Abstract: An example first device disclosed includes interface circuitry, machine readable instructions, and programmable circuitry to operate based on the machine readable instructions to update configuration data based on a telemetry pattern from a second device, the second device to satisfy a neighbor condition, generate telemetry data based on the configuration data, and update the first set of data based on feedback from a recipient of the telemetry data.

Abstract: In one embodiment, an apparatus comprises a communication interface and a processor. The communication interface is to communicate with a plurality of cameras. The processor is to obtain metadata associated with an initial state of an object, wherein the object is captured by a first camera in a first video stream at a first point in time, and wherein the metadata is obtained based on the first video stream. The processor is further to predict, based on the metadata, a future state of the object at a second point in time, and identify a second camera for capturing the object at the second point in time. The processor is further to configure the second camera to capture the object in a second video stream at the second point in time, wherein the second camera is configured to capture the object based on the future state of the object.

Abstract: In one embodiment, an apparatus comprises a communication interface and a processor. The communication interface is to communicate with a plurality of cameras. The processor is to obtain metadata associated with an initial state of an object, wherein the object is captured by a first camera in a first video stream at a first point in time, and wherein the metadata is obtained based on the first video stream. The processor is further to predict, based on the metadata, a future state of the object at a second point in time, and identify a second camera for capturing the object at the second point in time. The processor is further to configure the second camera to capture the object in a second video stream at the second point in time, wherein the second camera is configured to capture the object based on the future state of the object.

Abstract: Methods, apparatus, systems and articles of manufacture to provide machine assisted programming are disclosed. An example apparatus includes processor circuitry to execute computer readable instructions to: execute a machine learning model to generate a first code recommendation for programming code, the first code recommendation being associated with security of the programming code; cause output of the first code recommendation via a user interface; update the machine learning model based on feedback obtained via the user interface; determine a performance of the programming code; generate a second code recommendation, the second code recommendation being associated with the performance of the programming code; and cause output of the second code recommendation via the user interface.

Abstract: Methods, apparatus, systems and articles of manufacture to provide machine assisted programming are disclosed. An example apparatus includes a feature extractor to convert compiled code into a first feature vector; a first machine leaning model to identify a cluster of stored feature vectors corresponding to the first feature vector; and a second machine learning model to recommend a second algorithm corresponding to a second feature vector of the cluster based on a comparison of a parameter of a first algorithm corresponding to the first feature vector and the parameter of the second algorithm.

Abstract: Methods, apparatus, systems and articles of manufacture to provide machine assisted programming are disclosed. An example apparatus includes a feature extractor to convert compiled code into a first feature vector; a first machine leaning model to identify a cluster of stored feature vectors corresponding to the first feature vector; and a second machine learning model to recommend a second algorithm corresponding to a second feature vector of the cluster based on a comparison of a parameter of a first algorithm corresponding to the first feature vector and the parameter of the second algorithm.

Abstract: Apparatus, devices, systems, methods, and articles of manufacture for intent-based machine programming are disclosed. An example system categorize source code blocks includes a code repository accessor to access a code repository and select a source code block. The example system also includes a signature generator to generate a signature for the source code block, a collateral miner to extract collateral associated with the source code block, and a tokenizer to transform the source code block into tokens. In addition, the example system includes a function assessor to determine a function of the source code block based on the collateral and the tokens and an input/output determiner to determine an input and an output of the source code block based on the collateral and the signature. The example system further includes a tagger to categorize the source code block with the function, input, and output.

Abstract: In one embodiment, an apparatus comprises a communication interface and a processor. The communication interface is to communicate with a plurality of cameras. The processor is to obtain metadata associated with an initial state of an object, wherein the object is captured by a first camera in a first video stream at a first point in time, and wherein the metadata is obtained based on the first video stream. The processor is further to predict, based on the metadata, a future state of the object at a second point in time, and identify a second camera for capturing the object at the second point in time. The processor is further to configure the second camera to capture the object in a second video stream at the second point in time, wherein the second camera is configured to capture the object based on the future state of the object.

Abstract: Apparatus, devices, systems, methods, and articles of manufacture for intent-based machine programming are disclosed. An example system categorize source code blocks includes a code repository accessor to access a code repository and select a source code block. The example system also includes a signature generator to generate a signature for the source code block, a collateral miner to extract collateral associated with the source code block, and a tokenizer to transform the source code block into tokens. In addition, the example system includes a function assessor to determine a function of the source code block based on the collateral and the tokens and an input/output determiner to determine an input and an output of the source code block based on the collateral and the signature. The example system further includes a tagger to categorize the source code block with the function, input, and output.

Abstract: In one embodiment, an apparatus comprises a communication interface and a processor. The communication interface is to communicate with a plurality of cameras. The processor is to obtain metadata associated with an initial state of an object, wherein the object is captured by a first camera in a first video stream at a first point in time, and wherein the metadata is obtained based on the first video stream. The processor is further to predict, based on the metadata, a future state of the object at a second point in time, and identify a second camera for capturing the object at the second point in time. The processor is further to configure the second camera to capture the object in a second video stream at the second point in time, wherein the second camera is configured to capture the object based on the future state of the object.

Abstract: Methods, apparatus, systems and articles of manufacture to provide machine assisted programming are disclosed. An example apparatus includes a feature extractor to convert compiled code into a first feature vector; a first machine leaning model to identify a cluster of stored feature vectors corresponding to the first feature vector; and a second machine learning model to recommend a second algorithm corresponding to a second feature vector of the cluster based on a comparison of a parameter of a first algorithm corresponding to the first feature vector and the parameter of the second algorithm.

Abstract: Apparatus, devices, systems, methods, and articles of manufacture for intent-based machine programming are disclosed. An example system categorize source code blocks includes a code repository accessor to access a code repository and select a source code block. The example system also includes a signature generator to generate a signature for the source code block, a collateral miner to extract collateral associated with the source code block, and a tokenizer to transform the source code block into tokens. In addition, the example system includes a function assessor to determine a function of the source code block based on the collateral and the tokens and an input/output determiner to determine an input and an output of the source code block based on the collateral and the signature. The example system further includes a tagger to categorize the source code block with the function, input, and output.

Abstract: In one embodiment, an apparatus comprises a communication interface and a processor. The communication interface is to communicate with a plurality of cameras. The processor is to obtain metadata associated with an initial state of an object, wherein the object is captured by a first camera in a first video stream at a first point in time, and wherein the metadata is obtained based on the first video stream. The processor is further to predict, based on the metadata, a future state of the object at a second point in time, and identify a second camera for capturing the object at the second point in time. The processor is further to configure the second camera to capture the object in a second video stream at the second point in time, wherein the second camera is configured to capture the object based on the future state of the object.