Using laser light instead of traditional mechanics, researchers have built micro-gears that can spin, shift direction, and even power tiny machines. These breakthroughs could soon lead to revolutionary medical tools working at the scale of cells.

حقق العلماء تقدمًا ثوريًا من خلال إنشاء محركات دقيقة أصغر من شعرة الإنسان، باستخدام ضوء الليزر بدلاً من الميكانيكا التقليدية. يمكن أن تدور هذه التروس الدقيقة المبتكرة، وتغير الاتجاه، بل وتغذي آلات صغيرة، مما يمهد الطريق لأدوات طبية ثورية يمكن أن تعمل على مستوى الخلايا. هذا التطور مهم لأنه يفتح آفاقًا جديدة للتطبيقات الطبية، مما قد يحول طريقة تعاملنا مع العلاجات والتشخيصات.

Científicos han logrado un avance revolucionario al crear micromotores más pequeños que un cabello humano, utilizando luz láser en lugar de mecánica tradicional. Estos innovadores microengranajes pueden girar, cambiar de dirección e incluso alimentar pequeñas máquinas, allanando el camino para herramientas médicas revolucionarias que podrían operar a nivel celular. Este desarrollo es significativo ya que abre nuevas posibilidades para aplicaciones médicas, transformando potencialmente nuestra forma de abordar tratamientos y diagnósticos.

Des scientifiques ont réalisé une avancée révolutionnaire en créant des micromoteurs plus petits qu'un cheveu humain, utilisant la lumière laser au lieu de la mécanique traditionnelle. Ces micro-engrenages innovants peuvent tourner, changer de direction et même alimenter de minuscules machines, ouvrant la voie à des outils médicaux révolutionnaires capables d'opérer à l'échelle cellulaire. Ce développement est important car il ouvre de nouvelles possibilités pour les applications médicales, transformant potentiellement notre approche des traitements et des diagnostics.

Scientists have made a groundbreaking advancement by creating micromotors smaller than a human hair, utilizing laser light instead of traditional mechanics. These innovative micro-gears can spin, change direction, and even power tiny machines, paving the way for revolutionary medical tools that could operate at the cellular level. This development is significant as it opens up new possibilities for medical applications, potentially transforming how we approach treatments and diagnostics.

Scientists build micromotors smaller than a human hair

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

<p>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.</p>

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

<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>

<p><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></p>

<p>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.</p>

<p>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.</p>

<p>In this blog, we’ll unpack:</p>

<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>

<p>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.</p>

<p>Several factors contribute:</p>

<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>

<p>Empirical studies confirm three recurring failure modes on spatial benchmarks:</p>

<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>

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

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

<p>Two broad strategies have tried to patch this gap:</p>

<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>

<p>In short, we’ve lacked a solution that is:</p>

<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>

<p>This is where Learn-to-Steer enters.</p>

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

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

<p>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:</p>

<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>

<p>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.</p>

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

<p>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.).</p>

<p>The pipeline looks like this:</p>

<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>

<p><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></p>

<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>

<p>Naively, however, this leads to a subtle but serious issue: relation leakage.</p>

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

<p>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.</p>

<p>To prevent this, Learn-to-Steer uses a dual inversion strategy:</p>

<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>

<p>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.</p>

<p>To improve robustness, NVIDIA combines:</p>

<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>

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

<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>

<p><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></p>

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

<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>

<p>It can also handle prompts with multiple constraints, such as:</p>

<p>“A frog above a sneaker below a teapot.”</p>

<p>Here, Learn-to-Steer alternates attention between relations:</p>

<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

<p>The $5tn firm handily beat expectations, but analysts are awaiting projections for future demand for firm’s AI chips</p><p>Nvidia shares are rising in after-market trading after the company posted third quarter earnings that beat Wall Street estimates.<strong> </strong>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.</p><p>“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.”</p> <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

