Intel says Lip-Bu Tan will lead the chipmaker's AI efforts himself, as recently promoted CTO and AI chief quits to lead infrastructure efforts for OpenAI.
Read more: <a rel="nofollow" href="https://www.siliconrepublic.com/business/intel-ceo-lip-bu-tan-to-lead-ai-efforts-as-cto-departs-for-openai">Intel CEO Lip-Bu Tan to lead AI efforts as CTO departs for OpenAI</a>

أعلنت إنتل أن الرئيس التنفيذي ليب-بو تان سيتولى مبادرات الذكاء الاصطناعي في الشركة بعد مغادرة المدير الفني (CTO) الذي تم ترقيته مؤخرًا، والذي يغادر لقيادة جهود البنية التحتية في OpenAI. يُعتبر هذا التغيير في القيادة مهمًا لأنه يضع استراتيجية الذكاء الاصطناعي مباشرة تحت إشراف الرئيس التنفيذي، مما قد يؤثر على اتجاه إنتل في مشهد الذكاء الاصطناعي التنافسي. تعكس هذه الخطوة التزام إنتل بتعزيز قدراتها في الذكاء الاصطناعي في قطاع التكنولوجيا الذي يتطور بسرعة.

Intel ha anunciado que el CEO Lip-Bu Tan asumirá las iniciativas de inteligencia artificial de la empresa tras la salida del recientemente promovido Director de Tecnología (CTO), quien se va para liderar los esfuerzos de infraestructura en OpenAI. Este cambio de liderazgo es significativo ya que coloca la estrategia de IA directamente bajo la responsabilidad del CEO, lo que podría influir en la dirección de Intel en el competitivo panorama de la IA. Este movimiento refleja el compromiso de Intel de avanzar en sus capacidades de IA en un sector tecnológico que evoluciona rápidamente.

Intel a annoncé que le PDG Lip-Bu Tan prendra en charge les initiatives d'intelligence artificielle de l'entreprise suite au départ du directeur technique (CTO) récemment promu, qui s'en va pour diriger les efforts d'infrastructure chez OpenAI. Ce changement de leadership est significatif car il place la stratégie IA directement sous la responsabilité du PDG, ce qui pourrait influencer la direction d'Intel dans le paysage concurrentiel de l'IA. Ce mouvement reflète l'engagement d'Intel à faire avancer ses capacités en IA dans un secteur technologique en rapide évolution.

Intel has announced that CEO Lip-Bu Tan will take over the company's artificial intelligence initiatives following the departure of the recently promoted Chief Technology Officer (CTO), who is leaving to lead infrastructure efforts at OpenAI. This leadership change is significant as it places AI strategy directly under the CEO's purview, potentially influencing Intel's direction in the competitive AI landscape. The move reflects Intel's commitment to advancing its AI capabilities amidst a rapidly evolving technology sector.

Intel CEO Lip-Bu Tan to lead AI efforts as CTO departs for OpenAI

The story of the Ghost in the Shell’s main villain the Puppet Master hinted at a future where governments use hackers for espionage, at a time when most of the world had never connected to the internet.

الأنمي الكلاسيكي 'Ghost in the Shell' يقدم شخصية Puppet Master، التي تتنبأ بمستقبل تستخدم فيه الحكومات القراصنة للتجسس. ظهرت هذه التنبؤات في وقت كانت فيه غالبية سكان العالم لم تتصل بعد بالإنترنت، مما يبرز رؤية العرض لمشكلات الأمن السيبراني.

El clásico anime 'Ghost in the Shell' presenta al Puppet Master, un personaje que anticipa un futuro en el que los gobiernos utilizan hackers para el espionaje. Esta predicción surgió en un momento en que la mayoría de la población mundial aún no estaba conectada a Internet, destacando la previsión del programa sobre los problemas de ciberseguridad.

L'anime classique 'Ghost in the Shell' présente le Puppet Master, un personnage qui préfigure un avenir où les gouvernements utilisent des hackers pour l'espionnage. Cette prédiction est survenue à une époque où la majorité de la population mondiale n'était pas encore connectée à Internet, soulignant la prévoyance de l'émission concernant les problèmes de cybersécurité.

The classic anime 'Ghost in the Shell' features the Puppet Master, a character that foreshadows a future where governments utilize hackers for espionage. This prediction emerged at a time when the majority of the global population had yet to connect to the internet, highlighting the show's foresight regarding cybersecurity issues.

How the classic anime ‘Ghost in the Shell’ predicted the future of cybersecurity 30 years ago

Text-to-image diffusion models have become the workhorses of generative imaging. They can paint photorealistic scenes, mimic art styles, and blend concepts in ways that were science fiction a few years ago. Yet they stumble embarrassingly on a skill that even small children master: basic spatial reasoning.

Ask a state-of-the-art model for “a dog to the right of a teddy bear” and you often get:

<ul>
<li>The dog on the left</li>
<li>One of the objects missing</li>
<li>Or a bizarre hybrid where dog and teddy are fused into a single creature</li>
</ul>

<a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F49rtb08366xdl284o4z0.jpg" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F49rtb08366xdl284o4z0.jpg" alt=" " width="800" height="532"></a>

These failures become more severe for unusual compositions like “a giraffe above an airplane”. Traditional fixes range from expensive fine-tuning to brittle, hand-written loss functions at inference time—but both options come with significant downsides.

NVIDIA’s Learn-to-Steer framework (accepted to WACV 2026) proposes a different path: instead of hard-coding spatial rules or retraining the entire model, it learns a data-driven objective that can “steer” diffusion at inference time. The method reads the model’s own cross-attention maps, trains a lightweight classifier to detect spatial relations, and then uses that classifier’s gradient as a learned loss to nudge the generation towards layouts that match the prompt.

In this blog, we’ll unpack:

<ul>
<li>What makes spatial reasoning so fragile in current diffusion models</li>
<li>How Learn-to-Steer learns spatial constraints from the model itself</li>
<li>How it steers images during generation without changing model weights</li>
<li>The top gains on spatial benchmarks like GenEval and T2I-CompBench</li>
<li>The trade-offs in compute cost and generality, and what this implies for future generative systems</li>
</ul>

<h1>
 
 
 Why Spatial Reasoning Fails in Text-to-Image Diffusion
</h1>

<h2>
 
 
 What Makes Spatial Relations So Difficult for Diffusion Models?
</h2>

Modern diffusion models (e.g., Stable Diffusion, Flux) are excellent at what should appear in an image—objects, styles, textures—but much less reliable at where those objects should be.

