Graphical user interface to an artificial intelligence engine utilized to generate one or more trained artificial intelligence models
First Claim
1. A computing system, comprising a processor, and a storage device holding instructions executable by the processor to:
- receive 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 the pedagogical programming language is configured to enable the author to define schemas describing one or more data types to be streamed through connected nodes of the mental model, the data types including a constrained data type with range expressions limiting the data of the constrained data type;
generate an assembly code from the source code with a compiler of an artificial intelligence (“
AI”
) engine configured to work with the GUI;
propose 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;
build 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
train the AI model on the mental model with an instructor AI-engine module of the AI engine.
2 Assignments
0 Petitions
Accused Products
Abstract
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.
48 Citations
19 Claims
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1. A computing system, comprising a processor, and a storage device holding instructions executable by the processor to:
-
receive 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 the pedagogical programming language is configured to enable the author to define schemas describing one or more data types to be streamed through connected nodes of the mental model, the data types including a constrained data type with range expressions limiting the data of the constrained data type; generate an assembly code from the source code with a compiler of an artificial intelligence (“
AI”
) engine configured to work with the GUI;propose 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; build 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 train the AI model on the mental model with an instructor AI-engine module of the AI engine. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. An artificial intelligence (“
- AI”
) system including an AI engine configured to work with an integrated development environment (“
IDE”
), comprising;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; andii) 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;
wherein the one or more remote servers are configured to interact withone 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 IDE is further configured to send the source code through the API exposed to the IDE.
- AI”
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12. A computing system, comprising a processor, and a storage device holding instructions executable by the processor to:
-
receive 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, wherein the GUI is further configured to enable the author to set a breakpoint in the source code, and to automatically highlight one or more concept nodes of the mental model corresponding to the breakpoint; generate an assembly code from the source code with a compiler of an artificial intelligence (“
AI”
) engine configured to work with the GUI;propose 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; build 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 train the AI model on the mental model with an instructor AI-engine module of the AI engine. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19)
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Specification