A recent study at UC Davis had AI chatbots send messages to people's phones to remind them to get their steps in. Those messages were interactive. Sometimes the chatbot would tell a joke based on this example provided by the research team:

أظهرت دراسة حديثة أجريت في جامعة كاليفورنيا في دافيس استخدام روبوتات المحادثة الذكية لتشجيع النشاط البدني من خلال إرسال رسائل تفاعلية إلى هواتف الأفراد. تضمنت هذه الرسائل تذكيرات لزيادة الخطوات وأحيانًا نكات لجذب انتباه المستخدمين. تسلط هذه الطريقة المبتكرة الضوء على إمكانيات الذكاء الاصطناعي في تعزيز أنماط الحياة الصحية من خلال التكنولوجيا.

Un estudio reciente realizado en UC Davis exploró el uso de chatbots de IA para fomentar la actividad física mediante el envío de mensajes interactivos a los teléfonos de las personas. Estos mensajes incluían recordatorios para dar pasos y, a veces, chistes para involucrar a los usuarios. Este enfoque innovador destaca el potencial de la IA para promover estilos de vida más saludables a través de la tecnología.

Une étude récente menée à l'UC Davis a examiné l'utilisation de chatbots IA pour encourager l'activité physique en envoyant des messages interactifs sur les téléphones des individus. Ces messages comprenaient des rappels pour faire des pas et parfois des blagues pour engager les utilisateurs. Cette approche innovante met en lumière le potentiel de l'IA pour promouvoir des modes de vie plus sains grâce à la technologie.

A recent study conducted at UC Davis explored the use of AI chatbots to encourage physical activity by sending interactive messages to individuals' phones. These messages included reminders to take steps and sometimes featured jokes to engage users. This innovative approach highlights the potential of AI in promoting healthier lifestyles through technology.

Dangers to consider as AI gets smarter, more rapidly adopted

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

You've just finished a strenuous hike to the top of a mountain. You're exhausted but elated. The view of the city below is gorgeous, and you want to capture the moment on camera. But it's already quite dark, and you're not sure you'll get a good shot. Fortunately, your phone has an AI-powered night mode that can take stunning photos even after sunset.

يتناول المقال دور البيانات الاصطناعية في تدريب أنظمة الذكاء الاصطناعي (AI) لمواجهة التحديات الواقعية. يبرز سيناريو حيث يستخدم شخص ما، بعد رحلة شاقة، وضع الليل المدعوم بالذكاء الاصطناعي في هاتفه لالتقاط صور مذهلة في ظروف الإضاءة المنخفضة. وهذا يعكس التطبيق العملي لتكنولوجيا الذكاء الاصطناعي في تحسين التجارب اليومية، خاصة في التصوير الفوتوغرافي، من خلال استخدام البيانات الاصطناعية لتحسين الأداء في البيئات الصعبة.

El artículo aborda el papel de los datos sintéticos en la formación de sistemas de inteligencia artificial (IA) para enfrentar desafíos del mundo real. Destaca un escenario en el que una persona, tras una agotadora caminata, utiliza el modo nocturno impulsado por IA de su teléfono para capturar impresionantes fotos en condiciones de poca luz. Esto muestra la aplicación práctica de la tecnología de IA para mejorar las experiencias cotidianas, especialmente en la fotografía, al utilizar datos sintéticos para mejorar el rendimiento en entornos desafiantes.

L'article traite du rôle des données synthétiques dans la formation des systèmes d'intelligence artificielle (IA) pour relever des défis du monde réel. Il met en avant un scénario où une personne, après une randonnée fatigante, utilise le mode nuit alimenté par l'IA de son téléphone pour capturer des photos époustouflantes dans des conditions de faible luminosité. Cela illustre l'application pratique de la technologie IA pour améliorer les expériences quotidiennes, notamment en photographie, en utilisant des données synthétiques pour améliorer les performances dans des environnements difficile…

The article discusses the role of synthetic data in training artificial intelligence (AI) systems to address real-world challenges. It highlights a scenario where an individual, after a tiring hike, uses an AI-powered night mode on their phone to capture stunning photos in low light conditions. This showcases the practical application of AI technology in enhancing everyday experiences, particularly in photography, by utilizing synthetic data to improve performance in challenging environments.

