Abstract: The present disclosure provides methods and systems for providing machine learning model service. The method may comprise: (a) generating, by a first computing system, a first output data using a first machine learning model, wherein the first machine learning model is trained on a first training dataset; (b) transmitting the first output data to a second computing system, wherein the first training dataset and the first machine learning model are inaccessible to the second computing system; (c) creating an input data by joining the first output data with a selected set of input features accessible to the second computing system; and (d) generating a second output data using a second machine learning model to process the input data.

Abstract: According to an embodiment, a learning device includes a memory and one or more processors coupled to the memory. The one or more processors are configured to: generate a transformation matrix from learning data in which feature quantities and target values are held in a corresponding manner; and learn about parameters of a neural network which includes nodes equal in number to the number of rows of the transformation matrix, a first output layer representing first estimation distribution according to the values of the nodes, and a second output layer representing second estimation distribution decided according to the product of the transformation matrix and the first estimation distribution.

Abstract: One or more computing devices, systems, and/or methods for generating time-preserving embeddings are provided. User trails of user activities performed by users are generated. Frequencies at which the activities were performed are identified. Indices are assigned to a set of activities identified from the activities as having frequencies above a threshold. Activity descriptions of the set of activities are mapped to the indices to generate a vocabulary. A model is trained using the user trails, timestamps of the activities, and the vocabulary to learn a set of time-preserving embeddings.

Abstract: A computer-implemented method of training a model for making time-series predictions of a computer-controlled system. The model uses a stochastic differential equation (SDE) comprising a drift component and a diffusion component. The drift component has a predefined part representing domain knowledge, that is received as an input to the training; and a trainable part. When training the model, values of the set of SDE variables at a current time point are predicted based on their values at a previous time point, and based on this, the model is refined. In order to predict the values of the set of SDE variables, the predefined part of the drift component is evaluated to get a first drift, and the first drift is combined with a second drift obtained by evaluating the trainable part of the drift component.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a document classification neural network. One of the methods includes training an autoencoder neural network to autoencode input documents, wherein the autoencoder neural network comprises the one or more LSTM neural network layers and an autoencoder output layer, and wherein training the autoencoder neural network comprises determining pre-trained values of the parameters of the one or more LSTM neural network layers from initial values of the parameters of the one or more LSTM neural network layers; and training the document classification neural network on a plurality of training documents to determine trained values of the parameters of the one or more LSTM neural network layers from the pre-trained values of the parameters of the one or more LSTM neural network layers.

Abstract: In implementations of object detection in images, object detectors are trained using heterogeneous training datasets. A first training dataset is used to train an image tagging network to determine an attention map of an input image for a target concept. A second training dataset is used to train a conditional detection network that accepts as conditional inputs the attention map and a word embedding of the target concept. Despite the conditional detection network being trained with a training dataset having a small number of seen classes (e.g., classes in a training dataset), it generalizes to novel, unseen classes by concept conditioning, since the target concept propagates through the conditional detection network via the conditional inputs, thus influencing classification and region proposal. Hence, classes of objects that can be detected are expanded, without the need to scale training databases to include additional classes.

Abstract: A computer implemented method, computer program product, and system for managing execution of a workflow comprising a set of subworkflows, comprising optimizing the set of subworkflows using a deep neural network, wherein each subworkflow of the set of subworkflows has a set of tasks, wherein each task of the sets of tasks has a requirement of resources of a set of resources; wherein each task of the sets of tasks is enabled to be dependent on another task of the sets of tasks, training the deep neural network by: executing the set of subworkflows, collecting provenance data from the execution, and collecting monitoring data that represents the state of said set of resources, wherein the training causes the neural network to learn relationships between the states of said set of resources, the said sets of tasks, their parameters and the obtained performance, optimizing an allocation of resources of the set of resources to each task of the sets of tasks to ensure compliance with a user-defined quality metric b

Abstract: In some aspects, a computing system can generate and optimize a neural network for risk assessment. The neural network can be trained to enforce a monotonic relationship between each of the input predictor variables and an output risk indicator. The training of the neural network can involve solving an optimization problem under a monotonic constraint. This constrained optimization problem can be converted to an unconstrained problem by introducing a Lagrangian expression and by introducing a term approximating the monotonic constraint. Additional regularization terms can also be introduced into the optimization problem. The optimized neural network can be used both for accurately determining risk indicators for target entities using predictor variables and determining explanation codes for the predictor variables. Further, the risk indicators can be utilized to control the access by a target entity to an interactive computing environment for accessing services provided by one or more institutions.

