There is disclosed a novel system and method for building artificial neural networks for a given task. In an embodiment, the method utilizes one or more network models that define the probabilities of nodes and/or interconnects, and/or the probabilities of groups of nodes and/or interconnects, from sets of possible nodes and interconnects existing in a given artificial neural network. These network models can be constructed based on the properties of one or more artificial neural networks, or constructed based on desired architecture properties. These network models are then used to build combined network models using a model combiner module. The combined network models and random numbers generated by a random number generator module are then used to build one or more new artificial neural network architectures. New artificial neural networks are then built based on the newly built artificial neural network architectures and are trained for a given task. These trained artificial neural networks can then be used to generate network models for building subsequent artificial neural network architectures. This iterative building process can be repeated in order to learn how to build new artificial neural network architectures, and this learning may be stored to build future artificial neural network architectures based on past neural network architectures.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method of building an artificial neural network architecture for a given task, comprising: (i) constructing, utilizing a processor, one or more network models based on properties of one or more artificial neural networks and one or more desired artificial neural network architecture properties, the one or more network models defining probabilities of one or more nodes and/or interconnects from a set of possible nodes and interconnects existing in a given artificial neural network; (ii) combining, utilizing a model combiner module, the one or more network models into combined network models; (iii) generating, utilizing a random number generator module, random numbers; (iv) building, utilizing a network architecture builder module, one or more new artificial neural network architectures based on the combined network models and the random numbers generated from the random number generator module; (v) building one or more artificial neural networks based on the new artificial neural network architectures built by the network architecture builder module; and (vi) training one or more artificial neural networks built based on the new artificial neural network architectures. 2. The computer-implemented method of claim 1, further comprising: (vii) generating, utilizing a processor, one or more subsequent network models based on properties of one or more artificial neural networks and one or more desired artificial neural network architecture properties; and (viii) repeating steps (ii) to (vi) to iteratively build new artificial neural network architectures. 3. The computer-implemented method of claim 2, further comprising: (ix) storing the iteratively learned knowledge on how to build new artificial neural network architectures, thereby to build future artificial neural network architectures based on past neural network architectures. 4. The computer-implemented method of claim 1, further comprising: (x) training one or more artificial neural networks built based on the new artificial neural network architectures and desired bit-rates of interconnect weights in the one or more artificial neural networks. 5. The computer-implemented method of claim 1, wherein building one or more new artificial neural network architectures in step (iv) comprises removing all nodes and interconnects that are not connected to other nodes and interconnects in the one or more new artificial neural network architectures. 6. The computer-implemented method of claim 1, wherein building one or more new artificial neural network architectures in step (iv) comprises removing all interconnects that have interconnect weights equal to 0 and all nodes that are not connected to other nodes and interconnects in the trained artificial neural networks. 7. The computer-implemented method of claim 1, wherein the given task is object recognition from images or video, and the method further comprises building one or more artificial neural networks trained for the task of object recognition from images or video. 8. The computer-implemented method of claim 7, wherein the given task of object recognition from images or video comprises recognition of one or more predefined abstract objects or a class of predefined abstract objects. 9. The computer-implemented method of claim 7, wherein the given task of object recognition from images or video comprises recognition of one or more predefined physical objects or a class of predefined physical objects. 10. The computer-implemented method of claim 7, wherein the one or more predefined physical objects comprise one or more identifiable biometric features or a class of biometric features. 11. A computer-implemented system for building an artificial neural network architecture for a given task, the system comprising a processor and a memory, and adapted to: (i) construct, utilizing a processor, one or more network models based on properties of one or more artificial neural networks and one or more desired artificial neural network architecture properties, the one or more network models defining probabilities of one or more nodes and/or interconnects from a set of possible nodes and interconnects existing in a given artificial neural network; (ii) combine, utilizing a model combiner module, the one or more network models into combined network models; (iii) generate, utilizing a random number generator module, random numbers; (iv) build, utilizing a network architecture builder module, one or more new artificial neural network architectures based on combined network models and the random numbers generated from the random number generator module; (v) build one or more artificial neural networks based on the new artificial neural network architectures built by the network architecture builder module; and (vi) train one or more artificial neural networks built based on the new artificial neural network architectures. 12. The computer-implemented system of claim 11, wherein the system is further adapted to: (vii) generate, utilizing a processor, one or more subsequent network models based on properties of one or more artificial neural networks and one or more desired artificial neural network architecture properties; and (viii) repeat (ii) to (vi) to iteratively learn build new artificial neural network architectures. 13. The computer-implemented system of claim 12, wherein the system is further adapted to: (ix) store the iteratively learned knowledge on how to build new artificial neural network architectures, thereby to build future artificial neural network architectures based on past neural network architectures. 14. The computer-implemented system of claim 11, wherein the system is further adapted to: (x) train one or more artificial neural networks built based on the new artificial neural network architectures and desired bit-rates of interconnect weights in the one or more artificial neural networks. 15. The computer-implemented system of claim 11, wherein the system is further adapted to remove all nodes and interconnects that are not connected to other nodes and interconnects in the one or more new artificial neural network architectures when building one or more new artificial neural network architectures. 16. The computer-implemented system of claim 11, wherein the system is further adapted to remove all interconnects that have interconnect weights equal to 0 and all nodes that are not connected to other nodes and interconnects in the trained artificial neural networks when building one or more new artificial neural network architectures. 17. The computer-implemented system of claim 11, wherein, for the given task of object recognition from images or video, the system is further adapted to build one or more artificial neural networks trained for the task of object recognition from images or video. 18. The computer-implemented system of claim 17, wherein the given task of object recognition from images or video comprises recognition of one or more predefined abstract objects or a class of predefined abstract objects. 19. The computer-implemented system of claim 17, wherein the given task of object recognition from images or video comprises recognition of one or more predefined physical objects or a class of predefined physical objects. 20. The computer-implemented system of claim 17, wherein the one or more predefined physical objects comprise one or more identifiable biometric features or a class of biometric features. 21. An integrated circuit having a plurality of electrical circuit components arranged and configured to replicate the nodes and interconnects of the artificial neural network architecture built by the system of claim 11.