Abstract: Methods, computer systems, computer storage media, and graphical user interfaces are provided for facilitating identification of audience insights based on persona representations using AI. In one implementation, a persona representation associated with a target audience is identified for a campaign asset. Thereafter, via one or more generative artificial intelligence (AI) models, an audience insight is determined in relation to the campaign asset based on the persona representation associated with the target audience. The audience insight may be displayed in relation to the campaign asset.

Abstract: learned from digital content items and their corresponding performance metrics. In accordance with some aspects, a training dataset is accessed that comprises training samples that each include a digital content item and a performance metric, and the multimodal generative model is trained using the training data. The training can include, for a training sample, using encoders of the multimodal generative model to generate a latent representation of a digital content item from the training sample and a latent representation of a performance metric from the training sample. The latent representations are merged to provide a combined latent representation, and decoders of the multimodal generative model decode the combined latent representation to provide an output digital content item and output performance metric. Losses are determined from the outputs and used to update parameters of the multimodal generative model.

Abstract: Some aspects relate to technologies for performance-guided content generation and exploration using a multimodal generative model with a joint latent space learned from digital content items and their corresponding performance metrics. In accordance with some aspects, input is received for content generation. The input includes a digital content item and is encoded by one or more encoders of the multimodal generative model into a latent representation in the joint latent space. A latent space transformation from the latent representation of the input is performed to provide a transformed latent representation, which is decoded by one or more decoders of the multimodal generative model to generate an output digital content item. In some aspects, the one or more decoders also decode the transformed latent representation to generate a predicted performance metric for the output digital content item.

Abstract: Methods, computer systems, computer storage media, and graphical user interfaces are provided for facilitating identification of relevant data using data embeddings. In one implementation, a query embedding representing a query is generated. Using the query embedding, a data embedding representing data in a hyperspace that is similar to the query embedding is identified. Thereafter, the data, represented by the data embedding identified to be similar to the query embedding, is identified as relevant to the query. Content may then be generated via one or more generative artificial intelligence (AI) models based on at least a portion of the query and the data identified as relevant to the query. Such content may be displayed via a graphical user interface.

Abstract: Methods, computer systems, computer storage media, and graphical user interfaces are provided for facilitating identification of relevant data using a set of hierarchical knowledge graphs. In one implementation, a data source relevant to a query is identified using a root knowledge graph. Thereafter, a data knowledge graph associated with the data source identified as relevant to the query is identified from among a plurality of data knowledge graphs. Such a data knowledge graph is used to identify a set of data relevant to the query. In embodiments, content may be generated, via one or more generative artificial intelligence (AI) models, based on at least a portion of the query and at least a portion of the set of data identified as relevant to the query. Such content may be provided for display via a graphical user interface.

Abstract: Artificial intelligence techniques for query management are described. A method comprises generating, by a context detection module, context information for a first query comprising natural language information to request a result from one of a plurality of machine learning models, modifying, by a query modification module, the first query based the context information to form a first modified query, determining, by an intent module, an intent type for the first modified query, selecting, by a routing module, a machine learning model from the plurality of machine learning models based on the intent type, and routing, by the routing module, the first modified query to the selected machine learning model. Other embodiments are described and claimed.

Abstract: In various examples, direct and indirect feedback is obtained and used to update a machine learning model. For example, feedback indicating interactions with the machine learning model are obtained from various entities. Continuing this example, the feedback is used to determine a set of scores associated with a particular response generated by the machine learning model. In various embodiments, the set of scores includes a response score, a multi-turn score, and a session score. Furthermore, the set of scores, in this examples, are combined to generate a single score associated with the response that is then used to update the machine learning model.

Abstract: Artificial intelligence techniques for query management are described. A method comprises generating, by a context detection module, context information for a first query comprising natural language information to request a result from one of a plurality of machine learning models, modifying, by a query modification module, the first query based the context information to form a first modified query, determining, by an intent module, an intent type for the first modified query, selecting, by a routing module, a machine learning model from the plurality of machine learning models based on the intent type, and routing, by the routing module, the first modified query to the selected machine learning model. Other embodiments are described and claimed.

Abstract: Techniques for data question answering with auxiliary recommendations are described to enable efficient querying of data sets for answers to data questions based on a natural language input. In an example, a processing device is operable to receive a natural language input including a query, determine an additional query based on a context of the query, and query a machine-learning model using the query and the additional query. The processing device is further operable to receive, from the machine-learning model, a result including a quantitative answer to the query, an additional answer based on the additional query, and an explanation by the machine-learning model of how the machine-learning model generated the quantitative answer or the additional answer in response to the querying. The processing device is operable to present the result for display in a user interface.

Abstract: Various disclosed embodiments are directed to controllable text generation that is optimized for natural language fluency and particular conditions, such as specific metrics. In other words, various embodiments generate text that is both fluent and predicted to meet particular metric scores. For example, various embodiments generate text that is not only concise and human-readable, but also is associated with particular user engagement metric scores, such as a high click rate or the like.

