Abstract: For one embodiment of the present disclosure, a knowledge anchored artificial intelligence (KAAI) system and method are disclosed herein. A computer implemented method includes receiving a query to KAAI system; creating the necessary actions to respond to the query using a KAAI tree (literally by traversing the KAAI tree) to create the chain of thoughts and also make sure that the actions are anchored using existing knowledge graph in the system;maintaining a KV memory called session which will be preserved and changed while processing the query and traversing the tree; At each node in the tree execute Compute Agent logic i. Get data from the backend ii. Call into API iii. Run data transformations; b. May further interact with the user through UI; c. May amend the tree if needed; d. Update the context memory (session) e. Update the values in the tree.

Abstract: A dynamic, distributed directed activity network comprising a directed activity control program specifying tasks to be executed including required individual task inputs and outputs, the required order of task execution, permitted parallelism in task execution, task adjacency to subsequent tasks, and reachability from each task to other tasks; a plurality of task execution agents, individual of said agents having a set of dynamically changing agent attributes and capable of executing different required tasks; a plurality of task execution controllers, each controller associated with one or more of the task execution agents with access to dynamically changing agent attributes; a directed activity controller for communicating with said task execution controllers for directing execution of said activity control program; and, a communications network supporting communication between said directed activity controller and task execution controllers for directing execution of said directed activity control program u

Abstract: One example method includes consolidating a data point concerning an autonomous mobile robot that operates in an environment, calculating a lazy feature importance for an available feature of the data point, combining the lazy feature importance with a health score of a sensor that collected data associated with the available feature, to obtain a final feature score for the available feature, selecting a subset of features of the data point, and performing, with a machine learning model, an inference, using only those features in the subset of features, and the inference concerns an aspect of the autonomous mobile robot or the environment.

Abstract: A process includes responsive to an issue occurring with the computer system, receiving, by a troubleshooting analysis engine, data from the computer system representing information about the computer system. The process includes processing, by the troubleshooting analysis engine, the data to identify a parameter of the computer system having an unexpected value; and searching, by the troubleshooting analysis engine, a design database to identify a design infrastructure of the computer system that is causally linked to the issue. The process includes analyzing, by the troubleshooting analysis engine, the design infrastructure using machine learning to identify a candidate cause of the issue.

Abstract: A domain transformation system may determine a source ontology for a source domain of a first environment. The source domain relates to a process performed in the first environment. The process may be transformed from the source domain to a target domain of a second environment, such as a virtual environment. The domain transformation system may determine a first portion of a target ontology for the target domain. The domain transformation system may generate, using a machine learning technique, a first embedding of the source ontology and a second embedding of the target ontology. The domain transformation system may generate a joint embedding based on the first embedding and the second embedding domain transformation system and may determine a second portion of the target ontology, based on the joint embedding, using a transfer learning technique. The domain transformation system may refine the target ontology using a neuro-symbolic artificial intelligence technique.

Abstract: A device and method for predicting exhaust emissions on the basis of data analysis is proposed. The method includes collecting, by a collection unit, raw data in real time from a power generation facility comprising a gas turbine, extracting, by a prediction unit, analysis data from the raw data, and predicting, by the prediction unit, the exhaust emissions to be emitted from the power generation facility a pre-derived delay time after the collecting of the raw data by analyzing the analysis data using a prediction model trained by learning.

Abstract: Systems and methods for determining contextual rules are described herein. In some embodiments, an apparatus may identify a context datum and an interaction datum as a function of a user datum. In some embodiments, an apparatus may determine an interaction feature and a reaction datum as a function of an interaction datum. In some embodiments, an apparatus may determine a contextual rule as a function of the context datum, interaction feature, and reaction datum. In some embodiments, an apparatus may display a visual element to a suer as a function of a contextual rule.

Abstract: This disclosure describes one or more implementations of a video inference system that utilizes machine-learning models to efficiently and flexibly process digital videos utilizing various improved video inference architectures. For example, the video inference system provides a framework for improving digital video processing by increasing the efficiency of both central processing units (CPUs) and graphics processing units (GPUs). In one example, the video inference system utilizes a first video inference architecture to reduce the number of computing resources needed to inference digital videos by analyzing multiple digital videos utilizing sets of CPU/GPU containers along with parallel pipeline processing. In a further example, the video inference system utilizes a second video inference architecture that facilitates multiple CPUs to preprocess multiple digital videos in parallel as well as a GPU to continuously, sequentially, and efficiently inference each of the digital videos.

