A variation testing system environment for simulating adaptive experiments of objects is disclosed. An experiment system conducts one or more simulations of an adaptive experiment that includes a plurality of variants of an object. Simulation results based on the one or more simulations are generated that are indicative of at least an estimated amount of time to conduct a real-world adaptive experiment based on the one or more simulations.

Generating a transformed dataset for use by a machine learning model in an artificial intelligence infrastructure that includes one or more storage systems and one or more graphical processing unit (GPU) servers, including: storing, within one or more storage systems, a transformed dataset generated by applying one or more transformations to a dataset that are identified based on one or more expected input formats of data received as input data by one or more machine learning models to be executed on one or more servers; and transmitting, from the one or more storage systems to the one or more servers without reapplying the one or more transformations on the dataset, the transformed dataset including data in the one or more expected formats of data to be received as input data by the one or more machine learning models.

A system and method for generating and optimizing marketing campaigns. More specifically, a campaign management system leverages a Large Language Model (LLM) to create multiple variations of an existing campaign tailored to specific target groups. The system employs a cluster-based approach and a click-through rate (CTR) prediction model to generate revised campaigns for targeted readers, thereby creating a feedback loop for further fine-tuning of the LLM for future campaigns.

Provided are computer-implemented methods and systems for generating an large language model (LLM)-based user interface for a user at a user device, including: providing, a memory comprising a database, the database comprising at least one historical content version and at least one criteria prompt; automatically transmitting, at a network device, a content collection request; receiving, at the network device based on the content collection request, a content collection response comprising an updated content version; generating, at a processor in communication with the memory and the network device, an LLM request comprising the at least one historical content version, the updated content version, and the at least one criteria prompt; transmitting, from the network device to an LLM system, the LLM request; receiving, at the network device from the LLM system, an LLM response; and generating, at the processor, a user interface for content analysis based on the LLM response.

One or more computing devices, systems, and/or methods for isolated budget utilization are provided. A first budget pacing component is assigned to control bidding by a first content serving component for a set of content items. A second budget pacing component is assigned to control bidding by a second content serving component for the set of content items. The first budget pacing component controls the bidding by the first content serving component according to a first portion of a content item budget based upon a traffic share of the first content serving component. The second budget pacing component controls the bidding by the second content serving component according to a second portion of the content item budget based upon a traffic share of the second content serving component.

Systems and methods for campaign data visualization. A system includes a campaign tool kit manager implemented on a server. The campaign toolkit manager includes a page generator configured to generate one or more pages visualizing campaign data, a donor manager configured to manage donor information relating to a campaign, and a pyramid builder configured to generate pyramid configuration data representative of allocated donor slots for a campaign. A computer-implemented method for managing campaign data visualization includes setting a campaign goal for the campaign, generating pyramid configuration data representative of allocated donor slots for the campaign based on initial campaign data, and generating a gift pyramid display page based on the pyramid configuration data for output to the campaign interface tool. A user can view or edit gift pyramid information shown in the gift pyramid display page.

Described herein are various embodiments for intelligent advertisement generation and distribution. An embodiment operates by determining website history information about a plurality of consumers and an ad history information about the plurality of consumers. The advertisement is provided for display on a computing device for each consumer of at least a subset of consumers of the plurality of consumers responsive to a first bid opportunity for the advertisement to be displayed. Website actions of the subset of consumers to whom the advertisement was provided for display are tracked. A predictive model that the bid opportunity lead to a conversion is generated based on the website history, ad history, and the tracking of website actions. A price to bid for a second bid opportunity is generated. The generated price to bid is submitted for the second bid opportunity to display the advertisement to a marketplace.

In some implementations, a computing device may determine, from multiple data sources, multiple event timelines, with each event timeline associated with a customer. Each event in an event timeline represents an interaction between the customer and a vendor of goods and/or services. For N (N>1) marketing campaigns, N augmented timelines may be created for each timeline by augmenting each event timeline with the individual marketing campaigns. Thus, for M (M>1) customers, MN augmented event timelines may be created. A trained machine learning model may perform an analysis of each augmented event timeline to predict results of executing each marketing campaign. The results may include total predicted revenue and total predicted cost resulting from executing each marketing campaign. A particular marketing campaign from the N marketing campaigns may be selected and execution of one or more marketing events may be initiated.

According to various embodiments described herein, mechanisms are provided for employing artificial intelligence (AI), such as in the form of large language models, to simulate responses to stimuli. A study may be created, a set of AI subjects having specified characteristics may be generated, and stimuli may be generated and presented to such AI subjects. Responses may be recorded and analyzed, and reports may be generated based on such analysis. The described system and method provide mechanisms for making research and/or product decisions based on observed responses by the AI subjects to stimuli.

Executing a machine learning model in an artificial intelligence infrastructure that includes one or more storage systems and one or more graphical processing unit (GPU) servers, including: receiving, by a graphical processing unit (GPU) server, a dataset transformed by a storage system that is external to the GPU server; and executing, by the GPU server, one or more machine learning algorithms using the transformed dataset as input.