Abstract: A computing system includes a processor, and a storage device holding instructions executable by the processor. The instructions are executable to receive a source code through an application programming interface (“API”) exposed to a graphical user interface (“GUI”). The GUI is configured to enable an author to define a proposed model with a pedagogical programming language, the proposed model including an input, one or more concept nodes, and an output. The GUI is further configured to enable the author to provide a program annotation indicating an execution behavior for the source code, to generate an assembly code from the source code with a compiler of an artificial intelligence (“AI”) engine configured to work with the GUI; and to build an executable, trained AI model including a neural-network layout having one or more layers derived from the assembly code.

Abstract: An AI engine is disclosed that is configured to work with a graphical user interface (“GUI”) including, in some embodiments, one or more AI-engine modules and a visual debugging module of the GUI. A learner AI-engine module is configured to train one or more AI models on one or more concepts of a mental model defined in a pedagogical programming language. An instructor AI-engine module is configured to coordinate with one or more simulators for respectively training the one or more AI models on the mental model. The visual debugging module is configured to provide a visualization window for each AI model while the one or more AI models are at least training with the learner module respectively in the one or more simulators. A viewer can glean insight and explainability into the training of the AI models while the simulations are running and arriving at various states.

Abstract: An AI engine having an architect module to create a number of nodes and how the nodes are connected in a graph of concept nodes that make up a resulting AI model. The architect module also creates a first concept node by wrapping an external entity of code into a software container with an interface configured to exchange information in a protocol of a software language used by the external entity of code. The architect module also creates a second concept node derived from its description in a scripted file coded in a pedagogical programming language, and connects the second concept node into the graph of nodes in the resulting AI model.

Abstract: An AI engine is disclosed that is configured to work with a graphical user interface (“GUI”) including, in some embodiments, one or more AI-engine modules and a visual debugging module of the GUI. A learner AI-engine module is configured to train one or more AI models on one or more concepts of a mental model defined in a pedagogical programming language. An instructor AI-engine module is configured to coordinate with one or more simulators for respectively training the one or more AI models on the mental model. The visual debugging module is configured to provide a visualization window for each AI model while the one or more AI models are at least training with the learner module respectively in the one or more simulators. A viewer can glean insight and explainability into the training of the AI models while the simulations are running and arriving at various states.

Abstract: An AI engine having an architect module to create a number of nodes and how the nodes are connected in a graph of concept nodes that make up a resulting AI model. The architect module also creates a first concept node by wrapping an external entity of code into a software container with an interface configured to exchange information in a protocol of a software language used by the external entity of code. The architect module also creates a second concept node derived from its description in a scripted file coded in a pedagogical programming language, and connects the second concept node into the graph of nodes in the resulting AI model.

Abstract: An AI engine having an architect module to create a number of nodes and how the nodes are connected in a graph of concept nodes that make up a resulting AI model. The architect module also creates a first concept node by wrapping an external entity of code into a software container with an interface configured to exchange information in a protocol of a software language used by the external entity of code. The architect module also creates a second concept node derived from its description in a scripted file coded in a pedagogical programming language, and connects the second concept node into the graph of nodes in the resulting AI model.

Abstract: Provided herein in some embodiments is an artificial intelligence (“AI”) engine hosted on one or more remote servers configured to cooperate with one or more databases including one or more AI-engine modules and one or more server-side client-server interfaces. The one or more AI-engine modules include an instructor module and a learner module configured to train an AI model. An assembly code can be generated from a source code written in a pedagogical programming language describing a mental model of one or more concept modules to be learned by the AI model and curricula of one or more lessons for training the AI model. The one or more server-side client-server interfaces can be configured to enable client interactions from a local client such as submitting the source code for training the AI model and using the trained AI model for one or more predictions.

Abstract: The multiple independent processes run in an AI engine on its cloud-based platform. The multiple independent processes are configured as an independent process wrapped in its own container so that multiple instances of the same processes can run simultaneously to scale to handle one or more users to perform actions. The actions to solve AI problems can include 1) running multiple training sessions on two or more AI models at the same time, 2) creating two or more AI models at the same time, 3) running a training session on one or more AI models while creating one or more AI models at the same time, 4) deploying and using two or more trained AI models to do predictions on data from one or more data sources, 5) etc. A service handles scaling by dynamically calling in additional computing devices to load on and run additional instances of one or more of the independent processes as needed.

Abstract: A computing system includes a processor, and a storage device holding instructions executable by the processor. The instructions are executable to receive a source code through an application programming interface (“API”) exposed to a graphical user interface (“GUI”). The GUI is configured to enable an author to define a proposed model with a pedagogical programming language, the proposed model including an input, one or more concept nodes, and an output. The GUI is further configured to enable the author to provide a program annotation indicating an execution behavior for the source code, to generate an assembly code from the source code with a compiler of an artificial intelligence (“AI”) engine configured to work with the GUI; and to build an executable, trained AI model including a neural-network layout having one or more layers derived from the assembly code.

Abstract: Provided herein in some embodiments is an artificial intelligence (“AI”) engine configured to operate with multiple user interfaces to accommodate different types of users solving different types of problems with AI. The AI engine can include AI-engine modules including an architect module, an instructor module, and a learner module. An assembly code can be generated from a source code written in a pedagogical programming language. The architect module can be configured to propose a neural-network layout from the assembly code; 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 built by the learner module. The multiple user interfaces can include an integrated development environment, a web-browser interface, or a command-line interface configured to enable an author to define a mental model for the AI model to learn.

