A processor may receive data and generate a quantified representation of the data by processing the data using at least one machine learning (ML) algorithm, the quantified representation of the data indicating a sentiment of content of the data. The processor may automatically revise the content of the communications data. The revising may include determining a reaction to the content of the communications data, generating a quantified representation of the reaction, determining a difference between the quantified representation of the reaction and the quantified representation of the communications data, identifying, based on the difference, a portion of the content having an unintended sentiment, and replacing the portion of the content with different content.

A processor may receive data and generate a quantified representation of the data by processing the data using at least one machine learning (ML) algorithm, the quantified representation of the data indicating a sentiment of content of the data. The processor may automatically revise the content of the communications data. The revising may include determining a reaction to the content of the communications data, generating a quantified representation of the reaction, determining a difference between the quantified representation of the reaction and the quantified representation of the communications data, identifying, based on the difference, a portion of the content having an unintended sentiment, and replacing the portion of the content with different content.

An upper-level integrated processing device recognizes an inquiry, generates a search request in a primitive form to be output to each of the individual AI search devices in response to the inquiry, receives an individual reply and the probability thereof corresponding to the search request in the primitive form from a plurality of individual AI search devices, normalizes the probabilities of the individual replies from the individual AI search devices to acquire normalized probabilities, and generates an inquiry reply corresponding to the individual replies and the normalized probabilities thereof, and the lower-level individual AI search device searches Individual AI database using artificial intelligence in response to reception of the search request in the primitive form, acquires an individual reply and the probability thereof, and outputs the same in the primitive form to the integrated processing device. The upper-level devices output reply using a plurality of AI search devices in combination.

An upper-level integrated processing device recognizes an inquiry, generates a search request in a primitive form to be output to each of the individual AI search devices in response to the inquiry, receives an individual reply and the probability thereof corresponding to the search request in the primitive form from a plurality of individual AI search devices, normalizes the probabilities of the individual replies from the individual AI search devices to acquire normalized probabilities, and generates an inquiry reply corresponding to the individual replies and the normalized probabilities thereof, and the lower-level individual AI search device searches Individual AI database using artificial intelligence in response to reception of the search request in the primitive form, acquires an individual reply and the probability thereof, and outputs the same in the primitive form to the integrated processing device. The upper-level devices output reply using a plurality of AI search devices in combination.

A computing device is coupled to a display device, and includes a data monitoring software application program executing on a processor within a data monitoring system. Via the data monitoring software application program, various techniques are performed for generating user interfaces for data monitoring and event response. In a first technique, the data monitoring software application program displays a user interface that includes a first region including a data visualization and a second region including one or more images of a video stream. In a second technique, the data monitoring software application program generates a user interface associated with an event, receive an input corresponding to interaction with a user interface element in the user interface, and initiates an event channel associated with the event in response to the input.

A computing device is coupled to a display device, and includes a data monitoring software application program executing on a processor within a data monitoring system. Via the data monitoring software application program, various techniques are performed for generating user interfaces for data monitoring and event response. In a first technique, the data monitoring software application program displays a user interface that includes a first region including a data visualization and a second region including one or more images of a video stream. In a second technique, the data monitoring software application program generates a user interface associated with an event, receive an input corresponding to interaction with a user interface element in the user interface, and initiates an event channel associated with the event in response to the input.

Technologies are described for generating, acquiring, transferring, and manipulating sets of interaction representations, where an interaction representation represents user interaction with content on a computer device, typically using a software application. The set can be represented as an interaction representation. To facilitate set creation, including adding items to a set, a request can be sent to an application to provide an interaction representation, such as an interaction representation of a current state of user-content interaction associated with the software application. Sets can be associated with different types, where the set type can determine whether, and what types, of interaction representations can be added to a set. Sets can be associated with expiration events, where the interaction representation for the set, and in some cases the component interaction representations, can be deleted upon the occurrence of the expiration event. In some cases, a set can be designated not to expire.

Technologies are described for generating, acquiring, transferring, and manipulating sets of interaction representations, where an interaction representation represents user interaction with content on a computer device, typically using a software application. The set can be represented as an interaction representation. To facilitate set creation, including adding items to a set, a request can be sent to an application to provide an interaction representation, such as an interaction representation of a current state of user-content interaction associated with the software application. Sets can be associated with different types, where the set type can determine whether, and what types, of interaction representations can be added to a set. Sets can be associated with expiration events, where the interaction representation for the set, and in some cases the component interaction representations, can be deleted upon the occurrence of the expiration event. In some cases, a set can be designated not to expire.

Various embodiments, methods and systems for implementing a distributed computing system machine-learning training service are provided. Initially a machine learning model is accessed. A plurality of synthetic data assets are accessed, where a synthetic data asset is associated with asset-variation parameters that are programmable for machine-learning. The machine learning model is retrained using the plurality of synthetic data assets. The machine-learning training service is further configured for executing real-time calls to generate an on-the-fly-generated synthetic data asset such that the on-the-fly-generated synthetic data asset is rendered in real-time to preclude pre-rendering and storing the on-the-fly-generated synthetic data asset. The machine-learning training service further supports hybrid-based machine learning training, where the machine learning model is trained based on a combination of the plurality of synthetic data assets, a plurality of non-synthetic data assets, and synthetic data asset metadata associated with the plurality of synthetic data assets.

Various embodiments, methods and systems for implementing a distributed computing system machine-learning training service are provided. Initially a machine learning model is accessed. A plurality of synthetic data assets are accessed, where a synthetic data asset is associated with asset-variation parameters that are programmable for machine-learning. The machine learning model is retrained using the plurality of synthetic data assets. The machine-learning training service is further configured for executing real-time calls to generate an on-the-fly-generated synthetic data asset such that the on-the-fly-generated synthetic data asset is rendered in real-time to preclude pre-rendering and storing the on-the-fly-generated synthetic data asset. The machine-learning training service further supports hybrid-based machine learning training, where the machine learning model is trained based on a combination of the plurality of synthetic data assets, a plurality of non-synthetic data assets, and synthetic data asset metadata associated with the plurality of synthetic data assets.