When fake data is a good thing: How synthetic data trains AI to solve real problems

Over the past few years, we have repeatedly been told artificial intelligence (AI) is coming for our jobs.

تشير دراسة استقصائية حديثة إلى أن العديد من الشركات كانت بطيئة في اعتماد الذكاء الاصطناعي (AI)، على الرغم من التنبؤات الواسعة بأن الذكاء الاصطناعي سيؤثر بشكل كبير على سوق العمل. تثير هذه الترددات تساؤلات حول استعداد الشركات لدمج التقنيات المتقدمة في عملياتها. تشير النتائج إلى وجود فجوة بين التبني السريع المتوقع للذكاء الاصطناعي والسرعة الفعلية التي تقوم بها الشركات بتنفيذ هذه التقنيات.

Una encuesta reciente indica que muchas empresas han sido lentas en adoptar la inteligencia artificial (IA), a pesar de las predicciones generalizadas de que la IA tendría un impacto significativo en el mercado laboral. Esta reticencia plantea preguntas sobre la preparación de las empresas para integrar tecnologías avanzadas en sus operaciones. Los hallazgos sugieren una brecha entre la rápida adopción anticipada de la IA y el ritmo real al que las empresas están implementando estas tecnologías.

Une enquête récente indique que de nombreuses entreprises ont été lentes à adopter l'intelligence artificielle (IA), malgré les prévisions selon lesquelles l'IA aurait un impact significatif sur le marché de l'emploi. Cette hésitation soulève des questions sur la préparation des entreprises à intégrer des technologies avancées dans leurs opérations. Les résultats suggèrent un écart entre l'adoption rapide anticipée de l'IA et le rythme réel auquel les entreprises mettent en œuvre ces technologies.

A recent survey indicates that many businesses have been slow to adopt artificial intelligence (AI), despite widespread predictions that AI would significantly impact job markets. This hesitance raises questions about the readiness of companies to integrate advanced technologies into their operations. The findings suggest a gap between the anticipated rapid adoption of AI and the actual pace at which businesses are implementing these technologies.

Some businesses have actually been slow to adopt AI, survey finds

Bipedal (two-legged) robots are sophisticated machines, but they are not the most graceful when things go wrong. A simple push, fall or an obstacle can send them crashing to the ground, often resulting in expensive damage to sensitive components such as cameras.

طورت ديزني تقنية لتعليم الروبوتات ثنائية الساقين كيفية السقوط برشاقة وإجراء هبوط ناعم. على الرغم من أن هذه الروبوتات متقدمة، إلا أنها غالبًا ما تواجه صعوبة في الحفاظ على توازنها ويمكن أن تتعرض لأضرار كبيرة نتيجة السقوط أو الاصطدامات. تهدف الطريقة الجديدة إلى تعزيز قدرتها على التحمل وتقليل تكاليف الإصلاح المرتبطة بالمكونات الحساسة مثل الكاميرات، التي تكون عرضة للتلف أثناء الحوادث.

Disney ha desarrollado una técnica para enseñar a los robots bípedos a caer con gracia y realizar aterrizajes suaves. Estos robots, aunque avanzados, a menudo tienen dificultades para mantener el equilibrio y pueden sufrir daños significativos por caídas o colisiones. El nuevo método busca mejorar su resistencia y reducir los costos de reparación asociados con componentes sensibles como las cámaras, que son propensas a dañarse durante accidentes.

Disney a développé une technique pour apprendre aux robots bipèdes à tomber gracieusement et à effectuer des atterrissages en douceur. Bien que ces robots soient avancés, ils ont souvent du mal à maintenir leur équilibre et peuvent subir des dommages importants lors de chutes ou de collisions. La nouvelle méthode vise à améliorer leur résilience et à réduire les coûts de réparation associés aux composants sensibles comme les caméras, qui sont vulnérables aux dommages lors d'accidents.

Disney has developed a technique to teach bipedal robots how to fall gracefully and make soft landings. These robots, while advanced, often struggle with maintaining balance and can sustain significant damage from falls or collisions. The new method aims to enhance their resilience and reduce repair costs associated with sensitive components like cameras, which are prone to damage during accidents.

Disney teaches a robot how to fall gracefully and make a soft landing

Georgia Tech researchers are using AI to quickly train exoskeleton devices, making it much more practical to develop, improve, and ultimately deploy wearable robots for people with impaired mobility.