Several factors contribute:

<h3>
 
 
 Weak supervision of spatial language
</h3>

<ul>
<li>Training data rarely comes with precise annotations like “object A is left of object B”.
</li>
<li>Captions often describe content loosely, so phrases like “on top of” or “to the right of” are under-specified.</li>
</ul>

<h3>
 
 
 Entangled visual concepts
</h3>

<ul>
<li>When two objects frequently co-occur, models may treat them as a single visual blob.</li>
<li>This leads to object fusion, where a “cat on a bookshelf” becomes a cat-bookshelf chimera.</li>
</ul>

<h3>
 
 
 Benchmark saturation without spatial coverage
</h3>

<ul>
<li>Many standard text-to-image benchmarks emphasize realism and style, not relational accuracy.</li>
<li>Models can score highly while still being spatially confused.</li>
</ul>

Empirical studies confirm three recurring failure modes on spatial benchmarks:

<ul>
<li>Incorrect placement: Objects appear in the wrong relative position.</li>
<li>Missing entities: One or more requested objects never appear.</li>
<li>Merged entities: Two objects get mashed into a single, incoherent form.</li>
</ul>

The model “knows” the objects you asked for, but it doesn’t reliably understand where to place them.

<h1>
 
 
 Why Fine-Tuning and Handcrafted Losses Are Not Enough
</h1>

Two broad strategies have tried to patch this gap:

<h2>
 
 
 Fine-tuning for spatial awareness
</h2>

<ul>
<li>Retrain the diffusion model on datasets with explicit layouts or spatial annotations.</li>
<li>Methods like COMPASS show that this can significantly improve spatial accuracy.</li>
<li>But this comes at a cost: expensive retraining, sensitivity to dataset bias, and often regressions in other capabilities such as color fidelity or counting.</li>
</ul>

<h2>
 
 
 Handcrafted test-time losses
</h2>

<ul>
<li>At inference, inject extra loss terms that penalize spatial errors (e.g., overlapping activation maps, incorrect ordering).</li>
<li>These losses must be manually designed to approximate relations like “left of” or “above”.</li>
<li>In practice, these heuristics are fragile, often over-fitting simple cases and failing on more complex layouts.</li>
</ul>

In short, we’ve lacked a solution that is:

<ul>
<li>Data-driven rather than rule-based</li>
<li>Plug-and-play at inference time (no full retraining)</li>
<li>Targeted enough to improve spatial reasoning without damaging other strengths</li>
</ul>

This is where Learn-to-Steer enters.

<h1>
 
 
 How Learn-to-Steer Works: Data-Driven Steering at Inference
</h1>

<h2>
 
 
 How Cross-Attention Maps Provide a Spatial Signal
</h2>

During diffusion, at each denoising step, the model computes cross-attention maps that connect text tokens to image regions. For a prompt like “a dog to the right of a teddy bear”, you can think of:

<ul>
<li>One set of attention maps for “dog”</li>
<li>Another set for “teddy bear”</li>
<li>Additional context around words like “right” or “of”</li>
</ul>

These maps form a rich, high-dimensional signal describing where in the image the model currently believes each word should manifest. Prior work has used cross-attention to locate objects or edit images; Learn-to-Steer goes further by treating them as a feature space in which spatial relations can be learned.

<h2>
 
 
 How a Relation Classifier Becomes a Learned Loss
</h2>

The core idea of Learn-to-Steer is to train a small relation classifier that takes cross-attention maps for two objects and predicts the spatial relation between them (left-of, right-of, above, below, etc.).

The pipeline looks like this:

<h3>
 
 
 Collect supervision
</h3>

<ul>
<li>Use images where the true relation between object A and object B is known (from datasets like GQA and synthetic layouts).</li>
<li>For each image, invert it through the diffusion model with a descriptive prompt to recover cross-attention maps for the relevant tokens.</li>
</ul>

<a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fi9dbjsdc4c8yjz2r88k4.jpg" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fi9dbjsdc4c8yjz2r88k4.jpg" alt=" " width="800" height="446"></a>

<h3>
 
 
 Train a classifier on attention patterns
</h3>

<ul>
<li>Input: attention maps for object A and object B.</li>
<li>Output: predicted relation (e.g., “A is left of B”).</li>
</ul>

Naively, however, this leads to a subtle but serious issue: relation leakage.

<h2>
 
 
 How Dual Inversion Solves the “Relation Leakage” Problem
</h2>

If you always invert images with a correct prompt (e.g., “a dog to the left of a cat”), hints about the word “left” can leak into the attention patterns. A naïve classifier might then “cheat” by reading out linguistic artefacts instead of learning genuine visual geometry.

To prevent this, Learn-to-Steer uses a dual inversion strategy:

<ul>
<li>For each image with a true relation (say, dog left of cat), create two prompts:

<ul>
<li>A positive prompt with the correct relation (“dog to the left of a cat”).</li>
<li>A negative prompt with an incorrect relation (“dog above a cat”).</li>
</ul>


</li>

<li>Run inversion with both prompts, obtaining two sets of attention maps.</li>

<li>Label both sets with the true relation (left-of), because that is what the image actually depicts.</li>

</ul>

The classifier sees pairs of attention maps that share the same underlying geometry but differ in the relation words used in the prompt. To succeed, it must ignore the unreliable linguistic cue and zero in on the geometric evidence in the attention patterns. This breaks the leakage shortcut and yields a classifier that actually understands “left-of” in terms of where things appear in the model’s internal vision.