Abstract: Disclosed are techniques for aggregating explanations of a predictive AI models' predictions to diagnose and improve the performance of a support stack. An indication of desired statistical parameters to be optimized may be received, the desired statistical parameters relating to performance of a support stack. A prediction model may be trained to predict resolution statistics corresponding to the desired statistical parameters. Each of a plurality of support tickets input to the support stack may be analyzed using a predictive AI model to generate a set of predicted resolution statistics including predicted values for each of the desired statistical parameters and the set of predicted resolution statistics may be analyzed using an explainable artificial intelligence (XAI) algorithm to generate a set of explanations for the predicted resolution statistics. The set of explanations for each of the plurality of support tickets may be aggregated to generate insights regarding the desired statistical parameters.

Abstract: A vector-based search method and system generates affinity vectors within a multidimensional affinity space by applying neural networks to stored content objects. A neural network is applied to content that is input by a user to generate an affinity vector, which is then compared to the affinity vectors corresponding to the stored content objects using a mathematical-based vector comparison algorithm. One or more of the stored content objects are selected and provided to the user based on the comparison. The selecting of the one or more stored content objects may be further performed based on an inference of a preference from user behaviors.

Abstract: Disclosed is a Bayesian classification recognition system based on an industrial PaaS platform, comprising: an IaaS infrastructure service layer, a G-PaaS graph neural network processing layer, an O-PaaS docking service layer and an SaaS system application layer. The G-PaaS graph neural network processing layer is configured for point cloud feature generation, point cloud feature learning, point cloud structure estimation and point cloud model classification; and the recognition accuracy of a workpiece point cloud model is improved through the Bayesian classification recognition system based on the industrial PaaS platform.

Abstract: A method includes receiving a set of execution paths for a directed acyclic graph. The directed acyclic graph includes multiple nodes and multiple edges. The nodes include sets of executable code. The edges represent an operational relationship between at least two nodes. The execution paths include a subset of the nodes connected by a sequence of edges. The method further includes setting a current training level to a maximum training level. The method further includes constructing a transition probability set for the current training level and adding the transition probability set to a transition probability dictionary. The method further includes storing the transition probability dictionary as a final transition probability dictionary.

Abstract: A program product comprising; formulating a map of a plurality of nodes; establishing a set of causal relationships between the plurality of nodes, creating a connection between the nodes where a causal relationship is determined, and assigning a linguistic term describing the connection strength to each such connection; manipulating the plurality of nodes based on the set of exogenous nodes and a set of non-exogenous nodes; repositioning the plurality of nodes in the map; iterating the map until a convergence state is reached for each non-exogenous node; assigning a linguistic term to the relationships between the nodes based on a relationship between word index values and their corresponding linguistic terms; calculating the states of the at least one non-exogenous node based on the linguistic terms associated with all of the connected nodes; and generating a graphical representation in a user interface of the state of the selected non-exogenous node.

Abstract: A modular quantum computer architecture is developed with a hierarchy of interactions that can scale to very large numbers of qubits. Local entangling quantum gates between qubit memories within a single modular register are accomplished using natural interactions between the qubits, and entanglement between separate modular registers is completed via a probabilistic photonic interface between qubits in different registers, even over large distances. This architecture is suitable for the implementation of complex quantum circuits utilizing the flexible connectivity provided by a reconfigurable photonic interconnect network. The subject architecture is made fault-tolerant which is a prerequisite for scalability.

Abstract: A quantum computing platform may receive application programming interface (API) information. The quantum computing platform may convert the API information to a two qubit state. The quantum computing platform may apply, to the converted API information, a CNOT gate, which may produce a trip wire flag value of zero or one. Based on identifying a trip wire flag value of zero, the quantum computing platform may activate a quantum trip wire, which may cause at least a portion of the API information to be routed to an overflow processing system.

