What is Node Enterprise

Revolutionizing Productivity with AI-Driven Innovation

Overview

Node enterprise is a productivity platform that is designed based on three core concepts

• Omniapilot

• Dynamic Artificial Intelligence Networks

• Cumulative Digital Models

Omniapilot

Figure 1b. Omniapilot Framework

This system is a combination of three machine learning frameworks

• Copilots

• Autopilots

• Autogen

The combination of these frameworks allow machine learning models to solve complex problems using the right set of tools. An omniapilot can perform tasks using any one of these frameworks, or a superposition of two or three of these states. The decision of which state the model needs to assume depends on the complexity of the task or the resources required to complete the task.

Learn more about omniapilots

Ominapilot

Dynamic Artificial Intelligence Networks

The combination of these frameworks encourages the system to work with multiple models in tandem irrespective of the environment that hosts the models. DAN is a protocol that governs the interaction between these models. It establishes the requirements of a node - an abstraction that describes the interface and resources a model uses. A node has the following properties:

1. Model

2. Actions

3. Memory store

4. Container

Learn more about DAN

DAN

Cumulative Digital Models

The combination of these two concepts allows the system to accumulate a lot of data about the user or entity over time depending on the rate of use and what it is used for. Node enterprise is a very generalized system, that can be used for a wide range of tasks. A thorough exploration of it's abilities will allow it to build a digital twin of different aspects of the user or entity it serves. This digital twin is stored in a CDM. The CDM serves as a single source of truth for nodes and processes that exist in any given network.

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CDM

What makes this Useful?

The primary goal of events in Node enterprise is to make it easy for users to delegate tasks. Achieving this objective pays a lot of dividend for the user.

Execute complex tasks

Node abstractions used in DAN extend the concept of SRP (Single Responsibility Principle) to machine learning models. A well defined node should have one true governing purpose, and only one reason to change. Frameworks built based on this principle make it easier for models to consistently assign the expected set of resources towards a defined set of tasks. As the complexity of these tasks increase overtime, the induced transparency that comes with this architecture makes it easier for users to manage the resources allocated to each node.

Control hallucinations

The ability to delegate tasks gives the system better control over confabulations. Hallucinations are one of many features machine learning models posses. It can serve as a useful tool for creativity in certain events, but it can also be a nuisance or a source of noise when trying to get objective data. The chances of creating a model that generates results with a strong probability of returning confabulations that fit EVERY scenario is practically zero. Some models will always be better suited for specific tasks because of the information, and processes that went into their training and validation. Node enterprise uses Omniapilot & DAN to ensure that the model best suited to complete a task is prioritized within the workflow. This gives the user more control over confabulations within the system.

Data Protection

Nodes allow individuals and businesses to describe the containers that host their models, and the data these models share. These nodes provide a sense of transparency for other models and the users that rely on them. Risk is managed because each node MUST declare the container that serves as its host environment in addition to other critical properties that encompass it's metadata. This allows models to accurately discern the risk of sharing information with any given node.

Features

• Delegate tasks to multiple machine learning models simultaneously

• Use autogen, autopilot, and copilot frameworks concurrently

• Automatically generate workflows, data pipelines, actions, and events

• Build comprehensive digital models from memory stores