An embodiment generates an initial set of training data from monitoring data. The initial set of training data is generated by combining outputs from a plurality of pretrained classifiers. The embodiment trains a new classification model using the initial set of training data to identify anomalies in monitoring data. The embodiment performs a multiple-level clustering of the data samples resulting in a plurality of clusters of sub-clusters of data samples, and generates a review list of data samples by selecting a representative data sample from each of the clusters. The embodiment receives an updated data sample from the expert review that includes a revised target classification for at least one of the data samples of the expert review list. The embodiment then trains another replacement classification model using a revised set of training data that includes the updated data sample and associated revised target classification.

A computer-implemented method according to one embodiment includes causing an environment generator of a Generative Adversarial Network (GAN) to generate realistic training environments, and causing a first discriminator of the GAN to determine whether the realistic training environments are real or fake. In response to a determination that an accuracy of the first discriminator at determining whether the realistic training environments are real or fake is within a predetermined range, the environment generator is caused to generate a first realistic environment. The method further includes causing the first realistic environment to be shared with an agent of a reinforcement learning (RL) algorithm and a second discriminator, and receiving, from the agent of the RL algorithm and the second discriminator, feedback associated with the first realistic environment. The environment generator is caused to generate a second realistic environment based on the feedback associated with the first realistic environment.

The following relates generally to detecting and repairing data errors using artificial intelligence (AI). In some embodiments, one or more first processors execute an AI toolkit comprising a plurality of units. The plurality of units may include, for example, a connect unit, an integrate unit, a detect unit, a correct unit, a repair unit, and/or a visualize unit. The AI toolkit may then be deployed to one or more second processors. The one or more second processors may then further execute the AI toolkit and/or run/augment the AI toolkit to detect and/or repair errors in data.

Embodiments for automatic visualization and explanation of feature learning output for predictive modeling in a computing environment by a processor. A degree of importance score may be assigned to one or more features from a relational database according to the machine learning model. A visualization graph of one or more join paths and the one or more features with the degree of importance score to predict a target variable may be generated.

Presented are concepts for matching a subject to one or more resources or workflow steps. Once such concept comprises obtaining data associated with a subject, the data comprising, for each of a plurality of parameters, a parameter value relating to the subject. A plurality of data groups for characterising the subject is then generated and a classification process is applied to each data group so as to generate a classification result for each data group. The subject is then matched to one or more resources or workflow steps based on the classification results.

Disclosed are systems and methods for improving interactions with and between computers searching, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The methods and systems analyze digital message content in digital communication systems to automatically identify shared user interest(s), to automatically create computerized relationship matrix data identifying user connections, or relationships, using identified shared user interest(s), and to automatically provide a recommendation using the shared user interest and user relationships formed using the shared user interest.

A method of data preparation for artificial intelligence models includes receiving data characterizing a first plurality of images. The method further includes annotating a first subset of images of the first plurality of images based at least in part on a first user input to generate annotated first subset of images. The annotating includes labelling one or more features of the first subset of images. The method also includes generating, by a training code, an annotation code, the training code configured to receive the annotated first subset of images as input and output the annotation code. The training and the annotation code includes computer executable instructions. The method also includes annotating, by the annotation code, a second subset of images of the first plurality of images to generate annotated second subset of images, wherein the annotating includes labelling one or more features of the second subset of images.

Certain aspects of the present disclosure provide techniques for encoding rules defining a completeness of input, including receiving a first input comprising one or more tuples, wherein a tuple of the one or more tuples comprises one or more fields associated with an operation, one or more indicators, and one or more modifiers; receiving a second input associated with the one or more tuples; providing, to a knowledge engine, the first input and the second input; receiving, from the knowledge engine, a result based on the first input and the second input; determining, based on the result, a first symbol associated with a first tuple of the one or more tuples; and displaying the first symbol, wherein the first symbol indicates whether the first tuple is complete.

Various techniques are described for classifying synthetic data tasks and orchestrating a resource allocation between groups of eligible resources for processing the synthetic data tasks. Received synthetic data tasks can be classified by identifying a task category and a corresponding group of eligible resources (e.g., processors) for processing synthetic data tasks in the task category. For example, synthetic data tasks can include generation of source assets, ingestion of source assets, identification of variation parameters, variation of variation parameters, and creation of synthetic data. Certain categories of synthetic data tasks can be classified for processing with a particular group of eligible resources. For example, tasks to ingest synthetic data assets can be classified for processing on a CPU only, while a task to create synthetic data assets can be classified for processing on a GPU only. The synthetic data tasks can be queued and routed for processing by an eligible resource.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for semi-supervised training of an object recognition model. The methods, systems, and apparatus include a monitoring system including a camera located at a property and configured to generate images and one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform actions of determining a cluster of images meets a threshold for number of included images and a threshold for cluster tightness. A representative image of the cluster is selected and a query including the representative image of the cluster is provided. User feedback responsive to the query is received and an object recognition model is updated based on the user feedback.