Abstract: An apparatus to facilitate partially-frozen neural networks for efficient computer vision systems is disclosed. The apparatus includes a frozen core to store fixed weights of a machine learning model, one or more trainable cores coupled to the frozen core, the one or more trainable cores comprising multipliers for trainable weights of the machine learning model, and wherein the alpha blending layer includes a trainable alpha blending parameter, and wherein the trainable alpha blending parameter is a function of a trainable parameter, a sigmoid function, and outputs of frozen and trainable blocks in a preceding layer of the machine learning model.

Abstract: Implementing a convolutional neural network (CNN) includes configuring a crosspoint array to implement a convolution layer in the CNN. Convolution kernels of the layer are stored in crosspoint devices of the array. Computations for the CNN are performed by iterating a set of operations for a predetermined number of times. The operations include transmitting voltage pulses corresponding to a subpart of a vector of input data to the crosspoint array. The voltage pulses generate electric currents that are representative of performing multiplication operations at the crosspoint device based on weight values stored at the crosspoint devices. A set of integrators accumulates an electric charge based on the output electric currents from the respective crosspoint devices. The crosspoint array outputs the accumulated charge after iterating for the predetermined number of times. The accumulated charge represents a multiply-add result of the vector of input data and the one or more convolution kernels.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an action selection neural network used to select actions to be performed by an agent interacting with an environment. In one aspect, a system comprises a plurality of actor computing units and a plurality of learner computing units. The actor computing units generate experience tuple trajectories that are used by the learner computing units to update learner action selection neural network parameters using a reinforcement learning technique. The reinforcement learning technique may be an off-policy actor critic reinforcement learning technique.

Abstract: A computer-implemented method includes receiving a neural network model that includes a tensor operation, dividing the tensor operation into a set of sub-operations, and generating instructions for performing a plurality of sub-operations of the set of sub-operations on respective computing engines of a plurality of computing engines on a same integrated circuit device or on different integrated circuit devices. Each sub-operation of the set of sub-operations generates a portion of a final output of the tensor operation. An inference is made based on a result of a sub-operation of the plurality of sub-operations, or based on results of the plurality of sub-operations.

Abstract: 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.

Abstract: Systems and methods for monitoring of icon in an external display device are disclosed. Images of an icon displayed in a display device may be continually captured as video frames by a video camera of an icon monitoring system. While operating in a first mode, video frames may be continually analyzed to determine if the captured image matches an active template icon known to match the captured image of the icon. While the captured image matches the active template icon, operating in the first mode continues. Upon detecting a failed match to the active template icon, the system starts operating in a second to search among known template icons for a new match. Upon finding a new match, the active template icon may be updated to the new match, and operation switches back to the first mode. Times of transitions between the first and second modes may be recorded.

Abstract: Some embodiments of the invention provide efficient, expressive machined-trained networks for performing machine learning. The machine-trained (MT) networks of some embodiments use novel processing nodes with novel activation functions that allow the MT network to efficiently define with fewer processing node layers a complex mathematical expression that solves a particular problem (e.g., face recognition, speech recognition, etc.). In some embodiments, the same activation function (e.g., a cup function) is used for numerous processing nodes of the MT network, but through the machine learning, this activation function is configured differently for different processing nodes so that different nodes can emulate or implement two or more different functions (e.g., two or more Boolean logical operators, such as XOR and AND). The activation function in some embodiments is a periodic function that can be configured to implement different functions (e.g., different sinusoidal functions).

Abstract: A model configuration selection system, the model configuration selection system comprising a processing circuitry configured to: (A) obtain: (a) one or more model configurations, each model configuration includes a set of parameters utilized to generate respective models, and (b) a training data-set comprising a plurality of unlabeled records, each unlabeled record including a collection of features describing a given state of a physical entity; (B) cluster the training data-set into two or more training data-set clusters using a clustering algorithm; (C) label (a) the unlabeled records of a subset of the training data-set clusters with a synthetic normal label, giving rise to a normal training data-set, and (b) the unlabeled records of the training data-set clusters not included in the subset with a synthetic abnormal label; (D) train, for each model configuration, using the normal training data-set, a corresponding model utilizing the corresponding set of parameters, each model capable of receiving the unl

Abstract: One embodiment includes a method for generating representations of inputs with missing values. The method includes steps for receiving an input includes a set of one or more values for several features, wherein the set of values for at least one of the several features includes values for each of several points in time, and for identifying a missingness pattern of the input, wherein the missingness pattern for the at least one feature indicates whether the set of values is missing a value for each of the several points in time. The method further includes steps for determining a set of one or more transformation weights based on the missingness pattern, and transforming the input based on the determined transformation weights.