To improve robustness, NVIDIA combines:

<ul>
<li>Real images (complex, natural scenes)</li>
<li>Synthetic images (simpler, cleaner attention patterns akin to generation scenarios)</li>
</ul>

<h1>
 
 
 How Learn-to-Steer Guides Images During Generation
</h1>

<h2>
 
 
 Step-by-Step: From Prompt to Steered Latent
</h2>

Once the relation classifier is trained, Learn-to-Steer uses it at inference time as a learned objective:

<h3>
 
 
 Parse the spatial prompt
</h3>

<ul>
<li>Extract subject, relation, and object from the text (e.g., subject = dog, relation = right-of, object = teddy bear).</li>
</ul>

<h3>
 
 
 Run diffusion as usual—but with checkpoints
</h3>

<ul>
<li>As the model denoises latent noise into an image, periodically extract cross-attention maps for the subject and object tokens.</li>
</ul>

<h3>
 
 
 Evaluate spatial correctness
</h3>

<ul>
<li>Feed these maps into the relation classifier, which outputs a probability distribution over relations.</li>
<li>Compare this distribution to the desired relation from the prompt, and compute a loss (e.g., cross-entropy).</li>
</ul>

<h3>
 
 
 Backpropagate into the latent
</h3>

<ul>
<li>Compute the gradient of this loss with respect to the latent representation at that timestep.</li>
<li>Nudge the latent in the direction that increases the classifier’s confidence in the correct relation.</li>
</ul>

<h3>
 
 
 Continue the diffusion process
</h3>

<ul>
<li>Let the denoising proceed from the adjusted latent.</li>
<li>Repeat this steering a number of times (often during the earlier half of the diffusion steps).</li>
</ul>

<h2>
 
 
 Support for Multiple Architectures and Relations
</h2>

<a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F578a9bjc7gmtemh0jbsj.jpg" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F578a9bjc7gmtemh0jbsj.jpg" alt=" " width="800" height="477"></a>

A key advantage of Learn-to-Steer is that it’s architecture-agnostic:

<ul>
<li>It has been demonstrated on both UNet-based models (like Stable Diffusion 1.4/2.1) and MMDiT-style models (like Flux).</li>
<li>The only requirement is access to a text-image alignment signal (cross-attention or similar).</li>
</ul>

It can also handle prompts with multiple constraints, such as:

“A frog above a sneaker below a teapot.”

Here, Learn-to-Steer alternates attention between relations:

<ul>
<li>At one timestep, optimize the frog–sneaker relation.</li>
<li>At another, optimize the sneaker–teapot relation.</li>
</ul>

يهدف Learn-to-Steer من NVIDIA إلى معالجة قيد كبير في نماذج الانتشار من النص إلى الصورة، التي تعاني من ضعف في التفكير المكاني الأساسي. يمكن لهذه النماذج إنشاء صور فوتوغرافية واقعية، لكنها غالبًا ما تضع الأشياء في غير موضعها، مثل وضع كلب على اليسار بدلاً من اليمين بجانب دمية دب. تهدف هذه الخطوة إلى تحسين دقة الصور المولدة من خلال تعزيز الفهم المكاني.

El Learn-to-Steer de NVIDIA busca abordar una limitación significativa en los modelos de difusión de texto a imagen, que luchan con el razonamiento espacial básico. Estos modelos pueden crear imágenes fotorealistas, pero a menudo colocan mal los objetos en relación entre sí, como poner un perro a la izquierda de un oso de peluche en lugar de a la derecha. Este avance tiene como objetivo mejorar la precisión de las imágenes generadas al mejorar la comprensión espacial.

Le Learn-to-Steer de NVIDIA vise à résoudre une limitation importante des modèles de diffusion texte-image, qui ont du mal avec le raisonnement spatial de base. Ces modèles peuvent créer des images photoréalistes mais placent souvent mal les objets les uns par rapport aux autres, comme mettre un chien à gauche d'un ours en peluche au lieu de la droite. Cette avancée vise à améliorer l'exactitude des images générées en renforçant la compréhension spatiale.

NVIDIA's Learn-to-Steer is set to address a significant limitation in text-to-image diffusion models, which struggle with basic spatial reasoning. These models can create photorealistic images but often misplace objects in relation to one another, such as placing a dog to the left of a teddy bear instead of the right. This advancement aims to enhance the accuracy of generated images by improving spatial understanding.

What Is Learn-to-Steer? NVIDIA’s 2025 Spatial Fix for Text-to-Image Diffusion

The $5tn firm handily beat expectations, but analysts are awaiting projections for future demand for firm’s AI chipsNvidia shares are rising in after-market trading after the company posted third quarter earnings that beat Wall Street estimates. All eyes were on Nvidia, the bellwether for the AI industry and the most valuable publicly traded company in the world, as analysts and investors hoped the chipmaker’s third-quarter earnings would assuage concerns about whether the high-flying valuations of AI firms have peaked.“Blackwell sales are off the charts, and cloud GPUs are sold out,” said Jensen Huang, founder and CEO of Nvidia in a press release. “Compute demand keeps accelerating and compounding across training and inference – each growing exponentially. We’ve entered the virtuous cycle of AI. The AI ecosystem is scaling fast – with more new foundation model makers, more AI startups, across more industries, and in more countries. AI is going everywhere, doing everything, all at once.” <a href="https://www.theguardian.com/technology/2025/nov/19/nvidia-earning-report">Continue reading...</a>

تجاوزت شركة إنفيديا توقعات وول ستريت مع نتائجها للربع الثالث، مما أظهر طلبًا قويًا على شرائح الذكاء الاصطناعي الخاصة بها. ارتفعت أسهم الشركة في التداول بعد السوق، مما يعكس ثقة المستثمرين وسط مخاوف بشأن تقييم سوق الذكاء الاصطناعي. وأبرز الرئيس التنفيذي جينسن هوانغ مبيعات قياسية ونظامًا بيئيًا سريع التوسع في مجال الذكاء الاصطناعي، مما يشير إلى نظرة إيجابية لمستقبل الشركة.

Nvidia superó las expectativas de Wall Street con sus ganancias del tercer trimestre, mostrando una fuerte demanda por sus chips de IA. Las acciones de la compañía aumentaron en el comercio posterior al cierre, reflejando la confianza de los inversores en medio de preocupaciones sobre la valoración del mercado de IA. El CEO Jensen Huang destacó las ventas récord y un ecosistema de IA en rápida expansión, indicando una perspectiva positiva para el futuro de la empresa.

Nvidia a dépassé les attentes de Wall Street avec ses résultats du troisième trimestre, montrant une forte demande pour ses puces d'IA. Les actions de l'entreprise ont augmenté lors des échanges après la clôture, reflétant la confiance des investisseurs face aux préoccupations concernant la valorisation du marché de l'IA. Le PDG Jensen Huang a souligné des ventes record et un écosystème IA en pleine expansion, indiquant une perspective positive pour l'avenir de l'entreprise.

Nvidia exceeded Wall Street expectations with its third-quarter earnings, showcasing strong demand for its AI chips. The company's shares rose in after-market trading, reflecting investor confidence amid concerns about the AI market's valuation. CEO Jensen Huang highlighted record sales and a rapidly expanding AI ecosystem, indicating a positive outlook for the company's future.

