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October 1, 2026
HSE Researchers Show How Congenital Motor Disorders Affect Brain Development
Researchers from HSE University’s Institute for Cognitive Neuroscience have synthesised the findings of their previous studies on brain development in children with obstetric brachial plexus palsy and arthrogryposis. Their analysis shows that impaired motor function in early childhood not only limits children’s motor experience but also affects memory, categorical thinking, and information processing. The study has been published in Frontiers in Psychology.
October 1, 2026
Window into the Body: Scientists Develop Neural Network to Detect Risk of 15 Diseases from Retinal Images
Russian universities, with the participation of HSE University, Sber, and Z-union, have developed a neural network that can simultaneously assess the risk of 15 types of pathology from retinal photographs, including not only eye diseases but also cardiovascular conditions. The AI system can help clinicians detect potentially concerning changes at an early stage, identify signs reflecting the condition of retinal blood vessels, and determine whether a patient may need further examination. The paper has been published in Frontiers in Medicine.
September 30, 2026
'We Did Not Limit the Time for Questions'
The International Laboratory for Supercomputer Atomistic Modelling and Multi-Scale Analysis at HSE University held a major conference on molecular dynamics. Participants had the opportunity to attend all the presentations, while speakers were given as much time as they needed to answer questions. The HSE News Service interviewed Grigory Smirnov, Head of the Laboratory, and Genri Norman, Chief Research Fellow, about the conference preparations and the discussions it generated.

 

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Может ли ИИ производить значение? Семантический капитал, «стохастический попугай» и пределы антропоцентризма

Эпистемология и философия науки. 2026. Т. 63. № 2. С. 154–171.
Филатова А. А.

intelligence (hereafter, AI), particularly in the form of large lan guage models, has prompted a renewed wave of philosophical debates concerning the semantic competences of technical sys tems and the criteria for evaluating the outputs they generate, especially textual outputs. This article examines a set of central problem areas and their treatment in contemporary philosophical literature. Drawing on the theory of L. Floridi, the paper first raises the question of whether AI systems can possess and gene rate semantic capital (hereafter, SC), understood as any content that enhances an agent’s capacity for semantization. The paper argus that Floridi’s account remains largely anthropocentric, as signing neural networks the role of a “semantic broker”, that is, an entity capable of increasing SC through computational capaci ties while lacking meaning and content in the strict sense. The paper then analyzes the “stochastic parrot” problem within the framework of competing metasemantic theories. It shows that the same conceptual resources function both to deny and to support the claim that AI systems can produce meaning, thereby revealing a general limitation of anthropocentric approaches. Using the method of “anthropocentric abstraction”, proposed by H. Cap pelen and J. Dever, the paper illustrates how Kripkean externalist semantics can be applied to the analysis of AI-based decision-mak ing systems. Particular attention attracts the problem of alien con tent generated by neural networks and to the challenges it poses for human understanding; the paper outlines several scenarios for bridging the communicative gap between humans and AI systems. The conclusion argues for a symmetric approach to evaluating the semantic competences of humans and AI systems, taking into account differences in their internal organization and modes of operation. It further emphasizes the cognitive co-evolution of humans and AI systems, as well as the importance of investi gating the social mechanisms that sustain SC.

Research target: Philosophy, Ethics, and Religious Studies
Language: Russian
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Keywords: искусственный интеллекттекстtextlarge language models artificial intelligenceбольшие языковые модели (LLM)semantic capitalalien contentmetasemanticсемантический капиталчуждое содержаниеметасемантика
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