Abstract: Some embodiments provide a compiler for optimizing the implementation of a machine-trained network (e.g., a neural network) on an integrated circuit (IC). The compiler of some embodiments receives a specification of a machine-trained network including multiple layers of computation nodes and generates a graph representing options for implementing the machine-trained network in the IC. In some embodiments, the graph includes nodes representing options for implementing each layer of the machine-trained network and edges between nodes for different layers representing different implementations that are compatible. The graph is used, in some embodiments, to select an optimum set of cores for implementing the received machine-trained network. The compiler, in some embodiments, optimizes memory storage such that input and output layers of a single layer are not stored in a same memory unit.

Abstract: There is a need for more effective and efficient data evaluation. This need can be addressed by, for example, techniques for data evaluation in accordance with a shared decision tree data object. In one example, a method includes generating, using a plurality of feature extraction threads, shared evidentiary data; generating, based on a selected shared evidentiary data subset of the shared evidentiary data that correspond to one or more selected nodes of the shared decision tree data object, refined evidentiary data; processing the refined evidentiary data in accordance with the shared decision tree data object to generate an evaluation output and an explanation output; and displaying an evaluation output user interface comprising user interface data describing the evaluation output and the explanation output.

Abstract: This invention relates generally to classification systems. More particularly this invention relates to a system, method, and computer program to dynamically generate a domain of information synthesized by a classification system or semantic network. The invention discloses a method, system, and computer program providing a means by which an information store comprised of knowledge representations, such as a web site comprised of a plurality of web pages or a database comprised of a plurality of data instances, may be optimally organized and accessed based on relational links between ideas defined by one or more thoughts identified by an agent and one or more ideas embodied by the data instances. Such means is hereinafter referred to as a “thought network”.

Abstract: Disclosed are a system and method for training and managing a prediction model, and a master apparatus and a slave apparatus for the same. there is provided a system for training and managing a prediction model, the system including a master apparatus configured to generate a prediction model, train the prediction model, and obtain the trained prediction model; and a slave apparatus configured to collect data, transmit the data to the master apparatus, receive the prediction model or the trained prediction model from the master apparatus, and operate based on the prediction model or the trained prediction model. The master apparatus is further configured to generate the prediction model or train the prediction model based on the data transmitted from the slave apparatus.

Abstract: A system includes a centralized repository for tracking rule content and managing subscriptions to rule content by organizations and providers utilizing the system; a rule-evaluation server for receiving requests for rule-evaluations for specific patients, wherein the server determines content needing to be evaluated and retrieves the content to be used; a rule engine for performing the evaluations, wherein content, patient data, and rule evaluation parameters are provided to the engine, and the engine returns recommendations triggered by the evaluation, if any; an aggregator for aggregating recommendations from multiple sources, detecting and coordinating related recommendations, and applying configuration settings based on the patient and/or provider in context; and a client component for coordinating communication between an electronic health records system, the server, and the aggregator.

Abstract: An example method comprises receiving historical sensor data of a first time period, the historical data including sensor data of a renewable energy asset, extracting features, performing a unsupervised anomaly detection technique on the historical sensor data to generate first labels associated with the historical sensor data, performing at least one dimensionality reduction technique to generate second labels, combining the first labels and the second labels to generate combined labels, generating one or more models based on supervised machine learning and the combined labels, receiving current sensor data of a second time period, the current sensor data including sensor data of the renewable energy asset, extracting features, applying the one or more models to the extracted features of the current sensor data to create a prediction of a future fault in the renewable energy asset, and generating a report including the prediction of the future fault in the energy asset.

Abstract: In an approach to improve chatbot workspaces by updating chatbot workspaces through documentation updating and chatbot skill updating. Embodiments determine a chatbot knowledge base contains a set of updated information and updates a chatbot dialog decision tree based on one or more identified new topics in a set of updated information using natural language processing techniques to determine a set of intents, a set of entities, and a set of keywords. Further, embodiments identify a starting decision for traversing the chatbot dialogue decision tree based on the updated set of entities and the updated set of keywords. Additionally, embodiments interact, via a user interface, with an end user according to one or more interactions traversing the chatbot dialogue decision tree for a response.

