technocracy

arXiv:2601.12303v1 Announce Type: new Abstract: Deep learning has achieved remarkable success in image recognition, yet their inherent opacity poses challenges for deployment in critical domains. Concept-based interpretations aim to address this by explaining model reasoning through human-understandable concepts. However, existing post-hoc methods and ante-hoc concept bottleneck models (CBMs), suffer from limitations such as unreliable concept relevance, non-visual or labor-intensive concept definitions, and model or data-agnostic assumptions. This paper introduces Post-hoc Concept Bottleneck Model via Representation Decomposition (PCBM-ReD), a […]

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<p>A conversation about the decision-making and communication required to take a mission-driven company to new heights.</p>

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arXiv:2604.05120v1 Announce Type: new Abstract: Digital humans are lifelike virtual agents capable of natural conversation and are increasingly deployed in domains like retail and finance. However, most current digital humans operate in isolation from their surroundings and lack contextual awareness beyond the dialogue itself. We address this limitation by integrating ambient intelligence (AmI) – i.e., environmental sensors, IoT data, and contextual modeling – with digital human systems. This integration enables situational awareness of the user’s environment, anticipatory and […]

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arXiv:2605.15202v1 Announce Type: new Abstract: Presentations are a primary medium for scholarly communication, yet most AI slide generators optimize the artifact (a visually plausible deck) while under-optimizing the delivery process (pacing, narrative, and presentation preparation). We present DeepSlide, a human-in-the-loop multi-agent system that supports preparing the full presentation process, from requirement elicitation and time-budgeted narrative planning, to evidence-grounded slide–script generation, attention augmentation, and rehearsal support. DeepSlide integrates (i) a controllable logical-chain planner with per-node time budgets, (ii) a […]

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arXiv:2212.14511v4 Announce Type: replace-cross Abstract: We study the task of learning state representations from potentially high-dimensional observations, with the goal of controlling an unknown partially observable system. We pursue a cost-driven approach, where a dynamic model in some latent state space is learned by predicting the costs without predicting the observations or actions. In particular, we focus on an intuitive cost-driven state representation learning method for solving Linear Quadratic Gaussian (LQG) control, one of the most fundamental partially […]

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Multimodal Machine Learning offers a holistic view of a patient’s status, integrating structured and unstructured data from electronic health records (EHR). We propose a framework to predict metastasis risk one month prior to diagnosis, using six months of clinical history from EHR data. Data from four cancer cohorts collected at Karolinska University Hospital (Stockholm, Sweden) were analyzed: breast (n = 743), colon (n = 387), lung (n = 870), and prostate (n = 1890). The dataset included demographics, […]

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Teilhard casts scientific research as humanity’s next evolutionary pulse—our collective mind awakening to shape its own future. He argues that the modern hunger to know and create is nothing less than evolution reflecting on itself, a divine power working through human inquiry. For him, research becomes sacred: the meeting point where faith in God and faith in human potential fuse into a single, world-transforming spiritual force.

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arXiv:2601.15640v1 Announce Type: cross Abstract: Bayesian optimisation is a sample efficient method for finding a global optimum of expensive black-box objective functions. Historic datasets from related problems can be exploited to help improve performance of Bayesian optimisation by adapting transfer learning methods to various components of the Bayesian optimisation pipeline. In this study we perform an empirical analysis of various ensemble-based transfer learning Bayesian optimisation methods and pipeline components. We expand on previous work in the literature by […]

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arXiv:2603.02260v1 Announce Type: new Abstract: There exist many techniques for automatically deriving parametric resource (or cost) bounds by analyzing the source code of a program. These techniques work effectively for a large class of programs and language features. However, non-local transfer of control as needed for exception or effect handlers has remained a challenge. This paper presents the first automatic resource bound analysis that supports non-local control transfer between exceptions or effects and their handlers. The analysis is […]

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