<p>BIO-key International, Inc. (NASDAQ: BKYI), a leading provider of Identity and Access Management (IAM) and biometric authentication solutions, has executed a strategic partnership with VaporVM, a global leader in cloud, cybersecurity, and managed services headquartered in Dubai, U.A.E. This collaboration expands the potential for BIO-key to deliver its robust identity, authentication, and...</p>
<p>The post <a href="https://ai-techpark.com/bio-key-teams-with-vaporvm-to-grow-iam-biometric-security-in-mea/">BIO-key Teams with VaporVM to Grow IAM, Biometric Security in MEA</a> first appeared on <a href="https://ai-techpark.com">AI-Tech Park</a>.</p>

أقامت شركة BIO-key International، Inc. شراكة استراتيجية مع شركة VaporVM، الرائدة في خدمات السحابة والأمن السيبراني ومقرها دبي. تهدف هذه الشراكة إلى تعزيز تقديم حلول إدارة الهوية والوصول (IAM) والمصادقة البيومترية في منطقة الشرق الأوسط وأفريقيا (MEA). من المتوقع أن توسع هذه الشراكة من قدرات BIO-key في تقديم خدمات الهوية والمصادقة القوية.

BIO-key International, Inc. ha formado una asociación estratégica con VaporVM, un líder en servicios de nube y ciberseguridad con sede en Dubái. Esta colaboración tiene como objetivo mejorar la entrega de soluciones de Gestión de Identidad y Acceso (IAM) y autenticación biométrica en la región de Medio Oriente y África (MEA). Se espera que esta asociación amplíe las capacidades de BIO-key para proporcionar servicios robustos de identidad y autenticación.

BIO-key International, Inc. a établi un partenariat stratégique avec VaporVM, un leader basé à Dubaï dans les services cloud et de cybersécurité. Cette collaboration vise à améliorer la fourniture de solutions de gestion des identités et des accès (IAM) et d'authentification biométrique dans la région du Moyen-Orient et de l'Afrique (MEA). Ce partenariat devrait élargir les capacités de BIO-key à fournir des services d'identité et d'authentification robustes.

BIO-key International, Inc. has formed a strategic partnership with VaporVM, a Dubai-based leader in cloud and cybersecurity services. This collaboration aims to enhance the delivery of Identity and Access Management (IAM) and biometric authentication solutions in the Middle East and Africa (MEA) region. The partnership is expected to expand BIO-key's capabilities in providing robust identity and authentication services.

BIO-key Teams with VaporVM to Grow IAM, Biometric Security in MEA

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

<p>Groundbreaking power delivery chiplet cuts compute power needs by more than 50% to unlock the next era of AI PowerLattice, the company reimagining power delivery for next-generation AI accelerators, today announced its emergence from stealth with $25 million in Series A funding jointly led by Playground Global and Celesta Capital....</p>
<p>The post <a href="https://ai-techpark.com/powerlattice-raises-25-million-to-break-the-ai-power-wall/">PowerLattice Raises $25 Million to Break the AI Power Wall</a> first appeared on <a href="https://ai-techpark.com">AI-Tech Park</a>.</p>

أعلنت شركة PowerLattice عن خروجها من مرحلة التطوير السري مع نجاحها في جمع 25 مليون دولار في جولة تمويل من الفئة A، بقيادة Playground Global وCelesta Capital. تركز الشركة على تطوير شرائح توصيل الطاقة التي تقلل بشكل كبير من احتياجات الطاقة الحاسوبية بأكثر من 50%. من المتوقع أن يسهل هذا التقدم الجيل التالي من تقنيات الذكاء الاصطناعي، مما يعالج التحديات الحرجة للطاقة التي تواجه مسرعات الذكاء الاصطناعي.

PowerLattice ha salido de su fase de desarrollo secreto con una exitosa ronda de financiación de 25 millones de dólares en la serie A, liderada por Playground Global y Celesta Capital. La empresa se centra en el desarrollo de chiplets de entrega de energía que reducen significativamente las necesidades de potencia de cálculo en más del 50%. Este avance se espera que facilite la próxima generación de tecnologías de IA, abordando los desafíos críticos de potencia que enfrentan los aceleradores de IA.

PowerLattice a émergé de sa phase de développement secret avec une levée de fonds de 25 millions de dollars lors d'un tour de financement de série A, dirigé par Playground Global et Celesta Capital. L'entreprise se concentre sur le développement de chiplets de distribution d'énergie qui réduisent considérablement les besoins en puissance de calcul de plus de 50 %. Cette avancée devrait faciliter la prochaine génération de technologies d'IA, en répondant aux défis critiques de puissance rencontrés par les accélérateurs d'IA.

PowerLattice has emerged from stealth mode with a successful $25 million Series A funding round, led by Playground Global and Celesta Capital. The company is focused on developing innovative power delivery chiplets that significantly reduce compute power requirements by over 50%. This advancement is expected to facilitate the next generation of AI technologies, addressing the critical power challenges faced by AI accelerators.

