Abstract: A domain adaptation module is used to optimize a first domain derived from a second domain using respective outputs from respective parallel hidden layers of the domains.

Abstract: A property vector representing extractable measurable properties, such as musical properties, of a file is mapped to semantic properties for the file. This is achieved by using artificial neural networks “ANNs” in which weights and biases are trained to align a distance dissimilarity measure in property space for pairwise comparative files back towards a corresponding semantic distance dissimilarity measure in semantic space for those same files. The result is that, once optimised, the ANNs can process any file, parsed with those properties, to identify other files sharing common traits reflective of emotional-perception, thereby rendering a more liable and true-to-life result of similarity/dissimilarity. This contrasts with simply training a neural network to consider extractable measurable properties that, in isolation, do not provide a reliable contextual relationship into the real-world.

Abstract: An example operation may include one or more of connecting to a blockchain containing chains of reasoning data and related premise data, receiving an inference query, retrieving from the blockchain chain of reasoning data and related premise data corresponding to the inference query, executing inference steps using the retrieved chain of reasoning data and the related premise data to generate conclusion data, tracking the execution of the inference steps, and sending to the blockchain the tracked inference steps and the conclusion data to be entered into the blockchain as transactions.

Abstract: An online system receives explicit user data and explicit event data, and implicit user data and implicit event data from a third party system. The online system generates an implicit users/implicit events data feature, an explicit users/explicit events data feature, and an explicit users/implicit events data feature. The online system generates a prediction of the counterfactual rate based on the implicit users/implicit events data feature, the explicit users/explicit events data feature, and the explicit users/explicit events data feature, the counterfactual rate indicating the likelihood that target users matching certain characteristics caused an event to occur when the target are not been presented with content by the online system, the content configured to induce users to cause the event to occur. A combined prediction rate is presented to the third party system based on the counterfactual rate.

Abstract: A method and apparatus that detect wheel misalignment are provided. The method includes predicting a self-aligning torque parameter based on a regression model determined from a dataset including one or more from among a steering wheel angle parameter, a speed parameter, a torsion bar torque parameter, a lateral acceleration parameter, and a power steering torque parameter, comparing a measured self-aligning torque parameter and the predicted self-aligning torque parameter, and outputting a wheel alignment condition indicating whether the wheel alignment is proper if the self-aligning torque parameter and the predicted self-aligning torque parameter are within a predetermined value based on the comparing.

Abstract: The present disclosure provides a method and apparatus for evaluating a matching degree based on artificial intelligence, a device and a storage medium, wherein the method comprises: respectively obtaining word expressions of words in a query and word expressions of words in a title; respectively obtaining context-based word expressions of words in the query and context-based word expressions of words in the title according to the word expressions; generating matching features according to obtained information; determining a matching degree score between the query and the title according to the matching features. The solution of the present disclosure may be applied to improve the accuracy of the evaluation result.

Abstract: The present disclosure provides a method and apparatus for evaluating a matching degree of multi-domain information based on artificial intelligence, a device and a medium.

Abstract: Methods, apparatuses and systems directed to pattern learning, recognition, and metrology. In some particular implementations, the invention provides a flexible pattern recognition platform including pattern recognition engines that can be dynamically adjusted to implement specific pattern recognition configurations for individual pattern recognition applications. In certain implementations, the present invention provides for methods and systems suitable for analyzing and recognizing patterns in biological signals such as multi-electrode array waveform data. In other implementations, the present invention also provides for a partition configuration where knowledge elements can be grouped and pattern recognition operations can be individually configured and arranged to allow for multi-level pattern recognition schemes. In other implementations, the present invention provides methods and systems for dynamic learning of patterns in supervised and unsupervised manners.

Abstract: Single loop inductive sensors are widely deployed in infrastructure for traffic data collection, however, these loops currently provide little more than vehicle detection. A system and method are provided that enable single loop inductive sensors to be used for vehicle classification (e.g., identification as motorcycle, passenger car, bus, etc.). Classification may be done using the Federal Highway Administration's 13 class system. Initially a signature library is built from vehicle signatures with known classifications. Vehicle signature waveforms of unknown classification obtained from inductive loop sensors are analyzed to identify specific features in the waveform including the number of “peaks”, the first peak location and its magnitude. A classifier (e.g., K-nearest neighbor) uses a representation of the vehicle signature and the features to determine from the signature library the classification of the vehicle.

Abstract: In one embodiment, a character engine models a character that interacts with users. The character engine receives user input data from a user device, and analyzes the user input data to determine a user intent and an assessment domain. Subsequently, the character engine selects inference algorithm(s) that include machine learning capabilities based on the intent and the assessment domain. The character engine computes a response to the user input data based on the selected inference algorithm(s) and a set of personality characteristics that are associated with the character. Finally, the character engine causes the user device to output the response to the user. In this fashion, the character engine includes sensing functionality, thinking and learning functionality, and expressing functionality.

