AI Task Orchestration Via Private Real-Time Data Networks
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Solution Overview
Problem
Existing AI entities face challenges in securely connecting and collaborating due to isolation and the risks associated with using public networks, leading to security concerns and inefficiencies in data and model sharing.
Innovation Solution
A private, real-time, and secure network is established using an AI controller to facilitate connections between AI-related entities, utilizing a global, real-time, private, and secure network with four functional layers: experience, control, data, and infrastructure planes, and AI on-ramps for private connectivity, enabling secure data exchange and task orchestration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If entities use public networks to connect and share data, then ease of operation and accessibility are improved, but security and reliability deteriorate
Solution Approach 1:
The network is segmented into public and private portions, allowing entities to access public resources while maintaining isolated private networks for sensitive data exchange. This segmentation enables simultaneous public accessibility and private security through network virtualization and dedicated private connectivity options.
Solution Approach 2:
An intermediary private network infrastructure is introduced between entities, providing secure data transmission channels without requiring direct public network exposure. This intermediary layer enables secure collaboration while maintaining entity isolation and data protection.
2Reliability
If entities isolate themselves for security, then security is improved, but ease of operation and collaboration deteriorate
Solution Approach 1:
The private network infrastructure provides multi-functional capabilities including secure data exchange, task orchestration, model sharing, and entity discovery within a single unified system. This universal platform enables diverse collaboration functions while maintaining security through consistent private network protocols and authentication mechanisms.
3Productivity
If specialized entities are created for specific AI functions, then productivity and capability quality are improved, but device complexity and coordination difficulty increase
Solution Approach 1:
The private network implements feedback mechanisms where entities publish their capabilities and requirements, and the system automatically matches compatible entities for collaboration. This feedback-driven matching reduces coordination complexity by enabling automated discovery and pairing of specialized entities based on their functional requirements and available capabilities.
Data Source
AI summary
In an embodiment, a method provides an environment for privately exchanging data for AI tasks. Identification of a task to perform, and a characteristic describing data needed to execute the task, is received. A data provider within the environment is located that has access to a data set according to the characteristic. A task provider within the environment is located. The located task provider is configured to execute the task. A real-time, private, and secure network connection between the data provider and the task provider is established. The established connection is configured such that the data provider and the task provider are able to communicate via the network connection without using publicly accessible network addresses. The data set is transferred from the data provider to the task provider via the established network connection. In response to the transfer, the task provider executes the task using the data set.