It has been said, arguably, that causality analysis should pave a promising way to interpretable deep learning and generalization. Incorporation of causality into artificial intelligence (AI) algorithms, however, is challenged with its vagueness, non-quantitiveness, computational inefficiency, etc. During the past 18 years, these challenges have been essentially resolved, with the establishment of a rigorous formalism of causality analysis initially motivated from atmospheric predictability. This not only opens a new field in the atmosphere-ocean science, namely, information flow, but also has led to scientific discoveries in other disciplines, such as quantum mechanics, neuroscience, financial economics, etc., through various applications. This note provides a brief review of the decade-long effort, including a list of major theoretical results, a sketch of the causal deep learning framework, and some representative real-world applications in geoscience pertaining to this journal, such as those on the anthropogenic cause of global warming, the decadal prediction of El Niño Modoki, the forecasting of an extreme drought in China, among others. Keywords: Causality, Liang-Kleeman information flow, Causal artificial intelligence, Fuzzy cognitive map, Interpretability, Frobenius-Perron operator, Weather/Climate forecasting 1. Introduction Causality analysis is a fundamental problem in scientific research, as commented by Einstein in 1953 in response to a question on the status quo of science in China at that time (cf. the historical record in Hu, 2005).The recent rush in artificial intelligence (AI) has stimulated enormous interest in causal inference, partly due to the realization that it may take the field to the next level to approach human intelligence (see Pearl, 2018; Bengio, 2019; Schölkopf, 2022). In the fields pertaining to this journal, assessment of the cause-effect relations between dynamic events makes a natural objective for the corresponding researches.

The large language model (LLM) based agents have demonstrated their capacity to automate and expedite software development processes. In this paper, we focus on game development and propose a multi-agent collaborative framework, dubbed GameGPT, to automate game development. While many studies have pinpointed hallucination as a primary roadblock for deploying LLMs in production, we identify another concern: redundancy. Our framework presents a series of methods to mitigate both concerns. These methods include dual collaboration and layered approaches with several in-house lexicons, to mitigate the hallucination and redundancy in the planning, task identification, and implementation phases. Furthermore, a decoupling approach is also introduced to achieve code generation with better precision.

In this paper, we describe and analyze an island-based random dynamic voltage scaling (iRDVS) approach to thwart power side-channel attacks. We first analyze the impact of the number of independent voltage islands on the resulting signal-to-noise ratio and trace misalignment. As part of our analysis of misalignment, we propose a novel unsupervised machine learning (ML) based attack that is effective on systems with three or fewer independent voltages. Our results show that iRDVS with four voltage islands, however, cannot be broken with 200k encryption traces, suggesting that iRDVS can be effective. We finish the talk by describing an iRDVS test chip in a 12nm FinFet process that incorporates three variants of an AES-256 accelerator, all originating from the same RTL. This included a synchronous core, an asynchronous core with no protection, and a core employing the iRDVS technique using asynchronous logic. Lab measurements from the chips indicated that both unprotected variants failed the test vector leakage assessment (TVLA) security metric test, while the iRDVS was proven secure in a variety of configurations.

Large number of ReLU and MAC operations of Deep neural networks make them ill-suited for latency and compute-efficient private inference. In this paper, we present a model optimization method that allows a model to learn to be shallow. In particular, we leverage the ReLU sensitivity of a convolutional block to remove a ReLU layer and merge its succeeding and preceding convolution layers to a shallow block. Unlike existing ReLU reduction methods, our joint reduction method can yield models with improved reduction of both ReLUs and linear operations by up to 1.73x and 1.47x, respectively, evaluated with ResNet18 on CIFAR-100 without any significant accuracy-drop.