Oracle International Corporation

Microsoft Technology Licensing, LLC

Bonsai Ai, Inc.

Marcos M Campos

Boriana L Milenova

Mark A McCracken

Matthew James Brown

Joseph Yarmus

Keen McEwan Browne

Mark Hammond

Ruofan Kong

Ari Wolfe Mozes

Pablo Tamayo

Megan Adams

Peter J Stengard

Krisztian Danko

Francisco V Casas

Jooyoung John Kim

Mark F Hornick

Pavani Kuntala

Ross Ian Story

Jacek Myczkowski

Prashanth Kini

Tod Frye

Richard Tauriello

Charlie Sum

Aditya Gudimella

Mahesh Jagannath

Shiby Thomas

Gary L Drescher

Chitra Balwant Bhagwat

Joe Yarmus

Victor Shnayder

Mihaela Dediu

Mark Kelly

William Guss

Kevin Timoney

Andres V Lubomirsky

Gayathri P Ayyappan

Matineh Shaker

George G Tang

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

Systems, methods, and other embodiments are disclosed for partitioning models in a database. In one embodiment, a set of training data is parsed into multiple data partitions based on partition keys, where the data partitions are identified by the partition keys and are used for training data mining models. The multiple data partitions are analyzed to generate partition metrics data. Algorithm data, identifying at least one algorithm for processing the multiple data partitions, and resources data, identifying available modeling resources for processing the multiple data partitions, are read. The partition metrics data, the algorithm data, and the resources data are processed to generate an organization data structure. The organization data structure is configured to control distribution and processing of the multiple data partitions across the available modeling resources to generate a composite model object that includes a separately trained data mining model for each partition of the multiple partitions.

System and method for partitioning models in a database

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

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

A data importer for a sales prospecting system imports one or more data tables that each may include one or more records. The data importer first (a) imports a data table into an intermediate table. The data importer then (b) determines if the imported data table depends on another data table and moves one or more records from the imported data table that have no missing dependencies to a corresponding working table; and (c) determines a set of previously imported data tables that refer to the imported data table. The data importer then, for each previously imported data table, repeats (b) and (c) above.

Data importer for a sales prospector

Processes, machines, and stored machine instructions are provided for approximating value densities in data. While generating a resulting density model to approximate value densities in a set of data, density modeling logic selects a functional component of a first model to vary based at least in part on how much the functional component contributes to how well the first model approximates the value densities. The density modeling logic then uses at least the functional component and a variation of the functional component as seed components to determine adjusted functional components of a second model by iteratively determining, in an expectation step, how much the seed components contribute to how well the second model explains the values, and, in a maximization step, new seed components, optionally to be used in further iterations, based at least in part on how much of the values are attributable to the seed components.

Approximating value densities

Methods, machines, and stored instructions are provided for partitioning a graph of nodes into clusters of nodes by iteratively excluding edges in the graph. For each node of at least a subset of nodes in the graph, a graph partitioning module determines whether to exclude edges for the node and, if so, selects for exclusion edge(s) to at least a subset of the node's neighbor(s). The module selects edge(s) to the node's neighbor(s) for exclusion based at least in part on a degree of overlap between the node's neighbor(s) and neighbor(s) of the node's neighbor(s). For any subset(s) that are yet not sufficiently partitioned into clusters, the module repeats the step of determining whether to exclude edges and, if so, selecting nodes for exclusion, and determining whether or not the nodes are sufficiently partitioned. Subset(s) of nodes that are already sufficiently partitioned may be skipped during the repeated steps.

Partitioning a graph by iteratively excluding edges

Processes, machines, and stored machine instructions are provided for grouping interdependent fields. Field grouping logic may include specially configured machines and/or stored instructions that identify group(s) of interdependent fields of a data set. The field grouping logic may receive, from a client on a customizable interface, a request for interdependent fields in a data set and, in response, cause generation of an output object that identifies the similar fields in the data set. The field grouping logic may exclude field(s) of the data set that are not interdependent, are not frequently accessed, or do not consume much space in storage, even though the request may not identify which fields are interdependent. The output object identifies the similar fields in set(s) or list(s) of fields, or in a hierarchy or hierarchies of groups and sub-groups.

Grouping interdependent fields

Processes, machines, and stored machine instructions are provided for estimating how many rows would be processed by a query. In one embodiment, model aggregating logic may generate a global model based on separate models for separate portions of a set of data. For example, the separate portions may be assigned to separate partitions of the set of data based at least in part on key values in the separate portions. Generating the global model may include generating an uncompressed model that includes components of the separate models before generating a compressed model to estimate the uncompressed model. Query analysis logic may use the global model to estimate how many rows would be processed by queries that, when executed, would access portion(s) of the set of data.

Generating estimates for query optimization

An implementation of SVM functionality improves efficiency, time consumption, and data security, reduces the parameter tuning challenges presented to the inexperienced user, and reduces the computational costs of building SVM models. A system for support vector machine processing comprises data stored in the system, a client application programming interface operable to provide an interface to client software, a build unit operable to build a support vector machine model on at least a portion of the data stored in the system, the portion of the data selected using a stratified sampling method with respect to a target distribution, an apply unit operable to apply the support vector machine model using the data stored in the system.

Support vector machines processing system

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Cambridge, MA, United States of America

Marcos M Campos