ASI data center
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
a processor configured to execute the code and implement an embedded Artificial Intelligence (AI) Superintelligence (ASI) Data Center configured to:
provision AI compute, based on how many parameters a hosted ASI Application (App) constitutes,
assemble an AI-Model with the parameters constituting the hosted ASI App,
populate the AI-Model with instantiated values of the parameters comprising real, accurate, dedicated, absolute data (RADAD) as a machine learning (M/L) training dataset accessed via a stub and a skeleton embedded in interconnected agents across an end-to-end connected AI application layer network from the hosted ASI App at the one end to a transactional application at the other end that returns the RADAD to train the AI-Model,
operate a tracking service to count actions performed by the ASI App, and parameters and instantiated values invoked from the AI-Model,
store tracked actions, parameters and instantiated values in an ASI database populated with the RADAD as the M/L training dataset,
maintain the ASI database with the RADAD,
administrate a reporting service from the ASI database, and
apportion AI compute for the ASI App, based on at least reporting from the ASI database by the reporting service.
2 . The apparatus of claim 1 , wherein the AI compute is an AI application layer network resource.
3 . The apparatus of claim 1 , wherein the ASI App is hosted for an enterprise.
4 . The apparatus of claim 1 , wherein the assembled AI-Model with the parameters constituting the hosted ASI App is implemented in firmware.
5 . The apparatus of claim 1 , wherein the AI-Model and the interconnected agents across the AI application layer network containing the parameters constituting the hosted ASI App are implemented in firmware.
6 . The apparatus of claim 1 , wherein the AI application layer network is an Artificial General Intelligence (AGI) application layer network.
7 . The apparatus of claim 1 , wherein the AI application layer network is an ASI application layer network.
8 . The apparatus of claim 1 , wherein the AI application layer network is a Generative AI application layer network.
9 . The apparatus of claim 1 , wherein the reporting service displays results in a spreadsheet.
10 . 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.
11 . The apparatus of claim 1 , wherein the ASI App is hosted on at least one of a smart vehicle, a drone, a robot, a watch, or a mobility device.
12 . The apparatus of claim 1 , wherein the ASI App is implemented at least partially in a Graphical Processing Unit (GPU).
13 . The apparatus of claim 1 , wherein the ASI database contains the RADAD from an enterprise or from an application requiring personalized input of the RADAD.
14 . The apparatus of claim 1 , wherein the hosted ASI App is an ASI wellness App.
15 . The apparatus of claim 1 , wherein the ASI App is an ASI banking application, a military application, or a supply chain application.
16 . The apparatus of claim 1 , wherein the embedded ASI Data Center is an M/L, adaptive, interdisciplinary, cross-domain Data Center.
17 . The apparatus of claim 1 , wherein the stub and the skeleton are multi-Agent AI.
18 . The apparatus of claim 1 , wherein the AI compute is provisioned, operated, administered, maintained, apportioned and enforced for payment by billing per trackable unit.
19 . A method implemented by a computer-based embedded Artificial Intelligence Superintelligence (ASI) Data Center, the method comprising:
provisioning AI compute, based on how many parameters a hosted ASI Application (App) constitutes,
assembling an AI-Model with the parameters constituting the hosted ASI App,
populating the AI-Model with instantiated values of the parameters comprising real, accurate, dedicated, absolute data (RADAD) as a machine learning (M/L) training dataset, accessed via a stub and a skeleton embedded in interconnected agents across an end-to-end connected AI application layer network from the hosted ASI App at the one end to a transactional application at the other end that returns the RADAD to train the AI-Model,
operating a tracking service to count actions performed by the ASI App, and parameters and instantiated values invoked from the AI-Model,
storing tracked actions, parameters and instantiated values, in an ASI database populated with the RADAD as the M/L training dataset,
maintaining the ASI database with the RADAD,
administrating a reporting service from the ASI database, and
apportioning AI compute for the ASI App, based on at least reporting from the ASI database by the reporting service.
20 . A non-transitory computer-readable medium having code stored thereon that when executed by a processor causes the processor to execute a method implemented by a computer-based embedded Artificial Intelligence Superintelligence (ASI) Data Center, the method comprising:
provisioning AI compute, based on how many parameters a hosted ASI Application (App) constitutes,
assembling an AI-Model with the parameters constituting the hosted ASI App, populating the AI-Model with instantiated values of the parameters comprising real, accurate, dedicated, absolute data (RADAD) as a machine learning (M/L) training dataset, accessed via a stub and a skeleton embedded in interconnected agents across an end-to-end connected AI application layer network from the hosted ASI App at the one end to a transactional application at the other end that returns the RADAD to train the AI-Model,
operating a tracking service to count actions performed by the ASI App, and parameters and instantiated values invoked from the AI-Model,
storing tracked actions, parameters and instantiated values, in an ASI database populated with the RADAD as the M/L training dataset,
maintaining the ASI database with the RADAD,
administrating a reporting service from the ASI database, and
apportioning AI compute for the ASI App, based on at least reporting from the ASI database by the reporting service.