A system for selecting an ameliorative output using artificial intelligence includes at least a server configured to receive at least a prognostic output. At least a server is configured to generate a plurality of ameliorative outputs as a function of at least a prognostic output wherein the plurality of ameliorative outputs include at least a short-term indicator and at least a long-term indicator. At least a server is configured to receive at least a user life element datum wherein the at least a user life element datum includes at least a user life quality response. At least a server is configured to generate a loss function of the plurality of short-term indicators and the plurality of long-term indicators using at least a user life element datum. At least a server is configured to select at least an ameliorative output from a plurality of ameliorative outputs to minimize the loss function.

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.

An apparatus for automatic target recognition with reinforcement learning is provided. The apparatus receives an image of a scene and performs an automatic target recognition on the image to detect objects in the image as candidate targets. The apparatus divides the candidate targets into subsets of candidate targets and performs a verification of the automatic target recognition to identify true targets in the image. In the verification, the apparatus solicits user input to manually identify some true targets in the image. The verification is performed according to a reinforcement learning process to minimize a total verification time.

Systems, methods and apparatus of drowsiness detection for vehicle control. For example, a vehicle includes: a camera configured to face a driver of the vehicle and generate a sequence of images of the driver driving the vehicle; an artificial neural network configured to analyze the sequence of images and classify, based on the sequence of images, whether the driver is in a drowsy state; and an infotainment system configured to provide instructions to the driver in response to a classification by the artificial neural network that the driver is in the drowsy state.

A system includes a memory module configured to store image data captured by a camera and an electronic controller communicatively coupled to the memory module. The electronic controller is configured to receive image data captured by the camera, implement a neural network trained to predict a drivable portion in the image data of an environment. The neural network predicts the drivable portion in the image data of the environment. The electronic controller is configured to implement a support vector machine. The support vector machine determines whether the predicted drivable portion of the environment output by the neural network is classified as drivable based on a hyperplane of the support vector machine and output an indication of the drivable portion of the environment.

An automated learning system of a content provider includes a database, an image processing unit, and a server. The database stores data related to visual marks, features of the visual marks, a set of discriminating instances, a position of a region of interest, and pre-defined threshold values. The image processing unit includes a detection module, a determination module, and a feature generation module. The detection module detects frames from a primary display device. The determination module extracts a static visual area, and determines a visual mark. The feature generation module generates discriminating features of the visual mark. The server maps the discriminating features with the stored data, identifies at least one closest visual mark, and transmits the updated visual mark and the discriminating features to secondary display devices.

In one example, a method performed by a processing system in a telecommunications network includes acquiring live footage of a event, acquiring sensor data related to the event, wherein the sensor data is collected by a sensor positioned in a location at which the event occurs, extracting an analytical statistic related to a target participating in the event, wherein the extracting is based on content analysis of the live footage and the sensor data, filtering data relating to the target based on the analytical statistic to identify content of interest in the data, wherein the data comprises the live footage, the sensor data, and data relating to historical events that are similar to the event, and generating computer-generated content to present the content of interest, wherein when the computer-generated content is synchronized with the live footage on an immersive display, an augmented reality media is produced.

An electronic device that specifies or determines information associated with an application is described. During operation, the electronic device may identify one or more objects of interest in an acquired image. Then, the electronic device may determine or specify classifications (such as names) for the one or more objects of interest. Moreover, the electronic device may analyze a context of the one or more objects of interest in order to determine one or more inspection criteria. Once the one or more inspection criteria are determined, the electronic device may receive publishing information (such as designated recipients) and privacy settings (such as is the application private or public). Next, the electronic device may generate the application using the specified or determined information, and may publish or provide the application to one or more other electronic devices associated with the designated recipients using the publishing information and the privacy settings.

One embodiment includes a computer-implemented data labeling platform. The platform provides a confidence-driven workflow (CDW) executable to receive and process labeling requests to label data items. The CDW comprises a set of executable labelers, each labeler in having a dynamically modeled confidence range. The execution path for processing a labeling request to label a data item is dynamically determined. Dynamically determining the execution path comprises dynamically determining a bounded number of candidate paths through the set of labelers using dynamically calculated cost and confidence metrics for the labelers in the set of labelers to estimate a probability of each candidate path to satisfy a set of constraints on cost and final result confidence, selecting a candidate path that minimizes cost for a specified confidence from the candidate paths as a selected path, executing a next labeler consultation according to the selected path to label the data item, and dynamically re-determining the remaining execution path using calculated results arising from executing the completed path steps.

An automatic content styling system receives digital content, an indication of a style, and an indication of a named entity category. The occurrences of the indicated named entity category in the digital content are identified using a trained machine learning system and the indicated style is automatically applied to the identified occurrences, resulting in styled digital content. User inputs to the styled digital content are also monitored and false positives (occurrences of the indicated named entity category that were not actually the named entity category) and false negatives (occurrences of the indicated named entity category that were not identified) are identified. These false positives and false negatives are used to further train the machine learning system.