An inference apparatus comprises a plurality of PEs (Processing Elements) and a control part. The control part operates a convolution operation in a convolutional neural network using each of a plurality of pieces of input data and a weight group including a plurality of weights corresponding to each of the plurality of pieces of input data by controlling the plurality of PEs. Further, each of the plurality of PEs executes a computation including multiplication of a single piece of the input data by a single weight and also executes multiplication included in the convolution operation using an element with a non-zero value included in each of the plurality of pieces of input data.

A processing load in a case where a plurality of different sensors is used can be reduced. An information processing apparatus according to an embodiment includes: a recognition processing unit (15, 40b) configured to perform recognition processing for recognizing a target object by adding, to an output of a first sensor (23), region information that is generated according to object likelihood detected in a process of object recognition processing based on an output of a second sensor (21) different from the first sensor.

A processing load in a case where a plurality of different sensors is used can be reduced. An information processing apparatus according to an embodiment includes: a recognition processing unit (15, 40b) configured to perform recognition processing for recognizing a target object by adding, to an output of a first sensor (23), region information that is generated according to object likelihood detected in a process of object recognition processing based on an output of a second sensor (21) different from the first sensor.

Included are: an information acquiring unit to acquire information; a periphery recognizing unit to acquire peripheral environment information regarding a state of a peripheral environment based on the information acquired by the information acquiring unit and a first machine learning model and to acquire calculation process information indicating a calculation process when the peripheral environment information has been acquired; an explanatory information generating unit to generate explanatory information indicating information having a large influence on the peripheral environment information in the calculation process among the information acquired by the information acquiring unit based on the calculation process information acquired by the periphery recognizing unit; and an evaluation information generating unit to generate evaluation information indicating adequacy of the peripheral environment information acquired by the periphery recognizing unit based on the information acquired by the information acquiring unit and the explanatory information generated by the explanatory information generating unit.

Included are: an information acquiring unit to acquire information; a periphery recognizing unit to acquire peripheral environment information regarding a state of a peripheral environment based on the information acquired by the information acquiring unit and a first machine learning model and to acquire calculation process information indicating a calculation process when the peripheral environment information has been acquired; an explanatory information generating unit to generate explanatory information indicating information having a large influence on the peripheral environment information in the calculation process among the information acquired by the information acquiring unit based on the calculation process information acquired by the periphery recognizing unit; and an evaluation information generating unit to generate evaluation information indicating adequacy of the peripheral environment information acquired by the periphery recognizing unit based on the information acquired by the information acquiring unit and the explanatory information generated by the explanatory information generating unit.

An autonomous electric mower for mowing a lawn comprises a frame, drive wheels, cutting deck, computer, a Lidar sensor, at least one color and depth sensing camera. The computer is programmed and operable to: determine the location of the mower; detect obstacles; and to instruct the mower to avoid the obstacles. Advantageously, the system is operable to analyze the data from the multiple sensors and to instruct the mower to continue to safely operate and cut the lawn despite one or more of the sensors being obstructed. Novel route planning methods are also described.

An autonomous electric mower for mowing a lawn comprises a frame, drive wheels, cutting deck, computer, a Lidar sensor, at least one color and depth sensing camera. The computer is programmed and operable to: determine the location of the mower; detect obstacles; and to instruct the mower to avoid the obstacles. Advantageously, the system is operable to analyze the data from the multiple sensors and to instruct the mower to continue to safely operate and cut the lawn despite one or more of the sensors being obstructed. Novel route planning methods are also described.

The present invention provides a food and/or beverage item counting device to be provided in a food and/or beverage item provision system including a transport path that passes along a customer table to transport carriers each configured to allow a food and/or beverage item to be placed thereon, the food and/or beverage item counting device including: a first information acquiring unit disposed upstream of the table and configured to acquire information relating to each of the carriers on the transport path; a second information acquiring unit disposed downstream of the table and configured to acquire information relating to each of the carriers on the transport path; and a first calculating unit configured to calculate the number of the food and/or beverage items taken out from the transport path to the table, wherein each of the carriers is provided with identification information for identifying the carrier.

A method described herein includes receiving a digital image, wherein the digital image includes a first element that corresponds to a first domain and a second element that corresponds to a second domain. The method also includes automatically assigning a label to the first element in the digital image based at least in part upon a computed probability that the label corresponds to the first element, wherein the probability is computed through utilization of a first model that is configured to infer labels for elements in the first domain and a second model that is configured to infer labels for elements in the second domain. The first model receives data that identifies learned relationships between elements in the first domain and elements in the second domain, and the probability is computed by the first model based at least in part upon the learned relationships.

A method includes obtaining first training data having multiple first linguistic samples. The method also includes generating second training data using the first training data and multiple symmetries. The symmetries identify how to modify the first linguistic samples while maintaining structural invariants within the first linguistic samples, and the second training data has multiple second linguistic samples. The method further includes training a machine learning model using at least the second training data. At least some of the second linguistic samples in the second training data are selected during the training based on a likelihood of being misclassified by the machine learning model.