Abstract: The present disclosure relates to quantum computing methods and devices, wherein a computation corresponding to a certain desired unitary transformation of the Hilbert space of a quantum system, such as a set of qubits, is implemented by selective application of control pulses in order realize a continuous time quantum computation rather than by decomposing the unitary transformation into a sequence of gates taken from a fixed set of gates. In this way an effective Hamiltonian having an input matrix A encoded as an off-diagonal block connecting a first and a second subspace of the quantum system is then applied alternatingly with some standard Hamiltonian in order to obtain a time evolution corresponding to a given function f applied to the input matrix A.

Abstract: In a quantum sequence triplication embodiment with quantum operation supremacy, the algorithmic software process and system database are utilized to critically verify information by Quantum Sequence Triplication, which utilizes three qubit bits to achieve quantum supremacy without gray area bias and enables quantum supremacy in the absence of gray area bias.

Abstract: A non-transitory computer-readable recording medium stores an information processing program for causing a computer to execute processing including: acquiring a quantum circuit to perform quantum chemical calculation by a variational quantum eigensolver, which includes a first quantum circuit to create a wave function that expresses an electron orbit of a molecule to be calculated and a second quantum circuit to transform a base of the wave function; changing a first rotation angle applied to a rotation operation in the first quantum circuit according to a second rotation angle applied to a partial circuit that indicates a rotation operation in the second quantum circuit; and deleting the partial circuit from the quantum circuit.

Abstract: Techniques described herein are directed toward univariate series truncation policy using change point detection. An example method can include a device determining a first time series comprising a first set of data points indexed over time. The device can determine a first and second change point of the first time series based on a relative position and a category of the change points. The device can generate a first and second truncated time series based on the change points. The device can generate a first and second forecasted value using a first forecasting technique. The device can compare the first forecasted value and the second forecasted value using a second time series. The device can select one of the forecasting techniques to generate a final forecasted value based on the comparison. The device can generate, using the selected first forecasting technique, the final forecasted value.

Abstract: A method for executing collaborative content creation in a computing environment using artificial intelligence. The method includes receiving, by a content creation system, a first input from a first user and a second input from a second user, analyzing, by an input fusion system, the first input and the second input to determine a presence of duplicate data, redundancy data, and/or prompt data by using a machine learning model, upon determining the presence of the prompt data from at least one of the first input or the second input, transmitting, by the input fusion system, the prompt data to an action generation system, generating, by the action generation system, first action data based on the prompt data and the machine learning model, and executing, by the action generation system, a first action in the computing environment based on the first action data.

Abstract: A method for processing data in a multi-mode single-engine system, an apparatus, and a non-transitory computer-readable storage medium are provided. In the method, a graphic processing engine receives a first input query. Further, the graphic processing engine obtains a first set of model parameters by switching between multiple sets of model parameters based on the first input query. Moreover, the graphic processing engine infers a first output for the first input query based on the first set of parameters.

Abstract: A method for providing a canonical data model is disclosed. The method includes receiving, via a graphical user interface, requests to generate machine learning models, the requests including configuration data for the machine learning models; identifying the canonical data model that corresponds to the requested machine learning models, the canonical data model including various predetermined parameters; automatically mapping the configuration data to the various predetermined parameters; automatically generating the machine learning models based on a result of the mapping; and outputting the machine learning models in response to the requests.

Abstract: A system and a computer-implemented method of training a global student model is disclosed. The global student model and a teacher model are stored on a server and each include a first layer. The method includes transmitting local student models based on the global student model, the local student models each including an embedding layer and a first layer. The method includes receiving an embedding layer output of one of the local student models. The method includes performing a forward pass on the first layer of the teacher model, with the embedding layer output as an input, to generate a teacher model first layer output. The method includes transmitting the teacher model first layer output. The method includes receiving first layer weights of the local student models. The method includes calculating first layer weights of the global student model using the received first layer weights of the local student models.

Abstract: A computer-implemented method, system and computer program product for imputing missing data in the presence of data quality disparity. An optimization problem of imputing the missing values in the dataset with a presence of data quality disparity is formulated as a black-box optimization problem with an objective of jointly maximining both the fairness metric and an accuracy of the model (machine learning model) trained to identify the missing values to be imputed in the dataset for the sensitive group. Missing values to be imputed in the dataset may then be identified based on maximizing the fairness metric and the accuracy of the model. In this manner, the disparity of the data quality in machine learning datasets involving missing data among sensitive groups is effectively handled.