Abstract: A method, apparatus, non-transitory computer readable medium, and system for query disambiguation include obtaining a query including an ambiguous element, where the ambiguous element corresponds to an ambiguity category, and selecting a plurality of candidate elements by retrieving the plurality of candidate elements based on the ambiguity category and computing a distance between the ambiguous element and each of the plurality of candidate elements. Some embodiments include generating a plurality of modified queries based on the query by replacing the ambiguous element from the query with each of the plurality of candidate elements, respectively.

Abstract: Techniques for data question answering with auxiliary recommendations are described to enable efficient querying of data sets for answers to data questions based on a natural language input. In an example, a processing device is operable to receive a natural language input including a query, determine an additional query based on a context of the query, and query a machine-learning model using the query and the additional query. The processing device is further operable to receive, from the machine-learning model, a result including a quantitative answer to the query, an additional answer based on the additional query, and an explanation by the machine-learning model of how the machine-learning model generated the quantitative answer or the additional answer in response to the querying. The processing device is operable to present the result for display in a user interface.

Abstract: Techniques for data question answering with auxiliary recommendations are described to enable efficient querying of data sets for answers to data questions based on a natural language input. In an example, a processing device is operable to receive a natural language input including a query, determine an additional query based on a context of the query, and query a machine-learning model using the query and the additional query. The processing device is further operable to receive, from the machine-learning model, a result including a quantitative answer to the query, an additional answer based on the additional query, and an explanation by the machine-learning model of how the machine-learning model generated the quantitative answer or the additional answer in response to the querying. The processing device is operable to present the result for display in a user interface.

Abstract: Some aspects relate to technologies for neural network model adaptation and inference for multiple tasks via matrix sharing. In accordance with some aspects, a neural network model is accessed that has a pre-trained matrix at a layer of the neural network model. A shared matrix and a task matrix are added to the pre-trained matrix at the layer of the neural network model. The neural network model is trained for a plurality of tasks by updating the task matrix for each task to provide a trained task matrix for each task while maintaining the pre-trained matrix and the shared matrix the same for all tasks.

Abstract: Artificial intelligence techniques for query management are described. A method comprises generating, by a context detection module, context information for a first query comprising natural language information to request a result from one of a plurality of machine learning models, modifying, by a query modification module, the first query based the context information to form a first modified query, determining, by an intent module, an intent type for the first modified query, selecting, by a routing module, a machine learning model from the plurality of machine learning models based on the intent type, and routing, by the routing module, the first modified query to the selected machine learning model. Other embodiments are described and claimed.

Abstract: A system debiases image translation models to produce generated images that contain minority attributes. A balanced batch for a minority attribute is created by over-sampling images having the minority attribute from an image dataset. An image translation model is trained using images from the balanced batch by applying supervised contrastive loss to output of an encoder of the image translation model and an auxiliary classifier loss based on predicted attributes in images generated by a decoder of the image translation model. Once trained, the image translation model is used to generate images with the minority image when given an input image having the minority attribute.

Abstract: Techniques for video manipulation detection are described to detect one or more manipulations present in digital content such as a digital video. A detection system, for instance, receives a frame of a digital video that depicts at least one entity. Coordinates of the frame that correspond to a gaze location of the entity are determined, and the detection system determines whether the coordinates correspond to a portion of an object depicted in the frame to calculate a gaze confidence score. A manipulation score is generated that indicates whether the digital video has been manipulated based on the gaze confidence score. In some examples, the manipulation score is based on at least one additional confidence score.

Abstract: Some aspects relate to technologies for generating data filters from natural language queries and using the data filters to retrieve data from a structured dataset. In accordance with some aspects, a natural language query is received. A generative model generates an initial filter based on the natural language query, where the initial filter includes an initial attribute name and an initial attribute value. A valid attribute value corresponding to the initial attribute value is identified, where the valid attribute value comprises an attribute value in the structured dataset. Additionally, a valid attribute name corresponding to the initial attribute name is identified, where the valid attribute name comprises an attribute name in the structured dataset. A valid filter is generated using the valid attribute value and the valid attribute name, and data is retrieved from the structured dataset using the valid filter.

Abstract: Techniques for web bot detection using behavioral analysis and machine learning are disclosed. In an example method, a processing device receives an indication of a network interaction by a client agent, from which behaviors of the client agent can be determined. A heuristics module may classify the client agent as in an unknown class based on the behaviors of the client agent. A trained adversarial neural network may also classify the client agent as in the unknown class. The processing device then generates a graph representation of the network interaction. A trained graph convolutional neural network may classify the client agent as in a bot class using the graph representation. Based on the classification of the client agent as a bot, the processing device executes a command to cause a bot countermeasure and generates a notification including information about the behaviors of the client agent.

Abstract: Embodiments are disclosed for performing content authentication. A method of content authentication may include dividing a query video into a plurality of chunks. A feature vector may be generated, using a fingerprinting model, for each chunk from the plurality of chunks. Similar video chunks are identified from a trusted chunk database based on the feature vectors using a multi-chunk search policy. One or more original videos corresponding to the query video are then returned.