In some embodiments, user-selectable/connectable model representations may be provided via a user interface to facilitate artificial intelligence development. The model representations may comprises first and second machine learning model (ML) representations corresponding to first and second ML models, and non-ML model representations corresponding to non-ML models. Based on user input indicating selection of the first and second ML model representations and a non-ML model representation corresponding to a non-ML model, at least a portion of a software application may be generated such that the software application comprises (i) an instance of the first ML model, an instance of the second ML model, and an instance of the non-ML model and (ii) an input/output data path between the instance of the first ML model and at least one other instance, the at least one other instance comprising the instance of the second ML model or the instance of the non-ML model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of generating a neural-network-incorporated software application via user-selectable model representations of an interactive user interface, the method being implemented by a computer system that comprises one or more processors executing computer program instructions that, when executed, perform the method, the method comprising: causing user-selectable model representations to be available via a user interface, the user-selectable model representations comprising a first neural network representation corresponding to a first neural network, a second neural network representation corresponding to a second neural network, and non-neural-network model representations corresponding to non-neural-network models; obtaining, via the user interface, user input indicating (i) selection of the first neural network representation, the second neural network representation, and a non-neural-network model representation corresponding to a non-neural-network model, and (ii) connection of the first neural network representation to at least one other model representation, the at least one other model representation comprising the second neural network representation or the non-neural-network model representation; causing, via the user interface, based on the user input, a presentation of (i) the first neural network representation, the second neural network representation, and the non-neural-network model representation and (ii) a connection component shared between the first neural network representation and the at least one other model representation; and generating, based on the user input, at least a portion of a software application such that the software application comprises (i) an instance of the first neural network, an instance of the second neural network, and an instance of the non-neural-network model and (ii) an input/output data path between the instance of the first neural network and at least one other instance, the at least one other instance comprising the instance of the second neural network or the instance of the non-neural-network model. 2. The method of claim 1, wherein the at least one other model representation comprises the second neural network representation, and the at least one instance comprises the instance of the second neural network, and wherein at least the portion of the software application is generated such that the software application comprises (i) the instance of the first neural network, the instance of the second neural network, and the instance of the non-neural-network model and (ii) the input/output data path between the instance of the first neural network and the instance of the second neural network. 3. The method of claim 1, wherein at least the portion of the software application is generated such that the software application is configured to cause a prediction outputted by the instance of the first neural network to be provided as input to the at least one other instance for further processing by the at least one other instance. 4. The method of claim 1, further comprising: processing, via the instance of the first neural network, a first input provided to the instance of the first neural network to generate a prediction output; obtaining a reference indication corresponding to the prediction output or a derivative of the prediction output; and updating the software application's instance of the first neural network based on the reference indication. 5. The method of claim 4, wherein updating the software application's instance of the first neural network comprises providing the first input and the reference indication together as input to at least one instance of the first neural network. 6. The method of claim 5, wherein the at least one instance of the first neural network comprises a second instance of the first neural network different from the instance of the first neural network, and updating the software application's instance of the first neural network comprises replacing, for the software application, the first instance of the first neural network with the second instance of the first neural network. 7. The method of claim 5, wherein the at least one instance of the first neural network comprises the instance of the first neural network. 8. The method of claim 1, further comprising: processing, via the instance of the first neural network, a first input provided to the instance of the first neural network to generate a first prediction output; processing, via the instance of the second neural network, a second input provided to the instance of the second neural network to generate a second prediction output, the second input comprising the first prediction output or another output derived from the first prediction output; obtaining a reference indication corresponding to the second prediction output or another output derived from the second prediction output; and updating the software application's instance of the second neural network based on the reference indication. 9. The method of claim 8, wherein updating the software application's instance of the second neural network comprises providing the second input and the reference indication together as input to at least one instance of the second neural network. 10. The method of claim 9, wherein the at least one instance of the second neural network comprises a second instance of the second neural network different from the instance of the second neural network, and wherein updating the software application's instance of the second neural network comprises replacing, for the software application, the instance of the second neural network with the second instance of the second neural network. 11. The method of claim 9, further comprising: processing, via the instance of the second neural network, the second input and the reference indication provided to the instance of the second neural network to generate another reference indication corresponding to the first prediction output; and updating the software application's instance of the first neural network based on the other reference indication. 