<img width="800" height="450" src="https://analyticsindiamag.com/wp-content/uploads/2023/05/ibm-1300x731.jpg" class="webfeedsFeaturedVisual wp-post-image" alt="" style="display: block; margin: auto; margin-bottom: 5px;max-width: 100%;" link_thumbnail="" decoding="async" loading="lazy" srcset="https://analyticsindiamag.com/wp-content/uploads/2023/05/ibm-1300x731.jpg 1300w, https://analyticsindiamag.com/wp-content/uploads/2023/05/ibm-768x432.jpg 768w, https://analyticsindiamag.com/wp-content/uploads/2023/05/ibm-1536x864.jpg 1536w, https://analyticsindiamag.com/wp-content/uploads/2023/05/ibm.jpg 1920w" sizes="auto, (max-width: 800px) 100vw, 800px" /><p>‘The acquisition gives IBM more than 50% share in the event broker market.’</p>
<p>The post <a href="https://analyticsindiamag.com/global-tech/ibm-bought-a-nervous-system-for-11-billion/">IBM Bought a ‘Nervous System’ for $11 Billion </a> appeared first on <a href="https://analyticsindiamag.com">Analytics India Magazine</a>.</p>

قامت شركة IBM بشراء شركة Confluent، وهي منصة لتدفق البيانات، مقابل حوالي 11 مليار دولار، مما يعزز بشكل كبير قدراتها في الذكاء الاصطناعي ومعالجة البيانات في الوقت الحقيقي. تضع هذه الصفقة IBM في موقع يمكنها من الاستحواذ على أكثر من 50% من سوق وسطاء الأحداث، مما يمثل خطوة استراتيجية في المشهد التنافسي لتكنولوجيا الذكاء الاصطناعي.

IBM ha adquirido Confluent, una plataforma de transmisión de datos, por aproximadamente 11 mil millones de dólares, mejorando significativamente sus capacidades en inteligencia artificial y procesamiento de datos en tiempo real. Esta adquisición posiciona a IBM para capturar más del 50% del mercado de corredores de eventos, marcando un movimiento estratégico en el competitivo panorama de la tecnología de IA.

IBM a acquis Confluent, une plateforme de streaming de données, pour environ 11 milliards de dollars, renforçant ainsi ses capacités en intelligence artificielle et en traitement de données en temps réel. Cette acquisition permet à IBM de capturer plus de 50 % du marché des courtiers d'événements, marquant un mouvement stratégique dans le paysage concurrentiel de la technologie AI.

IBM has acquired Confluent, a data-streaming platform, for approximately $11 billion, significantly enhancing its capabilities in artificial intelligence and real-time data processing. This acquisition positions IBM to capture over 50% of the event broker market, marking a strategic move in the competitive landscape of AI technology.

IBM Bought a ‘Nervous System’ for $11 Billion

arXiv:2510.05038v2 Announce Type: replace 
Abstract: Multimodal encoders have pushed the boundaries of visual document retrieval, matching textual query tokens directly to image patches and achieving state-of-the-art performance on public benchmarks. Recent models relying on this paradigm have massively scaled the sizes of their query and document representations, presenting obstacles to deployment and scalability in real-world pipelines. Furthermore, purely vision-centric approaches may be constrained by the inherent modality gap still exhibited by modern vision-language models. In this work, we connect these challenges to the paradigm of hybrid retrieval, investigating whether a lightweight dense text retriever can enhance a stronger vision-centric model. Existing hybrid methods, which rely on coarse-grained fusion of ranks or scores, fail to exploit the rich interactions within each model's representation space. To address this, we introduce Guided Query Refinement (GQR), a novel test-time optimization method that refines a primary retriever's query embedding using guidance from a complementary retriever's scores. Through extensive experiments on visual document retrieval benchmarks, we demonstrate that GQR allows vision-centric models to match the performance of models with significantly larger representations, while being up to 14x faster and requiring 54x less memory. Our findings show that GQR effectively pushes the Pareto frontier for performance and efficiency in multimodal retrieval. We release our code at https://github.com/IBM/test-time-hybrid-retrieval

تقدم دراسة حديثة مفهوم Guided Query Refinement (GQR)، وهو نهج جديد يهدف إلى تحسين أنظمة الاسترجاع الهجينة متعددة الوسائط من خلال دمج مسترجع نصوص كثيف خفيف مع نماذج تركز على الرؤية. تعالج هذه الطريقة التحديات المتعلقة بتوسيع تمثيلات الاستعلام والمستندات، بينما تسد الفجوة بين الوسائط في النماذج الحالية للغة والرؤية.

Un estudio reciente presenta el Guided Query Refinement (GQR), un enfoque novedoso destinado a mejorar los sistemas de recuperación híbrida multimodal al integrar un recuperador de texto denso ligero con modelos centrados en la visión. Este método aborda los desafíos de escalar las representaciones de consultas y documentos, al tiempo que cierra la brecha de modalidad en los modelos actuales de visión-lenguaje.

Une étude récente présente le Guided Query Refinement (GQR), une nouvelle approche visant à améliorer les systèmes de récupération hybride multimodale en intégrant un récupérateur de texte dense léger avec des modèles centrés sur la vision. Cette méthode répond aux défis de mise à l'échelle des représentations de requêtes et de documents tout en comblant l'écart de modalité dans les modèles actuels de vision-langage.

A recent study introduces Guided Query Refinement (GQR), a novel approach aimed at enhancing multimodal hybrid retrieval systems by integrating a lightweight dense text retriever with vision-centric models. This method addresses challenges in scaling query and document representations while bridging the modality gap in current vision-language models.

IBM Bought a ‘Nervous System’ for $11 Billion

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