A computer-implemented method for power grid anomaly detection using a convolutional neural network (CNN) trained to detect anomalies in electricity demand data and electricity supply data includes receiving (i) electricity demand data comprising time series measurements of consumption of electricity by a plurality of consumers, and (ii) electricity supply data comprising time series measurements of availability of electricity by one or more producers. An input matrix is generated that comprises the electricity demand data and the electricity supply data. The CNN is applied to the input matrix to yield a probability of anomaly in the electricity demand data and the electricity supply data. If the probability of anomaly is above a threshold value, an alert message is generated for one or more system operators.

Disclosed is an artificial intelligence (AI) server. The AI server includes a communication unit configured to communicate with an AI device; and an AI unit configured to receive feature data from the AI device, wherein the received feature data is generated by the AI device by obtaining sensing data and compressing the sensing data while preserving a feature of the sensing data; and input the received feature data to a deep learning model to obtain second sensing data for use in a recognition model related to an AI function of the AI device.

A learning device includes one or more processors. The processors calculate a likelihood of belonging to a plurality of estimated classes, of learning data, by using an estimation model for estimating to which of the estimated classes input data belongs. The processors calculate a weight of a loss function to be used in learning the estimation model such that, when a likelihood of a first class that is closer to correct data than other estimated classes among the estimated classes and likelihoods of a second class and a third class that are adjacent to the first class are applied to a function having a predetermined shape, a position that has an extreme value of the function corresponds to the correct data. The processors learn the estimation model by using the loss function.

Methods, apparatus, systems and articles of manufacture are disclosed to map workloads. An example apparatus includes a constraint definer to define performance characteristic targets of the neural network, an action determiner to apply a first resource configuration to candidate resources corresponding to the neural network, a reward determiner to calculate a results metric based on (a) resource performance metrics and (b) the performance characteristic targets, and a layer map generator to generate a resource mapping file, the mapping file including respective resource assignments for respective corresponding layers of the neural network, the resource assignments selected based on the results metric.

Entity record pairs are extracted from a selected cluster of entity records. Attribute value pairs are obtained from the entity record pairs. Labels are assigned to the attribute value pairs based at least in part on entity-level similarity scores of the entity records from which the attribute value pairs were obtained. A machine learning model is trained, using a data set which includes at least some attribute value pairs to which the labels are assigned, to generate attribute similarity scores for pairs of attribute values.

A quantum computer includes a quantum computing system; a transducer disposed inside the quantum computing system, the transducer being configured to receive an optical control propagating wave and output a microwave control propagating wave; and a quantum processor comprising a plurality of qubits, the plurality of qubits being disposed in the quantum computing system, each qubit of the plurality of qubits being configured to receive at least a portion of the microwave control propagating wave to control a quantum state of each qubit of the plurality of qubits.

A quantum computer includes a quantum computing system; a transducer disposed inside the quantum computing system, the transducer being configured to receive an optical control propagating wave and output a microwave control propagating wave; and a quantum processor comprising a plurality of qubits, the plurality of qubits being disposed in the quantum computing system, each qubit of the plurality of qubits being configured to receive at least a portion of the microwave control propagating wave to control a quantum state of each qubit of the plurality of qubits.

A method and apparatus comprising generating a dynamic personalized webpage is disclosed. At least two webpages are loaded in a fashion that is hidden from the user. Content from the at least two webpages is extracted based on classification “of interest” by an artificial intelligence algorithm. A dynamic personalized webpage comprising extracted content is then generated and displayed to the user. In the preferred embodiment, the user's dynamic personalized webpage will be filled with advertisements tailored to the user and the user would receive at least some revenue from advertisements.

An apparatus includes a plurality of first optical devices and a second optical device. Each first optical device includes a respective first pair of waveguides comprising a respective first waveguide and a respective second waveguide that are coupled together, a respective second pair of waveguides comprising a respective third waveguide and a respective fourth waveguide that are coupled together, and a first fusion gate that includes a detector. Each first fusion gate is configured to perform a fusion on the respective second waveguide and the respective third waveguide of a respective first optical device. The fusion produces a detection pattern for the respective first optical device. The apparatus further includes a multiplexer to select a respective first optical device of the plurality of first optical devices based at least in part on the detection pattern for the respective first optical device and output photons from the respective first optical device.

An apparatus includes a plurality of first optical devices and a second optical device. Each first optical device includes a respective first pair of waveguides comprising a respective first waveguide and a respective second waveguide that are coupled together, a respective second pair of waveguides comprising a respective third waveguide and a respective fourth waveguide that are coupled together, and a first fusion gate that includes a detector. Each first fusion gate is configured to perform a fusion on the respective second waveguide and the respective third waveguide of a respective first optical device. The fusion produces a detection pattern for the respective first optical device. The apparatus further includes a multiplexer to select a respective first optical device of the plurality of first optical devices based at least in part on the detection pattern for the respective first optical device and output photons from the respective first optical device.