<p>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:</p>

<h2>
  
  
  Key Improvements
</h2>

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

<ul>
<li>
<strong>Floating-point printing</strong>: 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>
<strong>Better diagnostics</strong>: New <code>EmbeddedRestrictions</code> diagnostic group warns about unsupported language constructs</li>
<li>
<strong>Swift MMIO 0.1.x</strong>: Includes code generation from SVD files and improved debugging with SVD2LLDB plugin</li>
</ul>

<h3>
  
  
  C Interoperability
</h3>

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

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

</div>



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

<h3>
  
  
  Debugging
</h3>

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

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

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




<p><em>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</em></p>

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><br />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....<br /><br /><a href="https://www.techspot.com/news/110317-judge-dismisses-lawsuit-twice-due-alleged-deepfake-video.html">Read Entire Article</a><br /><br />

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

<a href="https://www.techspot.com/news/110296-chip-could-transform-quantum-computing-generating-rainbow-laser.html" target="_blank"><img src="https://www.techspot.com/images2/news/ts3_thumbs/2025/11/2025-11-19-ts3_thumbs-8d2.jpg" width="800" height="560" style="padding: 15px 0" title="This microscopic chip turns one beam of light into three &ndash; and it could reshape quantum computing" /></a><br />For decades, scientists have sought ways to compress the tools of optical science – lasers, lenses, mirrors – onto chips that fit on a fingertip. Such miniaturization is key to unlocking faster communication systems, ultra-precise atomic clocks, and scalable quantum computers that rely on light rather than electronic signals. But...<br /><br /><a href="https://www.techspot.com/news/110296-chip-could-transform-quantum-computing-generating-rainbow-laser.html">Read Entire Article</a><br /><br />

طور العلماء شريحة مجهرية قادرة على تحويل شعاع ضوء واحد إلى ثلاثة أشعة متميزة. من المتوقع أن تعزز هذه الابتكارات بشكل كبير الحوسبة الكمومية من خلال تصغير المكونات البصرية الأساسية مثل الليزر والعدسات على شريحة، مما قد يؤدي إلى أنظمة اتصالات أسرع وأجهزة كمبيوتر كمومية أكثر كفاءة.

Científicos han desarrollado un chip microscópico capaz de transformar un solo haz de luz en tres haces distintos. Esta innovación está destinada a avanzar significativamente en la computación cuántica al miniaturizar componentes ópticos esenciales como láseres y lentes en un chip, lo que podría llevar a sistemas de comunicación más rápidos y computadoras cuánticas más eficientes.

Des scientifiques ont développé une puce microscopique capable de transformer un faisceau de lumière unique en trois faisceaux distincts. Cette innovation pourrait considérablement faire progresser l'informatique quantique en miniaturisant des composants optiques essentiels tels que les lasers et les lentilles sur une puce, ce qui pourrait conduire à des systèmes de communication plus rapides et à des ordinateurs quantiques plus efficaces.

Scientists have developed a microscopic chip capable of transforming a single beam of light into three distinct beams. This innovation is poised to significantly advance quantum computing by miniaturizing essential optical components like lasers and lenses onto a chip, potentially leading to faster communication systems and more efficient quantum computers.

This microscopic chip turns one beam of light into three &ndash; and it could reshape quantum computing

arXiv:2410.15584v3 Announce Type: replace 
Abstract: An in-depth exploration of object detection and semantic segmentation is provided, combining theoretical foundations with practical applications. State-of-the-art advancements in machine learning and deep learning are reviewed, focusing on convolutional neural networks (CNNs), YOLO architectures, and transformer-based approaches such as DETR. The integration of artificial intelligence (AI) techniques and large language models for enhancing object detection in complex environments is examined. Additionally, a comprehensive analysis of big data processing is presented, with emphasis on model optimization and performance evaluation metrics. By bridging the gap between traditional methods and modern deep learning frameworks, valuable insights are offered for researchers, data scientists, and engineers aiming to apply AI-driven methodologies to large-scale object detection tasks.

