While the rise of AI has benefited professionals, it also induces technostress that threatens their expertise and jobs. To ensure the human-centered advancement of technology, a deep understanding of users technostress and how to cope with it is essential. Despite technostress having long been discussed, the growing integration of AI tools into professionals’ everyday work amplifies these challenges and calls for further exploration. Accordingly, this is a timely moment to examine their real-world experiences and voices. Thus, our study aims to investigate AI-induced technostress experienced by professionals, and the coping strategies they employ. Through focus group interviews with 19 professionals from diverse fields, we identified seven AI-induced technostressors and examined their coping strategies along two dimensions: stress Coping Style (problem-focused and emotion-focused) and Value Orientation (AI-oriented and humanness-oriented). Drawing on professionals’ coping strategies, we suggest practical implications to support users in coping with AI-induced technostress.

AI clones are evolving to include digital representations of real world individuals as chatbots. While often used to replicate famous figures, as the technology becomes more accessible, it is crucial to understand whether everyday users would create their own clones and how they interact with them. In this study, within the scope of AI-generated personas and their role in representing users’ needs and identities, we focus on personas that directly reflect the qualities of real humans. We define this as AI self clones—conversational AI representations that reflect their human creators—and examine how creators construct and engage with them. We conducted a 7-day study in which participants (N=12) created and interacted with their text based AI self clones using CloneBuilder , a web-based authoring interface for configuring and tuning AI self clones. The system enables individuals to create AI representations that encapsulate their unique personality, values, and interaction style. Our findings reveal that each participant developed a clone tailored to their personal circumstances. As the participants iteratively refined and tested their clone, their direction and expectation of AI clones evolved from performing specific roles to evolving entities that facilitated self exploration and relationship formation. Unexpected responses from the clone prompted self reflection and identity questioning. Overall, this paper explores the motivations for creating these clones, the strategies participants use to build and refine them, and the moments of emotional connection and break out experiences that emerge during the crafting process, along with key design implications, challenges, and ethical considerations in developing AI self clones.

Deep spiking neural networks (SNNs) have shown remarkable progress due to improvements, such as training algorithms. However, most of them have not considered the features of neuromorphic devices. Their improvement has relied on soft resets, which are computationally expensive and unsuitable for neuromorphic devices. To address this, this paper proposes gradient clipping in hard reset-based deep SNNs and explores how device noise enhances learning performance. According to our experiments on various datasets and models, the proposed approach improved the training performance of deep SNNs with hard reset. These findings bridge gaps between SNN algorithms and hardware constraints, paving the way for efficient neuromorphic computing.

Recently, many large-scale neuromorphic systems that emulate spiking neural networks have been presented. Biological evidence emphasizes the importance of the log-normal distribution of biological neural and synaptic parameters in the brain; however, this fact is easily ignored sometimes, and the parameters are excessively optimized to scale up a system. This is because high-precision parameters require floating-point arithmetic–an operation known to consume high-energy and result in a high implementation cost in digital hardware. In this study, we propose a novel neuron implementation model that enhances neural and synaptic dynamics using the time-embedded floating-point arithmetic for better biological plausibility and low-power consumption. The proposed algorithm enables sharing temporal information with a membrane potential by time-embedded floating-point arithmetic, thus minimizing the memory usage of the neural state. In addition, this method need not access the static random-access memory at every time step, thus reducing the dynamic power consumption, even with a floating-point precision neural and synaptic dynamics. Using the proposed model, we implemented a core group with a total of 8,192 neurons on a field-programmable gate array device, Xilinx XC7K160T. The core group is designed for use in large-scale neuromorphic systems. We tested the neuron model in a core under various experimental conditions.