Microsoft Technology Licensing, LLC

Leapfrog Enterprises, Inc.

Bonsai Ai, Inc.

Other

Matthew James Brown

Marcos M Campos

Keen McEwan Browne

Mark Hammond

Ruofan Kong

Michael C Wood

Megan Adams

James L Marggraff

Ross Ian Story

Aditya Gudimella

Tod Frye

Jen Sward

Matt Fishbach

Jett Evan Jones

Matthew Haigh

Shane Arney

Victor Shnayder

Michael Estee

Chetan Desh

Matineh Shaker

William Guss

The AI engine operates with the common API. The common API supports i) any of multiple different training sources and/or prediction sources installed on ii) potentially different sets of customer computing hardware in a plurality of on-premises' environments, where the training sources, prediction sources as well as the set of customer computing hardware may differ amongst the on-premises' environments. The common API via its cooperation with a library of base classes is configured to allow users and third-party developers to interface with the AI-engine modules of the AI engine in an easy and predictable manner through the three or more base classes available from the library. The common API via its cooperation with the library of base classes is configured to be adaptable to the different kinds of training sources, prediction sources, and the different sets of hardware found a particular on-premises environment.

Interface for working with simulations on premises

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.

Artificial intelligence engine for mixing and enhancing features from one or more trained pre-existing machine-learning models

The AI engine has a first module that chooses from a library of algorithms to use when automatically assembling and building different learning topologies to solve different concepts making up a resulting AI model. The AI engine may integrate both i) one or more dynamic programming training algorithms and ii) one or more policy optimization algorithms, to build the different learning topologies to solve the different concepts contained with an AI model in order to solve a wide variety of problem types. Each concept contained in the AI model can use a most appropriate approach for achieving a mission of that concept. A learning topology representing a first concept can be built by the first module with a first dynamic programming training algorithm, while a learning topology representing a second concept in the same AI model can be built by the first module with a first policy optimization algorithm.

Artificial intelligence engine having various algorithms to build different concepts contained within a same AI model

An artificial intelligence ('AI') engine is disclosed with AI-engine modules and a plurality of learning agents. The AI-engine modules include instructor, learner, and predictor modules. The learner module is configured to train a plurality of AI models in parallel, and the instructor module is configured to coordinate with a plurality of simulators for respectively training the AI models. The learning agents are configured to process training requests from the instructor on data from the simulators for training the AI models. The learner module is further configured to first train the AI models on a first batch of similar data synchronously pooled in a memory of the learner module with a first processor. The learner module is further configured to subsequently train the AI models on a second, different batch of similar data synchronously pooled in the memory of the learner module with the first processor.

Artificial intelligence engine with enhanced computing hardware throughput

Methods and apparatuses that apply a hierarchical-decomposition reinforcement learning technique to train one or more AI objects as concept nodes composed in a hierarchical graph incorporated into an AI model. The individual sub-tasks of a decomposed task may correspond to its own concept node in the hierarchical graph and are initially trained on how to complete their individual sub-task and then trained on how the all of the individual sub-tasks need to interact with each other in the complex task in order to deliver an end solution to the complex task. Next, during the training, using reward functions focused for solving each individual sub-task and then a separate one or more reward functions focused for solving the end solution of the complex task. In addition, where reasonably possible, conducting the training of the AI objects corresponding to the individual sub-tasks in the complex task, in parallel at the same time.

For hierarchical decomposition deep reinforcement learning for an artificial intelligence model

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.

Artificial intelligence engine hosted on an online platform

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.

Artificial intelligence engine having multiple independent processes on a cloud based platform configured to scale

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.

Artificial intelligence engine having an architect module

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.

Searchable database of trained artificial intelligence objects that can be reused, reconfigured, and recomposed, into one or more subsequent artificial intelligence models

Some embodiments of the present disclosure include a traction device for relieving lower back pain and stiffness and reducing lumbar spine intervertebral disc displacement. The device may include a sliding spine assembly attached to a head rest pad, a torso pad, and a hip pad, an adjustable length vector bar extending outwardly from the spine assembly, wherein the vector bar is rotatably attached to the sliding spine assembly such that the vector bar is configured to move in a plurality of vectors, a cross bar foot brace tube attached to the vector bar; a foot rest positioned on the foot brace tube, a belt positioned proximate to the torso pad, and a strap assembly attached to the foot brace tube or the distal end of the vector bar, the strap assembly configured to attach to the belt and apply traction to a user's back.

Multi-vector traction device for the lumbar spine

A method, a user interface, and an educational content server for assisting a user in learning using interactive learning appliances are disclosed. The performance information of the user, which may be in the form of a user log file in an interactive learning appliance, may be received at a server computer from a site where the user is present. The performance information and profile information can then be presented in the user interface and used to generate an electronic content package. The electronic content package can then be received and loaded into the interactive learning appliance.

Method and system for providing content for learning appliances over an electronic communication medium

An interactive communications appliance for broadcasting personalized information is disclosed. The interactive communications appliance comprises a transceiver for receiving personalized information transmitted via a wireless link. A memory stores the personalized information and an audio device broadcasts the personalized information to a user.

Interactive communications appliance

A method for assisting a user in learning is provided. The method may comprise receiving performance information of a user after the user uses an interactive learning appliance. The user performance information is generated by the user's use of the interactive learning appliance which is adapted to educate the user about one or more predetermined subjects. The performance information, which may be in the form of a user log file, may be received at a server computer from a site where the user is present.

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San Francisco, CA, United States of America

Matthew James Brown