Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed for data usage monitoring to identify and mitigate ethical divergence. Disclosed example apparatus are to orchestrate resources in an edge environment based on ingested network traffic on an edge network, the ingested network traffic associated with a source node that is to source a target data stream and a target artificial intelligence (AI) application node that is to consume at least a portion of the target data stream. Disclosed example apparatus are also to execute a machine learning model based on the ingested network traffic to generate one or more outputs including at least one a first value representative of a data stream characteristic or a second value representative of an AI application node characteristic, determine the one or more outputs satisfy a threshold value, and generate an alert in response to the one or more outputs satisfying the threshold value.

Abstract: In one embodiment, a computing device includes interface circuitry and processing circuitry. The processing circuitry receives, via the interface circuitry, a data stream captured at least partially by sensor(s), which contains feature values corresponding to an unlabeled instance of a feature set. The processing circuitry then groups the data stream into a data stream group, which is assigned from a set of data stream groups based on the feature values in the data stream. The processing circuitry then selects a predictive model for the data stream group from a set of predictive models, which are each trained to predict a target variable for a corresponding data stream group. The processing circuitry then predicts the target variable for the data stream using the predictive model, which infers the target variable based on the set of feature values in the data stream.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed for automatically updating artificial intelligence models operating on data of a first factory production line, the apparatus comprising, an intelligent trigger circuitry to trigger an automated model update process, an automated model search circuitry to, in response to a model update, generate a plurality of candidate artificial intelligence models, and an intelligent model deployment circuitry to output a prediction of an artificial intelligence model combination to improve prediction performance over time.

Abstract: Methods and apparatus for Time-Sensitive Networking Coordinated Transfer Learning in industrial settings are disclosed. An example apparatus includes at least one memory, instructions in the apparatus, and processor circuitry to execute the instructions to cause performance of an operation by a first machine according to a first configuration, process a performance metric of the performance of the operation by the first machine to determine whether the performance metric is within a threshold range, and in response to a determination that the performance metric is not within the threshold range, cause performance of the operation by a second machine according to a second configuration different from the first configuration.