‘AI is going everywhere, doing everything:’ Nvidia beats Wall Street estimates amid market selloff and AI bubble fears

The SanDisk ExtremeFit USB-C flash drive is barely three grams, but offers 1TB of external storage and impressive speeds.

I refused to believe this coin-sized gadget was a storage drive, until I tried it for myself

Swift 6.3 is bringing significant enhancements to Embedded Swift, the subset of Swift designed for resource-constrained environments like microcontrollers. Here's what's new:

<h2>
 
 
 Key Improvements
</h2>

<h3>
 
 
 Libraries &amp; Standard Library
</h3>

<ul>
<li>
Floating-point printing: The <code>description</code> and <code>debugDescription</code> properties now work for Float, Double, and other floating-point types with a new all-Swift implementation</li>
<li>
Better diagnostics: New <code>EmbeddedRestrictions</code> diagnostic group warns about unsupported language constructs</li>
<li>
Swift MMIO 0.1.x: Includes code generation from SVD files and improved debugging with SVD2LLDB plugin</li>
</ul>

<h3>
 
 
 C Interoperability
</h3>

<ul>
<li>
<code>@c</code> attribute: Define C-compatible functions and enums (from SE-0495)
</li>
</ul>

<div class="highlight js-code-highlight">
<pre class="highlight swift"><code>@c(MyLib_initialize)
public func initialize() { ... }
</code></pre>

</div>



<ul>
<li>
Improved type matching: Better tolerance for mismatching C signatures, eliminating cryptic deserialization errors</li>
</ul>

<h3>
 
 
 Debugging
</h3>

<ul>
<li>
Enhanced LLDB support: Better value printing for Embedded Swift types</li>
<li>
Core dump inspection: Dictionary, Array, and other common types now inspectable without a live process</li>
<li>
ARMv7m exception unwinding: Complete backtraces through exception frames</li>
</ul>

<h3>
 
 
 Linking &amp; Compilation
</h3>

<ul>
<li>
<code>@section</code> and <code>@used</code> attributes: Control where globals are emitted and ensure symbols aren't stripped (SE-0492)</li>
<li>
Weak symbol definitions: Fixes duplicate symbol errors in diamond dependencies</li>
<li>
<code>@export</code> attribute: Better control over function visibility (SE-0497)</li>
</ul>




Want to dive deeper? Read the <a href="https://www.swift.org/blog/embedded-swift-improvements-coming-in-swift-6.3/" rel="noopener noreferrer">full announcement</a> on Swift.org

تقدم Swift 6.3 تحسينات كبيرة على Embedded Swift، مما يعزز وظيفته في البيئات ذات الموارد المحدودة مثل المتحكمات الدقيقة. تشمل التحسينات الرئيسية قدرات جديدة لطباعة الأعداد العشرية، وتشخيصات أفضل مع مجموعة EmbeddedRestrictions، وإدخال Swift MMIO 0.1.x لتوليد الشيفرة وتصحيح الأخطاء.

Swift 6.3 presenta mejoras significativas en Embedded Swift, aumentando su funcionalidad para entornos con recursos limitados como microcontroladores. Las mejoras clave incluyen nuevas capacidades de impresión de números de punto flotante, mejores diagnósticos con el grupo EmbeddedRestrictions y la introducción de Swift MMIO 0.1.x para la generación de código y la depuración.

Swift 6.3 apporte des améliorations significatives à Embedded Swift, renforçant sa fonctionnalité pour les environnements à ressources limitées comme les microcontrôleurs. Les principales améliorations comprennent de nouvelles capacités d'impression de nombres à virgule flottante, de meilleurs diagnostics avec le groupe EmbeddedRestrictions, et l'introduction de Swift MMIO 0.1.x pour la génération de code et le débogage.

Swift 6.3 introduces significant upgrades to Embedded Swift, enhancing its functionality for resource-constrained environments like microcontrollers. Key improvements include new floating-point printing capabilities, better diagnostics with the EmbeddedRestrictions group, and the introduction of Swift MMIO 0.1.x for code generation and debugging.

Embedded Swift Gets Major Upgrades in Swift 6.3

<a href="https://www.techspot.com/news/110317-judge-dismisses-lawsuit-twice-due-alleged-deepfake-video.html" target="_blank"><img src="https://www.techspot.com/images2/news/ts3_thumbs/2025/11/2025-11-19-ts3_thumbs-252.jpg" width="800" height="560" style="padding: 15px 0" title="Judge dismisses lawsuit twice due to alleged deepfake video testimony" /></a> A California housing dispute is getting media attention over allegations that lawyers presented a deepfake video as witness testimony. NBC News reports that Judge Victoria Kolakowski became suspicious after the supposed witness showed signs that something was not right, including a monotone voice, fuzzy facial features, and repeated facial expressions.... <a href="https://www.techspot.com/news/110317-judge-dismisses-lawsuit-twice-due-alleged-deepfake-video.html">Read Entire Article</a>

تجذب نزاع سكني في كاليفورنيا الانتباه الإعلامي بعد ظهور مزاعم بأن المحامين قدموا فيديو مزيف كدليل شهود. أعربت القاضية فيكتوريا كولاكوفسكي عن شكوكها بشأن الفيديو، مشيرة إلى صوت الشاهد الأحادي، وملامح الوجه غير الواضحة، وتكرار التعبيرات. أدى ذلك إلى رفض الدعوى القضائية مرتين.

Una disputa de vivienda en California ha llamado la atención de los medios tras las alegaciones de que los abogados presentaron un video deepfake como testimonio. La jueza Victoria Kolakowski expresó su escepticismo sobre el video, señalando la voz monótona del testigo, rasgos faciales borrosos y expresiones repetitivas. Esto llevó al desestimado de la demanda en dos ocasiones.

Un litige immobilier en Californie suscite l'attention des médias après des allégations selon lesquelles des avocats auraient présenté une vidéo deepfake comme témoignage. La juge Victoria Kolakowski a exprimé des doutes sur la vidéo, notant la voix monotone du témoin, des traits faciaux flous et des expressions répétitives. Cela a conduit à l'annulation de la poursuite à deux reprises.

