Abstract: In one embodiments, one or more computer systems receive, from a client device of a user, a request to access content. The computer systems access a plurality of assets representing the content. The computer devices calculate, for each asset, using a deep-learning model, a probability of an interaction by the user upon providing the asset to the user, wherein the deep-learning model is based at least in part on one or more features associated with the user, the asset, or the content. The computer devices selects one of the assets based on the probability. The computer devices send, to the client device, the selected asset.

Abstract: Methods and apparatus for detecting abusive language are disclosed. In one embodiment, a set of character N-grams is ascertained for a set of text. Feature values for a plurality of features of the set of text are determined, based, at least in part, on the set of character N-grams. A computer-generated model is applied to the feature values for the plurality of features to generate a score for the set of text, where the model includes a plurality of weights, each of the weights corresponding to one of the features. It may then be determined whether the set of text includes abusive language based, at least in part, on the score.

Abstract: A method for using machine learning techniques to analyze past decisions made by administrators concerning account opening requests and to recommend whether an account opening request should be allowed or denied. Further, the machine learning techniques determine various other products that the customer may be interested in and prioritizes the choices of options that the machine learning algorithm determines appropriate for the customer.

Abstract: A controllable resistive element and method for updating the resistance of the same includes a state device configured to provide a voltage-controlled resistance responsive to a voltage input. A battery is configured to apply a voltage to the voltage input of the state device based on a charge stored in the battery. A write device is configured to charge the battery responsive to a write signal. An erase device is configured to discharge the battery responsive to an erase signal.

Abstract: A method for updating the resistance of a controllable resistance element includes determining an amount of resistance change for the controllable resistive element. A charge difference for a battery is determined corresponding to the resistance change for the controllable resistive element. The battery is charged or discharged to effect the resistance change in the controllable resistive element.

Abstract: The technology disclosed relates to an end-to-end neural network for question answering, referred to herein as “dynamic coattention network (DCN)”. Roughly described, the DCN includes an encoder neural network and a coattentive encoder that capture the interactions between a question and a document in a so-called “coattention encoding”. The DCN also includes a decoder neural network and highway maxout networks that process the coattention encoding to estimate start and end positions of a phrase in the document that responds to the question.

Abstract: Feature engineering can be performed on time series data making the data easy to manipulate and accessible to business users for analysis according to existing best practices. A computer system can, after receiving time series data related to a device, contextualize the time series data based on business data related to the device from, for example, an enterprise resource planning database. The contextualized data can be windowed by a selected feature based on execution data related to the device from, for example, a manufacturing execution system database. The windowed data can be transformed into summary data using a time series transformation. The summary data can be easily manipulated by, for example, generating genetic maps of the segmented and transformed data for clustering or searching for anomalies and patterns in response to user requests or automatically.

Abstract: At a network-accessible artificial intelligence service for generating content-based recommendations based on multi-perspective learned descriptors, text sections associated with a plurality of description perspectives, including a single-character perspective and a multi-character perspective, are extracted from various text sources. Using the text sections as input, a machine learning model which includes respective portions corresponding to the different perspectives is trained to reconstruct the input using intermediary descriptors learned from the input. An indication that a second text source is recommended with respect to a first text source is generated using a set of the learned descriptors and transmitted.

Abstract: A system including one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the one or more computers to implement a memory and memory-based neural network is described. The memory is configured to store a respective memory vector at each of a plurality of memory locations in the memory. The memory-based neural network is configured to: at each of a plurality of time steps: receive an input; determine an update to the memory, wherein determining the update comprising applying an attention mechanism over the memory vectors in the memory and the received input; update the memory using the determined update to the memory; and generate an output for the current time step using the updated memory.

Abstract: Technologies for using a shifted neural network include a compute device to determine a shift-based activation function of the shifted neural network. The shift-based activation function is a piecewise linear approximation of a transcendental activation function and is defined by a plurality of line segments such that a corresponding slope of each line segment is a power of two. The compute device further trains the shifted neural network based on shift-based input weights and the determined shift-based activation function.

Abstract: A safety system associated with a travel coordination system collects safety data describing safety incidents by providers and generates a plurality of safety incident prediction models using the safety data. The safety incident prediction models predict likelihoods that providers in the computerized travel coordination system will be involved in safety incidents. Two types of safety incidents predicted by the safety system include dangerous driving incidents and interpersonal conflict incidents. The safety system uses the plurality of safety incident prediction models to generate a set of predictions indicating probabilities that a given provider in the travel coordination system will be involved in a safety incident in the future. The safety system selects a safety intervention for the given provider responsive to the set of predictions and performs the selected safety intervention on the given provider.

Abstract: A method, system and computer program product for accelerating a deep neural network (DNN) in a field-programmable gate array (FPGA) are disclosed. The method includes receiving a DNN net file and weights, converting the received DNN net file to one or more source files, generating an executable FPGA bit file using the one or more source files, and downloading the executable FPGA bit file from the DNN conversion platform to the FPGA. Converting of the received DNN net file and the weights to the one or more source files can further include analyzing the DNN net file to identify a plurality of neural layers, decomposing one or more neural layers of the plurality of neural layers to one or more operation blocks, instantiating the one or more source files, based on the one or more operation blocks.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for improving operational efficiency within a data center by modeling data center performance and predicting power usage efficiency. An example method receives a state input characterizing a current state of a data center. For each data center setting slate, the state input and the data center setting slate are processed through an ensemble of machine learning models. Each machine learning model is configured to receive and process the state input and the data center setting slate to generate an efficiency score that characterizes a predicted resource efficiency of the data center if the data center settings defined by the data center setting slate are adopted t. The method selects, based on the efficiency scores for the data center setting slates, new values for the data center settings.

Abstract: Systems and methods associated with neural network verification are disclosed. One example method may be embodied on a non-transitory computer-readable medium storing computer-executable instructions. The instructions, when executed by a computer, may cause the computer to train a neural network with a training data set to perform a predefined task. The instructions may also cause the computer to train the neural network with a sentinel data set. The sentinel data set may cause the neural network to provide an identification signal in response to a predefined query set. The instructions may also cause the computer to verify whether a suspicious service operates an unauthorized copy of the neural network. The suspicious service may be verified by extracting the identification signal from responses the suspicious service provides to the predefined query set.

Abstract: An information handling system includes a memory that stores code, and a processor that executes code stored in memory to derive a distribution of impedances for parameters of a trace within a printed circuit board (PCB). The processor further to determine impedance corners of the distribution of impedances, to select the impedance corners as first, second, and third trace models, and to derive first, second, and third distribution of losses based on the first, second, and third trace models. The processor further to store loss corners of the first, second, and third distribution of losses as modeling points, and to determine whether all of modeling points pass within tolerance levels of loss and impedance of the trace.

Abstract: A method of identifying substances in a material handling work environment that analyzes sensor input from the environment with an image processor to extract features of the substance using optical flow and object recognition, and operates a vector modeler and a particle modeler to generate multiple predictions about particle movement within the substance based on existing data models for physical properties to generate multiple predictions of physical properties of the substance.

Abstract: Novel tools and techniques for the machine discovery of aberrant states are provided. A system includes a plurality of network devices, and a decision system in communication with the plurality of network devices. Each of the plurality of network devices may be configured to generate a respective data stream. The decision system may include a processor and a non-transitory computer readable medium including instructions executable by the processor to obtain, via the plurality of network devices, one or more data streams. The decision system may build a historic model of a data stream, and determine a predicted value of the data stream at a future time, based on the historic model. The decision system may be configured to determine whether an anomaly has occurred based on a variation between a current value of the data stream and the predicted value of the data stream.