C3.ai, Inc.

Techniques for machine learning-based degradation optimization are disclosed. In embodiments, a method includes identifying a power module, wherein the power module is controlled with a set of variables; determining, using functional relations of degradation of a degradation machine learning model, optimal set-point values that minimize degradation of the power module while utilizing minimal resources; and reducing a degradation rate of the power module by adjusting one or more of the variables that control the power module based on the determined optimal set-point values.

Examples include a method of identifying background information for a target product; identifying one or more potential comparison products based on the background information using a vector store which stores data using searchable embedding values, wherein a specification is associated with each of the one or more potential comparison products; and generating an assessment of the target product using a multimodal model, wherein the multimodal model determines similarities between the target product and one or more potential comparison products and passages from the specifications associated with each of the one or more potential comparison products based on the similarities.

The present disclosure provides systems and methods that may advantageously apply machine learning to accurately manage and predict inventory variables with future uncertainty. In an aspect, the present disclosure provides a system that can receive an inventory dataset comprising a plurality of inventory variables that indicate at least historical (i) inventory levels, (ii) inventory holding costs, (iii) supplier orders, or (iv) lead times over time. The plurality of inventory variables can be characterized by having one or more future uncertainty levels. The system can process the inventory dataset using a trained machine learning model to generate a prediction of the plurality inventory variables. The system can provide the processed inventory dataset to an optimization algorithm. The optimization algorithm can predict one or more inventory management parameters that result in a particular probability of achieving a target service level while minimizing a cost. The optimization algorithm can comprise constraint conditions.

Described herein are machine learning methods and systems for locating and tracking performance of latent themes in changing data from disparate sources. Themes may be indirect goals or consequential impacts indicated by latent topics. Identifying performance indicators of latent themes in large changing data sets uncovers underlying trends or previously concealed behaviors that may be accelerating or undermining goals.

A method includes obtaining data associated with operation of a monitored system. The method also includes using one or more first machine learning models to identify anomalies in the monitored system based on the obtained data, where each anomaly identifies an anomalous behavior. The method further includes using one or more second machine learning models to classify each of at least some of the identified anomalies into one of multiple classifications. Different ones of the classifications are associated with different types of cyberthreats to the monitored system, and the identified anomalies are classified based on risk scores determined using the one or more second machine learning models. In addition, the method includes identifying, for each of at least some of the anomalies, one or more actions to be performed in order to counteract the cyberthreat associated with the anomaly.

A method includes obtaining information associated with multiple collocation events. Each collocation event is associated with an occurrence where multiple entities were collocated with one another. The information associated with each collocation event identifies the entities associated with the collocation event, a duration of the collocation event, one or more locations associated with the collocation event, and a time associated with the collocation event. The method also includes identifying strengths of different relationships involving different ones of the entities based on the information. The strengths are determined using one or more features of the collocation events.

In some embodiments, an artificial intelligence vision system may be integrated with multiple enterprise software applications and the artificial intelligence vision system and method may transform the human-machine interface (HMI). The artificial intelligence vision system and method may use augmented reality, virtual reality, and/or mixed reality devices to create a new level of data interaction and collaboration for enterprise processes. Synchronization may be enabled between virtual environments presented via multiple computing devices and/or between virtual environments presented via different applications executing on the same user device. The artificial intelligence vision system and method may provide a seamless immersive and interactive experience for users by providing accurate multi-dimensional virtual environments mapped to physical environments. Enterprise insights may be generated and provided to users to enable the users to perform real-time or near real-time action regarding assets of an enterprise.

The present disclosure provides systems and methods that may advantageously apply machine learning to accurately manage and predict inventory variables with future uncertainty. In an aspect, the present disclosure provides a system that can receive an inventory dataset comprising a plurality of inventory variables that indicate at least historical (i) inventory levels, (ii) inventory holding costs, (iii) supplier orders, and/or (iv) lead times over time. The plurality of inventory variables can be characterized by having one or more future uncertainty levels. The system can process the inventory dataset using a trained machine learning model to generate a prediction of the plurality inventory variables. The system can provide the processed in inventory dataset to an optimization algorithm. The optimization algorithm can be used to predict a target inventory level for optimizing an inventory holding cost. The optimization algorithm can comprise one or more constraint conditions.

A method includes obtaining time-series data of at least one type and at least one textual description of the at least one type of time-series data and processing the time-series data and the at least one textual description using a foundation machine learning model. Processing the time-series data and the at least one textual description using the foundation machine learning model includes generating at least one embedding of the at least one textual description, combining the time-series data and the at least one embedding of the at least one textual description to generate combined data, and generating embedding vectors using the combined data.

Disclosed herein are system, method, and computer program product aspects for generating a data pipeline. A model prompt including a received natural language description and a prompt template is generated. The prompt template includes action labels and a processing example. Each action label indicates a respective data processing action, and the processing example includes a sample query and a sample answer comprising one or more sample action labels associated with a sample natural language description of a sample data pipeline. A multimodal model (MM) is queried with the model prompt. The MM response includes one or more action labels corresponding to the natural language description of the requested data pipeline in a format guided by the prompt template. A data pipeline project template can then be generated using one or more executable nodes corresponding to the action labels.