technocracy

In this quiz, you’ll test your understanding of Python’s __all__: Packages, Modules, and Wildcard Imports. By working through this quiz, you’ll revisit how wildcard imports work, what role the __all__ variable plays in modules and packages, and how to define a clean public API for your Python code. You’ll need to know the basics of Python modules and packages and the import system to get the most out of this quiz. Good luck! [ Improve Your Python With […]

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arXiv:2602.02559v1 Announce Type: new Abstract: Recent advances have enabled large language model (LLM) agents to solve complex tasks by orchestrating external tools. However, these agents often struggle in specialized, tool-intensive domains that demand long-horizon execution, tight coordination across modalities, and strict adherence to implicit tool constraints. Earth Observation (EO) tasks exemplify this challenge due to the multi-modal and multi-temporal data inputs, as well as the requirements of geo-knowledge constraints (spectrum library, spatial reasoning, etc): many high-level plans can […]

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arXiv:2604.18768v1 Announce Type: new Abstract: Buildings shape how people feel, yet the mechanisms through which specific facade properties drive affective states remain empirically underspecified. Here we introduce the Cambridge Facade Affect Dataset (CFAD), 86 orthogonally rectified facade images annotated with continuous arousal and valence ratings from 85 participants, and establish a validated pipeline linking machine-vision-derived surface metrics to human affective responses. Focusing on three quantifiable attributes, complexity, transparency (window-to-wall ratio), and materiality (proportion of natural versus artificial surface […]

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See how LSEG uses OpenAI to scale trusted AI across its global business, accelerating insights, shrinking release cycles, and empowering 4,000 employees.

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arXiv:2601.13458v2 Announce Type: replace Abstract: The increasing reliance on human preference feedback to judge AI-generated pseudo labels has created a pressing need for principled, budget-conscious data acquisition strategies. We address the crucial question of how to optimally allocate a fixed annotation budget between ground-truth labels and pairwise preferences in AI. Our solution, grounded in semi-parametric inference, casts the budget allocation problem as a monotone missing data framework. Building on this formulation, we introduce Preference-Calibrated Active Learning (PCAL), a […]

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Immersive applications such as virtual and augmented reality impose stringent requirements on frame rate, latency, and synchronization between physical and virtual environments. To meet these requirements, an edge server must render panoramic content, predict user head motion, and transmit a portion of the scene that is large enough to cover the user viewport while remaining within wireless bandwidth constraints. Each portion produces two feedback signals: prediction feedback, indicating whether the selected portion covers the actual viewport, and transmission […]

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arXiv:2405.16958v3 Announce Type: replace Abstract: We prove a large deviation principle for deep neural networks with Gaussian weights and at most linearly growing activation functions, such as ReLU. This generalises earlier work, in which bounded and continuous activation functions were considered. In practice, linearly growing activation functions such as ReLU are most commonly used. We furthermore simplify previous expressions for the rate function and provide a power-series expansions for the ReLU case.

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A new working research from Perplexity and Harvard offers field evidence on what AI agents do to knowledge work. It draws on production data from two Perplexity products: Search and Computer. The setup is a natural comparison. Search is a conversational answer engine. Computer is an agent that plans and executes tasks end to end. The same users touch both products, so the team can hold the task roughly constant. What the Study Actually Measures The research study […]

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