According to one implementation, a system for automating inferential content annotation includes a computing platform having a hardware processor and a system memory storing a software code including a set of rules trained to annotate content inferentially. The hardware processor executes the software code to utilize one or more feature analyzer(s) to apply labels to features detected in the content, access one or more knowledge base(s) to validate at least one of the applied labels, and to obtain, from the knowledge base(s), descriptive data linked to the validated label(s). The software code then infers, using the set of rules, one or more label(s) for the content based on the validated label(s) and the descriptive data, and outputs tags for annotating the content, where the tags include the validated label(s) and the inferred label(s).

A device, system, and method to enable the automatic search of personal profiles in the context of on-line dating that includes the ability to select personal profile images which a likelihood of being perceived as attractive to the person conducting the search. Additionally, by way of further non-limiting example, the device, system, and method is useful in applications such as automatically searching hundreds of actor or model headshots and selecting the ones the director/photographer will approve of for a particular photoshoot, film, or commercial.

A computer implemented service for identifying and classifying damage. The algorithm may be implemented on a device, such as a computer or mobile device, or on a remote server. The remote server may be a website or cloud-based platform. A user may access the service by sending a request to the remote server including an image, video, or live feed containing an item to be inspected. The service may identify and classify any damage found on the item. The output of the service may include the location of the damaged item, a determination of the presence of damage, a certainty level of this determination, and a heatmap indicating the areas of the image that are most likely to contain damage. The output of the service may be stored on a remote server or may be integrated into existing damage reporting systems.

Computer systems, methods and program products for automating pseudonymization of personal identifying information (PII) using machine learning, metadata, and crowdsourcing patterns to identify and replace PII. Machine learning models are trained for classifying known column names or key names for processing, using metadata. Column or key names are classified to be unprocessed, anonymized or pseudonymized by a pseudonymizer without revealing PII or scrubbing data into a useless format. A library of crowdsourced patterns are utilized for matching PII to data values within column or key names and PII is mapped to replacement methods. Feedback from user annotations retrains the algorithms to improve classification accuracy and Deep Learning algorithms automate the identification of PII using regular expression generation to concisely articulate how pseudonymizers search for PII patterns within a data set. PII replacement is mapped consistently across entire data packages and the crowdsourced pattern library is updated with generated regular expressions.

A system for confirming an advisory interaction with an artificial intelligence platform. The system includes a constitutional generator module configured to receive a first advisory input, retrieve an expert input, select a machine-learning process as a function of the expert input, and generate a therapeutic corrector. The system includes a constitutional advisory module configured to display a therapeutic corrector on a graphical user interface and receive a second advisory input. The system includes a best practices module the best practices module designed and configured to retrieve from an expert database a best practices training set, calculate an optimal vector output, generate an optimal vector output containing an expected therapeutic corrector implementation response, authenticate a second advisory input, and update the best practices module.

Systems and methods are provided for providing personalized content based on interest levels determined using machine learning. An online marketplace system determines an interest profile for a client user using profile data of the client user as input into an interest extraction model. The online marketplace system selects a content item for the client user based on the interest profile and causes presentation of the content item to the client user via a channel that is separate from the online marketplace system. In response to receiving a request to access the online marketplace system originated from the content item, the online marketplace system selects product recommendations based on interest profile and the content item. The online marketplace system generates a personalized landing webpage including the product recommendations and causes presentation of the personalized landing webpage to the client user.

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.

A method of ascertaining disparities in sensor data uses at least one neural network implemented in a controller of a vehicle. The method involves capturing (101) a learning data record from temporally successive raw sensor data, evaluating (102) the learning data record to train the neural network exclusively based on the learning data record of the captured raw sensor data, ascertaining (103) expected sensor data, comparing (104) the ascertained expected sensor data with sensor data currently captured by the sensor arrangement, and ascertaining (105) a disparity between the currently captured sensor data and the ascertained expected sensor data.

Embodiments of the present application provide a method and apparatus for updating an online self-learning event detection model. The method includes: presenting, when at least one target alerting event generated by the current event detection model is detected, to a user the at least one target alerting event, so that the user provides an event result for each target alerting event based on the at least one presented target alerting event; obtaining the event result for each target alerting event provided by the user based on the at least one presented target alerting event; determining whether a target alerting event for which an event result has been provided by the user satisfies a predetermined update condition, and if so, training and obtaining a target event detection model based on at least one target alerting event for which an event result has been provided by the user and corresponding event result, and predetermined training samples; and replacing the current event detection model with the target event detection model. By means of the method and apparatus according to the present application, the current event detection model may be continually updated, and thus improving the accuracy of the online learning.

Methods of determining, and using, a patterning process model that is a machine learning model. The process model is trained partially based on simulation or based on a non-machine learning model. The training data may include inputs obtained from a design layout, patterning process measurements, and image measurements.