This paper presents a novel research analytical IT system grounded in Martin Heidegger's Fundamental Ontology, distinguishing between beings (das Seiende) and Being (das Sein). The system employs two modally distinct, descriptively complete languages: a categorical language of beings for processing user inputs and an existential language of Being for internal analysis. These languages are bridged via a phenomenological reduction module, enabling the system to analyze user queries (including questions, answers, and dialogues among IT specialists), identify recursive and self-referential structures, and provide actionable insights in categorical terms. Unlike contemporary systems limited to categorical analysis, this approach leverages Heidegger's phenomenological existential analysis to uncover deeper ontological patterns in query processing, aiding in resolving logical traps in complex interactions, such as metaphor usage in IT contexts. The path to full realization involves formalizing the language of Being by a research team based on Heidegger's Fundamental Ontology; given the existing completeness of the language of beings, this reduces the system's computability to completeness, paving the way for a universal query analysis tool. The paper presents the system's architecture, operational principles, technical implementation, use cases--including a case based on real IT specialist dialogues--comparative evaluation with existing tools, and its advantages and limitations.

--This paper investigates deploying semantic edge inference systems for performing a common image clarification task. In particular, each system consists of multiple Internet of Things (IoT) devices that first locally encode the sensing data into semantic features and then transmit them to an edge server for subsequent data fusion and task inference. The inference accuracy is determined by efficient training of the feature encoder/decoder using labeled data samples. Due to the difference in sensing data and communication channel distributions, deploying the system in a new environment may induce high costs in annotating data labels and re-training the encoder/decoder models. T o achieve cost-effective transferable system deployment, we propose an efficient Domain Adaptation method for Semantic Edge INference systems (DASEIN) that can maintain high inference accuracy in a new environment without the need for labeled samples. Specifically, DASEIN exploits the task-relevant data correlation between different deployment scenarios by leveraging the techniques of unsupervised domain adaptation and knowledge distillation. It devises an efficient two-step adaptation procedure that sequentially aligns the data distributions and adapts to the channel variations. Numerical results show that, under a substantial change in sensing data distributions, the proposed DASEIN outperforms the best-performing benchmark method by 7.09 % and 21.33 % in inference accuracy when the new environment has similar or 25 dB lower channel signal to noise power ratios (SNRs), respectively. This verifies the effectiveness of the proposed method in adapting both data and channel distributions in practical transfer deployment applications. Index T erms --Semantic communications, edge inference, transfer learning, unsupervised domain adaptation. Hanks to the advancement of artificial intelligence (AI), it becomes prevalent in recent years to deploy smart Internet of Things (IoT) systems using deep neural networks (DNNs) to perform complex inference tasks, e.g., computer vision based object recognition [1]-[3]. In particular, wireless IoT devices, such as video surveillance cameras, are systematically deployed at target locations to collect real-time sensing data and collaboratively accomplish specific inference tasks. The performance of on-device AI inference, however, is significantly constrained by the limited battery energy and computing power of IoT devices.

I don't know, but am inclined to think "no," if sentience involves anything like a sense of self-consciousness, interiority, care for the world, or existential motivation. But this is a question that is alive in our culture, an ersatz theology for the secular age. This "dialogue" between a Google Engineer and an AI is quite remarkable, the stuff of science fiction: The dialogue recalls Kierkegaard's quip about St. Anselm, when he heard that the great rationalist had prayed for days asking God to send him "proof" of his existence. "Does the loving bride in the embrace of her beloved ask for proof that he is alive and real?" Who needs ontological arguments for divine existence when you have something more immediate, a relationship?

AI has a long history of making fine tools, and an equally long history of trying to simulate human intelligence, without, I contend, really understanding what intelligence consists in: the ability to deal with the world, which presupposes having a stake in one's own being. The tools are very nifty, but I don't see how it is even possible to simulate having a stake in one's own being.