A California housing dispute has drawn attention after allegations surfaced that lawyers presented a deepfake video as witness testimony. Judge Victoria Kolakowski expressed skepticism about the video, noting the witness's monotone voice, unclear facial features, and repetitive expressions. This led to the dismissal of the lawsuit on two occasions.

Judge dismisses lawsuit twice due to alleged deepfake video testimony

Two weeks ago I read a line about tool use with Claude that stuck in my head. Paraphrased:

<blockquote>
Direct tool calls don’t really scale. 
Have the model write code that uses tools, and execute that code instead.
</blockquote>

At the same time, I was knee-deep in wiring a JavaScript execution environment into Contenox, my self-hosted runtime for deterministic, chat-native AI workflows.

So of course the thought was:

<blockquote>
What if I just let the model write the JavaScript and run it inside the runtime? 😅
</blockquote>

This post is about what happened when I tried exactly that.




<h2>
 
 
 What is Contenox?
</h2>

Very short version:

<blockquote>
Contenox is a self-hostable runtime for sovereign GenAI applications. 
It models AI behavior as explicit state machines, not opaque prompt chains.
</blockquote>

Some key properties:

Runtime, not a library, Explicit state machines, Chat-native interface, Vendor-agnostic &amp; self-hosted, written in go with lots of passion and zero tolerance for shortcuts




<h2>
 
 
 The experiment: ask it to fetch and summarize a TODO
</h2>

Once the JS execution hook was in place (a Goja VM with some globals exposed), I wired up a new state machine: 


<div class="highlight js-code-highlight">
<pre class="highlight plaintext"><code>mux_input → moderate → generate_js → run_js → eval_js_result → (repair_js?) → answer
</code></pre>

</div>



Then I opened the Contenox chat UI and typed:

<blockquote>
Me: “Hey Contenox, fetch the TODO item from jsonplaceholder and summarize it.”
</blockquote>

The first attempt didn’t go as planned.

Instead of a neat summary, I got:

<blockquote>
Contenox: 
“I'm sorry, but it seems there was a problem fetching the TODO item from the provided URL due to an Internal Server Error (status code: 500). Unfortunately, I couldn't retrieve any data at this time. My apologies for the inconvenience!”
</blockquote>

I checked manually – Cloudflare was having a moment, JSONPlaceholder was behind it, and the request really was failing. Okay, fair. Long coffee break.

A bit later, I went back to the same chat and typed:

<blockquote>
Me: “okay let's try again now.”
</blockquote>

This time the reply was:

<blockquote>
Contenox: 
<code>Task titled "delectus aut autem" is incomplete. Nothing to summarize here.</code>
</blockquote>

Wait. It worked? Just like that?




<h2>
 
 
 Under the hood: LLM → JavaScript → tools → LLM
</h2>

Looking at the execution trace made it clear what happened.

<h3>
 
 
 1. Input is normalized and moderated
</h3>

The workflow starts with:

<ol>
<li><code>mux_input</code></li>
</ol>

<ul>
<li>
Parses the incoming chat history into a normalized <code>messages</code> array.

<ol>
<li><code>moderate</code></li>
</ol>
</li>
<li>Uses a small model to classify the input as safe/unsafe.</li>
<li>Output: <code>0</code> → safe → continue.</li>
</ul>

So far this is standard workflow stuff.

<h3>
 
 
 2. <code>generate_js</code>: the LLM writes JavaScript
</h3>

Next, the <code>generate_js</code> task runs with a system instruction like:

<ul>
<li>“You are an expert JavaScript agent for the Contenox runtime…”</li>
<li>“You must output JSON: { "code": "" }”</li>
<li>“You can use httpFetch, executeTask, executeTaskChain, …”</li>
<li>“Script must be synchronous, no async/await, and must return a JSON-serializable object.”</li>
</ul>

Given the chat history, the model produced something like this (simplified): 


<div class="highlight js-code-highlight">
<pre class="highlight json"><code>{
 "code": "var result;
var answer;
var meta = { steps: [] };

var todoUrl = \"https://jsonplaceholder.typicode.com/todos/1\";

// Step 1: Fetch the TODO item
meta.steps.push(\"Fetching TODO item from \" + todoUrl);
var fetchResponse = httpFetch(todoUrl);

if (!fetchResponse.ok) {
 answer = \"I couldn't access that item right now because there was some trouble with getting it. Please check the URL or try again later.\";
 meta.error = {
 message: \"HTTP fetch failed\",
 url: todoUrl,
 status: fetchResponse.status,
 statusText: fetchResponse.statusText,
 body: fetchResponse.body,
 error: fetchResponse.error
 };
 result = { answer: answer, meta: meta };
} else {
 var todoItem;
 try {
 todoItem = JSON.parse(fetchResponse.body);
 meta.raw = todoItem;
 } catch (e) {
 answer = \"I fetched the item, but I couldn't understand its format. It doesn't seem to be valid JSON.\";
 meta.error = {
 message: \"JSON parse error\",
 rawBody: fetchResponse.body,
 parseError: e.toString()
 };
 result = { answer: answer, meta: meta };
 }

 if (todoItem) {
 // Step 2: Summarize the TODO item using an LLM
 meta.steps.push(\"Summarizing TODO item with LLM\");
 var prompt = \"Summarize the following TODO item in a concise sentence: \" + JSON.stringify(todoItem);
 var summarizeTask = executeTask(prompt, \"gpt-4o-mini\", \"openai\");

 if (!summarizeTask.success) {
 answer = \"While I managed to fetch your TODO details, the summary tool had a hiccup and couldn't provide me with a concise sentence right now. We seem to be experiencing some temporary issues.\";
 meta.error = {
 message: \"LLM task execution failed\",
 task_id: summarizeTask.task_id,
 error: summarizeTask.error
 };
 result = { answer: answer, meta: meta };
 } else {
 answer = summarizeTask.response;
 meta.llmResponse = summarizeTask.response;
 result = { answer: answer, meta: meta };
 }
 }
}

result;"
}
</code></pre>

</div>



A few important details here:

<ul>
<li>
It encodes error handling:

<ul>
<li>If HTTP fails → user-friendly <code>answer</code>, raw error in <code>meta.error</code>.</li>
<li>If JSON parse fails → similar pattern.</li>
<li>If the inner LLM call fails → fallback message.</li>
</ul>


</li>

<li>It delegates the actual summarization to another model via <code>executeTask</code>.</li>

<li>It returns a structured <code>result</code> with both <code>answer</code> and <code>meta</code>.</li>

</ul>

This is not the model “calling tools” directly. It’s the model writing a program that calls tools.

