Microsoft Technology Licensing, LLC

Bonsai Ai, Inc.

Clara Kliman-Silver

Keen McEwan Browne

Mark Hammond

Mike Estee

Shane Arney

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.

Graphical user interface to an artificial intelligence engine utilized to generate one or more trained artificial intelligence models

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.

Visual aids for debugging

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.

Multiple user interfaces of an artificial intelligence system to accommodate different types of users solving different types of problems with artificial intelligence

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.

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Berkeley, CA, United States of America

Clara Kliman-Silver