Abstract: A modular neural network system comprising: a plurality of neural network modules; and a controller configured to select a combination of at least one of the neural network modules to construct a neural network, dedicated for a specific task.

Abstract: A modular neural network system comprising: a plurality of neural network modules; and a controller configured to select a combination of at least one of the neural network modules to construct a neural network, dedicated for a specific task.

Abstract: A modular neural network system comprising: a plurality of neural network modules; and a controller configured to select a combination of at least one of the neural network modules to construct a neural network, dedicated for a specific task.

Abstract: A modular neural network system comprising: a plurality of neural network modules: and a controller configured to select a combination of at least one of the neural network modules to construct a neural network, dedicated for a specific task.

Abstract: A modular neural network system comprising: a plurality of neural network modules; and a controller configured to select a combination of at least one of the neural network modules to construct a neural network, dedicated for a specific task.

Abstract: A modular neural network system comprising: a plurality of neural network modules; and a controller configured to select a combination of at least one of the neural network modules to construct a neural network, dedicated for a specific task.

Abstract: A robotic system (“new robot”) operative for performing at least one task in an environment, the system comprising: learn-from-predecessor functionality governed by a data exchange protocol, which controls short-range wireless knowledge transfer from a short-range wireless transmitter in a predecessor robot system (“old robot”) to a short-range wireless receiver in said robotic system, said knowledge comprising at least one environment-specific datum previously stored by the predecessor robot.

Abstract: A robotic system (“new robot”) operative for performing at least one task in an environment, the system comprising: learn-from-predecessor functionality governed by a data exchange protocol, which controls short-range wireless knowledge transfer from a short-range wireless transmitter in a predecessor robot system (“old robot”) to a short-range wireless receiver in said robotic system, said knowledge comprising at least one environment-specific datum previously stored by the predecessor robot.

Abstract: A method of controlling output of a neural network, the method including receiving or training the neural network; wherein the neural network is an application executed on a computer that receives input from sensors and provides an output comprising predictions and/or decisions based on the input, identifying a region of the neural network that contains information of interest, finding within the identified region a specific node or group of nodes that contains specific information of interest; and applying a manipulation application external to the neural network to operate on and alter the output of the specific node or group of nodes within the neural network; wherein the altered output of the specific node affects the output of the neural network without altering the input of the neural network.

Abstract: A modular neural network system comprising: a plurality of neural network modules; and a controller configured to select a combination of at least one of the neural network modules to construct a neural network, dedicated for a specific task.

Abstract: A system and method for implementation of attention mechanism in artificial neural networks, the method comprising: receiving sensor data from at least one sensor sensing properties of an environment, classifying the received data by a multi-regional neural network, wherein each region of the network is trained to classify sensor data with a different property of the environment, and wherein each region has an individually adjustable contribution to the classification, calculating based on the classification a current environment state including at least one property of the environment, and based on the at least one property, selecting corresponding regions of the network and adjusting contribution of the selected regions to the classification.

Abstract: A robotic system (“new robot”) operative for performing at least one task in an environment, the system comprising: learn-from-predecessor functionality governed by a data exchange protocol, which controls short-range wireless knowledge transfer from a short-range wireless transmitter in a predecessor robot system (“old robot”) to a short-range wireless receiver in said robotic system, said knowledge comprising at least one environment-specific datum previously stored by the predecessor robot.

Abstract: The present invention includes computer vision based driver assistance devices, systems, methods and associated computer executable code (hereinafter collectively referred to as: “ADAS”). According to some embodiments, an ADAS may include one or more fixed image/video sensors and one or more adjustable or otherwise movable image/video sensors, characterized by different dimensions of fields of view. According to some embodiments of the present invention, an ADAS may include improved image processing. According to some embodiments, an ADAS may also include one or more sensors adapted to monitor/sense an interior of the vehicle and/or the persons within. An ADAS may include one or more sensors adapted to detect parameters relating to the driver of the vehicle and processing circuitry adapted to assess mental conditions/alertness of the driver and directions of driver gaze. These may be used to modify ADAS operation/thresholds.

Abstract: The present invention includes computer vision based driver assistance devices, systems, methods and associated computer executable code (hereinafter collectively referred to as: “ADAS”). According to some embodiments, an ADAS may include one or more fixed image/video sensors and one or more adjustable or otherwise movable image/video sensors, characterized by different dimensions of fields of view. According to some embodiments of the present invention, an ADAS may include improved image processing. According to some embodiments, an ADAS may also include one or more sensors adapted to monitor/sense an interior of the vehicle and/or the persons within. An ADAS may include one or more sensors adapted to detect parameters relating to the driver of the vehicle and processing circuitry adapted to assess mental conditions/alertness of the driver and directions of driver gaze. These may be used to modify ADAS operation/thresholds.

Abstract: According to some embodiments of the present invention there may be provided one or more video surveillance units including: (i) video capturing equipment (e.g. a video camera), (ii) processing circuitry adapted to modify/sanitize video streams captured by the video capturing equipment to generate sanitized video streams devoid of privacy infringing data/images, and (iii) communication circuitry for transmitting sanitized video streams to one or more monitoring units for analysis. The monitoring units, in turn, may analyze the sanitized video streams to identify security events occurring within the area being captured by the surveillance units.