<h3>
 
 
 3. <code>run_js</code>: execute the code in a sandbox
</h3>

The next task is <code>run_js</code>, which is just a Contenox <code>hook</code> that calls the JS sandbox: 


<div class="highlight js-code-highlight">
<pre class="highlight json"><code>{
 "name": "js_sandbox",
 "tool_name": "execute_js",
 "args": {
 "code": "{{.generate_js.code}}"
 }
}
</code></pre>

</div>



Inside the trace you can see:

<ul>
<li>An <code>httpFetch</code> log for the JSONPlaceholder URL.</li>
<li>A response with <code>status: 200 OK</code> when things finally worked.</li>
<li>
An <code>executeTask</code> log with the summarization prompt:

<ul>
<li><code>Summarize the following TODO item in a concise sentence: {"userId":1,"id":1,"title":"delectus aut autem","completed":false}</code></li>
</ul>


</li>

</ul>

The sandbox result looked roughly like: 


<div class="highlight js-code-highlight">
<pre class="highlight json"><code>{
 "ok": true,
 "result": {
 "answer": "Task titled \"delectus aut autem\" is incomplete.",
 "meta": {
 "llmResponse": "Task titled \"delectus aut autem\" is incomplete.",
 "raw": {
 "userId": 1,
 "id": 1,
 "title": "delectus aut autem",
 "completed": false
 },
 "steps": [
 "Fetching TODO item from https://jsonplaceholder.typicode.com/todos/1",
 "Summarizing TODO item with LLM"
 ]
 }
 },
 "logs": [ ... ],
 "code": "var result; ..."
}
</code></pre>

</div>



<h3>
 
 
 4. <code>eval_js_result</code>: success or retry?
</h3>

Now comes the evaluator:

<ul>
<li>It receives a description of the JS sandbox output.</li>
<li>
The system prompt is very strict:

<ul>
<li>If <code>ok</code> is true and there is a non-empty <code>result.answer</code> → respond with <code>success</code>.</li>
<li>Otherwise → respond with <code>retry</code>.</li>
</ul>


</li>

</ul>

On the successful run, it answered: 


<div class="highlight js-code-highlight">
<pre class="highlight plaintext"><code>success
</code></pre>

</div>



So the workflow does not go into <code>repair_js</code> or <code>run_js_retry</code>. Happy path.

<h3>
 
 
 5. <code>answer</code>: extract the final user message
</h3>

The final task, <code>answer</code>, is intentionally boring:

<ul>
<li>System prompt: “You are a purely extractive post-processor. Do NOT invent content. Just surface the best existing <code>answer</code> field.”
</li>
<li>
It gets:

<ul>
<li>First run (<code>run_js</code> result).</li>
<li>Second run (<code>run_js_retry</code>), if any.</li>
</ul>


</li>

<li>

Selection rule:

<ul>
<li>Take the last non-empty <code>answer</code> you see.</li>
<li>Output it verbatim.</li>
</ul>


</li>

</ul>

In our case it found: 


<div class="highlight js-code-highlight">
<pre class="highlight plaintext"><code>Task titled "delectus aut autem" is incomplete.
</code></pre>

</div>



And that’s exactly what Contenox replied in chat.




<h2>
 
 
 Why this is interesting (to me, at least)
</h2>

What I originally set out to build:

<blockquote>
A runtime for deterministic, observable GenAI workflows. 
Tasks, transitions, hooks – all explicit and replayable.
</blockquote>

What I accidentally stumbled into:

<blockquote>
A multi-model, self-orchestrating agent pattern, 
where LLMs write code that uses tools, and the runtime executes and evaluates that code.
</blockquote>

The pattern looks like this:

<ol>
<li>
Planner LLM (<code>generate_js</code>)</li>
</ol>

<ul>
<li>Reads user intent + history.</li>
<li>Emits JavaScript that calls <code>httpFetch</code>, <code>executeTask</code>, <code>executeTaskChain</code>, hooks, etc.</li>
</ul>

<ol>
<li>
Execution environment (<code>run_js</code> in Goja)</li>
</ol>

<ul>
<li>Deterministic execution of that JS.</li>
<li>Full logs of every HTTP call, every inner LLM call, every step.</li>
</ul>

<ol>
<li>
Controller LLM (<code>eval_js_result</code>)</li>
</ol>

<ul>
<li>Looks at the sandbox result.</li>
<li>Decides: is this good enough? Retry? Repair?</li>
</ul>

<ol>
<li>
Repair LLM (<code>repair_js</code>, if needed)</li>
</ol>

<ul>
<li>Gets the previous code + error output.</li>
<li>Writes a fixed version of the JS.</li>
</ul>

<ol>
<li>
Answer LLM (<code>answer</code>)</li>
</ol>

<ul>
<li>Doesn’t “reason” at all.</li>
<li>Just extracts the final <code>answer</code> text safely.</li>
</ul>

All of that is expressed as an explicit state machine in Contenox.

No hidden loops, no undocumented retries, no magic glue code inside some SDK. It’s all visible in the workflow graph and trace.




To me, that’s the exciting part:

<blockquote>
You don’t have to choose between “boring deterministic workflows” and “fancy agents”. 
You can build the agent on top of deterministic workflows. 
And everything stays **self-hosted, inspectable, and auditable if you want.
</blockquote>

يتناول المقال تجربة تم فيها السماح لنموذج ذكاء اصطناعي بكتابة كود جافا سكريبت داخل بيئة مستقلة تُدعى كونتينوكس. يتأمل الكاتب في مفهوم يتعلق باستخدام الأدوات في الذكاء الاصطناعي، مقترحًا أن النماذج يجب أن تولد كودًا لاستخدام الأدوات بدلاً من إجراء مكالمات مباشرة. تم اختبار هذا النهج من خلال تنفيذ كود جافا سكريبت الذي تم إنشاؤه داخل بيئة كونتينوكس، بهدف تحسين كفاءة سير العمل في الذكاء الاصطناعي.

El artículo discute un experimento en el que se permitió a un modelo de IA escribir código JavaScript dentro de un entorno autónomo llamado Contenox. El autor reflexiona sobre un concepto relacionado con el uso de herramientas en IA, sugiriendo que los modelos deberían generar código para utilizar herramientas en lugar de realizar llamadas directas. Este enfoque se probó ejecutando el JavaScript generado dentro del entorno Contenox, con el objetivo de mejorar la eficiencia de los flujos de trabajo de IA.

