Abstract: In one implementation, a device receives a natural language input for processing by a first agent executed by the device. The first agent generates, based on the natural language input, an output in a first embedding space using an artificial intelligence model. The first agent identifies a second agent for further processing of the output. The first agent provides the first agent, the output for further processing by a second agent.

Abstract: In one implementation, a device obtains metrics regarding software executed in a computer network. The device determines, based on the metrics, a probability of the software representing a potential vulnerability. The device generates an attack graph that represents the potential vulnerability along a particular attack path. The device provides the attack graph for analysis.

Abstract: In one implementation, a device obtains a prompt from a user that describes a particular type of network traffic by specifying one or more constraints. The device converts the prompt into a latent space representation. The device generates, based on the latent space representation, synthetic network traffic data for the particular type of network traffic using a diffusion model. The device provides the synthetic network traffic data.

Abstract: In one implementation, a method is disclosed comprising: associating, by a device in a service mesh, a security function with a portion of an online application that is executed in a distributed manner across the service mesh; executing, by the device, the security function and the portion of the online application within a trusted execution environment of the device to produce output data; generating, by the device, a cryptographic proof for the output data based on the security function; and providing, by the device, the output data and the cryptographic proof to a remote execution environment within the service mesh to establish a verifiable data lineage for the output data.

Abstract: In one implementation, a device maintains a set of processing groups of which the device is a member in a distributed machine learning system. The device performs a machine learning task with respect to a portion of a machine learning model distributed across the distributed machine learning system. The device receives an indication of a change in the distributed machine learning system. The device adjusts, based on the indication, the set of processing groups of which the device is a member in the distributed machine learning system.

Abstract: In one implementation, a device obtains a prompt from a user that describes a particular type of network traffic by specifying one or more constraints. The device converts the prompt into a latent space representation. The device generates, based on the latent space representation, synthetic network traffic data for the particular type of network traffic using a diffusion model. The device provides the synthetic network traffic data.

Abstract: In one implementation, a device in a local network receives, via a user interface, a prompt for input to a large language model that is external to the local network. The device sends the prompt to the large language model, wherein the large language model sends an intermediate embedding as a response to the prompt for input to one or more model layers split from the large language model that is hosted in the local network. The device receives an answer to the prompt from the one or more model layers hosted in the local network. The device provides the answer to the user interface for presentation to a user.

Abstract: In one implementation, a device obtains one or more terms of interest. The device also obtains one or more bias terms. The device selects a generative model configured to generate an output given a textual prompt. The device generates a debiased model by pruning neuron connections in a text encoder of the generative model associated with the one or more terms of interest and the one or more bias terms.

Abstract: In one implementation, a device extracts, using an embedding model, one or more ideas from a particular document. The device determines a measure of similarity between the one or more ideas from the particular document and those of each of a body of existing documents, to identify a set of one or more similar documents. The device generates an access control list for the particular document, based on one or more access control lists associated with the set of one or more similar documents. The device restricts access to the particular document according to the access control list for the particular document.

Abstract: In one implementation, a device determines a set of overfitted prompts for each of a set of samples. The device trains a diffusion model to generate a set of diffusion prompts for each of the set of samples based on the set of overfitted prompts and features of each of the set of samples. The device generates a particular diffusion prompt using the diffusion model for an input sample for a vision-language model. The device inputs the particular diffusion prompt in conjunction with the input sample to the vision-language model to perform a downstream task.

Abstract: In one embodiment, a sidecar proxy executed by a device extracts label information from input data for input to a microservice associated with the sidecar proxy indicative of a lineage of the input data. The sidecar proxy makes, based on the label information, a determination as to whether the microservice processing the input data would violate a data governance policy. The sidecar proxy provides, based on the determination, the input data to the microservice. The sidecar proxy tags output data generated by the microservice with appended label information that includes the label information extracted from the input data and an indication of a data transformation performed by the microservice to the input data to form the output data.

Abstract: In one implementation, a device receives a superclass and an image specified via a user interface. The device identifies subclasses of the superclass using a language model. The device generates, for each of the subclasses, subclass image masks for the image. The device forms an ensemble segmentation mask for the image based on the subclass image masks that represents the superclass.

Abstract: In one implementation, a device receives, via a user interface, one or more parameters regarding formation of an embedding of an image. The device forms, in accordance with the one or more parameters, an embedding of the image by inputting it to a machine learning-based encoder model that was trained to maximize a measure of data utility of its output embeddings. The device provides the embedding for use to train an analytics model. The device causes the analytics model to be used to make inferences about embeddings derived from images captured by one or more cameras.

Abstract: In one embodiment, a device executing a first portion of a distributed application extracts label information from sensor data sent to the device by a sensor that indicates the sensor as a source of the sensor data and one or more data governance policies applicable to the sensor data. The device performs, based on the label information, a first data transformation of the distributed application on the sensor data using stored data, to form transformed data. The device forms combined label information for the transformed data by appending the label information with additional label information associated with the stored data and adding an indication of the first data transformation. The device provides the transformed data and combined label information to a remote device executing another portion of the distributed application.

Abstract: In one embodiment, a sidecar proxy executed by a device extracts label information from input data for input to a microservice associated with the sidecar proxy indicative of a lineage of the input data. The sidecar proxy makes, based on the label information, a determination as to whether the microservice processing the input data would violate a data governance policy. The sidecar proxy provides, based on the determination, the input data to the microservice. The sidecar proxy tags output data generated by the microservice with appended label information that includes the label information extracted from the input data and an indication of a data transformation performed by the microservice to the input data to form the output data.

Abstract: In one implementation, a method is disclosed comprising: associating, by a device in a service mesh, a security function with a portion of an online application that is executed in a distributed manner across the service mesh; executing, by the device, the security function and the portion of the online application within a trusted execution environment of the device to produce output data; generating, by the device, a cryptographic proof for the output data based on the security function; and providing, by the device, the output data and the cryptographic proof to a remote execution environment within the service mesh to establish a verifiable data lineage for the output data.

Abstract: In one implementation, a device intercepts return data for an application programming interface call to be sent to a requester via a network. The device converts the return data into an embedding. The device determines a similarity between the embedding and one or more embeddings in a database that were generated from one or more documents deemed sensitive. The device blocks, based on the similarity, the return data from being sent via the network to the requester.

Abstract: In one implementation, a device receives, via a user interface, one or more constraint parameters for each of a plurality of machine learning models that perform different analytics tasks. The device computes, based on the one or more constraint parameters, a set of weights for the plurality of machine learning models. The device generates a unified model by performing knowledge distillation on the plurality of machine learning models using the set of weights. The device deploys the unified model for execution by a particular node in a network.

Abstract: Methods and apparatuses for nonlinear scaling of video images. To match the aspect ratios of a video image and the target display area, at least one embodiment of the present invention scales the video image according to one or more nonlinear functions along the horizontal direction and/or the vertical direction. In one embodiment, the nonlinear functions are such that the original aspect ratio of the video image is preserved near the center region (or strip) of the image and the image is gradually stretched (or compressed) as it is mapped to the edges. In one example, the scaling is implemented by the texture mapping functionality of OpenGL using graphics hardware. In one embodiment of the present invention, the nonlinear mapping is constructed according to a polynomial mapping; and, the coefficients of the polynomial are adjustable by a user to trade off distortion between the image center and the image edges, giving the user control over the location and the amount of distortion.