Systems and methods are provided for feature detection such that users can apply advanced machine learning and artificial intelligence (AI) without the need for a deep understanding of existing algorithms and techniques. Embodiments of the present disclosure provide systems and methods than enable easy access to a suite of machine learning algorithms and techniques, an intuitive interface for training an AI to recognize image features based on geometric correct and refine recursion, and real-time visualizations of training effectiveness.

Aspects of the disclosure relate to anomaly detection in cybersecurity training modules. A computing platform may receive information defining a training module. The computing platform may capture a plurality of screenshots corresponding to different permutations of the training module. The computing platform may input, into an auto-encoder, the plurality of screenshots corresponding to the different permutations of the training module, wherein inputting the plurality of screenshots corresponding to the different permutations of the training module causes the auto-encoder to output a reconstruction error value. The computing platform may execute an outlier detection algorithm on the reconstruction error value, which may cause the computing platform to identify an outlier permutation of the training module. The computing platform may generate a user interface comprising information identifying the outlier permutation of the training module. The computing platform may send the user interface to at least one user device.

According to one implementation, a system for automating content annotation includes a computing platform having a hardware processor and a system memory storing an automation training software code. The hardware processor executes the automation training software code to initially train a content annotation engine using labeled content, test the content annotation engine using a first test set of content obtained from a training database, and receive corrections to a first automatically annotated content set resulting from the test. The hardware processor further executes the automation training software code to further train the content annotation engine based on the corrections, determine one or more prioritization criteria for selecting a second test set of content for testing the content annotation engine based on the statistics relating to the first automatically annotated content, and select the second test set of content from the training database based on the prioritization criteria.

A method includes accessing a structured content item from a first database and event data from a second database, the event data including sets of event attributes in a multi-dimensional namespace and associated with a respective point in time; determining a relevancy profile characterizing a metric of relevancy of the structured content item over a respective time interval, the metric of relevancy including a distance in the multi-dimensional namespace between attributes associated with the structured content and the sets of event attributes; generating, using the relevancy profile, second digital content including a subset of the structured content item; and providing the second digital content for rendering on a device. Related apparatus, systems, techniques and articles are also described.

An artificial intelligence (AI) system that utilizes a machine learning algorithm, such as deep learning, etc. and an application of the AI system is provided. A method, performed by a server, of integrating and managing a plurality of databases (DBs) includes obtaining a plurality of knowledge graphs related to DBs generated from the plurality of DBs having different structures from one another, inputting the plurality of knowledge graphs related to DBs into a learning model related to DB for determining a correlation between data in the plurality of DBs, and obtaining a virtual integrated knowledge graph output from the learning model related to DB and including information about a correlation extracted from the plurality of knowledge graphs related to DBs.

Provided is a process, including: obtaining a first identifier of a first user for whom a first presentation including a first natural language text summary of data is to be provided; selecting a first domain from among a plurality of domains based on the first identifier; selecting a first set of fields among a plurality of fields of the data based on the first domain; determining a first set of exposure-control rules based on the first set of fields of data; determining a first applicable subset of the first set exposure-control rules by comparing criteria of the first set of exposure-control rules to user attributes associated with the first identifier; generating with a trained captioning model, the first natural language text summary in the first domain of the data compliant with exposure permissions of the first applicable subset of the first set of exposure-control rules.

Provided are an information processing apparatus, an information processing method, and a program capable of accumulating appropriate relearning data. An information processing apparatus includes an input unit that inputs input data to a learned model acquired in advance through machine learning using learning data, an acquisition unit that acquires output data output from the learned model through the input using the input unit, a reception unit that receives correction performed by a user for the output data acquired by the acquisition unit, and a storage controller that performs control for storing, as relearning data of the learned model, the input data and the output data that reflects the correction received by the reception unit in a storage unit in a case where a value indicating a correction amount acquired by performing the correction for the output data is equal to or greater than a threshold value.

A method includes receiving a first set of sensor data including data representing an object or an event in a monitored environment, receiving a second set of sensor data representing a corresponding time period as a time period represented by the first set of sensor data, inputting to a tutor classifier data representing the first set of data and including data representing the object or the event, generating a classification of the object or event in the tutor classifier, receiving the second set of sensor data at an apprentice classifier training process, receiving the classification generated in the tutor classifier at the apprentice classifier training process, and training the apprentice classifier in the apprentice classifier training process using the second set of sensor data as input and using the classification received from the tutor classifier as a ground-truth for the classification of the second set of sensor data.

An information processing apparatus includes a hardware processor which (i) performs learning by a learning data set associated with a correct answer label for a preset problem and creates a machine learning model for estimating a correct answer to the preset problem for input data, (ii) estimates the correct answer to the preset problem for the input data by using the machine learning model, (iii) in response to a user operation, determines a label indicating a result of the estimation as a correct answer label of the input data or corrects the label to determine the corrected label as a correct answer label of the input data, and (iv) additionally registers the determined correct answer label as learning data in association with the input data in the learning data set.

Techniques related to improved video coding based on face detection, region extraction, and tracking are discussed. Such techniques may include performing a facial search of a video frame to determine candidate face regions in the video frame, testing the candidate face regions based on skin tone information to determine valid and invalid face regions, rejecting invalid face regions, and encoding the video frame based on valid face regions to generate a coded bitstream.