PowerLattice Raises $25 Million to Break the AI Power Wall

<p>48 MW Incheon campus is first phase of a 500 MW AI and cloud buildout plan for Korea Princeton Digital Group (PDG), Asia Pacific&#8217;s leading data center operator, today announced its entry into South Korea, its seventh country after Singapore, Japan, India, Indonesia, China and Malaysia. PDG plans to develop multiple...</p>
<p>The post <a href="https://ai-techpark.com/pdg-enters-south-korea-with-usd-700-million-data-center-investment/">PDG Enters South Korea with USD 700 Million Data Center Investment</a> first appeared on <a href="https://ai-techpark.com">AI-Tech Park</a>.</p>

أعلنت مجموعة برينستون الرقمية (PDG)، وهي مشغل رائد لمراكز البيانات في منطقة آسيا والمحيط الهادئ، عن دخولها إلى كوريا الجنوبية باستثمار كبير قدره 700 مليون دولار. يمثل هذا الاستثمار المرحلة الأولى من خطة أوسع لتطوير بنية تحتية للذكاء الاصطناعي والسحابة بقدرة 500 ميغاوات في البلاد. سيكون المشروع الأول في حرم إنشيون، الذي سيوفر قدرة 48 ميغاوات. تمثل هذه التوسعة في كوريا الجنوبية الدولة السابعة التي تعمل فيها PDG، بعد سنغافورة واليابان والهند وإندونيسيا والصين وماليزيا.

Princeton Digital Group (PDG), un operador líder de centros de datos en Asia-Pacífico, ha anunciado su entrada en Corea del Sur con una inversión significativa de 700 millones de dólares. Esta inversión marca la primera fase de un plan más amplio para desarrollar una infraestructura de IA y nube de 500 MW en el país. El proyecto inicial se ubicará en el campus de Incheon, que proporcionará 48 MW de capacidad. Esta expansión en Corea del Sur representa el séptimo país de operación de PDG, después de Singapur, Japón, India, Indonesia, China y Malasia.

Princeton Digital Group (PDG), un opérateur de centres de données de premier plan en Asie-Pacifique, a annoncé son entrée sur le marché sud-coréen avec un investissement significatif de 700 millions USD. Cet investissement marque la première phase d'un plan plus large visant à développer une infrastructure AI et cloud de 500 MW dans le pays. Le projet initial sera situé sur le campus d'Incheon, qui fournira une capacité de 48 MW. Cette expansion en Corée du Sud représente le septième pays d'opération de PDG, après Singapour, le Japon, l'Inde, l'Indonésie, la Chine et la Malaisie.

Princeton Digital Group (PDG), a leading data center operator in Asia Pacific, has announced its entry into South Korea with a significant investment of USD 700 million. This investment marks the first phase of a broader plan to develop a 500 MW AI and cloud infrastructure in the country. The initial project will be located at the Incheon campus, which will provide 48 MW of capacity. This expansion into South Korea represents PDG's seventh country of operation, following its presence in Singapore, Japan, India, Indonesia, China, and Malaysia.

PDG Enters South Korea with USD 700 Million Data Center Investment

<p>Integration of Dell Technologies infrastructure with QuEra quantum accelerator demonstrates practical pathways to hybrid quantum–classical workflows. QuEra Computing, the leader in neutral-atom quantum computers, announced today that it is working with Dell Technologies to help define the future of high-performance computing (HPC). Using Dell solutions, QuEra is demonstrating how quantum...</p>
<p>The post <a href="https://ai-techpark.com/quera-to-showcase-quantum-classical-integration-at-sc25/">QuEra to Showcase Quantum/Classical Integration at SC25</a> first appeared on <a href="https://ai-techpark.com">AI-Tech Park</a>.</p>

أعلنت شركة كويرا كومبيوتينغ، الرائدة في مجال الحواسيب الكمومية ذات الذرات المحايدة، عن تعاونها مع شركة ديل تكنولوجيز لعرض دمج الحوسبة الكمومية والتقليدية في مؤتمر SC25. يهدف هذا التعاون إلى إظهار طرق عملية لتدفقات العمل الهجينة بين الكم والتقليدي، مستفيدًا من بنية ديل التحتية إلى جانب مسرع كويرا الكمومي. من المقرر أن تحدد هذه المبادرة مستقبل الحوسبة عالية الأداء (HPC) وتبرز الأهمية المتزايدة للتكنولوجيا الكمومية في تعزيز القدرات الحاسوبية.

QuEra Computing, líder en computadoras cuánticas de átomos neutros, ha anunciado su colaboración con Dell Technologies para mostrar la integración de la computación cuántica y clásica en SC25. Esta asociación tiene como objetivo demostrar caminos prácticos para flujos de trabajo híbridos cuántico-clásicos, aprovechando la infraestructura de Dell junto con el acelerador cuántico de QuEra. La iniciativa está destinada a definir el futuro de la computación de alto rendimiento (HPC) y resalta la creciente importancia de la tecnología cuántica en la mejora de las capacidades computacionales.

QuEra Computing, leader dans le domaine des ordinateurs quantiques à atomes neutres, a annoncé sa collaboration avec Dell Technologies pour présenter l'intégration de l'informatique quantique et classique lors de SC25. Ce partenariat vise à démontrer des voies pratiques pour des flux de travail hybrides quantiques-classiques, en utilisant l'infrastructure de Dell aux côtés de l'accélérateur quantique de QuEra. Cette initiative est destinée à définir l'avenir de l'informatique haute performance (HPC) et souligne l'importance croissante de la technologie quantique dans l'amélioration des capacit…

QuEra Computing, a leader in neutral-atom quantum computers, has announced its collaboration with Dell Technologies to showcase the integration of quantum and classical computing at SC25. This partnership aims to demonstrate practical pathways for hybrid quantum-classical workflows, leveraging Dell's infrastructure alongside QuEra's quantum accelerator. The initiative is set to define the future of high-performance computing (HPC) and highlights the growing importance of quantum technology in enhancing computational capabilities.

BIO-key Teams with VaporVM to Grow IAM, Biometric Security in MEA

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