Interoperable composite data units for use in distributed computing execution environments
Disclosed implementations provide executable models, such as artificial intelligence models that can be owned, traded, and used in various execution environments. By coupling a model with a strictly defined interface definition, the model can be executed in various execution environments that support the interface. Coupling the model with a non-fungible cryptographic token allows the model and other components to be owned and traded as a unit. The tradeable composite units have utility across multiple supported execution environments, such as video game environments, chat bot environments and financial trading environments. Additionally, the interface allows for the creation of pipelines and systems from multiple complementary composite units.
1. A non-transitory computer readable media for providing a computation model that can be implemented in multiple execution environments, the data structure including software modules recorded thereon, the modules comprising:
a model module including model code that, when executed by a computer processor, causes computation on data in accordance with the computation model;
an interface definition module including a pointer to an interface definition associated with the computation model, that is used by a processor to communicate with the model code;
a non-fungible token module including a pointer to a non-fungible token associated with the computation module, where the non-fungible token is stored on a decentralized ledger; and
an input value matrix which holds multiple values that are mapped to input variables within the model code and wherein the multiple values of the input value matrix are used to train the learning module.
2. The non-transitory computer readable media of claim 1 , wherein the interface definition specifies a finite set of inputs and outputs for the computation model.
3. The non-transitory computer readable media data structure of claim 1 , wherein the model code includes a learning module.
4. The non-transitory computer readable media of claim 3 , wherein the learning module is an artificial intelligence model.
5. The non-transitory computer readable media of claim 1 , further comprising an input value matrix which holds multiple values that are mapped to input variables within the model code.
6. The non-transitory computer readable media of claim 2 , wherein the computation model represents activity of an entity in a video game.
7. The non-transitory computer readable media of claim 1 , wherein the interface definition specifies actions that can be taken by the entity.
8. The non-transitory computer readable media of claim 7 , wherein the values represent attributes of the entity.
9. The non-transitory computer readable media of claim 8 , wherein the values are changed as a result of entity activity.
10. A method for creating a composite data structure recorded on non-transitory computer readable media for providing a computation model that can be implemented in multiple execution environments, the method comprising:
providing a model module specifying model code that, when executed by a computer processor, causes computation on data in accordance with the computation model;
providing an interface definition module including a pointer to an interface definition associated with the computation model;
linking the computation model with a non-fungible token stored on a decentralized ledger; and
providing an input value matrix which holds multiple values that are mapped to input variables within the model code and wherein the multiple values of the input value matrix are used to train the learning module and wherein the model code includes a learning module.
11. The method of claim 10 , wherein the interface definition specifies a finite set of inputs and outputs for the computation model.
12. The method of claim 11 , wherein the computation model represents activity of an entity in a video game.
13. The method of claim 10 , wherein the model module includes the model code.
14. The method of claim 10 , wherein the learning module is an artificial intelligence model.
15. The method of claim 10 , further comprising providing an input value matrix which holds multiple values that are mapped to input variables within the model code.
16. The method of claim 10 , wherein the interface definition specifies actions that can be taken by the entity.
17. The method of claim 16 , wherein the values represent attributes of the entity.
18. The method of claim 17 , wherein the values are changed as a result of entity activity.