The subject technology detects anomalies in media campaign configuration settings. The anomaly detection system may leverage one or more deep learning models to detect anomalies and identify particular configuration settings that contribute to the detected anomalies. In various embodiments, two or more of the deep learning models may be combined into an ensemble model that boosts the accuracy of anomaly predictions made by the anomaly detection system. The anomaly detection system may review the configuration settings of media campaigns during the configuration process and before the media campaigns run on a publication system in order to reduce the amount of unsuccessful campaigns and minimize the amount of wasted resources spent on running campaigns that have a low likelihood of achieving user defined goals.

Methods, apparatus, and processor-readable storage media for determining configurations to be used in system testing processes using machine learning techniques are provided herein. An example computer-implemented method includes obtaining, from multiple data sources, configuration information associated with at least one system; filtering out a subset of the configuration information based at least in part on at least one user request related to testing of at least a portion of the at least one system; determining at least a portion of the subset of the configuration information to be used in the testing of the at least a portion of the at least one system by processing the subset of the configuration information using one or more machine learning techniques; and performing one or more automated actions based on the determined at least a portion of the subset of the configuration information to be used in the testing.

A system and a method are disclosed for anomaly detection. In some embodiments, a method includes: converting a plurality of received time-domain signals to a plurality of latent vectors with an encoder neural network; converting the plurality of latent vectors to random vectors with a temporal neural network and sampling operation; converting the plurality of latent vectors to a connectivity representation with a spatial neural network; merging the latent vectors and the connectivity representation; and transmitting a determination of whether the received time-domain signals are normal, based on the latent vectors and the connectivity representation.

The following relates generally to diagnosing problems with websites. In some embodiments, a webpage interaction processor receives a list of potential user experience problems. The webpage interaction processor then extracts click data from the website, and processes the extracted click data into grams. Subsequently, an analytics engine is trained based on the processed click data. The trained analytics engine may then diagnose the problem of the website with a potential user experience problem from the received list of potential user experience problems. In some embodiments, the process is entirely automated.

Run-time logic built-in self-test (LBIST) may be performed, while ensuring operational continuity. The compute elements in a machine learning accelerator contain LBIST circuitry that performs logic testing of the functional circuitry in the compute element. The LBIST circuitry may be self-sufficient, meaning that it contains the data and instructions needed to run and evaluate these tests. An LBIST manager enables the logic testing during idle time of the functional circuitry between blocks of statically scheduled instructions. As a result, the LBIST circuitry can perform the logic tests without disrupting the computation of the machine learning network.

A computer-implemented method for determining a signature embedding vector for information technology events. The method including: receiving a current data object representing an occurrence of an information technology event associated with a configurable item; obtaining a graph database of logical associations of the configurable item; applying a clustering algorithm to the graph database to determine a subset of graphs; extracting associated data of the subset of graphs; applying an embedding model to the extracted associated data to determine an embedding vector for each subset of graphs; aggregating the embedding vector for each subset of graphs to determine a signature embedding vector; and utilizing the signature embedding vector to perform further analysis of the current data object.

The present invention describes system for facilitating testing of a System under Test (SUT). The system comprises a control unit configured to dynamically assign at least one hardware configuration to each of the one or more devices based on SUT information. Further, the control unit is configured to create at least one device classification group to include the one or more devices based on the assigned configuration, and generate a test template for each of the generated at least one device classification group based on the SUT information. Finally, the control unit is configured to perform testing, during real time operation, on the one or more devices associated with the SUT present within the at least one device classification group, based on the generated test template, test sequence, and the assigned hardware configuration to determine health status of the one or more devices.

A test and measurement system includes one or more test and measurement instruments at least one of which connects to a device under test (DUT), one or more memories, a generative artificial intelligence (AI) model connected to the one or more test and measurement instruments, and the one or more memories, and one or more processors to provide an artificial intelligence (AI) assistant as an interface to the generative AI model, present a user interface that allows a user to enter a prompt, use the AI assistant to translate the prompt into one or more queries for the generative AI model, send commands to the test and measurement instrument connected to the DUT to perform one or more tests on the DUT, take results from the one or more tests and convert them to user-interpretable results, and provide the user with results from the prompt at the user interface.

Methods, apparatus, and processor-readable storage media for determining hardware issues and recommending corresponding actions using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining input image data pertaining to at least one hardware item; identifying one or more portions of the at least one hardware item by processing at least a portion of the input image data using a first set of one or more artificial intelligence techniques; detecting, in the one or more identified portions, at least one defect of the at least one hardware item by processing the at least a portion of the input image data using a second set of one or more artificial intelligence techniques; generating at least one recommendation, associated with the at least one hardware item, in connection with the at least one detected defect; and performing one or more automated actions based on the at least one recommendation.