Abstract: A system maintains a data store for managing machine-learning (ML) models and features that are used by the models. The system generates a graph including nodes for each model and a node for each feature, and edges linking models and features that are used by the models. For a new model to be trained, the system receives a proposed feature corresponding to a node in the graph, and identifies one or more candidate features corresponding to nodes in the graph based in part on relevancy scores between the proposed feature with other features corresponding to nodes in the graph. The system presents in a user interface a suggestion to use one or more candidate features with the new model. Responsive to receiving a user selection of at least one candidate feature, the system causes the new model to be trained using the selected candidate feature and the proposed feature.

Abstract: A system, method, and computer-readable medium for optimizing overall edge datacenter power and maintaining a compliant thermal state of edge devices in a cluster. Thermal compliant policy of the edge devices as to lower and upper limits is determined. Telemetry attributes from the edge devices are applied to a machine learning (ML) model to predict thermal condition of the edge devices over time. Workload is offloaded from one edge device to another edge device if predicted thermal condition of one of the edge devices is not within the limits of the thermal compliant policy.

Abstract: Techniques for enabling the building of general input data ML flows using a serverless data-representation-as-a-service (DRaaS) are provided. In one technique, in response to receiving a first data representation (DR) generation request from a first calling entity, first input data is retrieved based on the first DR generation request, a first set of DRs is generated (by a DR generator) based on the first input data, and the first set of DRs are made available to the first calling entity. In response to receiving a second DR generation request from a second calling entity that is different than the first calling entity, second input data is retrieved based on the second DR generation request, a second set of DRs is generated based on the second input data, and the second set of DRs are made available to the second calling entity.

Abstract: An information processing system enables searching for an optimum solution through annealing by converting, into an Ising model, a strong nonlinear objective function derived from machine learning. An objective function derivation system performs machine learning on a training database; and a function conversion system converts the objective function. The objective function derivation system includes: a machine learning setting unit; and a learning unit configured to derive the objective function.

Abstract: The present disclosure generally relates to the field of Machine Learning, more particularly, methods and apparatuses to tune learning rates to accelerate training and avoid non-convergence. The new method utilizes estimates of the first-order derivatives and the unmixed second-order derivatives of the cost function relative to model parameters and a rate adjustment function to update the model parameters iteratively during model training.

Abstract: A system is configured to train a machine learning tree network using path based features, such as leaf nodes or connections between nodes. A first machine learning tree network model, for example, may be trained using a first set of training data, and used to generate predictions for a second set of training data. The path based features are determined from the first machine learning tree network model when generating the predictions for the second set of training data. The path based features may then be used to train a second machine learning tree network model, e.g., using logistic regression.

Abstract: Methods and systems are described herein for novel uses and/or improvements to artificial intelligence applications for data-sparse environments. As one example, methods and systems are described herein for overcoming the problem of determining an appropriate digital asset to recommend to a user in real-time based on training data that may be shared between multiple intents (e.g., as would be found in responses to textual, verbal, and/or other real-time communications). In particular, the methods and systems overcome this technical problem by using an artificial intelligence trained to identify specific verbiage of a user to display digital assets corresponding to a user's intent thereby increasing productivity, organization, and collaboration, and reducing frustration.

Abstract: Techniques are provided for causal learning and prediction. In one embodiment, the techniques involve determining a first attribute value based on an output of a generative model trained on a first data set, generating a second attribute value based on a change of a latent space dimension of the generative model, generating a sensitivity map based on a difference between the first attribute value and the second attribute value; and generating a causal graph based on the sensitivity map.

Abstract: Intelligent self-adjusting metric collection is described. A first rule set is distributed that describes a first set of one or more metrics corresponding to operation of elements of the receiving entities. One or more metrics based on the first rule set are received. A second rule set is generated in response to an indication of a condition change. The second rule set can be generated using machine learning techniques. The second rule set that describes a second set of one or more metrics is distributed. Metrics based on the second rule set are received.

