Provided herein in some embodiments is an artificial intelligence (“AI”) engine configured to work with a graphical user interface (“GUI”). The AI engine can include an architect module, instructor module, and learner module AI-engine modules. The GUI can be configured with a text editor and a mental-model editor to enable an author to define a mental model to be learned by an AI model, the mental model including an input, one or more concept nodes, and an output. The architect module can be configured to propose a neural-network layout from an assembly code compiled from a source code in a pedagogical programming language, the learner module can be configured to build the AI model from the neural-network layout, and the instructor module can be configured to train the AI model on the one or more concept nodes.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An artificial intelligence (“AI”) engine configured to work with a graphical user interface (“GUI”), comprising: an application programming interface (“API”) exposed to the GUI, wherein the API is configured to receive a source code written in a pedagogical programming language for compiling by the AI engine into an assembly code, wherein the GUI is configured to enable an author to define a mental model to be learned by an AI model, the mental model including an input, one or more concept nodes, and an output, and wherein the GUI is further configured to enable the author to define the mental model in a textual mode, a graphical mode, or both the textual mode and the graphical mode; and one or more AI-engine modules in the memory including an architect module, an instructor module, and a learner module, wherein the architect module, upon execution of the architect module by the one or more processors, is configured to propose a neural-network layout with one or more neural-network layers from the assembly code, wherein the learner module, upon execution of the learner module by the one or more processors, is configured to build the AI model with the one or more neural-network layers from the neural-network layout proposed by the architect module, wherein the instructor module, upon execution of the instructor module by the one or more processors, is configured to train the AI model built by the learner module on the one or more concept nodes, and wherein the GUI is configured via interoperability of the textual mode and the graphical mode to facilitate development of the mental model, thereby optimizing the development of the mental model to reduce AI-model training time, memory, and computing cycles used by the AI engine for training the AI model on the mental model. 2. The AI engine of claim 1, further comprising a compiler in a memory, wherein the compiler, upon execution of the compiler by one or more processors, is configured to generate the assembly code from the source code received the API exposed to the GUI, wherein the GUI is an integrated development environment (“IDE”) including a text editor configured to enable the author to define the mental model by typing in the textual mode, and a mental-model designer configured to enable the author to define the mental model by mouse gestures in the graphical mode, wherein author-based modification of the mental model by the typing in the textual mode automatically modifies the mental model in the mental-model designer, and wherein author-based modification of the mental model by the mouse gestures in the graphical mode automatically modifies the mental-model in the text editor. 3. The AI engine of claim 2, wherein the text editor is further configured to enable the author to set one or more breakpoints in the source code written in the pedagogical programming language, and wherein the mental-model designer is further configured to automatically highlight the one or more concept nodes of the mental model corresponding to the one or more breakpoints if the one or more breakpoints are set at the one or more concept nodes. 4. The AI engine of claim 2, wherein the pedagogical programming language is configured to enable an author to define schemas describing one or more data types to be streamed through connected nodes of the mental model including the input, the one or more concept nodes, one or more stream nodes, and the output, and wherein the data types include primitive data types selected from strings, integers, floats, and Booleans; structured data types; constrained data types with range expressions limiting the data of the constrained data types; and combinations thereof. 5. The AI engine of claim 4, wherein each connector of a number of connectors between the connected nodes of the mental model includes a data type, and wherein the mental-model designer is further configured to color code the number of connectors in the mental model to indicate whether the data type between the connected nodes of the mental model is correct or not for each connector. 6. The AI engine of claim 2, wherein the IDE is configured to support one or more plug-ins for extended functionality of the IDE, and wherein the extended functionality of the IDE includes support for programming languages in addition to the pedagogical programming language; source code management; project management; a marketplace; finding software bugs; checking programming style; and themes within the IDE. 7. The AI engine of claim 2, wherein the IDE is configured as a window with a menu bar, a short cut bar, or both a menu bar and a short cut bar configured to provide access to the text editor, the mental-model designer, training data from a training-data source, analytical tools for analyzing aspects of training the AI model, and tools for deploying a trained AI model. 8. The AI engine of claim 2, wherein the text editor is further configured to enable the author to define one or more curriculums for training the AI model respectively on the one or more concept nodes. 9. The AI engine of claim 8, wherein the architect module is further configured to map the one or more concept nodes of the mental model on the one or more neural-network layers of the AI model, and wherein the instructor module is further configured to find a starting point among the one or more concept nodes and the corresponding one or more curriculums for training the AI model. 10. The AI engine of claim 9, wherein the instructor module is further configured to train the AI model in one or more training cycles with training data from one or more training-data sources selected from a simulator, a training-data generator, a training-data database, or a combination thereof based on training parameters in the pedagogical programming language, and wherein the instructor module is further configured to terminate neural-network training upon an instructor module-determined terminating condition. 