L'article traite d'une expérience où un modèle d'IA a été autorisé à écrire du code JavaScript au sein d'un environnement autonome appelé Contenox. L'auteur réfléchit à un concept concernant l'utilisation des outils en IA, suggérant que les modèles devraient générer du code pour utiliser des outils plutôt que d'effectuer des appels directs. Cette approche a été testée en exécutant le JavaScript généré dans l'environnement Contenox, visant à améliorer l'efficacité des flux de travail en IA.

The article discusses an experiment where an AI model was allowed to write JavaScript code within a self-hosted runtime called Contenox. The author reflects on a concept regarding tool usage in AI, suggesting that models should generate code to utilize tools instead of direct calls. This approach was tested by executing the generated JavaScript within the Contenox environment, aiming to enhance the efficiency of AI workflows.

I Let an LLM Write JavaScript Inside My AI Runtime. Here’s What Happened

<h1>
 
 
 Sector HQ Weekly Digest - November 17, 2025
</h1>

Who's shipping vs who's just talking? Here's this week's AI industry intelligence.

<h2>
 
 
 🏆 Top 10 Companies
</h2>

<ol>
<li>
OpenAI - Score: 442385.7 | 343 events this week</li>
<li>
Anthropic - Score: 266113.1 | 51 events this week</li>
<li>
Amazon - Score: 135242.8 | 22 events this week</li>
<li>
Nvidia - Score: 128360.7 | 161 events this week</li>
<li>
Google - Score: 111135.8 | 125 events this week</li>
<li>
Microsoft - Score: 95559.7 | 99 events this week</li>
<li>
Meta - Score: 78015.6 | 61 events this week</li>
<li>
Apple - Score: 64936.3 | 94 events this week</li>
<li>
Tesla - Score: 41242.1 | 12 events this week</li>
<li>
AMD - Score: 40235.9 | 43 events this week</li>
</ol>

<h2>
 
 
 📈 Biggest Movers
</h2>

<ul>
<li>↑ Sony jumped 277 positions to #58</li>
<li>↑ Stability AI jumped 183 positions to #78</li>
<li>↑ Bytedance jumped 143 positions to #68</li>
<li>↑ Scale AI jumped 122 positions to #66</li>
<li>↑ Palantir jumped 107 positions to #17</li>
</ul>

<h2>
 
 
 🚨 Hype Gap Alerts
</h2>

No high hype alerts this week

<h2>
 
 
 📊 Key Insights
</h2>

<ul>
<li>Total companies tracked: 100</li>
<li>Total events this week: 1317</li>
<li>Average activity per company: 13.2 events</li>
</ul>

<h2>
 
 
 💡 What This Means
</h2>

The AI industry continues to evolve rapidly. Companies that ship consistently rise in our rankings, while those focused on hype alone get flagged by our Hype Gap detector.

Methodology: Our leaderboard tracks real product releases, funding events, partnerships, and market traction - not just PR and social media buzz.




Want real-time updates? Check out the live leaderboard at <a href="https://sectorhq.co" rel="noopener noreferrer">sectorhq.co</a>

Track specific companies and get instant alerts when they move in the rankings.

<h2>
 
 
 Tags
</h2>

<h1>
 
 
 AI #ArtificialIntelligence #MachineLearning #TechIndustry #Startups #AILeaderboard
</h1>

تسليط الضوء على أحدث التطورات في صناعة الذكاء الاصطناعي في نشرة قطاع HQ الأسبوعية بتاريخ 17 نوفمبر 2025، مع التركيز على أداء الشركات الرائدة. تتصدر OpenAI القائمة برصيد 442385.7 و343 حدثًا، تليها Anthropic وAmazon. كما يشير التقرير إلى تحركات كبيرة، حيث قفزت Sony 277 مركزًا في الترتيب، مما يعكس الطبيعة الديناميكية للقطاع.

El Digest semanal de Sector HQ del 17 de noviembre de 2025 destaca los últimos desarrollos en la industria de la IA, centrándose en el rendimiento de las principales empresas. OpenAI lidera con una puntuación de 442385.7 y 343 eventos, seguido de Anthropic y Amazon. El informe también señala movimientos significativos, con Sony saltando 277 posiciones en el ranking, reflejando la naturaleza dinámica del sector de la IA.

Le Digest hebdomadaire du Secteur HQ du 17 novembre 2025 met en lumière les derniers développements de l'industrie de l'IA, en se concentrant sur la performance des principales entreprises. OpenAI est en tête avec un score de 442385,7 et 343 événements, suivi d'Anthropic et d'Amazon. Le rapport note également des mouvements significatifs, Sony ayant grimpé de 277 positions dans le classement, reflétant la nature dynamique du secteur de l'IA.

The Sector HQ Weekly Digest for November 17, 2025, highlights the latest developments in the AI industry, focusing on the performance of top companies. OpenAI leads with a score of 442385.7 and 343 events, followed by Anthropic and Amazon. The report also notes significant movements, with Sony jumping 277 positions in the rankings, reflecting the dynamic nature of the AI sector.

Sector HQ Weekly Digest - November 17, 2025

arXiv:2511.10693v1 Announce Type: new 
Abstract: Voice-based artificial intelligence is increasingly expected to adhere to human social conventions, but can it learn implicit cues that are not explicitly programmed? This study investigates whether state-of-the-art text-to-speech systems have internalized the human tendency to reduce speech rate to convey politeness - a non-obvious prosodic marker. We prompted 22 synthetic voices from two leading AI platforms (AI Studio and OpenAI) to read a fixed script under both "polite and formal" and "casual and informal" conditions and measured the resulting speech duration. Across both AI platforms, the polite prompt produced slower speech than the casual prompt with very large effect sizes, an effect that was statistically significant for all of AI Studio's voices and for a large majority of OpenAI's voices. These results demonstrate that AI can implicitly learn and replicate psychological nuances of human communication, highlighting its emerging role as a social actor capable of reinforcing human social norms.