يقدم هذا المقال استكشافًا عميقًا للكشف عن الكائنات والتجزئة الدلالية، حيث يجمع بين الأسس النظرية والتطبيقات العملية. يتم مراجعة التقدمات في التعلم الآلي والتعلم العميق، مع التركيز بشكل خاص على الشبكات العصبية التلافيفية (CNNs) وهياكل YOLO، وكذلك الأساليب القائمة على المحولات مثل DETR. كما يتم فحص دمج تقنيات الذكاء الاصطناعي ونماذج اللغة الكبيرة لتعزيز الكشف عن الكائنات في البيئات المعقدة، بالإضافة إلى تحليل شامل لمعالجة البيانات الضخمة وتقييم أداء النماذج.

Este artículo ofrece una exploración profunda de la detección de objetos y la segmentación semántica, combinando fundamentos teóricos con aplicaciones prácticas. Se revisan los avances en aprendizaje automático y aprendizaje profundo, centrándose especialmente en redes neuronales convolucionales (CNN), arquitecturas YOLO y enfoques basados en transformadores como DETR. El estudio también examina la integración de técnicas de IA y modelos de lenguaje grandes para mejorar la detección de objetos en entornos complejos, junto con un análisis exhaustivo del procesamiento de big data y la optimizaci…

Cet article propose une exploration approfondie de la détection d'objets et de la segmentation sémantique, alliant fondements théoriques et applications pratiques. Il passe en revue les avancées en apprentissage automatique et en apprentissage profond, en se concentrant particulièrement sur les réseaux de neurones convolutifs (CNN), les architectures YOLO et les approches basées sur des transformateurs comme DETR. L'étude examine également l'intégration des techniques d'IA et des grands modèles de langage pour améliorer la détection d'objets dans des environnements complexes, ainsi qu'une anal…

This article provides an in-depth exploration of object detection and semantic segmentation, merging theoretical foundations with practical applications. It reviews advancements in machine learning and deep learning, particularly focusing on convolutional neural networks (CNNs), YOLO architectures, and transformer-based approaches like DETR. The study also examines the integration of AI techniques and large language models to enhance object detection in complex environments, along with a comprehensive analysis of big data processing and model optimization.

Deep Learning and Machine Learning -- Object Detection and Semantic Segmentation: From Theory to Applications

Robots that can sense and respond to the world like humans may soon be a reality as scientists have created an artificial neuron capable of mimicking different parts of the brain.

طور العلماء نيوترونًا صناعيًا يمكنه تقليد أجزاء مختلفة من الدماغ، مما يمثل خطوة كبيرة نحو إنشاء روبوتات يمكنها الإحساس والاستجابة للعالم مثل البشر. قد تفتح هذه الابتكارات الطريق نحو روبوتات أكثر تطورًا، مما يعزز التفاعل بين الآلات وبيئتها.

Científicos han desarrollado un neurón artificial que puede imitar diferentes partes del cerebro, marcando un avance significativo hacia la creación de robots que pueden sentir y responder a su entorno como los humanos. Esta innovación podría allanar el camino para una robótica más sofisticada, mejorando la interacción entre las máquinas y su entorno.

Des scientifiques ont développé un neurone artificiel capable de mimer différentes parties du cerveau, marquant une avancée significative vers la création de robots capables de percevoir et de réagir à leur environnement comme des humains. Cette innovation pourrait ouvrir la voie à des robots plus sophistiqués, améliorant l'interaction entre les machines et leur environnement.

Scientists have developed an artificial neuron that can mimic various parts of the brain, marking a significant advancement toward creating robots that can sense and respond to their environment like humans. This innovation could pave the way for more sophisticated human-like robotics, enhancing the interaction between machines and their surroundings.

Artificial neuron can mimic different parts of the brain—a major step toward human-like robotics

arXiv:2511.11163v1 Announce Type: new 
Abstract: This article reviews modern optimization methods for training neural networks with an emphasis on efficiency and scale. We present state-of-the-art optimization algorithms under a unified algorithmic template that highlights the importance of adapting to the structures in the problem. We then cover how to make these algorithms agnostic to the scale of the problem. Our exposition is intended as an introduction for both practitioners and researchers who wish to be involved in these exciting new developments.

