Abstract: An artificial intelligence based chatbot development system discloses a method including receiving from a user a configuration file including chatbot framework information for configuring a framework for a chatbot for a team of end users, the user configuration file including at least one of a name of a team, one or more document sites related to the team, one or more incident identifications searched by the team, determining a plurality of data sources relevant to the team based on the chatbot framework information, downloading a plurality of document chunks from the data sources relevant to the team, processing the plurality of document chunks to generate metadata tags related to the document chunks, vectorizing the metadata tags to generate metadata embeddings for the plurality of document chunks, and in response to receiving a user query, using the metadata embeddings to select a collection of the document chunks that are passed to a language model (LM) with the user query.

Abstract: Various embodiments of systems and methods for an at-risk system identification via analysis of discussions from various online hacker communities are disclosed herein.

Abstract: Various embodiments of systems and methods for an at-risk system identification via analysis of discussions from various online hacker communities are disclosed herein.

Abstract: Various embodiments of systems and methods for an at-risk system identification via analysis of discussions from various online hacker communities are disclosed herein.

Abstract: Various embodiments of systems and methods for an at-risk system identification via analysis of discussions from various online hacker communities are disclosed herein.

Abstract: The present disclosure is directed toward systems and methods for ranking media effects. For example, systems and methods described herein enable a user to select and apply one or more media effects to a media communication such as a digital photograph or video. Systems and methods described herein identify and rank media effects based on characteristics associated with the user and the user's client-computing device. Furthermore, systems and methods described herein automatically present the top-ranked media effects to the user such that the user can easily select and apply one or more media effects that are tailored specifically to the user.

Abstract: The present disclosure is directed toward systems and methods for ranking media effects. For example, systems and methods described herein enable a user to select and apply one or more media effects to a media communication such as a digital photograph or video. Systems and methods described herein identify and rank media effects based on characteristics associated with the user and the user's client-computing device. Furthermore, systems and methods described herein automatically present the top-ranked media effects to the user such that the user can easily select and apply one or more media effects that are tailored specifically to the user.

Abstract: The present disclosure is directed toward systems and methods for ranking media effects. For example, systems and methods described herein enable a user to select and apply one or more media effects to a media communication such as a digital photograph or video. Systems and methods described herein identify and rank media effects based on characteristics associated with the user and the user's client-computing device. Furthermore, systems and methods described herein automatically present the top-ranked media effects to the user such that the user can easily select and apply one or more media effects that are tailored specifically to the user.

Abstract: The present disclosure is directed toward systems and methods for ranking media effects. For example, systems and methods described herein enable a user to select and apply one or more media effects to a media communication such as a digital photograph or video. Systems and methods described herein identify and rank media effects based on characteristics associated with the user and the user's client-computing device. Furthermore, systems and methods described herein automatically present the top-ranked media effects to the user such that the user can easily select and apply one or more media effects that are tailored specifically to the user.

Abstract: The present disclosure is directed toward systems and methods for ranking media effects. For example, systems and methods described herein enable a user to select and apply one or more media effects to a media communication such as a digital photograph or video. Systems and methods described herein identify and rank media effects based on characteristics associated with the user and the user's client-computing device. Furthermore, systems and methods described herein automatically present the top-ranked media effects to the user such that the user can easily select and apply one or more media effects that are tailored specifically to the user.

Abstract: Various embodiments of systems and methods for an at-risk system identification via analysis of discussions from various online hacker communities are disclosed herein.

Abstract: The present disclosure is directed toward systems and methods for ranking media effects. For example, systems and methods described herein enable a user to select and apply one or more media effects to a media communication such as a digital photograph or video. Systems and methods described herein identify and rank media effects based on characteristics associated with the user and the user's client-computing device. Furthermore, systems and methods described herein automatically present the top-ranked media effects to the user such that the user can easily select and apply one or more media effects that are tailored specifically to the user.

Abstract: The present disclosure is directed toward systems and methods for ranking media effects. For example, systems and methods described herein enable a user to select and apply one or more media effects to a media communication such as a digital photograph or video. Systems and methods described herein identify and rank media effects based on characteristics associated with the user and the user's client-computing device. Furthermore, systems and methods described herein automatically present the top-ranked media effects to the user such that the user can easily select and apply one or more media effects that are tailored specifically to the user.

Abstract: The present disclosure is directed toward systems and methods for ranking media effects. For example, systems and methods described herein enable a user to select and apply one or more media effects to a media communication such as a digital photograph or video. Systems and methods described herein identify and rank media effects based on characteristics associated with the user and the user's client-computing device. Furthermore, systems and methods described herein automatically present the top-ranked media effects to the user such that the user can easily select and apply one or more media effects that are tailored specifically to the user.

Abstract: An online system customizes video conversations between users of the online system by providing graphics that are likely to interest the users. The online system may present composite views overlaying selected graphics on a video stream, which may be part of an augmented reality (AR) environment. The graphics include, for example, background graphics, masks, props, visual or particular effects, frames or borders, etc. The online system may use a machine learning model to predict whether a user is likely to select or interact with a particular graphic. The online system can also filter or rank graphics according to user affinities or user connections on the online system. Users may be encouraged to interact with the graphics that are customized to their specific interests, which can promote an engaging video conversation or AR experience.

Abstract: The present disclosure is directed toward systems and methods for ranking media effects. For example, systems and methods described herein enable a user to select and apply one or more media effects to a media communication such as a digital photograph or video. Systems and methods described herein identify and rank media effects based on characteristics associated with the user and the user's client-computing device. Furthermore, systems and methods described herein automatically present the top-ranked media effects to the user such that the user can easily select and apply one or more media effects that are tailored specifically to the user.

Abstract: A client device receives from a user, in an application associated with a social networking service provided by a server system, a first user input for creating a first post on a social networking system. In response to the first user input, a plurality of icons for selection by the user are displayed. A selection of a first icon from the plurality of icons is received from the user. The first post including a first image associated with the user is posted to the social networking system. The first image includes the first icon overlaying at least a portion of a profile picture of the user.

Abstract: Systems, methods, and non-transitory computer readable media can obtain a media content item created by a first user that includes a media effect that can be applied to image data in a camera view. A plurality of content items including the media content item can be ranked. A ranking of the media content item can be adjusted. The media content item can be provided to a second user, based on the adjusted ranking of the media content item.

Abstract: An online system customizes video conversations between users of the online system by providing graphics that are likely to interest the users. The online system may present composite views overlaying selected graphics on a video stream, which may be part of an augmented reality (AR) environment. The graphics include, for example, background graphics, masks, props, visual or particular effects, frames or borders, etc. The online system may use a machine learning model to predict whether a user is likely to select or interact with a particular graphic. The online system can also filter or rank graphics according to user affinities or user connections on the online system. Users may be encouraged to interact with the graphics that are customized to their specific interests, which can promote an engaging video conversation or AR experience.

Abstract: An online system customizes video conversations between users of the online system by providing graphics that are likely to interest the users. The online system may present composite views overlaying selected graphics on a video stream, which may be part of an augmented reality (AR) environment. The graphics include, for example, background graphics, masks, props, visual or particular effects, frames or borders, etc. The online system may use a machine learning model to predict whether a user is likely to select or interact with a particular graphic. The online system can also filter or rank graphics according to user affinities or user connections on the online system. Users may be encouraged to interact with the graphics that are customized to their specific interests, which can promote an engaging video conversation or AR experience.