Provided is a simplified driving method of a synapse circuit. In a case where a first pre-spike pulse precedes a first post-spike pulse, a second pre-spike pulse from an input circuit 20a is used as a time window that allows writing of a coupling weight, and the first post-spike pulse from a neuron circuit 17 is used as a write pulse for controlling a write timing of the coupling weight. In a case where the first post-spike pulse precedes the first pre-spike pulse, a second post-spike pulse from the neuron circuit 17 is used as the time window, and the first pre-spike pulse from the input circuit 20a is used as the write pulse. The second pre-spike pulse and the second post-spike pulse are output in synchronization with the first pre-spike pulse and the first post-spike pulse, respectively.

Disclosed are an apparatus, a system and a non-transitory computer readable medium that implement processing circuitry that receives non-dialog information from a smart device and determines a data type of data in the received non-dialog information. Based on the determined data type, the processing circuitry transforms the received first data using an input from a machine learning algorithm into transformed data. The transformed data is standardized data that is palatable for machine learning algorithms such as those used implemented as chatbots. The standardized transformed data is useful for training multiple different chatbot systems and enables the typically underutilized non-dialog information to be used to as training input to improve context and conversation flow between a chatbot and a user.

A computational method is disclosed for the simulation of a hierarchical artificial neural network (ANN), wherein a single correlator pools, during a single time-step, two or more consecutive feed-forward inputs from previously predicted and now active neurons of one or more lower levels.

A method includes obtaining a plurality of training data sets each associated with a respective native language and includes a plurality of respective training data samples. For each respective training data sample of each training data set in the respective native language, the method includes transliterating the corresponding transcription in the respective native script into corresponding transliterated text representing the respective native language of the corresponding audio in a target script and associating the corresponding transliterated text in the target script with the corresponding audio in the respective native language to generate a respective normalized training data sample. The method also includes training, using the normalized training data samples, a multilingual end-to-end speech recognition model to predict speech recognition results in the target script for corresponding speech utterances spoken in any of the different native languages associated with the plurality of training data sets.

Systems and methods detecting a spoken sentence in a speech recognition system are disclosed herein. Speech data is buffered based on an audio signal captured at a computing device operating in an active mode. The speech data is buffered irrespective of whether the speech data comprises a signature word. The buffered speech data is processed to detect a presence of the sentence comprising at least one command and a query for the computing device. Processing the buffered speech data includes detecting the signature word in the buffered speech data, and in response to detecting the signature word in the speech data, initiating detection of the sentence in the buffered speech data.

A neuromorphic system includes an address translation device that translates an address corresponding to each of synaptic weights between presynaptic neurons and postsynaptic neurons to generate a translation address, and a plurality of synapse memories that store the synaptic weights based on the translation address. The translation address is generated such that at least two of synaptic weights corresponding to each of the postsynaptic neurons are stored in different synapse memories of the plurality of synapse memories and such that at least two of synaptic weights corresponding to each of the presynaptic neurons are stored in different synapse memories.

An apparatus including a processor to: within a kill container, in response to a set of error messages indicative of errors in executing multiple instances of a task routine to perform a task of a job flow with multiple data object blocks of a data object, and in response to the quantity of error messages reaching a threshold, output a kill tasks request message that identifies the job flow; within a task container, in response to the kill tasks request message, cease execution of the task routine and output a task cancelation message that identifies the task and the job flow; and within a performance container, in response to he task cancelation message, output a job cancelation message to cause the transmission of an indication of cancelation of the job flow, via a network, and to a requesting device that requested the performance of the job flow.

A method evaluates a data-based sensor model for determining a change-point time in a sensor signal time series. The method includes providing an evaluation signal time series within an evaluation time window of a sensor signal time series, and determining sensor signal extracts from the evaluation signal time series. The sensor signal extracts are (i) time-shifted with respect to one another, or (ii) respectively offset from one another by a number of sensing steps. The sensor signal extracts are shorter in length than the evaluation signal time series. The method further includes determining one or more frequency contributions from the sensor signal extracts using a fast Fourier transform (“FFT”) or a Goertzel algorithm, and evaluating the one or more frequency contributions in a trained data-based sensor model in order to determine a change-point time within the evaluation time window.

A method evaluates a data-based sensor model for determining a change-point time in a sensor signal time series. The method includes providing an evaluation signal time series within an evaluation time window of a sensor signal time series, and determining sensor signal extracts from the evaluation signal time series. The sensor signal extracts are (i) time-shifted with respect to one another, or (ii) respectively offset from one another by a number of sensing steps. The sensor signal extracts are shorter in length than the evaluation signal time series. The method further includes determining one or more frequency contributions from the sensor signal extracts using a fast Fourier transform (“FFT”) or a Goertzel algorithm, and evaluating the one or more frequency contributions in a trained data-based sensor model in order to determine a change-point time within the evaluation time window.

A method and system for generating training data for training a gesture detection machine-learning (ML) model includes receiving a request to generate training data for the gesture detection model, the training data being associated with a target gesture, retrieving data associated with an original gesture, the original gesture being a gesture made using a body part, retrieving skeleton data associated with the target gesture, the skeleton data displaying a skeleton representative of the body part and the skeleton displaying the target gesture, aligning a location of the body part in the data with a location of the skeleton in the skeleton data, providing the aligned data and the skeleton data to an ML model for generating a target data that displays the target gesture, receiving the target data as an output from the ML model, the target data preserving a visual feature of the data and displaying the target gesture, and providing the target data to the gesture detection ML model.