An AI database hosted on cloud platform is configured to cooperate with a search engine and an AI engine. The AI database stores and indexes trained AI objects and its class of AI objects have searchable criteria. The AI database cooperates with the search engine to utilize search criteria supplied from a user, from either or both 1) via scripted software code and 2) via data put into defined fields of a user interface. The search engine utilizes the search criteria in order for the search engine to retrieve one or more AI data objects that have already been trained as query results. The AI database is coupled to an AI engine to allow any of reuse, reconfigure ability, and recomposition of the one or more trained AI data objects from the AI database into a new trained AI model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An artificial intelligence (“AI”) database hosted on cloud platform and configured to cooperate with a search engine, comprising: where the AI database is configured to store and index trained AI objects and its class of AI objects have searchable criteria, where the AI database is configured to cooperate with the search engine to utilize search criteria supplied from a user, from either or both 1) via scripted software code and 2) via data put into defined fields of a user interface, in order for the search engine to retrieve one or more AI data objects that have already been trained as query results, where the AI database is coupled to an AI engine to allow any of reuse, reconfigure ability, and recomposition of the one or more trained AI data objects into a new trained AI model, where reuse, reconfigure ability, and recomposition of the one or more trained AI data objects improves the functioning of the AI engine by reducing an amount of memory used and processing cycles used as compared to start training an AI model from scratch, which has not previously been through training sessions to be trained. 2. The AI database configured to cooperate with the search engine of claim 1, where the search engine is a component within the AI database of the AI system, where the AI database further consists of a storage layer which is configured to know how to efficiently store database objects, in this case AI objects, an indexing mechanism to speed retrieval of the stored AI objects, where the search engine is configured to translate a query request into a retrieval strategy to retrieve AI objects that satisfy a query, and a query language which describes to the AI database what AI objects are desired to be retrieved. 3. The AI database configured to cooperate with the search engine of claim 1, a signature module is configured to calculate a signature for a mental model of concepts in a first trained AI model, where the signature is a form of hashing such that mental models of different trained AI models that have similar machine learning algorithmic characteristics have similar signatures. 4. The AI database configured to cooperate with the search engine of claim 3, where the search engine utilizes the user supplied criteria to query for relevant trained AI objects by referring to 1) the signatures of the stored AI objects as well as 2) any indexed parameters for the AI objects indexed by the AI database. 5. The AI database configured to cooperate with the search engine of claim 1, where the AI database is composed of a set of one or more databases in which each database has a different profile and indexing, where the set of databases are configured to operate in a parallel to then send back accurate, fast, and efficient returns of trained AI objects that satisfy the search query. 6. The AI database configured to cooperate with the search engine of claim 1, where each trained artificial intelligence model itself is a collection of trained AI objects corresponding to a main concept and a set of sub concepts feeding parameters into the main concept, where the AI database indexes AI objects corresponding to the main concept and the set of sub concepts making up a given trained artificial intelligence model so that reuse, recomposition, and reconfiguration of all or part of a trained artificial intelligence model is possible. 7. The AI database configured to cooperate with the search engine of claim 1, further comprising: a user interface to the AI database and search engine that is configured to present a population of known trained AI objects and the search engine cooperating with the AI database is configured to search the population of known trained AI objects to return a set of one or more already trained AI objects similar to a problem trying to be solved by the user supplying the search criteria. 8. The AI database configured to cooperate with the AI engine to supply one or more AI objects of claim 1, wherein the AI engine includes an architect module configured to create and optimize learning topologies of neural networks for the AI objects; an instructor module configured to carrying out a training plan codified in a pedagogical software programming language; and a learner module configured to carrying out an actual execution of underlying AI learning algorithms during a training session, where the architect module, when reconfiguring or recomposing the AI objects, composes the one or more trained AI data objects into a new AI model and the instructor module and learner module cooperate with one or more data sources to train the new AI model. 9. A method of causing an artificial intelligence (“AI”) database to cooperate with a search engine, comprising: causing the AI database to store and index trained AI objects and its class of AI objects to have searchable criteria; and utilizing search criteria supplied from a user, from either or both 1) via scripted software code and 2) via data put into defined fields of a user interface, in order for the search engine to retrieve one or more AI data objects that have already been trained as query results, where the AI database cooperates with an AI engine to cause any of reuse, reconfigure ability, and recomposition of the one or more trained AI data objects into a new trained AI model. 