System(s) and method(s) that analyze image data associated with a computing screen operated by a user, and learns the image data (e.g., using pattern recognition, historical information analysis, user implicit and explicit training data, optical character recognition (OCR), video information, 360°/panoramic recordings, and so on) to concurrently glean information regarding multiple states of user interaction (e.g., analyzing data associated with multiple applications open on a desktop, mobile phone or tablet). A machine learning model is trained on analysis of graphical image data associated with screen display to determine or infer user intent. An input component receives image data regarding a screen display associated with user interaction with a computing device. An analysis component employs the model to determine or infer user intent based on the image data analysis; and an action component provisions services to the user as a function of the determined or inferred user intent. In an implementation, a gaming component gamifies interaction with the user in connection with explicitly training the model.

An artificial intelligence apparatus for recognizing a user includes a camera, and a process configured to receive, via the camera, image data including a recognition target object, generate recognition information corresponding to the recognition target object from the received image data, calculate a confidence level of the generated recognition information, determine whether the calculated confidence level is greater than a reference value, if the calculated confidence level is greater than the reference value, perform a control corresponding to the generated recognition information, and if the calculated confidence level is not greater than the reference value, provide a feedback for the object recognition.

Techniques are provided for deep neural network (DNN) identification of realistic synthetic images generated using a generative adversarial network (GAN). According to an embodiment, a system is described that can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise, a first extraction component that extracts a subset of synthetic images classified as non-real like as opposed to real-like, wherein the subset of synthetic images were generated using a GAN model. The computer executable components can further comprise a training component that employs the subset of synthetic images and real images to train a DNN network model to classify synthetic images generated using the GAN model as either real-like or non-real like

Systems for self-organizing data collection and storage in a manufacturing environment are disclosed. A system may include a data collector for handling a plurality of sensor inputs from sensors in the manufacturing system, wherein the plurality of sensor inputs is configured to sense at least one of: an operational mode, a fault mode, a maintenance mode, or a health status of at least one target system. The system may also include a self-organizing system for self-organizing a storage operation of the data, a data collection operation of the sensors, or a selection operation of the plurality of sensor inputs. The self-organizing system may organize a swarm of mobile data collectors to collect data from a plurality of target systems.

Personalized video interjections based on a learner model and a learning objective. A method for adding interjections to a video includes analyzing the content of a plurality of videos based on a set of learning objectives, selecting a video based on a learning objective, determining types of video interjections using an analytics engine that compares a learner model and the learning objective, determining a location for the video interjections using the analytics engine, generating a video interjection for each video interjection type and inserting the video interjections into the video at the determined locations.

An authentication engine, residing at one or more computing machines, receives, from a vision device comprising one or more cameras, a probe image. The authentication engine generates, using a trained facial classification neural engine, one or more first labels for a person depicted in the probe image and a probability for at least one of the one or more first labels. The authentication engine determines that the probability is within a predefined low accuracy range. The authentication engine generates, using a supporting engine, a second label for the person depicted in the probe image. The supporting engine operates independently of the trained facial classification neural engine. The authentication engine further trains the facial classification neural engine based on the second label.

An x-ray image laterality detection system is provided. The x-ray image laterality detection system includes a detection computing device. The processor of the computing device is programmed to execute a neural network model for analyzing x-ray images, wherein the neural network model is trained with training x-ray images as inputs and observed laterality classes associated with the training x-ray images as outputs. The process is also programmed to receive an unclassified x-ray image, analyze the unclassified x-ray image using the neural network model, and assign a laterality class to the unclassified x-ray image. If the assigned laterality class is not target laterality, the processor is programmed to adjust the unclassified x-ray image to derive a corrected x-ray image having the target laterality and output the corrected x-ray image. If the assigned laterality class is the target laterality, the processor is programmed to output the unclassified x-ray image.

Disclosed herein an artificial intelligence apparatus for determining inattention of a driver including a vibration sensor or a gyro sensor configured to sense movement of a driver's seat of a vehicle, a camera configured to receive image data including a face of a driver, a communication modem configured to receive vehicle status information from an ECU (Electronic Control Unit) of the vehicle, and a processor configured to generate movement information of the driver's seat using vibration sensor information received from the vibration sensor or gyro sensor information received from the gyro sensor, generate driver status information corresponding to the driver from the received image data, determine whether the driver is in an inattention status based on the movement information of the driver's seat, the driver status information and the vehicle status information, and output an inattention alarm if the driver is in the inattention status.

Aspects of the disclosure relate to determining a sign type of an unfamiliar sign. The system may include one or more processors. The one or more processors may be configured to receive an image and identify image data corresponding to a traffic sign in the image. The image data corresponding to the traffic sign may be input in a sign type model. The processors may determine that the sign type model was unable to identify a type of the traffic sign and determine one or more attributes of the traffic sign. The one or more attributes of the traffic sign may be compared to known attributes of other traffic signs and based on this comparison, a sign type of the traffic sign may be determined. The vehicle may be controlled in an autonomous driving mode based on the sign type of the traffic sign.

A system includes a sensor and a remote processing and storage component in communication with the sensor. The sensor generates sensor data indicative of the presence of people or objects in a scene. The remote processing and storage component receives the sensor data from the sensor and performs an automated counting process to determine an automated count of people or objects. The component receives a request to perform a validation process for the sensor. The component also generates and transmits a link to a manual validation page that includes an interface for performing a manual count. The component further receives an indication to start a validation process from the manual validation page, generates an automated count value from the performed automated counting process, and receives manual count data. The component also generates a validation report based on the automated count value and the manual count data.