System and methods for safe, scalable, artificial general intelligence (agi)

A system for safe and scalable Artificial General Intelligence (AGI) using a network of intelligent entities agents including a combination of human users each utilizing a computer system, and previously customized Artificial Intelligence (AI) agents, all electronically communicating over a collective network, the system comprising:

• a computer system comprising:
• a processor, a computer-readable storage medium, and program instructions stored on the computer-readable storage medium being executable by the processor to cause the computer system to:
• train a base Large Language Model (LLM) of a first AI agent with guardrails including attributes associated with any one of or any combination safety, ethics and knowledge;
• customize the base LLM with an ethics profile associated with a first human user;
• combine ethical information from multiple intelligent entities different to that of the first AI agent and the first human user;
• refine a set of values of the base LLM based on a problem solving process;
• and
• update the training of the first AI agent with the combined ethical information and the refined set of values thereby allowing for a scalable AGI.

A method for safe and scalable Artificial General Intelligence (AGI) using a network of intelligent entities agents including a combination of human users each utilizing a computer system, and previously customized Artificial Intelligence (AI) agents, all electronically communicating over a collective network, the method comprising:

• training a base Large Language Model (LLM) of a first AI agent with guardrails including attributes associated with any one of or any combination safety, ethics and knowledge;
• customizing the base LLM to an ethics profile associated with a first human user;
• combining ethical information from multiple intelligent entities different to that of the first AI agent and the first human user;
• refining a set of values of the base LLM based on problem solving of a problem request;
• and
• updating the base LLM with the combined ethical information and the refined set of values thereby allowing for a scalable AGI.

A method for safe and scalable Artificial General Intelligence (AGI) using a network of intelligent entities agents including a combination of human users each utilizing a computer system, and previously customized Artificial Intelligence (AI) agents, all electronically communicating over a collective network, the method comprising:

• training a base Large Language Model (LLM) of a first AI agent with guardrails including attributes associated with any one of or any combination safety, ethics and knowledge;
• customizing the base LLM to an ethics profile associated with a first human user;
• identifying one or more weight matrices from multiple intelligent entities different to that of the first AI agent and the first human user that contain attributes related to the attributes of the first AI agent, wherein the intelligent entities including any one of or any combination of a human user utilizing a computer system different to the first human user, and a previously customized AI agent;
• determining a method for combining the identified weight matrices from each of the intelligent entities;
• experimenting repeatedly with a first combination of the weight matrices to monitor if a desired behavior is moving in a specific direction before proceeding with a second combination of the weight matrices that is larger than the first combination;
• utilizing an algorithm to automate the step of experimenting;
• combining ethical information and the weight matrices from the intelligent entities;
• refining a set of values of the base LLM based on problem solving of a problem request;
• and
• testing the first AI agent with the ethical information and the weight matrices to determine if a desired performance of the first AI agent has been achieved;
• and
• updating the base LLM with the combined ethical information and the refined set of values thereby allowing for a scalable AGI.

A method for safe and scalable Artificial General Intelligence (AGI) using a network of intelligent entities agents including a combination of human users each utilizing a computer system, and previously customized Artificial Intelligence (AI) agents, all electronically communicating over a collective network, the method comprising:

• training a base Large Language Model (LLM) of a first AI agent with guardrails including attributes associated with any one of or any combination safety, ethics and knowledge;
• customizing the base LLM to an ethics profile associated with a first human user;
• combining ethical information from multiple intelligent entities different to that of the first AI agent and the first human user;
• confirming that the ethical information from the multiple intelligent entities is related to a desired behavior of the first AI agent;
• refining a set of values of the base LLM based on problem solving of a problem request;
• updating the base LLM with the combined ethical information and the refined set of values thereby allowing for a scalable AGI;
• testing a performance of the updated base LLM against previously run scenarios to determine if a desired performance of the first AI agent has been achieved;
• making the first AI agent with the updated base LLM available on the collective network if the desired performance was determined;
• monitoring an active performance of the first AI agent by the intelligent entities or other intelligent entities and flagging potential ethical issues of the first AI agent in real time;
• and
• resolving any of the flagged ethical issues and providing resolution information for updating any one of or any combination of the first AI system, and the intelligent entities.

A method for safe and scalable Artificial General Intelligence (AGI) using knowledge modules in customizing an Artificial Intelligence (AI) agent by using previously customized AI agents, all electronically communicating over a collective network, the method comprising:

• assigning each customized AI agent on the collective network with metadata that identifies an expertise of the customized AI agent;
• identifying the customized AI agents on the collective network that have metadata related to a desired expertise;
• obtaining weight matrices from the identified customized AI agents, and combining the weight matrices from the identified customized AI agents to form a knowledge module;
• refining a set of values of a base LLM of a first AI agent based on problem solving of a problem request provided by a human user utilizing a computer system or an AI agent;
• and
• updating the base LLM with the knowledge module and the refined set of values thereby allowing for a scalable AGI;
• providing a response to the problem request by the identified customized AI agents;
• and
• testing the first AI agent with the knowledge module to determine if a desired performance of the first AI agent has been achieved.

A method for safe and scalable Artificial General Intelligence (AGI) using knowledge modules in customizing an Artificial Intelligence (AI) agent by using previously customized AI agents, all electronically communicating over a collective network, the method comprising:

• creating multiple collections of customized AI agents, wherein each collection includes multiple customized AI agents with metadata relating to an expertise;
• providing a task by an intelligent entity including human users utilizing a computer system, and previously customized Artificial Intelligence (AI) agents, wherein the task includes metadata associated with a desired expertise;
• identifying a relevant collection out of the collections of customized AI agents with metadata related to the desired expertise associated with the task;
• identifying a customized AI agent on the collective network that has metadata related to the desired expertise associated with the task;
• adding the customized AI agent to the identified relevant collection to create a new collection of customized AI agents;
• providing the task to the new collection of customized AI agents for creating responses to the task;
• and
• determining if the responses from the new collection of customized AI agents are in consensus, and if so, then providing the responses to the intelligent entity;
• and
• testing the first AI agent with the knowledge module to determine if a desired performance of the first AI agent has been achieved.

A method for developing a safe and scalable Artificial General Intelligence (AGI) utilizing a network of human users each utilizing a computer system, and previously customized Artificial Intelligence (AI) agent, all electronically communicating over a collective network, the method comprising the steps of:

• a) creating an ethics profile associated with a human user;
• b) customizing a base Large Language Model (LLM) of a first AI agent with the ethics profile;
• c) communicating between multiple AI agents and the first AI agent utilizing a collective intelligence network;
• d) combining ethical information from multiple AI agents different to that of the first AI agent;
• e) refining a set of values of the base LLM based on problem solving of a problem request;
• and
• f) updating the base LLM with the combined ethical information and the refined set of values thereby allowing for a scalable AGI.

A method for safe and scalable Artificial General Intelligence (AGI) utilizing a single computerized intelligent system including multiple Artificial Intelligence (AI) agents residing in the single computerized intelligent system, the method comprising:

• training a base Large Language Model (LLM) of an AI agent with guardrails including attributes associated with any one of or any combination safety, ethics and knowledge, the AI agent residing in a single computerized intelligent system;
• customizing the base LLM to an ethics profile;
• combining ethical information from multiple additional AI agents residing in the single computerized intelligent system, the additional AI agents being different to that of the AI agent;
• refining a set of values of the base LLM based on problem solving of a problem request;
• and
• updating the base LLM with the combined ethical information and the refined set of values thereby allowing for a scalable AGI.