Predicting Categorical Information for New Content in Application Recommender Systems

Generally, the present disclosure is directed to predicting categorical information for newly created content in an application recommender system. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict categorical information of new applications in an applications marketplace based on screenshots of such new applications in the applications marketplace

Generating Icons for Applications in an Applications Marketplace

Generally, the present disclosure is directed to generating one or more icons for one or more applications in an applications marketplace. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to generate an icon for an application based on one or more icons for existing applications

Improving Advertisement Delivery in Video Streaming

Generally, the present disclosure is directed to improving advertisement delivery based on the content of a video. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict a non-intrusive location for an advertisement based on the content of a video

Automatically Scaling Multi-Tenant Machine Learning

Generally, the present disclosure is directed to optimizing use of computing resources in a system. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict task allocation for a job serving a plurality of machine-learned models based on current system state and queries per second (QPS) data for the plurality of models. Alternatively, the tasks can be allocated according to one or more rules (e.g., a new task is allocated to a job until the compute usage for the job falls below a scaling threshold). Thus, the systems and methods of the present disclosure are able to efficiently serve a mix of high-QPS and low-QPS machine-learned models at low latency with minimal waste of compute resources (e.g., CPU, GPU, TPU, etc.) and memory (e.g., RAM)

Determining High-Level Topical Annotations for a Conversation

Generally, the present disclosure is directed to annotating a conversation with high-level topical annotations. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict topical annotations for a conversation based on audio data from the conversation

Determining Optimal Dimming of Displays

Generally, the present disclosure is directed to dimming a display to reduce power consumption and/or improve security of a device. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predictively dim a display connected to a device based on usage data and/or sensor data available from the device and/or signals derived from the usage data and/or sensor data

EVALUATING ARTIFICIAL INTELLIGENCE METHODS FOR USE IN KILL CHAIN FUNCTIONS

Current naval operations require sailors to make time-critical and high-stakes decisions based on uncertain situational knowledge in dynamic operational environments. Recent tragic events have resulted in unnecessary casualties, and they represent the decision complexity involved in naval operations and specifically highlight challenges within the OODA loop (Observe, Orient, Decide, and Assess). Kill chain decisions involving the use of weapon systems are a particularly stressing category within the OODA loop—with unexpected threats that are difficult to identify with certainty, shortened decision reaction times, and lethal consequences. An effective kill chain requires the proper setup and employment of shipboard sensors; the identification and classification of unknown contacts; the analysis of contact intentions based on kinematics and intelligence; an awareness of the environment; and decision analysis and resource selection. This project explored the use of automation and artificial intelligence (AI) to improve naval kill chain decisions. The team studied naval kill chain functions and developed specific evaluation criteria for each function for determining the efficacy of specific AI methods. The team identified and studied AI methods and applied the evaluation criteria to map specific AI methods to specific kill chain functions.Civilian, Department of the NavyCivilian, Department of the NavyCivilian, Department of the NavyCaptain, United States Marine CorpsCivilian, Department of the NavyCivilian, Department of the NavyApproved for public release. Distribution is unlimited

Anticipatory Product Development Using Design Suggestions

Generally, the present disclosure is directed to determining an optimal solution for building and/or designing a multi-dimensional product. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict an optimal design solution for building and/or designing a product based on design characteristics and information relating to existing designs