Abstract: An artificial intelligence platform system includes at least a server designed and configured to receive training data. Receiving training data includes receiving a first training set including a plurality of first data entries, each first data entry of the plurality of first data entries including at least an element of user data and at least a correlated first constitutional label. At least a server receives at least a user input datum from a user client device. At least a server generates at least an output as a function of the at least a user input datum and the training data. At least a server retrieves at least a stored user datum as a function of the at least a user input datum and the at least an output. At least a server transmits the at least a stored user datum to a user client device.

Abstract: A processing unit can extract salient semantics to model knowledge carryover, from one turn to the next, in multi-turn conversations. Architecture described herein can use the end-to-end memory networks to encode inputs, e.g., utterances, with intents and slots, which can be stored as embeddings in memory, and in decoding the architecture can exploit latent contextual information from memory, e.g., demographic context, visual context, semantic context, etc. e.g., via an attention model, to leverage previously stored semantics for semantic parsing, e.g., for joint intent prediction and slot tagging. In examples, architecture is configured to build an end-to-end memory network model for contextual, e.g., multi-turn, language understanding, to apply the end-to-end memory network model to multiple turns of conversational input; and to fill slots for output of contextual, e.g., multi-turn, language understanding of the conversational input.

Abstract: Provided are an artificial intelligence (AI) learning method and system and an answer relay method and system using an AI. According to an AI learning method, an AI may transmit a question to users through a messaging service and may acquire learning data for the AI through reactions of the user to the transmitted question.

Abstract: Embodiments for implementing intelligent recommendations of convenient event opportunities by a processor. A group of entities may be identified for one or more event opportunities or the one or more event opportunities may be identified for the group of entities according to one or more entity selection criteria and one or more event criteria. The one or more event opportunities and the group of entities may be matched according to a level of convenience for attending the one or more event opportunities of the group of entities. The one or more matching event opportunities may be ranked and suggested to the group of entities.

Abstract: A method, computer system, and computer program product for providing recommendations about processing datasets. A set of machine learning models are provided for use in respectively determining data processing action performable on a dataset based on a respective set of features of the dataset. A current dataset is received. A set of features of the current dataset are determined. One or more data processing actions are generated to be executed on the current dataset, which are determined by at least two machine learning models of the provided set, based on the determined set of features of the current dataset. One or more of the data processing actions are performed on the current dataset.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for evaluating reinforcement learning policies. One of the methods includes receiving a plurality of training histories for a reinforcement learning agent; determining a total reward for each training observation in the training histories; partitioning the training observations into a plurality of partitions; determining, for each partition and from the partitioned training observations, a probability that the reinforcement learning agent will receive the total reward for the partition if the reinforcement learning agent performs the action for the partition in response to receiving the current observation; determining, from the probabilities and for each total reward, a respective estimated value of performing each action in response to receiving the current observation; and selecting an action from the pre-determined set of actions from the estimated values in accordance with an action selection policy.

Abstract: Example aspects of the present disclosure are directed to systems and methods for learning classification models which satisfy constraints such as, for example, constraints that can be expressed as a predicted positive rate or negative rate on a subset of the training dataset. In particular, through the use of quantile estimators, the systems and methods of the present disclosure can transform a constrained optimization problem into an unconstrained optimization problem that is solved more efficiently and generally than the constrained optimization problem. As one example, the unconstrained optimization problem can include optimizing an objective function where a decision threshold of the classification model is expressed as an estimator of a quantile function on the classification scores of the machine-learned classification model for a subset of the training dataset at a desired quantile.

Abstract: In one embodiment, a character engine models a character that interacts with users. The character engine receives user input data from a user device, and analyzes the user input data to determine a user intent and an assessment domain. Subsequently, the character engine selects inference algorithm(s) that include machine learning capabilities based on the intent and the assessment domain. The character engine computes a response to the user input data based on the selected inference algorithm(s) and a set of personality characteristics that are associated with the character. Finally, the character engine causes the user device to output the response to the user. In this fashion, the character engine includes sensing functionality, thinking and learning functionality, and expressing functionality.

Abstract: A bias estimation apparatus according to an embodiment estimates a bias included in a measured values by each sensor. The bias estimation apparatus includes a reference model builder, a temporary bias generator, a corrected measured value calculator, a similarity calculator, a similarity selector, a score calculator, and an estimated bias determiner. The reference model builder builds a reference model of the measured value packs. The temporary bias generator generates a temporary bias pack. The corrected measured value calculator calculates corrected measured value packs. The similarity calculator calculates a similarity of each corrected measured value pack. The similarity selector selects a part of the similarities according to their values from among the similarities. The score calculator calculates a score based on the selected similarities. The estimated bias determiner determines an estimated bias which is an estimated value of the bias based on the score.

Abstract: A Web-analytics system analyzes user activity on a Web site of interest. The system receives a set of user-session logs that each track user behavior on the site during one session. The system identifies common patterns of user behavior recorded in the logs. The system stores each identified pattern in a reserved area and replaces each instance of a stored pattern in a log with a placeholder. During the analytics system's subsequent analysis of a session, the system need process only the reduced-size log of that session, referring to the previously stored patterns whenever encountering a corresponding placeholder.