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 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 artificial intelligence (“AI”) engine having multiple independent processes on one or more computing platforms is disclosed, where the one or more computing platforms are located on premises of an organization such that i) the one or more computing platforms are configurable for one or more users in the organization having at least administrative rights on the one or more computing platforms in order to configure hardware components thereof to execute and load the multiple independent processes of the AI engine; ii) the one or more users of the organization are able to physically access the one or more computing platforms; and iii) the hardware components of the one or more computing platforms are connected to each other through a Local Area Network (LAN), and the LAN is configurable such that the one or more users in the organization have a right to control an operation of the LAN.

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: 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 artificial intelligence (“AI”) engine having multiple independent processes on one or more computing platforms is disclosed, where the one or more computing platforms are located on premises of an organization such that i) the one or more computing platforms are configurable for one or more users in the organization having at least administrative rights on the one or more computing platforms in order to configure hardware components thereof to execute and load the multiple independent processes of the AI engine; ii) the one or more users of the organization are able to physically access the one or more computing platforms; and iii) the hardware components of the one or more computing platforms are connected to each other through a Local Area Network (LAN), and the LAN is configurable such that the one or more users in the organization have a right to control an operation of the LAN.

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.