AI superintelligence application state machine
View Patent ↗The present invention provides a method and apparatus for a thinking system using an ASI distributed Application State Machine that connects to real, accurate, dedicated absolute data, providing privacy, security, and eliminating hallucinated AI. Specifically, one embodiment of the present invention discloses an ASI Command, Control, Communications and Computer Intelligence (C4I) system for independent, intelligent AI Prompting and ASI Browsing. The ASI Application State Machine comprises: ASI Application data structure network; ASI Enterprise Interface State Machine, said data structures interfacing with a back-end channel, a Finite State Machine embedded in each data structure; and Application Service Information Base providing a uniform interface for ASI Application data structure identities. Additional embodiments disclose ASI Browser displaying ASI Applications; ASI Agents; ASI Application Network Nodes; ASI Operating System; ASI Switch; ASI Database of real, accurate, dedicated absolute data, for Machine Learning to train AI Models, for performing real-time, bi-directional transactions, connecting to billing.
1 . An apparatus, comprising:
a memory having code therein; and
circuitry configured to execute the code and implement an embedded Artificial Intelligence (AI) Superintelligence (ASI) Switch configured to
connect a user-device at one end of a Boundary Quadrant to an ASI Application (App) in an ASI Apps Quadrant in a Graphical Processing Unit (GPU) or other processing unit in a data center,
exchange instantiated values of user-requested action parameters with sending and receiving circuitry in an ASI Switching Quadrant,
invoke an application programming interface to remotely access data and actions of a data structure of the ASI App, wherein the application programming interface provides two types of a class, wherein the two types of the class are an ASI skeleton, wherein the ASI skeleton comprises the actions of the data structure of the ASI App, and an ASI stub, wherein the ASI stub allows remote access of the same ASI App data structure;
access real, accurate, dedicated absolute data (RADAD) at the other end of the Boundary Quadrant in an end-to-end connected AI application layer network,
return the real, accurate, dedicated absolute data (RADAD),
train an AI-Model with the real, accurate, dedicated absolute data (RADAD) as a Machine Learning (M/L) data set,
invoke a connection to an ASI Service Management Quadrant to track per usage of AI application layer network resources,
apply Operations, Administration, Maintenance and Provisioning (OAM&P) services provided by the ASI Service Management Quadrant, and
complete the actions in the AI application layer network.
2 . The apparatus of claim 1 , wherein the AI application layer network resources are provisioned, operated, administered, maintained and apportioned by providing billing per trackable unit of usage of the AI application layer network resources to enforce payment.
3 . The apparatus of claim 1 , wherein the AI application layer network resources include one or more of AI compute, ASI Apps, ASI App-specific action parameters, number of parameters in the AI-Model, and power and bandwidth consumption.
4 . The apparatus of claim 1 , wherein the application programming interface utilizes an ASI Application State Machine.
5 . The apparatus of claim 1 , wherein the AI-Model is a user-device specific AI-Model with ASI App-specific parameters as per a data definition file for the ASI App.
6 . The apparatus of claim 1 , wherein each of the ASI skeleton and the ASI stub is an ASI Agent.
7 . The apparatus of claim 1 , wherein the sending and receiving circuitry are ASI multi-Agents.
8 . The apparatus of claim 1 , wherein the ASI skeleton and ASI stub are implemented in firmware.
9 . The apparatus of claim 1 , wherein the AI-Model, ASI skeleton and ASI stub contain same ASI App-specific action parameters.
10 . The apparatus of claim 1 , wherein the AI application layer network is an Artificial General Intelligence (AGI) application layer network, or a Generative AI application layer network, or an ASI application layer network.
11 . The apparatus of claim 1 , wherein the OAM&P services provided by the ASI Service Management Quadrant include a tracking service that displays results in a spreadsheet or a database.
12 . The apparatus of claim 1 , wherein the AI-Model is an enterprise-specific ASI App-specific AI-Model or an end-user device-specific ASI App-specific AI-Model.
13 . The apparatus of claim 1 , wherein the ASI App is hosted on at least one of an intelligent vehicle, a drone, a robot, a watch, or a mobility device.
14 . The apparatus of claim 1 , wherein the ASI App is implemented at least partially in a Graphical Processing Unit (GPU) or other processing unit in a data center or in an enterprise.
15 . The apparatus of claim 11 , wherein the database is a Retrieval-Augmented Generation (RAG) database with embeddings of data sources, a database that retrieves text and other data with AI App state machine protocols, a database with native vector search, or a database for unstructured data.
16 . The apparatus of claim 1 , wherein the ASI App is user-specific to an ASI wellness App.
17 . The apparatus of claim 1 , wherein the ASI App is an enterprise AI App, ASI banking App, ASI military App, ASI search App, or ASI e-commerce App.
18 . A method implemented by a computer-based embedded Artificial Intelligence Superintelligence (ASI) Switch, the method comprising:
connecting a user-device at one end of a Boundary Quadrant to an ASI Application (App) in an ASI Apps Quadrant in a Graphical Processing Unit (GPU) or other processing unit in a data center,
exchanging instantiated values of user-requested action parameters with sending and receiving circuitry in an ASI Switching Quadrant,
invoking an application programming interface to remotely access data and actions of a data structure of the ASI App, wherein the application programming interface provides two types of a class, wherein the two types of the class are an ASI skeleton, wherein the ASI skeleton comprises the actions of the data structure of the ASI Application, and an ASI stub, wherein the ASI stub allows remote access of the same ASI App data structure;
accessing real, accurate, dedicated absolute data (RADAD) at the other end of the Boundary Quadrant in an end-to-end connected AI application layer network,
returning the real, accurate, dedicated absolute data (RADAD),
training an AI-Model with the real, accurate, dedicated absolute data (RADAD) as the Machine Learning (M/L) data set,
invoking a connection for the ASI database to an ASI Service Management Quadrant to track per usage of AI application layer network resources,
applying Operations, Administration, Maintenance and Provisioning (OAM&P) services provided by the ASI Service Management Quadrant, and
completing the actions in the AI application layer network.
19 . A non-transitory computer-readable medium having code stored thereon that when executed by circuitry causes the circuitry to execute a method implemented by a computer-based embedded Artificial Intelligence Superintelligence (ASI) Switch, the method comprising:
connecting a user-device at one end of a Boundary Quadrant to an ASI Application (App) in an ASI Apps Quadrant in a Graphical Processing Unit (GPU) or other processing unit in a data center,
exchanging instantiated values of user-requested action parameters with sending and receiving circuitry in an ASI Switching Quadrant,
invoking an application programming interface to remotely access data and actions of a data structure of the ASI App, wherein the application programming interface provides two types of a class, wherein the two types of the class are an ASI skeleton, wherein the ASI skeleton comprises the actions of the data structure of the ASI Application, and an ASI stub, wherein the ASI stub allows remote access of the same ASI App data structure;
accessing real, accurate, dedicated absolute data (RADAD) at the other end of the Boundary Quadrant in an end-to-end connected AI application layer network,
returning the real, accurate, dedicated absolute data (RADAD),
training an AI-Model with the real, accurate, dedicated absolute data (RADAD) as the Machine Learning (M/L) data set,
invoking a connection for the ASI database to an ASI Service Management Quadrant to track per usage of AI application layer network resources,
applying Operations, Administration, Maintenance and Provisioning (OAM&P) services provided by the ASI Service Management Quadrant, and
completing the actions in the AI application layer network.