for each autonomous generative AI vehicle.

Data Generators and Consumers (DGC)

The middle layer provides vast array of opportunities in terms of data generators and consumers. Dataset into Action (DiA): The final step is the conversion of the data generated by AI into actions without human involvement.

The content generated in form of text, images, audio or even video becomes irrelevant while simple data attribute and value pair becomes much more meaningful.

The datasets available at sources such as Kaggle.com, Data.Gov, Data.World show that most of the datasets still focus on statistical usage by humans. The future dataset format for machine to generate and consume are still in early stages of development.

Language of Datasets (LoD)

The intelligent machines will transfer the data based on attribute and value into action without any human interventions. For example, a dataset needed to drive a car is simple two data points including acceleration that can be positive or negative and direction of steering wheel in the range of zero to 360degrees. Similarly, the vehicles in space need only three datasets directing movements of ailerons, elevator, and rudder.

Simplification Process (SP)
Both the language models and security standards in autonomous vehicles generative AI are waiting to be simplified. The simplification in language models will be key factor in the effectiveness of generative AI in autonomous vehicles. The autonomous security is a whole new discipline and very different from cyber security. The human control over autonomous activities will require whole new suit of tools, dashboard and command centers.