تدرس دراسة حديثة نُشرت على arXiv ما إذا كانت أنظمة تحويل النص إلى كلام المتقدمة يمكن أن تتعلم الفروق الاجتماعية، وخاصة الميل البشري لتقليل سرعة الكلام للتعبير عن الأدب. اختبر الباحثون 22 صوتًا صناعيًا من AI Studio وOpenAI في ظروف رسمية وغير رسمية، ووجدوا أن العبارات المهذبة أدت إلى كلام أبطأ بشكل كبير عبر كلا النظامين. يشير هذا إلى أن الذكاء الاصطناعي يمكن أن يستوعب ويعيد إنتاج الإشارات النفسية الدقيقة في التواصل البشري.

Un estudio reciente publicado en arXiv investiga si los sistemas avanzados de texto a voz pueden aprender matices sociales, específicamente la tendencia humana a reducir la velocidad del habla para transmitir cortesía. Los investigadores probaron 22 voces sintéticas de AI Studio y OpenAI en condiciones de cortesía y casualidad, encontrando que los mensajes corteses resultaron en un habla significativamente más lenta en ambas plataformas. Esto sugiere que la IA puede internalizar y replicar sutiles señales psicológicas en la comunicación humana.

Une étude récente publiée sur arXiv examine si les systèmes avancés de synthèse vocale peuvent apprendre des nuances sociales, en particulier la tendance humaine à ralentir la parole pour exprimer la politesse. Les chercheurs ont testé 22 voix synthétiques d'AI Studio et d'OpenAI dans des conditions polies et décontractées, constatant que les prompts polis entraînaient un discours significativement plus lent sur les deux plateformes. Cela suggère que l'IA peut intérioriser et reproduire des indices psychologiques subtils dans la communication humaine.

A recent study published on arXiv investigates whether advanced text-to-speech systems can learn social nuances, specifically the human tendency to slow speech for politeness. Researchers tested 22 synthetic voices from AI Studio and OpenAI under polite and casual conditions, finding that the polite prompts resulted in significantly slower speech across both platforms. This suggests that AI can internalize and replicate subtle psychological cues in human communication.

Do AI Voices Learn Social Nuances? A Case of Politeness and Speech Rate

As the dust settled on the internet’s latest major disruption, Cloudflare linked the cause of the error to an inflated ‘feature’ configuration file.
Read more: <a rel="nofollow" href="https://www.siliconrepublic.com/enterprise/cloudflare-outage-bots-internet-disruption-cause">What caused the global Cloudflare outage?</a>

نسبت شركة كلاودفلير انقطاعها العالمي الأخير إلى ملف تكوين 'feature' المبالغ فيه. تسبب هذا الحادث في اضطرابات كبيرة عبر منصات الإنترنت المختلفة، مما أثر على المستخدمين في جميع أنحاء العالم.

Cloudflare atribuyó la reciente interrupción global a un archivo de configuración 'feature' inflado. Este incidente causó interrupciones significativas en varias plataformas en línea, afectando a usuarios en todo el mundo.

Cloudflare a attribué la récente panne mondiale à un fichier de configuration 'feature' gonflé. Cet incident a causé des perturbations significatives sur diverses plateformes en ligne, affectant des utilisateurs dans le monde entier.

Cloudflare attributed the recent global outage to an inflated 'feature' configuration file. This incident caused significant disruptions across various online platforms, affecting users worldwide.

What caused the global Cloudflare outage?

LearnUpon's new AI acquisition will allow businesses to generate content 50 times faster, the company said.
Read more: <a rel="nofollow" href="https://www.siliconrepublic.com/start-ups/learnupon-ai-course-creator-courseau-germany-ireland-acquisition">Dublin’s LearnUpon buys German AI course creator Courseau</a>

استحوذت شركة LearnUpon، ومقرها دبلن، على Courseau، وهي منشئ دورات الذكاء الاصطناعي الألماني. من المتوقع أن يمكّن هذا الاستحواذ الشركات من إنتاج المحتوى بسرعة تصل إلى 50 مرة، مما يعزز كفاءة عمليات التعلم والتدريب.

LearnUpon, con sede en Dublín, ha adquirido Courseau, un creador de cursos de IA alemán. Se espera que esta adquisición permita a las empresas generar contenido 50 veces más rápido, mejorando la eficiencia de los procesos de aprendizaje y capacitación.

LearnUpon, basé à Dublin, a acquis Courseau, un créateur de cours AI allemand. Cette acquisition devrait permettre aux entreprises de générer du contenu 50 fois plus rapidement, améliorant ainsi l'efficacité des processus d'apprentissage et de formation.

LearnUpon, based in Dublin, has acquired Courseau, a German AI course creator. This acquisition is expected to enable businesses to generate content 50 times faster, enhancing the efficiency of learning and training processes.

Dublin’s LearnUpon buys German AI course creator Courseau

The State of European Tech Report has highlighted the need for the region to ‘define its future on its own terms'.
Read more: <a rel="nofollow" href="https://www.siliconrepublic.com/business/optimism-europe-technology-sector-4trn-mark-funding-investment">Optimism at 10-year high, as Europe’s technology sector hits $4trn mark</a>

يكشف تقرير حالة التكنولوجيا الأوروبية أن التفاؤل في قطاع التكنولوجيا في أوروبا قد وصل إلى أعلى مستوى له منذ عشر سنوات، حيث تبلغ قيمة القطاع الآن 4 تريليونات دولار. يؤكد التقرير على أهمية أن تحدد أوروبا مستقبلها بمفردها، مشددًا على الحاجة إلى الابتكار والاستثمار في المنطقة.

El Informe sobre el Estado de la Tecnología Europea revela que el optimismo en el sector tecnológico de Europa ha alcanzado un máximo de diez años, con el sector ahora valorado en 4 billones de dólares. El informe enfatiza la importancia de que Europa defina su futuro de manera independiente, destacando la necesidad de innovación e inversión en la región.

Le rapport sur l'état de la technologie européenne révèle que l'optimisme dans le secteur technologique de l'Europe a atteint un niveau record en dix ans, le secteur étant désormais évalué à 4 trillions de dollars. Le rapport souligne l'importance pour l'Europe de définir son avenir de manière indépendante, mettant en avant la nécessité d'innovation et d'investissement dans la région.

The State of European Tech Report reveals that optimism in Europe's technology sector has reached a 10-year high, with the sector now valued at $4 trillion. The report emphasizes the importance of Europe defining its future independently, highlighting the need for innovation and investment in the region.

Intel CEO Lip-Bu Tan to lead AI efforts as CTO departs for OpenAI

Was this article worth reading? Share it