Example circuit implementations of Szegedy's quantization of the Metropolis-Hastings walk are presented. In certain disclosed embodiments, a quantum walk procedure of a Markov chain Monte Carlo simulation is implemented in which a quantum move register is reset at every step in the quantum walk. In further embodiments, a quantum walk procedure of a Markov chain Monte Carlo simulation is implemented in which an underlying classical walk is obtained using a Metropolis-Hastings rotation or a Glauber dynamics rotation. In some embodiments, a quantum walk procedure is performed in the quantum computing device to implement a Markov Chain Monte Carlo method; during the quantum walk procedure, an intermediate measurement is obtained; and a rewinding procedure of one or more but not all steps of the quantum walk procedure is performed if the intermediate measurement produces an incorrect outcome.

A method for providing a user interface (UI) for generating training data for an artificial intelligence (AI) model may include providing, for display via the UI, image information that depicts an object, a set of operations of the object, and a process associated with the set of operations. The method may include providing, for display via the UI, text information that describes the object, the set of operations of the object, and the process associated with the set of operations. The method may include receiving, via the UI, a user input that associates respective image information of the image information with corresponding text information of the text information. The method may include generating association information that associates the respective image information with the corresponding text information, based on the user input. The method may include generating discourse and semantic information from the text information associated to the image information.

In an approach for identifying troubled contracts using a health score, a processor receives a contract. A processor identifies a list of requirements of the contract using a first Natural Language Processing technique. A processor trains a model to recognize the list of requirements of the contract. A processor receives at least one deliverable document associated with the contract. A processor applies a plurality of health metrics to the at least one deliverable document associated with the contract to identify evidence of completion of each requirement of the list of requirements of the contract. A processor outputs the health score for each requirement of the list of requirements of the contract.

The innovation disclosed and claimed herein, in one aspect thereof, comprises systems and methods of autonomous asset configuration modeling and management. The innovation includes probing elements of a networked architecture to compile information about elements in the networked architecture. The innovation learns a configuration for the at least one element in the environment based on the probing and determines vulnerabilities in the learned configuration. The innovation develops a threat model based on the learned configuration. The innovation applies the threat model to the elements of the networked architecture and deploys a configuration that resolves the vulnerabilities based on the threat model to the elements in the networked architecture. The threat model can be developed over time using machine learning concepts and deep learning of data sources associated with the elements and vulnerabilities.

Provided is a process, including: obtaining a first training dataset, training a first machine-learning model on the first training dataset, obtaining a set of candidate question sequences, forming virtual subject-entity records, forming a second training dataset, training a second machine-learning model, and storing the adjusted parameters of the second machine-learning model in memory.

Apparatuses, systems, and techniques are disclosed to generate a derived artificial intelligence (AI) model from a plurality of AI models. In at least one embodiment, at least one common feature shared among the plurality of AI models are identified, and the derived AI model is generated based on the at least one common feature shared among the plurality of AI models.

A deep learning-based continuous federated learning network system is provided. The system includes a global site comprising a global model and a plurality of local sites having a respective local model derived from the global model. The plurality of model tuning modules having a processing system are provided at the plurality of local sites for tuning the respective local model. The processing system is programmed to receive incremental data and select one or more layers of the local model for tuning based on the incremental data. Finally, the selected layers are tuned to generate a retrained model.

A text correction engine meets different and changing end user requirements, with the ability to change a desired output by providing sufficient amounts of data, and by finetuning the appropriate text correction engine at the point of origin of the data. It is possible to retain confidentiality of data by retraining the base deep learning model at the base deep learning model's point of origin, to improve the base deep learning model's performance, making the base deep learning model more accurate for different contexts. Separate training of an end user model, leaving the base deep learning model intact, streamlines end user model training, and highlights desirable changes in the base deep learning model for further training or retraining.

According to an aspect of an embodiment, operations include receiving a set of NL descriptors and a corresponding set of PL codes. The operations further include determining a first vector associated with each NL descriptor and a second vector associated with each PL code, using language models. The operations further include determining a number of a set of semantic code classes to cluster the set of PL codes into the set of semantic code classes, based on the number, the first vector, and the second vector. The operations further include training a multi-class classifier model to predict a semantic code class, from the set of semantic code classes, corresponding to an input NL descriptor. The operations further include selecting an intra-class predictor model based on the predicted semantic code class. The operations further include training the intra-class predictor model to predict a PL code corresponding to the input NL descriptor.

This application disclose an information processing method and a related device. This application provide a first AI entity in an access network, and define a plurality of basic interaction modes between the first AI entity and a terminal device. In an interaction mode, the first AI entity may receive second AI model information sent by the terminal device. The second AI model information does not include user data of the terminal device. The first AI entity may update first AI model information of the first AI entity based on the second AI model information, and then send updated first AI model information to the terminal device, so that the terminal device trains and updates the second AI model information.