Embodiments of the system, as described herein leverage artificial intelligence, machine-learning, and/or other complex, specific-use computer systems to provide a novel approach for identifying patterns in input data and determine and implement necessary changes to a regulated ML model within the bounds of a regulatory control structure. The system utilizes a collection of machine learning models, either individually or clustered, to process incoming data to determine if specific data should be flagged as irregular or part of the formation of an emerging pattern. The system may intelligently analyze such patterns to determine any regulatory implications that may arise from acting on or adapting to the perceived patterns.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for automatically adjusting strategies. One of the methods includes: determining one or more characteristics of a plurality of complaints, wherein each of the complaints corresponds to an order; classifying the plurality of complaints into a plurality of categories based on the one or more characteristics by using a trained classifier; selecting a category from the plurality of categories based on a number of complaints in the selected category; from a group of strategies each associated with one or more conditions and one or more actions, identifying a candidate strategy causing the complaints of the selected category, wherein the one or more actions are executed in response to the one or more conditions being satisfied; and optimizing the candidate strategy using a reinforcement learning model at least based on a plurality of historical orders.

An information processing apparatus includes a processor configured to extract a mark specified in advance from an image of a document; and acquire a character string by performing character recognition on a region located in a particular direction with respect to a position of the mark, the direction being associated in advance with the mark.

An imaging system and method uses grouping and elimination to label images of unknown items. The items may be stacked together with known or unknown items. The items may be packages, such as packages of beverage containers. A machine learning model may be used to infer skus of the packages. The machine learning model is trained on known skus but is not trained on unknown skus. Multiple images of the same unknown sku are grouped using the machine learning model. Elimination based upon lists of expected skus is used to label each group of unknown skus.

A method of predicting patient deterioration includes receiving an electronic health record data set of the patient, determining a risk score corresponding to the patient by analyzing the electronic health record data set of the patient using a trained machine learning model, determining a threshold value using an online/reinforcement learning model, comparing the risk score to the threshold value, and when the risk score exceeds the threshold value, generating an alarm. A non-transitory computer readable medium includes program instructions that when executed, cause the computer to receive a list of patients, display selectable patient information corresponding to each of the list of patients according to an ordering established by a feature importance algorithm, receive a selection, retrieve vital sign information corresponding to the selection, and display the vital sign information.

A system for adversarial targeting mitigation is provided, the system generally comprising identifying, using an artificial intelligence and machine learning model engine, a user targeting pattern employed by an entity based on interaction data between the entity and one or more users, based on the identified pattern of targeting, training the machine learning model to identify specific user profile data correlated with specific responses from the entity, identifying, using the machine learning model, a subset of one or more favorable responses from the specific responses, and triggering the one or more favorable responses by altering the user profile data for the one or more users prior to interaction with the specific entity.

In an example, an apparatus can include an array of variable resistance memory cells and a neural memory controller coupled to the array of variable resistance memory cells and configured to apply a sub-threshold voltage pulse to a variable resistance memory cell of the array to change a threshold voltage of the variable resistance memory cell in an analog fashion from a voltage associated with a reset state to effectuate a first synaptic weight change; and apply additional sub-threshold voltage pulses to the variable resistance memory cell to effectuate each subsequent synaptic weight change.

A system includes a training system configured to display first image data and a remote expert system configured to display second image data that corresponds to the first image data, receive feedback data associated with the second image data, and transmit a command to the training system based on the feedback data. The command is configured to modify the first image data presented via the training system.

The disclosure relates to a system and method of configuring and validating multi-vendor and multi-region Internet-of-Things (IoT) devices using reinforcement learning. In some embodiments, the method includes generating a matching table for each of a plurality of IoT sensors based on a plurality of sensor attributes extracted from a product data associated with an IoT sensor; acquiring an identification information and operational information associated with the IoT sensor and a set of neighboring IoT sensors for each of the plurality of IoT sensors; identifying an appropriate set of IoT sensors from the plurality of IoT sensors, based on a user requirement, the matching table, the identification information and the operational information, using a Reinforcement Learning (RL) model; and dynamically configuring each of the appropriate set of IoT sensors based on a vendor type.

The present disclosure provides systems and methods for providing augmented reality experiences. Consistent with disclosed embodiments, one or more machine-learning models can be trained to selectively process image data. A pre-processor can be configured to receive image data provided by a user device and trained to automatically determine whether to select and apply a preprocessing technique to the image data. A classifier can be trained to identify whether the image data received from the pre-processor includes a match to one of a plurality of triggers. A selection engine can be trained to select, based on a matched trigger and in response to the identification of the match, a processing engine. The processing engine can be configured to generate an output using the image data, and store the output or provide the output to the user device or a client system.