10. The method of claim 9, further comprising: 1) parsing scripted software code written in a pedagogical software programming language with the search engine and then mapping that to one or more searchable criteria as well as 2) importing the data put into defined fields of the user interface to use as searchable criteria to find relevant trained AI objects indexed in the AI database. 11. The method of claim 9, further comprising: utilizing the user supplied criteria to query for relevant trained AI objects by referring to 1) the signatures of the stored AI objects as well as 2) any indexed parameters for the AI objects indexed by the AI database. 12. The method of claim 9, further comprising: configuring a set of one or more databases in which each database has a different profile and indexing to be the AI database, where the set of databases are configured to operate in a parallel to then send back accurate, fast, and efficient returns of trained AI objects that satisfy the search query. 13. The method of claim 9, further comprising: storing and indexing parts of a trained artificial intelligence model as a collection of trained AI objects corresponding to a main concept and a set of sub concepts feeding parameters into the main concept so that reuse, recomposition, and reconfiguration of all or part of a trained artificial intelligence model is possible. 14. The method of claim 9, further comprising: configuring the AI database to cooperate with the AI engine to supply one or more AI objects, wherein the AI engine includes an architect module configured to create and optimize learning topologies of neural networks for the AI objects; an instructor module configured to carrying out a training plan codified in a pedagogical software programming language; and a learner module configured to carrying out an actual execution of underlying artificial intelligence learning algorithms during a training session, where the architect module, when reconfiguring or recomposing the AI objects, composes the one or more trained AI data objects into a new AI model and the instructor module and learner module cooperate with one or more data sources to train the new AI model. 15. The method of claim 9, further comprising: presenting a population of known trained AI objects; and searching the population of known trained AI objects to return a set of one or more already trained AI objects similar to a problem trying to be solved by the user supplying the search criteria. 16. A non-transitory machine-readable medium configured to store data and instructions, which, when executed by one or more processors on a computing device, causes the following operations, comprising: causing an artificial intelligence (“AI”) database to cooperate with a search engine; causing the AI database to store and index trained AI objects and its class of AI objects to have searchable criteria; and utilizing search criteria supplied from a user, from either or both 1) via scripted software code and 2) via data put into defined fields of a user interface, in order for the search engine to retrieve one or more AI data objects that have already been trained as query results, where the AI database cooperates with an AI engine to cause any of reuse, reconfigure ability, and recomposition of the one or more trained AI data objects into a new trained AI model. 17. The non-transitory machine-readable medium of claim 16 storing instructions to cause further following operations, comprising: either or both 1) parsing scripted software code written in a pedagogical software programming language with the search engine and then mapping that to one or more searchable criteria as well as 2) importing the data put into defined fields of the user interface to use as searchable criteria to find relevant trained AI objects indexed in the AI database. 18. The non-transitory machine-readable medium of claim 16 storing instructions to cause further following operations, comprising: calculating a signature for a mental model of concepts in a first trained AI model, where the signature is a form of hashing such that mental models of different trained AI models that have similar machine learning algorithmic characteristics have similar signatures. 19. The non-transitory machine-readable medium of claim 16 storing instructions to cause further following operations, comprising: utilizing the user supplied criteria to query for relevant trained AI objects by referring to 1) the signatures of the stored AI objects as well as 2) any indexed parameters for the AI objects indexed by the AI database. 20. The non-transitory machine-readable medium of claim 16 storing instructions to cause further following operations, comprising: storing and indexing parts of a trained artificial intelligence model as a collection of trained AI objects corresponding to a main concept and a set of sub concepts feeding parameters into the main concept, where the AI database indexes AI objects corresponding to the main concept and the set of sub concepts making up a given trained artificial intelligence model so that reuse, recomposition, and reconfiguration of all or part of a trained artificial intelligence model is possible. 21. The non-transitory machine-readable medium of claim 16 storing instructions to cause further following operations, comprising: presenting a population of known trained AI objects; and searching the population of known trained AI objects to return a set of one or more already trained AI objects similar to a problem trying to be solved by the user supplying the search criteria. 22. The AI database configured to cooperate with the search engine of claim 1, where the search engine is configured to import the data put into defined fields of the user interface to use as searchable criteria to find relevant trained AI objects indexed in the AI database. 23. The AI database configured to cooperate with the search engine of claim 22, where the search engine is also configured to parse scripted software code written in a pedagogical software programming language and then map that to one or more searchable criteria.