تستعرض هذه المقالة طرق التحسين الحديثة لتدريب الشبكات العصبية، مع التركيز على الكفاءة والنطاق. تقدم خوارزميات متطورة ضمن إطار موحد، مما يبرز أهمية التكيف مع الهياكل المحددة في المشكلة. المحتوى موجه لكل من الممارسين والباحثين الذين يرغبون في الانخراط في هذه التطورات المثيرة.

El artículo revisa los métodos de optimización modernos para el entrenamiento de redes neuronales, centrándose en la eficiencia y la escalabilidad. Presenta algoritmos de vanguardia dentro de un marco unificado, enfatizando la necesidad de adaptarse a las estructuras específicas de los problemas. El contenido está diseñado para profesionales e investigadores interesados en los últimos avances en este campo.

Cet article passe en revue les méthodes d'optimisation modernes pour l'entraînement des réseaux de neurones, en mettant l'accent sur l'efficacité et l'échelle. Il présente des algorithmes à la pointe de la technologie dans un cadre unifié, soulignant la nécessité de s'adapter aux structures spécifiques des problèmes. Le contenu est destiné aux praticiens et aux chercheurs intéressés par les dernières avancées dans ce domaine.

The article reviews modern optimization methods for training neural networks, focusing on efficiency and scalability. It presents state-of-the-art algorithms within a unified framework, emphasizing the need to adapt to specific problem structures. The content is designed for both practitioners and researchers interested in the latest advancements in this field.

Training Neural Networks at Any Scale

Researchers combined deep learning with high-resolution physics to create the first Milky Way model that tracks over 100 billion stars individually. Their AI learned how gas behaves after supernovae, removing one of the biggest computational bottlenecks in galactic modeling. The result is a simulation hundreds of times faster than current methods.

طور الباحثون أول محاكاة لمجرة درب التبانة تتعقب أكثر من 100 مليار نجم بشكل فردي، باستخدام التعلم العميق والفيزياء عالية الدقة. تتيح هذه الطريقة المبتكرة للذكاء الاصطناعي تعلم كيفية تصرف الغاز بعد الانفجارات النجمية، مما يعالج تحديًا حسابيًا كبيرًا في نمذجة المجرات. تعمل المحاكاة الناتجة بمعدل أسرع بمئات المرات من الطرق الحالية، مما يمثل تقدمًا كبيرًا في البحث الفلكي.

Investigadores han desarrollado la primera simulación de la Vía Láctea que rastrea individualmente más de 100 mil millones de estrellas, utilizando aprendizaje profundo y física de alta resolución. Este enfoque innovador permite que la IA aprenda cómo se comporta el gas después de las supernovas, abordando un desafío computacional significativo en la modelización galáctica. La simulación resultante opera cientos de veces más rápido que los métodos existentes, marcando un avance sustancial en la investigación astrofísica.

Des chercheurs ont développé la première simulation de la Voie lactée qui suit individuellement plus de 100 milliards d'étoiles, en utilisant l'apprentissage profond et la physique haute résolution. Cette approche innovante permet à l'IA d'apprendre comment le gaz se comporte après les supernovae, ce qui résout un défi computationnel majeur dans la modélisation galactique. La simulation résultante fonctionne des centaines de fois plus vite que les méthodes existantes, marquant une avancée substantielle dans la recherche astrophysique.

Researchers have developed the first simulation of the Milky Way that tracks over 100 billion stars individually, utilizing deep learning and high-resolution physics. This innovative approach allows the AI to learn how gas behaves after supernovae, addressing a significant computational challenge in galactic modeling. The resulting simulation operates hundreds of times faster than existing methods, marking a substantial advancement in astrophysical research.

AI creates the first 100-billion-star Milky Way simulation

Electrons can freeze into strange geometric crystals and then melt back into liquid-like motion under the right quantum conditions. Researchers identified how to tune these transitions and even discovered a bizarre “pinball” state where some electrons stay locked in place while others dart around freely. Their simulations help explain how these phases form and how they might be harnessed for advanced quantum technologies.