Abstract: A system receives a predictive model and receives one or more runtime constraints. The system generates a directed acyclic graph (DAG) of the predictive model indicating dependencies. The system compiles the predictive model into first instructions for a first processor based on the one or more runtime constraints and the DAG. The system packages first instructions, the one or more runtime constraints, and the DAG of the predictive model in a first binary. The system recompiles the predictive model into second instructions for a second processor based on the runtime constraints and the DAG stored in the first processor. The system packages the second instructions, the DAG, and the runtime constraints in a second binary.

Abstract: A computer includes a processor and a memory storing instructions executable by the processor to predict a time to failure of a vehicle component according to a damage model generated from vehicle operating data, based on the predicted time to failure, retrieve a schematic of the vehicle component and input the schematic to an additive printer to manufacture a replacement vehicle component, and replace the vehicle component with the replacement vehicle component according to maintenance instructions that, based at least in part on the predicted time to failure, specify a time and location to replace the vehicle component.

Abstract: A device for generating training data sets for signal type recognition has at least one radio frequency signal generator for generating at least one artificial radio frequency signal, a radio frequency receiver connected to the at least one radio frequency signal generator for receiving the at least one artificial radio frequency signal generated by the at least one radio frequency signal generator, and a signal data recorder connected to the radio frequency receiver for storing the radio frequency signal received by the radio frequency receiver as a training data set. Further, a method for generating training data sets as well as a training data set are provided.

Abstract: Disclosed are various approaches for managing the status of machine-learning models using distributed ledgers. A registration request for a machine-learning model can be received. The registration request can include a model name for the machine-learning model, a version identifier for the machine-learning model, a network address from which the machine-learning model can be retrieved, a source code hash for a source code version of the machine learning model, and a runtime hash for a binary executable version of the machine-learning model. A registration identifier can then be created based at least in part on the source code hash and the runtime hash. Subsequently, an entry in the distributed ledger can be created for the machine-learning model. The entry can include the registration identifier, the model name, the model version, the network address, the source code hash, and the runtime hash.

Abstract: Disclosed is a multi-device based inference method and apparatus, where the multi-device based inference method includes receiving information related to operation devices performing an operation included in a neural network and a graph corresponding to the neural network, obtaining a size of an output of the operation in a forward direction of the graph based on the information and the graph, dividing an input of the operation in a backward direction of the graph based on the information, the graph, and the size of the output, and performing an inference based on the divided input.

Abstract: System, apparatus and method may permit users to collaboratively engage in inference on a computer and visualize structure of that inference, and provide a formal verification system for informal argumentation and inference. The system and method may generate and allow for modification of graphical structures that represent sequences of structured rational argumentation; and automatically monitor, compute and represent ratings or scores of nodes within the structure; indicate whether a node is supported by a chain of argumentation that has not been validly rebutted. The graphical structures may be displayed to bring into focus contentious and significant underlying points within an argument, and simulate the effects of alternative resolutions of these contentious points. The graphical displays may provide a transparent verification to other users of the state of what can be demonstrated and refuted, allow discovery of weak or missing points in a logical argument, and allow rational inference by users.

Abstract: A system and method for updating and correcting facts that receives proposed values for facts from users and determines a correctness score which is used to automatically accept or reject the proposed values.

Abstract: In a computer-implemented method of assessing driving performance using route scoring, driving data indicative of operation of a vehicle while the vehicle was driven on a driving route may be received. Road infrastructure data indicative of one or more features of the driving route may also be received. A route score for the driving route may be calculated using the road infrastructure data, and a driving performance score for a driver of the vehicle may be calculated using the driving data and the route score for the driving route. Data may be sent to a client device via a network to cause the client device to display the driving performance score and/or a ranking based on the driving performance score, and/or the driving performance score may be used to determine a risk rating for the driver of the vehicle.

Abstract: A social networking application provides for automated link and/or content recommendation to users of a social media platform by automated social graph refinement that augments a baseline social graph with predicted links and inferred labels by iteratively (a) propagating attribute labels through optimizing attribute label similarity between user nodes constrained by closeness of links between the users, and (b) predicting links between users through optimizing link closeness constrained by label similarity. Each label inference iteration is based on predicted labels generated in and immediately prior link prediction iteration, and each link prediction iteration is based on inferred labels generated in and immediately prior label inference iteration.

