Example systems perform complex optimization tasks with improved efficiency via neural meta-optimization of experts. In particular, provided is a machine learning framework in which a meta-optimization neural network can learn to fuse a collection of experts to provide a predicted solution. Specifically, the meta-optimization neural network can learn to predict the output of a complex optimization process which optimizes over outputs from the collection of experts to produce an optimized output. In such fashion, the meta-optimization neural network can, after training, be used in place of the complex optimization process to produce a synthesized solution from the experts, leading to orders of magnitude faster and computationally more efficient prediction or problem solution.

A method of executing a sequence of tasks includes receiving a natural language input indicative of the sequence of tasks. The natural language input may include a first task and a plurality of possible next tasks for the first task. The tasks may each be associated with a playback performance skeleton, indicative of a series of actions to be carried out on a web page. The series of action may have been generated, ahead of time, from a recorded performance of a similar task. The first task may be arranged to be performed. Then, based on a result of the performance of the first task, a successive task from among a plurality of possible next tasks associated with the result of performance of the first task may be selected. The successive task may then be arranged to be performed.

A method of executing a sequence of tasks includes receiving a natural language input indicative of the sequence of tasks. The natural language input may include a first task and a plurality of possible next tasks for the first task. The tasks may each be associated with a playback performance skeleton, indicative of a series of actions to be carried out on a web page. The series of action may have been generated, ahead of time, from a recorded performance of a similar task. The first task may be arranged to be performed. Then, based on a result of the performance of the first task, a successive task from among a plurality of possible next tasks associated with the result of performance of the first task may be selected. The successive task may then be arranged to be performed.

The present invention relates to a system and method for creating artificial intelligence-based knowledge bot application. The objective is to cater to various needs of knowledge transition at organizational level. Accordingly, the bot application can collect and extract knowledge from transition artifacts related to multiple applications in any format. Availability of existing knowledge or standard keys in collected artifacts is assessed along with associated knowledge gaps using a machine learning approach.

Systems and methods for architecture embeddings for efficient dynamic synthetic data generation are disclosed. The disclosed systems and methods may include a system for generating synthetic data configured to perform operations. The operations may include retrieving a set of rules associated with a first data profile and generating, by executing a hyperparameter search, a plurality of hyperparameter sets for generative adversarial networks (GANs) that satisfy the set of rules. The operations may include generating mappings between the hyperparameter sets and the first data profile and storing the mappings in a hyperparameter library. The operations may include receiving a request for synthetic data, the request indicating a second data profile and selecting, from the mappings in the hyperparameter library, a hyperparameter set mapped to the second data profile. The operations may include building a GAN using the selected hyperparameter set and generating, using the GAN, a synthetic data set.

An access management system includes a mobile device with a processor and a memory and a software platform including at least a processor and a memory. The software platform is configured to analyze data obtained from the mobile device and other devices connected to the software platform. Specifically, the software platform is operable to determine if an access key received, read, or captured by a mobile device matches an access key for an authorized account, object, device, or space for the mobile device, and to provide access to the mobile device if the access key received, read, or captured by the mobile device matches the authorized access key.

An access management system includes a mobile device with a processor and a memory and a software platform including at least a processor and a memory. The software platform is configured to analyze data obtained from the mobile device and other devices connected to the software platform. Specifically, the software platform is operable to determine if an access key received, read, or captured by a mobile device matches an access key for an authorized account, object, device, or space for the mobile device, and to provide access to the mobile device if the access key received, read, or captured by the mobile device matches the authorized access key.

This disclosure relates to the field of recommendation of learning competencies. The usage of competency-based learning platforms is growing as it provides a safe learning environment where learners/users can learn from the comfort of their homes at a convenient time. However, one of the challenges with competency-based learning platforms is to identify learning competencies/courses relevant to a learner as exhaustive material is available on the internet. The disclosure addresses the challenges by providing techniques for personalized recommendation of cognitive based learning competencies and skill based learning competencies. The disclosed techniques recommend cognitive based learning competencies and skill based learning competencies based on several techniques that include a machine learning, Natural Language Processing (NLP), a skill based recommendation technique and a collaborative cognitive recommendation technique. Hence the disclosure is an approach to enable a learner to assess the learner's current competencies and identify learning competencies required for a desired role.

A unified graph query system provides an abstraction layer that increases the interoperability of different graph technologies by exposing graphs stored in graph databases using a unified query language. The abstraction layer generates graph models for each of the available graph databases and extracts a graph component and other source data used to identify the source of the data requested by a query. The unified graph query system executes the query across the multiple graphs included in different graph databases by using the graph models to locate the graph component in each of the multiple graphs and extract the feature data associated with the graph component. The feature data is used to generate features that are used by a machine learning service to train machine learning models and is also used to make predictions in real time.

Embodiments of the present invention provide systems and methods of log optimization for television advertisements. Exemplary method and systems can comprise: receiving, at a computer software platform, (i) the digital log, (ii) constraint parameters, and (iii) viewership predictions; and generating, with the computer software platform, an optimized log based on the received digital log, the constraint parameters, and the viewership predictions.