اكتشف الباحثون حالة كمومية جديدة حيث يمكن للإلكترونات أن تتجمد في بلورات هندسية غريبة ثم تعود إلى حركة تشبه السائل تحت ظروف كمومية معينة. تكشف هذه الدراسة عن حالة فريدة تُعرف باسم 'حالة البينبول'، حيث تبقى بعض الإلكترونات ثابتة بينما تتحرك أخرى بحرية. توفر النتائج رؤى حول سلوك الإلكترونات وقد تمهد الطريق لتحقيق تقدم في التقنيات الكمومية.

Investigadores han descubierto un nuevo estado cuántico en el que los electrones pueden congelarse en extraños cristales geométricos y luego volver a un movimiento similar al líquido bajo condiciones cuánticas específicas. Este estudio revela un estado único de 'pinball', donde algunos electrones permanecen inmóviles mientras que otros se mueven libremente. Los hallazgos proporcionan información sobre el comportamiento de los electrones y podrían allanar el camino para avances en tecnologías cuánticas.

Des chercheurs ont découvert un nouvel état quantique où les électrons peuvent passer de cristaux géométriques étranges à un mouvement semblable à celui d'un liquide sous des conditions quantiques spécifiques. Cette étude révèle un état unique de 'flipper', où certains électrons restent immobiles tandis que d'autres se déplacent librement. Les résultats fournissent des informations sur le comportement des électrons et pourraient ouvrir la voie à des avancées dans les technologies quantiques.

Researchers have discovered a new quantum state where electrons can transition between strange geometric crystals and liquid-like motion under specific quantum conditions. This study reveals a unique 'pinball' state, where some electrons remain stationary while others move freely. The findings provide insights into the behavior of electrons and could pave the way for advancements in quantum technologies.

Physicists reveal a new quantum state where electrons run wild

Chimps may revise their beliefs in surprisingly human-like ways. Experiments showed they switched choices when presented with stronger clues, demonstrating flexible reasoning. Computational modeling confirmed these decisions weren’t just instinct. The findings could influence how we think about learning in both children and AI.

أظهرت التجارب الحديثة أن الشمبانزي يمكن أن يغيروا معتقداتهم بطرق تشبه البشر بشكل ملحوظ. عندما تم تقديم أدلة أقوى، قام الشمبانزي بتغيير خياراتهم، مما يدل على قدرة على التفكير المرن. أكدت النمذجة الحاسوبية أن هذه القرارات لم تكن مجرد غريزة، مما يشير إلى عملية معرفية معقدة. قد تؤثر هذه النتائج على كيفية تفكيرنا في التعلم لدى الأطفال والذكاء الاصطناعي.

Experimentos recientes han demostrado que los chimpancés pueden cambiar sus creencias de maneras sorprendentemente similares a los humanos. Al presentarse con pistas más fuertes, los chimpancés cambiaron sus elecciones, lo que demuestra un razonamiento flexible. La modelización computacional confirmó que estas decisiones no eran solo instintivas, sugiriendo un proceso cognitivo complejo. Estos hallazgos podrían influir en nuestra comprensión del aprendizaje tanto en niños como en inteligencia artificial.

Des expériences récentes ont montré que les chimpanzés peuvent changer leurs croyances de manière similaire aux humains. Lorsqu'ils sont confrontés à des preuves plus solides, les chimpanzés ont modifié leurs choix, indiquant une capacité de raisonnement flexible. La modélisation computationnelle a également confirmé que ces décisions n'étaient pas simplement instinctives, suggérant un processus cognitif complexe. Ces résultats pourraient avoir des implications pour comprendre l'apprentissage chez les enfants et l'intelligence artificielle.

Recent experiments have shown that chimpanzees can change their beliefs in ways similar to humans. When presented with stronger evidence, chimps switched their choices, indicating a capacity for flexible reasoning. Computational modeling further confirmed that these decisions were not merely instinctual, suggesting a complex cognitive process. These findings may have implications for understanding learning in both children and artificial intelligence.

Scientists build micromotors smaller than a human hair

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