12. The method of claim 11, wherein updating the software application's instance of the first neural network comprises providing the first input and the other reference indication together as input to at least one instance of the first neural network. 13. The method of claim 12, wherein the at least one instance of the first neural network comprises a second instance of the first neural network different from the first instance of the first neural network, and wherein updating the software application's instance of the first neural network comprises replacing, for the software application, the first instance of the first neural network with the second instance of the first neural network. 14. A system comprising: a computer system that comprises one or more processors programmed with computer program instructions that, when executed, cause the computer system to: cause user-selectable model representations to be available via a user interface, the user-selectable model representations comprising a first machine learning model representation corresponding to a first machine learning model, a second machine learning model representation corresponding to a second machine learning model, and non-machine-learning model representations corresponding to non-machine-learning models; obtain, via the user interface, user input indicating selection of the first machine learning model representation, the second machine learning model representation, and a non-machine-learning model representation corresponding to a non-machine-learning model; cause, via the user interface, based on the user input, a presentation of (i) the first machine learning model representation, the second machine learning model representation, and the non-machine-learning model representation and (ii) a connection component shared between the first machine learning model representation and at least one other model representation, the at least one other model representation comprising the second machine learning model representation or the non-machine-learning model representation; and generate, based on the user input, at least a portion of a software application such that the software application comprises (i) an instance of the first machine learning model, an instance of the second machine learning model, and an instance of the non-machine-learning model and (ii) an input/output data path between the instance of the first machine learning model and at least one other instance, the at least one other instance comprising the instance of the second machine learning model or the instance of the non-machine-learning model. 15. The system of claim 14, wherein the at least one other model representation comprises the second machine learning representation, and the at least one instance comprises the instance of the second machine learning, and wherein at least the portion of the software application is generated such that the software application comprises (i) the instance of the first machine learning model, the instance of the second machine learning model, and the instance of the non-machine-learning model and (ii) the input/output data path between the instance of the first machine learning model and the instance of the second machine learning model. 16. The system of claim 14, wherein at least the portion of the software application is generated such that the software application is configured to cause a prediction outputted by the instance of the first machine learning model to be provided as input to the at least one other instance for further processing by the at least one other instance. 17. The system of claim 14, wherein the computer system is caused to: process, via the instance of the first machine learning model, a first input provided to the instance of the first machine learning model to generate a prediction output; obtain a reference indication corresponding to the prediction output or a derivative of the prediction output; and update the software application's instance of the first machine learning model based on the reference indication. 18. The system of claim 17, wherein updating the software application's instance of the first machine learning model comprises providing the first input and the reference indication together as input to at least one instance of the first machine learning model. 19. The system of claim 18, wherein the at least one instance of the first machine learning model comprises a second instance of the first machine learning model different from the first instance of the first machine learning model, and wherein updating the software application's instance of the first machine learning model comprises replacing, for the software application, the first instance of the first machine learning model with the second instance of the first machine learning model. 20. A method being implemented by a computer system that comprises one or more processors executing computer program instructions that, when executed, perform the method, the method comprising: causing user-selectable model representations to be available via a user interface, the user-selectable model representations comprising a first machine learning model representation corresponding to a first machine learning model, a second machine learning model representation corresponding to a second machine learning model, and non-machine-learning model representations corresponding to non-machine-learning models; obtaining, via the user interface, user input indicating selection of the first machine learning model representation, the second machine learning model representation, and a non-machine-learning model representation corresponding to a non-machine-learning model; causing, via the user interface, based on the user input, a presentation of (i) the first machine learning model representation, the second machine learning model representation, and the non-machine-learning model representation and (ii) a connection component shared between the first machine learning model representation and at least one other model representation, the at least one other model representation comprising the second machine learning model representation or the non-machine-learning model representation; and generating, based on the user input, at least a portion of a software application such that the software application comprises (i) an instance of the first machine learning model, an instance of the second machine learning model, and an instance of the non-machine-learning model and (ii) an input/output data path between the instance of the first machine learning model and at least one other instance, the at least one other instance comprising the instance of the second machine learning model or the instance of the non-machine-learning model.