Learning from unlabeled data, or self-learning, can substantially reduce the complexity of machine learning (ML) utilization in real-time deployment. While the development of un/ semi-supervised algorithms shows promising results in learning with reduced labels, the fundamental assumption of data smoothness restricts its scope of application, especially with non-stationary data distributions in different domains. Leveraging trans-domain invariant causal relationships, causality has recently been employed to foster robust ML. In this paper, we have developed a generic method for self-labeling data that relies on known causality among interactive objects and learned temporal relations among causal events for identifying and associating labels and input data. The causal time-interval between asynchronous cause and effect events is studied to achieve self-labeling. We utilize dynamical system theory in a 1- $d$ setting to demonstrate that our proposed method outperforms traditional feature similarity based semi-supervised learning. A computer simulation experiment was conducted to generate high-dimensional data, and the comprehensive results reveal the potential of learning adaptation in dynamic environments to improve ML robustness against shifts in data distribution.

Adaptive machine learning (ML) aims to allow ML models to adapt to ever-changing environments with potential concept drift after model deployment. Traditionally, adaptive ML requires a new dataset to be manually labeled to tailor deployed models to altered data distributions. Recently, an interactive causality based self-labeling method was proposed to autonomously associate causally related data streams for domain adaptation, showing promising results compared to traditional feature similarity-based semi-supervised learning. Several unanswered research questions remain, including self-labeling’s compatibility with multivariate causality and the quantitative analysis of the auxiliary models used in the self-labeling. The auxiliary models, the interaction time model (ITM) and the effect state detector (ESD), are vital to the success of self-labeling. This paper further develops the self-labeling framework and its theoretical foundations to address these research questions. A framework for the application of self-labeling to multivariate causal graphs is proposed using four basic causal relationships, and the impact of non-ideal ITM and ESD performance is analyzed. A simulated experiment is conducted based on a multivariate causal graph, validating the proposed theory.

Machine learning (ML) has been demonstrated to improve productivity in many manufacturing applications. To host these ML applications, several software and Industrial Internet of Things (IIoT) systems have been proposed for manufacturing applications to deploy ML applications and provide real-time intelligence. Recently, an interactive causality-enabled self-labeling method has been proposed to advance adaptive ML applications in cyber–physical systems, especially manufacturing, by automatically adapting and personalizing ML models after deployment to counter data distribution shifts. The unique features of the self-labeling method require a novel software system to support dynamism at various levels. This paper proposes the AdaptIoT system, comprising an end-to-end data streaming pipeline, ML service integration, and an automated self-labeling service. The self-labeling service consists of causal knowledge bases and automated full-cycle self-labeling workflows to adapt multiple ML models simultaneously. AdaptIoT employs a containerized microservice architecture to deliver a scalable and portable solution for small and medium-sized manufacturers. A field demonstration of a self-labeling adaptive ML application is conducted with a makerspace and shows reliable performance with comparable accuracy at 98.3%.

This chapter describes an adaptive machine learning (ML) method for the utilization of unlabeled data for continual model adaptation after deployment. Current methods for the usage of unlabeled data, such as unsupervised and semi-supervised methods, rely on being both smooth and static in their distributions. In this chapter, a generic method for leveraging causal relationships to automatically associate labels with unlabeled data using state transitions of asynchronous interacting cause and effect events is discussed. This self-labeling method is predicated on a defined causal relationship and associated temporal spacing. The theoretical foundation of the self-supervised method is discussed and compared with its contemporary semi-supervised counterparts using dynamical systems theory. Implementations of this method to adapt action recognition ML models in semiconductor manufacturing and human assembly tasks as manufacturing cyber-physical systems (CPS) are provided to demonstrate the effectiveness of the proposed methodology.

This paper describes a system for modeling nonidealities in a silicon-photonic joint transform correlator (JTC) used as an accelerator for convolutional neural networks (CNNs). The system is used to determine which nonidealities have the greatest impact on end-to-end model accuracy, which helps determine what design choices can be made at the block and system level to improve performance. We create a digital twin of the on-chip 1D JTC using transfer functions fitted from simulations, then introduce a scalar α that mixes ideal and realized behaviors. We quantify the α -dependent model accuracy on CIFAR-10 and SNDR differences at both the joint-power spectrum (JPS) and the JTC output. The framework allows quantification of block- and system-level sensitivities, informing design choices by isolating the accuracy bottlenecks within the system.

Adaptive machine learning (ML) aims to allow ML models to adapt to ever-changing environments with potential concept drift after model deployment. Traditionally, adaptive ML requires a new dataset to be manually labeled to tailor deployed models to altered data distributions. Recently, an interactive causality based self-labeling method was proposed to autonomously associate causally related data streams for domain adaptation, showing promising results compared to traditional feature similarity-based semi-supervised learning. Several unanswered research questions remain, including self-labeling's compatibility with multivariate causality and the quantitative analysis of the auxiliary models used in the self-labeling. The auxiliary models, the interaction time model (ITM) and the effect state detector (ESD), are vital to the success of self-labeling. This paper further develops the self-labeling framework and its theoretical foundations to address these research questions. A framework for the application of self-labeling to multivariate causal graphs is proposed using four basic causal relationships, and the impact of non-ideal ITM and ESD performance is analyzed. A simulated experiment is conducted based on a multivariate causal graph, validating the proposed theory.