Abstract: A method of implementing a network-enabled secure door lock, comprising obtaining measurements of an environment associated with a door from a variety of sensor types; generating, based at least in part on the measurements, a set of inputs to a machine-learning model; inputting the set of inputs into the machine learning model to determine a status of the door; generating a message that indicates the status of the door; and transmitting the message to a user device.

Abstract: Methods of managing a fleet of devices are provided, as are methods for configuring a standby device for a job in a workflow environment, and methods for performing a job in a workflow environment. Device information is analyzed, such as information pertaining to verification systems. Device instructions are sent to various locations on a device network in response to a deviation from a parameter value having been detected. The deviation from the parameter value may correspond to printed media and/or indicia produced by one or more devices. A workflow device and a standby device are provided, and the workflow device sends configuration data to the standby device. The standby device installs configuration data and is introduced into the workflow environment.

Abstract: The technology disclosed herein enables configuration and sharing of a coverage map which displays asset data. In various implementations, an asset owner provides access to select portions of asset data, such as vehicle performance data, to an end-user. The asset owner configures a coverage map to display the selected data according to the type of asset data to be displayed, the scope of the data to be displayed, and other map attributes. Once configured, a URL is generated which the fleet operator provides to the end-user.

Abstract: A system and method processes a transaction between a merchant and a consumer. Data indicate of the security of a digital processing device being used by the consumer to carry out the transaction with the merchant is received. A determination as to whether the digital processing device is secure is made using the received data. In response to determining the digital processing device is insecure, the transaction is aborted. In response to determining the digital processing device is in secure, the transaction is processed by at least one of authenticating the consumer, authorizing the transaction using a payment processing supply chain, and capturing funds for the transaction using a payment processing supply chain.

Abstract: An approach to facilitate providing an event space associated with a primary virtual space is provided. An event space is provided to users of a primary virtual space, wherein the event space comprises one or more event objectives. Users of the primary virtual space having primary user accounts have associated event space accounts indicating event user parameters, event game parameters, and event inventory information for the first user. The user may be provided with event virtual items for purchase wherein the event virtual items are used in the event space during the event period. In response to a determination of the progress of the user associated with one or more event objectives an event award is determined for distribution to the user, wherein the event award may be used within the primary virtual space.

Abstract: The present invention generally relates to a system, method, and computer program for providing coupons and cash back in electronic commerce. Specifically, electronic coupon code(s) are tested against various retailer websites to ascertain the best combination and automatically applied into a user's cart during electronic checkout. Various embodiments of the present invention also provide the user-less tracking of an affiliate purchase in order to apportion user cash back without first requiring the creation of a user account.

Abstract: A system for anonymously connecting consumers to advertisers is disclosed. A computing device, upon receipt of a request from a consumer mobile device, sends a ready-to-send text message to the consumer mobile device to be displayed thereon, via a data network; receives the ready-to-send text message from the consumer mobile device; receives metadata from the consumer mobile device comprising at least a phone number associated with the consumer mobile device; determines an anonymous identity of the user of the consumer mobile device based on the metadata; composes and sends a text message with an embedded call-to-action link comprising a uniform resource locator (URL) to the consumer mobile device; and upon receipt of a request from the consumer mobile device comprising the URL, sends the anonymous identity of the user to a third-party server and redirect the request from the consumer mobile device to the third-party server.

Abstract: A computer-implemented method for providing location-based appointment operations is disclosed. The method includes receiving input indicating an instruction to perform an action related to appointments. Responsive to the input indicating the instruction to perform the action related to appointments, it may be determined that a computing device is proximate to a physical location where locations are schedulable. Then, responsive to determining that the computing device is proximate to the physical location, information related to scheduling appointments at the physical location is received via a network. The indication based on the received information is presented at the computing device. Input requesting an action in relation to an appointment at the physical location may be received at the computing device. An indication to initiate the requested action may be sent via the network. Related computing devices and computer-readable media are also disclosed.

Abstract: An information processing apparatus includes a processor configured to, in a reservation process of making a reservation for use of a facility, accept input of one purpose of use among plural purposes of use provided for the facility, and in accordance with the accepted purpose of use, change at least one of a function provided in the reserved facility or information for which input is accepted in the reservation process.