Abstract: An information handling system includes a storage and a processor. The storage stores a machine learning (ML) model. The processor receives first telemetry data associated with a second information handling system, and user survey data associated with the second information handling system. Based on the first telemetry data and the user survey data, the processor trains the ML model. The processor receives second telemetry data for the second information handling system. The processor executes the ML model to determine a composite score for the second information handling system.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to train machine learning models to reduce categorization bias, the apparatus comprising: interface circuitry; machine readable instructions; and programmable circuitry to at least one of instantiate or execute the machine readable instructions to: calculate category information corresponding to samples based on a plurality of models; calculate task loss values associated with respective ones of the samples and respective ones of the plurality of models based on product category information; calculate gating loss values for a model gate based on category frequency information; and train the model gate based on a sum of the task loss and the gating loss, the training to derive weights corresponding to respective ones of the plurality of models.

Abstract: A computer-implemented method for identifying relevant subgroups, which are relevant for training a subgroup-robust classifier, in a training dataset associated with a machine learning model includes receiving a classification dataset wherein subgroups are unlabeled. For each data point in the classification dataset, the method uses gradient space partitioning (GraSP) to identify a gradient representation of each data point by extracting an associated gradient of a logistic regression classification loss with respect to weights of a logistic regression. The gradient representations are clustered to provide estimated subgroup labels the cluster assignments are output as the estimated subgroup labels.

Abstract: Disclosed herein are systems and methods for determining data structures. In some embodiments, a classifier may be used to determine one or more attributes of an entity. In some embodiments, a clustering algorithm may be used to determine an attribute cluster. In some embodiments, an impact metric machine learning model may be used to determine an outlier cluster. In some embodiments, an outlier process may be determined as a function of the outlier cluster. In some embodiments, a visual element may be determined as a function of an outlier process and may be displayed to a user.

Abstract: An automated training-based data labeling method according to the present disclosure is configured to generate an artificial intelligence model in which a processor separates some of labeled data into training data as soon as it receives a certain amount of labeled data from a worker terminal, and automatically performs the data labeling on objects in source data through automated training of the training data. According to the present disclosure, since a proportion of worker participation is reduced when labeling the data for the objects in the source data, it is possible to dramatically reduce operation costs required for the data labeling.

Abstract: Described herein are systems and techniques to automatically and with minimal human intervention facilitate determining recommendations for improvement tools to improve user performance and/or modify user behavior based on a variety of data. An input data structure containing sentiment data for a user as well as any one or more pieces of data associated with the user may be provided as input to an improvement tool recommendation model that may be executed to generate improvement tool recommendations for the user. The recommendations may be provided in dedicated interfaces and/or integrated into existing communications and/or communications channels.

Abstract: A method of training a machine learning regression model includes defining a prediction accuracy grading function, the prediction accuracy grading function being a many-to-one function that maps prediction accuracies to proxies, each of the prediction accuracies being derivable from a respective prediction of the model and a corresponding actual. The method may further include receiving a plurality of proxies corresponding respectively to a plurality of predictions of the model and, for each of the plurality of proxies, deriving a corresponding approximated actual according to the prediction accuracy grading function. The method may further include calculating an approximated residual for each of the plurality of predictions of the model based on the corresponding approximated actual and adjusting the model based on the approximated residuals.

Abstract: In various aspects systems and methods are provided for using a Large Language Model(s) to analyze declarative deployment code as found in deployment manifest files for deploying resources in a cloud-based environment.

Abstract: A system includes at least one device configured to extract data from at least one internet-connected data source and preprocess the extracted data. The system also includes a database configured to store the preprocessed data. The system also includes at least one processor configured to perform vector extraction to extract features of at least a portion of the preprocessed data and generate a plurality of vectors from the extracted features of the preprocessed data, input the plurality of vectors into a machine learning model trained using historical data from the at least one internet-connected data source, and generate, using the machine learning model and the plurality of vectors, a willingness signature associated with an individual, wherein the willingness signature is a numerical value or a probability distribution that represents a propensity of the individual to engage in healthcare-related activities.