11. An artificial intelligence (“AI”) system including an AI engine configured to work with an integrated development environment (“IDE”), comprising: a) one or more remote servers including i) a compiler in a server memory, wherein the compiler, upon execution of the compiler by one or more server processors, is configured to generate an assembly code from a source code written in a pedagogical programming language, and wherein the compiler is further configured to receive the source code through an application programming interface (“API”) exposed to the IDE; and ii) one or more AI-engine modules in the server memory including an architect module, an instructor module, and a learner module, wherein the architect module, upon execution of the architect module by the one or more server processors, is configured to propose a neural-network layout with one or more neural-network layers from the assembly code, wherein the learner module, upon execution of the learner module by the one or more server processors, is configured to build an AI model with the one or more neural-network layers from the neural-network layout proposed by the architect module, and wherein the instructor module, upon execution of the instructor module by the one or more server processors, is configured to train the AI model built by the learner module on the one or more concept nodes respectively with one or more curriculums; and b) one or more local clients including i) the IDE in a client memory, wherein the IDE, upon execution of the IDE by one or more client processors, is configured to enable an author to generate the source code written in the pedagogical programming language, wherein the IDE is further configured to enable the author to define a mental model to be learned by the AI model, the mental model including an input, one or more concept nodes, and an output, wherein the IDE is further configured to enable the author to define the one or more curriculums for training the AI model respectively on the one or more concept nodes, wherein the ID is further configured to enable the author to define the mental model in both a textual mode in an text editor and a graphical mode in a mental-model designer, and wherein the IDE is further configured to send the source code through the API exposed to the IDE. 12. 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: receiving a source code through an application programming interface (“API”) exposed to a graphical user interface (“GUI”), wherein the GUI is configured to enable an author to define a mental model with a pedagogical programming language, the mental model including an input, one or more concept nodes, and an output, and wherein the GUI is further configured to enable the author to define the mental model in a textual mode, a graphical mode, or both the textual mode and the graphical mode; generating an assembly code from the source code with a compiler of an artificial intelligence (“AI”) engine configured to work with the GUI; proposing a neural-network layout including one or more neural-network layers from the assembly code with an architect AI-engine module of the AI engine; building an AI model including the one or more neural-network layers from the neural-network layout with a learner AI-engine module of the AI engine; and training the AI model on the mental model with an instructor AI-engine module of the AI engine. 13. The non-transitory machine-readable medium of claim 12, wherein the GUI is an integrated development environment (“IDE”) including a text editor configured to enable the author to define the mental model including one or more curriculums for training the AI model respectively on the one or more concept nodes by typing in the textual mode, and a mental-model designer configured to enable the author to define the mental model by mouse gestures in the graphical mode, wherein author-based modification of the mental model by the typing in the textual mode automatically modifies the mental model in the mental-model designer, and wherein author-based modification of the mental model by the mouse gestures in the graphical mode automatically modifies the mental-model in the text editor. 14. The non-transitory machine-readable medium of claim 13, wherein the text editor is further configured to enable the author to set one or more breakpoints in the source code written in the pedagogical programming language, and wherein the mental-model designer is further configured to automatically highlight the one or more concept nodes of the mental model corresponding to the one or more breakpoints if the one or more breakpoints are set at the one or more concept nodes. 15. The non-transitory machine-readable medium of claim 13, wherein the pedagogical programming language is configured to enable an author to define schemas describing one or more data types to be streamed through connected nodes of the mental model including the input, the one or more concept nodes, one or more stream nodes, and the output, and wherein the data types include primitive data types selected from strings, integers, floats, and Booleans; structured data types; constrained data types with range expressions limiting the data of the constrained data types; and combinations thereof. 16. The non-transitory machine-readable medium of claim 15, wherein each connector of a number of connectors between the connected nodes of the mental model includes a data type, and wherein the mental-model designer is further configured to color code the number of connectors in the mental model to indicate whether the data type between the connected nodes of the mental model is correct or not for each connector. 17. The non-transitory machine-readable medium of claim 13, wherein the IDE is configured to support one or more plug-ins for extended functionality of the IDE, and wherein the extended functionality of the IDE includes support for programming languages in addition to the pedagogical programming language; source code management; project management; a marketplace; finding software bugs; checking programming style; and themes within the IDE. 18. The non-transitory machine-readable medium of claim 13, wherein the IDE is configured as a window with a menu bar, a short cut bar, or both a menu bar and a short cut bar configured to provide access to the text editor, the mental-model designer, training data from a training-data source, analytical tools for analyzing aspects of training the AI model, and tools for deploying a trained AI model. 19. The non-transitory machine-readable medium of claim 13, further comprising mapping with the architect module the one or more concept nodes of the mental model on the one or more neural-network layers of the AI model; and finding with the instructor module a starting point among the one or more concept nodes and the one or more curriculums for training the AI model. 20. The non-transitory machine-readable medium of claim 19, further comprising training the AI model with the instructor module in one or more training cycles with training data from one or more training-data sources selected from a simulator, a training-data generator, a training-data database, or a combination thereof based on training parameters in the pedagogical programming language, and terminating the training of the AI model upon an instructor module-determined terminating condition.