Abstract: The disclosure provides a method for managing a public place in a smart city. The method may comprise obtaining pedestrian distribution information in a preset area during a current time period. The method may comprise determining, based on the pedestrian distribution information, at least one area location in the preset area for a future time period, and a population flow load of the area location may be greater than a first threshold. The method may comprise determining, based on the area location, prompt information. The method may comprise sending the prompt information to a user platform through a service platform.

Abstract: A system includes a computer processor configured to receive a vehicle identifier and a charging power dispenser identifier from a vehicle coupled to a specific charging power dispenser of a charging power dispenser chain. The system also includes a control program being configured to run on the computer processor, the control program being configured to determine a time to deliver power to the vehicle and an amount of power to deliver to the vehicle, the control program being further configured to send to a communication hub of a power cabinet electrically coupled to the charging power dispenser chain the time to deliver power to the vehicle, the amount of power to deliver to the vehicle, the vehicle identifier, and the charging power dispenser identifier.

Abstract: A network computing system can coordinate on-demand transport serviced by transport providers operating throughout a transport service region. The transport providers can comprise a set of internal autonomous vehicles (AVs) and a set of third-party AVs. The system can receive a transport request from a requesting user of the transport service region, where the transport request indicates a pick-up location and a destination. The system can determine a subset of the transport providers to service the respective transport request, and executing a selection process among the subset of the transport providers to select a transport provider to service the transport request. The system may then transmit a transport assignment to the selected transport provider to cause the selected transport provider to service the transport request.

Abstract: The present application discloses an improved transportation matching system, and corresponding methods and computer-readable media. According to the disclosed embodiments, the transportation matching system identifies low engagement transportation providers by analyzing information associated with the transportation providers to generate engagement levels. Furthermore, the system identifies an optimal match between a low engagement transportation provider (regardless of whether the transportation provider is online or offline) and a scheduled transportation request by utilizing attributes associated with a scheduled transportation request and attributes associated with transportation providers to generate rankings for the transportation providers. Additionally, the system provides the scheduled transportation request exclusively to a selected transportation provider based on the generated rankings.

Abstract: Generating a model for evaluating organizational skills to determine cost of entering a new market includes training a machine learning model to define business capabilities, processes and required skills of an organization based on a current business strategy, training the machine learning model to define a plurality of skill classes of the required skills of the organization using the cognitive computing processor device, training the machine learning model to define skill profiles of the available talent of the organization based on the plurality of skill classes, determining skill gaps of the available talent of the organization by analyzing the required skills of the organization and the skill profiles, assessing skills required for a new business strategy for the organization, and determining a cost of the organization executing the new business strategy based on the skill profiles, the at least one skill gap and the new business strategy skills.

Abstract: Arrangements described herein provide a reliability-aware method of determining a schedule for performing a set of tasks for agents. The arrangements determine the schedule based on an objective function that aims to provide a greater probability of completion of the schedule. This allows a more reliable schedule to be determined that takes into account the risk that one or more of the agents will fail during the operation of the schedule. This ensures that the schedule integrates sufficient fail-safes to avoid or at least reduce the need for rescheduling to account for agent failure.

Abstract: In an approach for real-time opportunity discovery for productivity enhancement of a production process, a processor extracts a set of features from time series data, through autoencoding using a neural network, based on non-control variables for the time series data. A processor identifies one or more operational modes based on the extracted features including a dimensional reduction with a representation learning from the time series data. A processor identifies a neighborhood of a current operational state based on the extracted features. A processor compares the current operational state to historical operational states based on the time series data at the same operational mode. A processor discovers an operational opportunity based on the comparison of the current operational state to the historical operational states using the neighborhood. A processor identifies control variables in the same mode which variables are relevant to the current operational state.

Abstract: Techniques to generate a digitally optimized schedule for a construction activity to meet a construction objective(s) of a construction project are disclosed. An artificial intelligence system receives a plurality of input data sets that impact the construction project. Each of the plurality of input data sets is processed to achieve the construction objective(s). The artificial intelligence system processes the plurality of input data sets using a respective ensemble of machine learning models. The artificial intelligence system generates machine learning validated intermediate output data sets corresponding to each of the plurality of input data sets. The artificial intelligence system implements a supervisory machine learning model to generate an optimized schedule for the construction activity based on the machine learning validated intermediate output data sets and the construction objective(s).