Methods and systems for implementing a traditional computer vision algorithm as a neural network. The method includes: receiving a definition of the traditional computer vision algorithm that identifies a sequence of one or more traditional computer vision algorithm operations; mapping each of the one or more traditional computer vision algorithm operations to a set of one or more neural network primitives that is mathematically equivalent to that traditional computer vision algorithm operation; linking the one or more network primitives mapped to each traditional computer vision algorithm operation according to the sequence to form a neural network representing the traditional computer vision algorithm; and configuring hardware logic capable of implementing a neural network to implement the neural network that represents the traditional computer vision algorithm.

An example embodiment includes a neural network unit to which a plurality of element values based on learning target data are input, and a learning unit that trains the neural network unit. The neural network unit has a plurality of learning cells each including a plurality of input nodes that perform predetermined weighting on each of the plurality of element values and an output node that sums the plurality of weighted element values and outputs the sum, and in accordance with an output value of each of the learning cells, the learning unit updates weighting coefficients of the plurality of input nodes of each of the learning cells or adds a new learning cell to the neural network unit.

A method for the patterning and control of single electrons on a surface is provided that includes implementing scanning tunneling microscopy hydrogen lithography with a scanning probe microscope to form charge structures with one or more confined charges; performing a series of field-free atomic force microscopy measurements on the charge structures with different tip heights, where interaction between the tip and the confined charge are elucidated; and adjusting tip heights to controllably position charges within the structures to write a given charge state. The present disclose also provides a Gibb's distribution machine formed with the method for the patterning and control of single electrons on a surface. A multi bit true random number generator and neural network learning hardware formed with the above described method are also provided.

The present disclosure relates to an automotive virtual personal assistant configured to provide intelligent support to a user, mindful of the user environment both in and out of a vehicle. Further, the automotive virtual personal assistant is configured to contextualize user-specific vehicle-based and cloud-based data to intimately interact with the user and predict future user actions. Vehicle-based data may include spoken natural language, visible and infrared camera video, as well as on-board sensors of the type commonly found in vehicles. Cloud-based data may include web searchable content and connectivity to personal user accounts, fully integrated to provide an attentive and predictive user experience. In contextualizing and communicating these data, the automotive virtual personal assistant provides improved safety and an enhanced user experience.

An object of the present invention is to achieve prediction of an indication, drug repositioning and/or drug repurposing for a drug with no known adverse events and/or side effects based on adverse events and/or side effects.

The problem is solved by a method for predicting an indication for a drug of interest or its equivalent substance, including inputting estimated adverse event-related information and/or estimated side effect-related information estimated from a set of data indicating the behavior of a biomarker in one or more organs collected from non-human animals to which the drug of interest or its equivalent substance has been administered as a test substance into an artificial intelligence model for prediction as test data to predict an indication for the drug of interest or its equivalent substance.

An electronic apparatus is provided. The electronic apparatus includes a camera, a processor and a memory configured to store at least one instruction executable by the processor where and the processor is configured to input audio data to an artificial intelligence model corresponding to user information, and obtain output audio data from the artificial intelligence model, and the artificial intelligence model is a model learned based on first learning audio data obtained by recording a sound source with a first recording device, second learning audio data obtained by recording the sound source with a second recording device, and information on a recording device for obtaining the second learning audio data, and the second learning audio data is binaural audio data.

This document relates to training and employing of quality estimation models to estimate the quality of different signal characteristics. One example includes a method or technique that can be performed on a computing device. The method or technique can include obtaining training signals exhibiting diverse impairments introduced when the training signals are captured or diverse artifacts introduced by different processing characteristics of a plurality of data enhancement models. The method or technique can also include obtaining quality labels for different signal characteristics of the training signals. The method or technique can also include training at least two different quality estimation models to estimate quality of at least two different signal characteristics based at least on the training signals and the quality labels.

A method for automatically training a neural network includes at a trainer having a first communication device and a perception recorder, continuously recording the surroundings in the vicinity of the first object; receiving, at the trainer, a message from a communication device associated with an object in the vicinity of the trainer, the message including information about the position and the type of the object; identifying a recording corresponding to the time at which the message is received from the object; correlating the received positional information about the second object with a corresponding location in the recording to identify the object in the recording; classifying the identified object based on the type of information received in the message from the object; and using the classified recording to train the neural network.

A tendency of an action of a robot may vary based on learning data used for training. The learning data may be generated by an agent performing an identical or similar task to a task of the robot. An apparatus and method for updating a policy for controlling an action of a robot may update the policy of the robot using a plurality of learning data sets generated by a plurality of heterogeneous agents, such that the robot may appropriately act even in an unpredicted environment.

Disclosed herein is a method implemented by a task mining engine. The task mining engine is stored as processor executable code on a memory. The processor executable code is executed by a processor that is communicatively coupled to the memory. The method includes receiving recorded tasks identifying user activity with respect to a computing environment and clustering the recorded user tasks into steps by processing and scoring each recorded user task. The method also includes extracting step sequences that identify similar combinations or repeated combinations of the steps to mimic the user activity.