Abstract: The present disclosure relates generally to the generation and deployment of a machine learning-enabled decision engine (MLDE). The MLDE includes decision options that are composed of a discrete list of selectable options. Further, the MLDE includes data inputs that can be used to influence decisions made by the machine learning models of the MLDE. Controls are applied to the MLDE to overlay and bound the decisioning within guidelines established by an operator of the MLDE. Once the MLDE is established, the MLDE is validated and deployed for use by software applications to make decisions.

Abstract: In an example, a non-transitory machine-readable storage medium storing instructions executable by a processor of a computing device to receive device usage data of an electronic device. Further, instructions may be executed by the processor to receive sensor data indicative of an internal state of the electronic device. The sensor data may include first data associated with a first characteristic of the internal state and second data associated with a second characteristic of the internal state. Furthermore, instructions may be executed by the processor to determine a deviation associated with a component of the electronic device by applying a machine learning model to the device usage data and the sensor data. The deviation may be associated with the first characteristic, the second characteristic, or both. Further, instructions may be executed by the processor to generate an alert notification based on the deviation.

Abstract: In a learning device, an inference means performs an inference with respect to training data using an inference model, and outputs a class score. A weight calculation means calculates each weight using a weight function which rapidly increases faster than a linear function for the class score that is over-estimated or under-estimated, based on output the class score. A weight sum calculation means calculates a total of weights over a mini-batch included in a predetermined number of training data. A regularization term calculation means calculates a regularization term by applying a rescale function which is a monotonically increasing function gradually increasing more than a linear function, to the regularization term. An optimization means optimizes the inference model using a total loss including the regularization term.

Abstract: A method including training, by one or more processors, a generative AI model using a plurality of first service requests handled by technicians for servicing building equipment. The generative AI model may be trained to predict root causes of a plurality of first problems corresponding to the plurality of first service requests. The method may include receiving, by the one or more processors, a second service request for servicing building equipment. The method may include predicting, by the one or more processors using the generative AI model, a root cause of a second problem corresponding to the second service request based on characteristics of the second service request and one or more patterns or trends identified from the plurality of first service requests using the generative AI model.

Abstract: A system that enables searching for listings for accommodation reservations is described. The system receives, by a network site of a listing network platform, input comprising search criteria and identifies a plurality of listings matching the search criteria. The system generates a graphical user interface comprising a plurality of graphical objects each associated with a respective one of the identified plurality of listings. The system determines that the search criteria satisfies an amenity criterion and, in response, causes one or more amenities associated with an individual listing of the identified plurality of listings to be presented in an individual graphical object of the plurality of graphical objects associated with the individual listing.

Abstract: A method of managing a digital queue includes accessing an application; transmitting a request based on a mobile device scanning a code or receiving a selection of a uniform resource locator (URL); transmitting information regarding a user of the mobile device and a reason for the request; receiving a real-time status of the digital queue including a number of clients in the digital queue before the user, the number of clients based on received indications of scanning the code or selecting the URL by each of the number of clients; displaying a graphical user interface (GUI) to depict a confirmation page indicating that the user has been added to the digital queue; automatically updating the GUI based on the real-time status of the digital queue; and transmitting, in response to determining the mobile device is within a geolocation area, a notification indicating the user has arrived.

Abstract: An operation plan creation device includes: a predicted value designation unit designating a predicted value of power to be supplied as first power from a first energy source to a power system; a constraint condition designation unit designating a constraint condition that includes a term indicating the first power and a term indicating second power and separately treats the first power and the second power for an operation of the power system; an objective function designation unit designating an objective function formulating an objective required for the power system; and an optimization calculation unit solving a problem defined by the predicted value of the power to be supplied, the constraint condition, and the objective function to obtain an operation plan for the power system.

Abstract: A method, a computer-readable medium, and an apparatus for transport service are provided. The apparatus may receive a plurality of transport service transactions associated with a point of interest entity. For each transport service transaction, the apparatus may determine a transport service location at which the transport service transaction is executed. The apparatus may cluster the transport service locations determined for the plurality of transport service transactions. The apparatus may determine one or more candidate transport service locations for the point of interest entity based on the clustering. The apparatus may provide the one or more candidate transport service locations to a client or a service provider associated with a transport service transaction that is to be executed at the point of interest entity.