Abstract: Provided herein in some embodiments is an artificial intelligence (“AI”) engine hosted on one or more servers configured to cooperate with one or more databases including one or more AI-engine modules. The one or more AI-engine modules include an architect module configured to propose an AI model from an assembly code. The assembly code can be generated from a source code written in a pedagogical programming language describing a mental model of one or more concept modules to be learned by the AI model and curricula of one or more lessons for training the AI model on the one or more concept modules in one or more training cycles. The AI engine can be configured to instantiate a trained AI model based on the one or more concept modules learned by the AI model in the one or more training cycles.

Abstract: Provided in some embodiments is an artificial intelligence (“AI”) engine configured to work with a pedagogical programming language configured to enable an author to 1) define a mental model to be learned by an AI model, the mental model including an input, one or more concept nodes, one or more stream nodes, and an output, as well as 2) define one or more curriculums for training the AI model respectively on the one or more concept nodes. A compiler can be configured to generate an assembly code from a source code authored in the pedagogical programming language. An architect module can be configured to propose a neural-network layout from the assembly code. A learner module can be configured to build the AI model the neural-network layout. An instructor module can be configured to train the AI model on the one or more concept nodes respectively with the one or more curriculums.

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.

Abstract: An AI database hosted on cloud platform is configured to cooperate with a search engine and an AI engine. The AI database stores and indexes trained AI objects and its class of AI objects have searchable criteria. The AI database cooperates with the search engine to utilize search criteria supplied from a user, from either or both 1) via scripted software code and 2) via data put into defined fields of a user interface. The search engine utilizes the search criteria in order for the search engine to retrieve one or more AI data objects that have already been trained as query results. The AI database is coupled to an AI engine to allow any of reuse, reconfigure ability, and recomposition of the one or more trained AI data objects from the AI database into a new trained AI model.

Abstract: An AI database hosted on cloud platform is configured to cooperate with a search engine and an AI engine. The AI database stores and indexes trained AI objects and its class of AI objects have searchable criteria. The AI database cooperates with the search engine to utilize search criteria supplied from a user, from either or both 1) via scripted software code and 2) via data put into defined fields of a user interface. The search engine utilizes the search criteria in order for the search engine to retrieve one or more AI data objects that have already been trained as query results. The AI database is coupled to an AI engine to allow any of reuse, reconfigure ability, and recomposition of the one or more trained AI data objects from the AI database into a new trained AI model.

Abstract: An AI engine is disclosed that is configured to work with a graphical user interface (“GUI”) including, in some embodiments, one or more AI-engine modules and a visual debugging module of the GUI. A learner AI-engine module is configured to train one or more AI models on one or more concepts of a mental model defined in a pedagogical programming language. An instructor AI-engine module is configured to coordinate with one or more simulators for respectively training the one or more AI models on the mental model. The visual debugging module is configured to provide a visualization window for each AI model while the one or more AI models are at least training with the learner module respectively in the one or more simulators. A viewer can glean insight and explainability into the training of the AI models while the simulations are running and arriving at various states.

Abstract: An AI engine having an architect module to create a number of nodes and how the nodes are connected in a graph of concept nodes that make up a resulting AI model. The architect module also creates a first concept node by wrapping an external entity of code into a software container with an interface configured to exchange information in a protocol of a software language used by the external entity of code. The architect module also creates a second concept node derived from its description in a scripted file coded in a pedagogical programming language, and connects the second concept node into the graph of nodes in the resulting AI model.

Abstract: The multiple independent processes run in an AI engine on its cloud-based platform. The multiple independent processes are configured as an independent process wrapped in its own container so that multiple instances of the same processes can run simultaneously to scale to handle one or more users to perform actions. The actions to solve AI problems can include 1) running multiple training sessions on two or more AI models at the same time, 2) creating two or more AI models at the same time, 3) running a training session on one or more AI models while creating one or more AI models at the same time, 4) deploying and using two or more trained AI models to do predictions on data from one or more data sources, 5) etc. A service handles scaling by dynamically calling in additional computing devices to load on and run additional instances of one or more of the independent processes as needed.

Abstract: Provided herein in some embodiments is an artificial intelligence (“AI”) engine hosted on one or more remote servers configured to cooperate with one or more databases including one or more AI-engine modules and one or more server-side client-server interfaces. The one or more AI-engine modules include an instructor module and a learner module configured to train an AI model. An assembly code can be generated from a source code written in a pedagogical programming language describing a mental model of one or more concept modules to be learned by the AI model and curricula of one or more lessons for training the AI model. The one or more server-side client-server interfaces can be configured to enable client interactions from a local client such as submitting the source code for training the AI model and using the trained AI model for one or more predictions.

Abstract: An AI database hosted on cloud platform is configured to cooperate with a search engine and an AI engine. The AI database stores and indexes trained AI objects and its class of AI objects have searchable criteria. The AI database cooperates with the search engine to utilize search criteria supplied from a user, from either or both 1) via scripted software code and 2) via data put into defined fields of a user interface. The search engine utilizes the search criteria in order for the search engine to retrieve one or more AI data objects that have already been trained as query results. The AI database is coupled to an AI engine to allow any of reuse, reconfigure ability, and recomposition of the one or more trained AI data objects from the AI database into a new trained AI model.