Abstract: In one implementation, a device receives a request to generate a set of video clips that depict a specified classification label. The device represents each of one or more objects depicted in a particular video clip over time as a set of timeseries of key points associated with that object. The device makes a determination as to whether the particular video clip depicts the specified classification label by analyzing the set of timeseries of key points associated with the particular video clip and in accordance with one or more constraint parameters. The device labels, based on the determination, the particular video clip with the specified classification label for inclusion in the set of video clips that depict the specified classification label.

Abstract: In one implementation, a device may classify a prompt from a user to a language model by a first prompt classifier and a second prompt classifier in parallel at inference time by the language model. The device may evaluate, based on a classification of the prompt by the first prompt classifier and a classification of the prompt by the second prompt classifier, an accuracy of the classification of the prompt by the first prompt classifier. The device may determine, based on the accuracy, whether feedback from the user on the classification of the prompt by the first prompt classifier is required. The device may cause, and responsive to a determination that the feedback is required, a solicitation for the feedback to be provided to the user.

Abstract: In one embodiment, a device receives a request for a machine learning model to make an inference about input data included in the request. The device retrieves metadata regarding training data used to train the machine learning model from a ledger associated with the machine learning model. The device assesses bias of the machine learning model by comparing the input data in the request to the metadata from the ledger. The device provides an indication of the bias of the machine learning model for display.

Abstract: According to one or more embodiments of the disclosure, a device obtains a data handling agreement associated with a subscription of a user to an online service. The device extracts data handling information for a type of data regarding the user from the data handling agreement by applying natural language processing to the data handling agreement. The device generates contextual data indicative of the type of data regarding the user being reported to the online service. The device provides the data handling information and the contextual data for presentation to the user.

Abstract: In one implementation, a device may identify a type of task and its context indicated by a prompt sent by a user for input to a language model. The device may authorize the prompt for input to the language model based on the type of task and its context. The device may make a determination as to whether a response of the language model to the prompt is authorized to be returned to the user based on the response, the type of task, and its context. The device may prevent the response from being returned to the user when the determination indicates that the response is not authorized to be returned.

Abstract: In one implementation, a method is disclosed comprising providing a prompt to a large language model to perform a task; determining a likelihood of an outcome of the task changing over a period of time; storing the prompt and the outcome in a cache when the likelihood indicates that the outcome of the task is unlikely to change over the period of time; and returning the outcome from the cache in response to a subsequent prompt asking the large language model to perform the task, in lieu of providing the subsequent prompt to the large language model.

Abstract: In one implementation, a device may identify features of a prompt to be sent to a language model that are indicative of a type of data that the prompt would cause the language model to access. The device may determine whether data control policies apply for the prompt based on the type of data that the prompt would cause the language model to access. The device may determine, based on the features, whether processing of the prompt by the language model violates an applicable data control policy. The device may prevent the language model from processing the prompt when the processing of the prompt violates the applicable data control policy.

Abstract: A method, computer system, and computer program product are provided for securely controlling playback of a recorded collaboration session. A plurality of video feeds is received from a plurality of client devices of users participating in a video meeting, wherein each video feed is encoded by each client device in an encoded format that distorts the video feed, wherein a unique key is used by each client device to encode the video feed. The plurality of video feeds are stored in the encoded format. A request is received to view a stored video feed, wherein the request includes the unique key used to encode the stored video feed. In response to determining that the requesting entity is authorized by a user of the stored video feed to view the stored video feed, the stored video feed is decoded and provided to the requesting entity.

Abstract: In one implementation, a device receives a request to generate a set of video clips that depict a specified classification label. The device represents each of one or more objects depicted in a particular video clip over time as a set of timeseries of key points associated with that object. The device makes a determination as to whether the particular video clip depicts the specified classification label by analyzing the set of timeseries of key points associated with the particular video clip and in accordance with one or more constraint parameters. The device labels, based on the determination, the particular video clip with the specified classification label for inclusion in the set of video clips that depict the specified classification label.

Abstract: A method, computer system, and computer program product are provided for securely controlling playback of a recorded collaboration session. A plurality of video feeds is received from a plurality of client devices of users participating in a video meeting, wherein each video feed is encoded by each client device in an encoded format that distorts the video feed, wherein a unique key is used by each client device to encode the video feed. The plurality of video feeds are stored in the encoded format. A request is received to view a stored video feed, wherein the request includes the unique key used to encode the stored video feed. In response to determining that the requesting entity is authorized by a user of the stored video feed to view the stored video feed, the stored video feed is decoded and provided to the requesting entity.

Abstract: According to one or more embodiments of the disclosure, a device obtains a data handling agreement associated with a subscription of a user to an online service. The device extracts data handling information for a type of data regarding the user from the data handling agreement by applying natural language processing to the data handling agreement. The device generates contextual data indicative of the type of data regarding the user being reported to the online service. The device provides the data handling information and the contextual data for presentation to the user.

Abstract: In one embodiment, a device receives a request for a machine learning model to make an inference about input data included in the request. The device retrieves metadata regarding training data used to train the machine learning model from a ledger associated with the machine learning model. The device assesses bias of the machine learning model by comparing the input data in the request to the metadata from the ledger. The device provides an indication of the bias of the machine learning model for display.

Abstract: Method for locating, in a fixed frame of reference (RG), a moving craft from the craft, comprising two characteristic points, the rolling and pitching of the craft in the fixed frame of reference having been determined, so as to determine the yawing and/or the coordinates of the craft in the fixed frame of reference: a/ a local frame of reference (Rn) comprising an axis deviating from the yaw axis of the fixed frame of reference by a known angle resulting from a rotation about a first axis of the first local frame of reference dependent on the roll (R) and a rotation dependent on the pitch (P) is defined; b/ the coordinates of each characteristic point (P1, P2) in said local frame of reference being equal, to within an unknown common factor, to the multiplying of a matrix defining said succession of rotations by a matrix of intrinsic image capture parameters and by the coordinates of a pixel corresponding to the characteristic point in an image captured from the craft, the values adopted by the quotients rela

Abstract: Method for locating, in a fixed frame of reference (RG), a moving craft from the craft, comprising two characteristic points, the rolling and pitching of the craft in the fixed frame of reference having been determined, so as to determine the yawing and/or the coordinates of the craft in the fixed frame of reference: a/ a local frame of reference (Rn) comprising an axis deviating from the yaw axis of the fixed frame of reference by a known angle resulting from a rotation about a first axis of the first local frame of reference dependent on the roll (R) and a rotation dependent on the pitch (P) is defined; b/ the coordinates of each characteristic point (P1, P2) in said local frame of reference being equal, to within an unknown common factor, to the multiplying of a matrix defining said succession of rotations by a matrix of intrinsic image capture parameters and by the coordinates of a pixel corresponding to the characteristic point in an image captured from the craft, the values adopted by the quotients rela