Abstract: The present application is at least directed to a system including a processor and non-transitory memory including computer-executable instructions, which when executed by the processor, perform receiving, via a user operating the system, a selection of a mode for recognizing objects. The processor is also configured to execute the instructions of causing a camera operably coupled to the system to operate in the selected mode for recognizing objects. The processor is further configured to execute the instructions of receiving, via the user operating the system, an image of a selected object. The processor is even further configured to execute the instructions of evaluating, via a trained machine learning model, one or more attributes of the selected object. The processor is yet further configured to execute the instructions of generating an image description based on at least a subset of the evaluated one or more attributes.

Abstract: In one embodiment, a method includes identifying a plurality of dyslexic users on an online social network. The plurality of dyslexic users may be identified based on content objects posted by these users over a particular time period, where the content objects may include one or more of word-level errors or sentence-level errors. A machine-learning model may be trained for text correction using a corpus of social network data, which may include at least the content objects with one or more of word-level errors or sentence-level errors, and a corresponding set of corrected content objects. A text string including one or more errors may be received from a client system associated with a first user. The text string may be transformed into a vector representation using an encoder of the machine-learning model. A corrected text string may be generated from the vector representation using a decoder of the machine-learning model.

Abstract: Systems, methods, and non-transitory computer-readable media can receive an image. One or more concepts depicted in the image are identified based on machine learning techniques. The one or more concepts are filtered based on filtering criteria to identify one or more selected concepts. An image description is generated comprising the one or more selected concepts.

Abstract: In one embodiment, a method includes identifying a plurality of dyslexic users on an online social network. The plurality of dyslexic users may be identified based on content objects posted by these users over a particular time period, where the content objects may include one or more of word-level errors or sentence-level errors. A machine-learning model may be trained for text correction using a corpus of social network data, which may include at least the content objects with one or more of word-level errors or sentence-level errors, and a corresponding set of corrected content objects. A text string including one or more errors may be received from a client system associated with a first user. The text string may be transformed into a vector representation using an encoder of the machine-learning model. A corrected text string may be generated from the vector representation using a decoder of the machine-learning model.

Abstract: In one embodiment, a method includes capturing one or more images of a physical environment surrounding the first user; determining that one or more of the images comprises a face; generating a facial image by cropping a region of the image comprising the face; sending instructions to perform facial recognition on the facial image; receiving a name associated with a second user of the online social network, wherein the face is associated with the second user, wherein the first user is represented by a first node on a social graph maintained by the social-networking system and the second user is represented by a second node on the social graph, and the first node and the second node are connected by at least one edge; and providing an audible indication of the name associated with the second user.

Abstract: Systems, methods, and non-transitory computer-readable media can receive an image. One or more concepts depicted in the image are identified based on machine learning techniques. The one or more concepts are filtered based on filtering criteria to identify one or more selected concepts. An image description is generated comprising the one or more selected concepts.

Abstract: In one embodiment, a method includes capturing one or more images of a physical environment surrounding the first user; determining that one or more of the images comprises a face; generating a facial image by cropping a region of the image comprising the face; sending instructions to perform facial recognition on the facial image; receiving a name associated with a second user of the online social network, wherein the face is associated with the second user, wherein the first user is represented by a first node on a social graph maintained by the social-networking system and the second user is represented by a second node on the social graph, and the first node and the second node are connected by at least one edge; and providing an audible indication of the name associated with the second user.

Abstract: Systems, methods, and non-transitory computer-readable media can receive a plurality of comments to a posted content item. Each of the plurality of comments is associated with at least one category of a plurality of categories based on a machine learning model. A first comment of the plurality of comments is selected for inclusion in a comment sample to be presented in a graphical user interface based on the first comment being associated with a first category of the plurality of categories. A second comment of the plurality of comments is selected for inclusion in the comment sample based on the second comment being associated with a second category of the plurality of categories.

Abstract: Methods and systems for generating influence scores are disclosed. A plurality of opinions of a selected topic are retrieved from a database. Influencers each associated with at least one opinion from the plurality of opinions are determined using the processor. A score is automatically generated for each influencer using the processor, the score related to the selected topic.

Abstract: Methods and systems for generating influence scores are disclosed. A plurality of opinions of a selected topic are retrieved from a database. Influencers each associated with at least one opinion from the plurality of opinions are determined using the processor. A score is automatically generated for each influencer using the processor, the score related to the selected topic.

Abstract: Methods and systems for generating influence scores are disclosed. A plurality of opinions of a selected topic are retrieved from a database. Influencers each associated with at least one opinion from the plurality of opinions are determined using the processor. A score is automatically generated for each influencer using the processor, the score related to the selected topic.