technocracy

GPT-5.5 prompting guide Now that GPT-5.5 is available in the API, OpenAI have released a wealth of useful tips on how best to prompt the new model. Here’s a neat trick they recommend for applications that might spend considerable time thinking before returning a user-visible response: Before any tool calls for a multi-step task, send a short user-visible update that acknowledges the request and states the first step. Keep it to one or two sentences. I’ve already noticed […]

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Interviews and discussions with conceptual artist Lawrence Weiner, biologist Rupert Sheldrake, Tibetan Buddhist teacher Sogyal Rinpoche, and director of Credit Lyonnais J.M. Leveque. Artists, scientists, spiritual leaders and economists gathered in Amsterdam in 1990 to explore the emerging paradigm of a holistic world view and the implications for a global economy. The five day conference was inspired by the artists Joseph Beuys and Robert Filliou, and manifested by Louwrien Wijers, who called it a “mental sculpture.”

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Multimodal contrastive learning is increasingly enriched by going beyond image-text pairs. Among recent contrastive methods, Symile is a strong approach for this challenge because its multiplicative interaction objective captures higher-order cross-modal dependence. Yet, we find that Symile treats all modalities symmetrically and does not explicitly model reliability differences, a limitation that becomes especially present in trimodal multiplicative interactions. In practice, modalities beyond image-text pairs can be misaligned, weakly informative, or missing, and treating them uniformly can silently degrade […]

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arXiv:2407.10417v2 Announce Type: replace Abstract: A fundamental challenge in machine learning is the choice of a loss as it characterizes our learning task, is minimized in the training phase, and serves as an evaluation criterion for estimators. Proper losses are commonly chosen, ensuring minimizers of the full risk match the true probability vector. Estimators induced from a proper loss are widely used to construct forecasters for downstream tasks such as classification and ranking. In this procedure, how does […]

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arXiv:2602.03906v1 Announce Type: cross Abstract: Information Bottleneck (IB) is widely used, but in deep learning, it is usually implemented through tractable surrogates, such as variational bounds or neural mutual information (MI) estimators, rather than directly controlling the MI I(X;Z) itself. The looseness and estimator-dependent bias can make IB “compression” only indirectly controlled and optimization fragile. We revisit the IB problem through the lens of information geometry and propose a textbf{Geo}metric textbf{I}nformation textbf{B}ottleneck (textbf{GeoIB}) that dispenses with mutual information […]

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Despite recent advances in machine learning and explainable AI, a gap remains in personalized preventive healthcare: predictions, interventions, and recommendations should be both understandable and verifiable for all stakeholders in the healthcare sector. We present a demonstration of how prototype-based learning can address these needs. Our proposed framework, ProtoPal, features both front- and back-end modes; it achieves superior quantitative performance while also providing an intuitive presentation of interventions and their simulated outcomes.

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Modern generative modeling methods have demonstrated strong performance in learning complex data distributions from clean samples. In many scientific and imaging applications, however, clean samples are unavailable, and only noisy or linearly corrupted measurements can be observed. Moreover, latent structures, such as manifold geometries, present in the data are important to extract for further downstream scientific analysis. In this work, we introduce Riemannian AmbientFlow, a framework for simultaneously learning a probabilistic generative model and the underlying, nonlinear data […]

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A cloud operation issue more than a decade old has been observability. To gain access to more complex systems, organizations have spent much on dashboards, monitoring tools, logs, metrics, and alerts. Signals were interpreted and responses taken in the shortest period of time possible to minimize the impact among users in case of failures. This model worked well when the workloads were predictable, as well as when the architecture was small. That is no longer the case.  Modern […]

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arXiv:2603.23659v1 Announce Type: new Abstract: When large language models make ethical judgments, do their internal representations distinguish between normative frameworks, or collapse ethics into a single acceptability dimension? We probe hidden representations across five ethical frameworks (deontology, utilitarianism, virtue, justice, commonsense) in six LLMs spanning 4B–72B parameters. Our analysis reveals differentiated ethical subspaces with asymmetric transfer patterns — e.g., deontology probes partially generalize to virtue scenarios while commonsense probes fail catastrophically on justice. Disagreement between deontological and utilitarian […]

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