يستخدم الباحثون في Georgia Tech الذكاء الاصطناعي لتسريع تدريب أجهزة الهيكل الخارجي. تهدف هذه التطورات إلى تحسين تطوير ونشر الروبوتات القابلة للارتداء للأشخاص ذوي الإعاقة الحركية.

Investigadores del Georgia Tech están utilizando inteligencia artificial para acelerar el entrenamiento de dispositivos de exoesqueleto. Este avance tiene como objetivo mejorar el desarrollo y la implementación de robots portátiles diseñados para personas con discapacidades de movilidad.

Des chercheurs du Georgia Tech utilisent l'intelligence artificielle pour accélérer la formation des dispositifs d'exosquelette. Cette avancée vise à améliorer le développement et le déploiement de robots portables destinés aux personnes ayant des problèmes de mobilité.

Researchers at Georgia Tech are utilizing artificial intelligence to expedite the training of exoskeleton devices. This advancement aims to enhance the development and deployment of wearable robots designed for individuals with mobility impairments.

Real-world helper exoskeletons come closer to reality with AI training

Virtual keyboards are a frequent source of frustration for augmented reality (AR) users. The virtual surfaces are slow and error prone, and raising an arm to type on them can cause muscle strain known as "gorilla arm."

تسبب لوحات المفاتيح الافتراضية في الواقع المعزز (AR) إحباطًا متكررًا للمستخدمين بسبب بطئها وارتفاع معدل الأخطاء. يعاني المستخدمون من عدم الراحة، المعروف باسم 'ذراع الغوريلا'، نتيجة رفع أذرعهم للكتابة على هذه الأسطح الافتراضية.

Los teclados virtuales en la realidad aumentada (RA) a menudo frustran a los usuarios debido a su lenta respuesta y alta tasa de errores. Los usuarios experimentan incomodidad, comúnmente conocida como 'brazo de gorila', al levantar los brazos para escribir en estas superficies virtuales.

Les claviers virtuels en réalité augmentée (RA) frustrent souvent les utilisateurs en raison de leur lenteur et de leur taux d'erreur élevé. Les utilisateurs ressentent un inconfort, communément appelé 'bras de gorille', en levant les bras pour taper sur ces surfaces virtuelles.

Virtual keyboards in augmented reality (AR) often frustrate users due to their slow response and high error rates. Users experience discomfort, commonly referred to as 'gorilla arm,' from raising their arms to type on these virtual surfaces.

New augmented reality tech can turn any surface into keyboard

Computer-aided design (CAD) is the go-to method for designing most of today's physical products. Engineers use CAD to turn 2D sketches into 3D models that they can then test and refine before sending a final version to a production line. But the software is notoriously complicated to learn, with thousands of commands to choose from. To be truly proficient in the software takes a huge amount of time and practice.

تم تطوير وكيل ذكاء اصطناعي جديد يمكنه إنشاء كائنات ثلاثية الأبعاد من رسومات ثنائية الأبعاد باستخدام برنامج التصميم بمساعدة الكمبيوتر (CAD). يعد CAD أساسيًا للمهندسين في تصميم المنتجات المادية، ولكنه غالبًا ما يكون معقدًا ويتطلب وقتًا طويلاً لإتقانه. قد تبسط هذه التطورات عملية التصميم وتحسن الإنتاجية في مجالات الهندسة المختلفة.

Se ha desarrollado un nuevo agente de IA que puede crear objetos 3D a partir de bocetos 2D utilizando software de diseño asistido por computadora (CAD). El CAD es esencial para que los ingenieros diseñen productos físicos, pero a menudo es complejo y requiere mucho tiempo para dominarlo. Este avance podría simplificar el proceso de diseño y mejorar la productividad en varios campos de la ingeniería.

Un nouvel agent d'IA a été développé pour créer des objets 3D à partir de croquis 2D en utilisant un logiciel de conception assistée par ordinateur (CAO). La CAO est essentielle pour les ingénieurs dans la conception de produits physiques, mais elle est souvent complexe et nécessite beaucoup de temps pour être maîtrisée. Cette avancée pourrait simplifier le processus de conception et améliorer la productivité dans divers domaines de l'ingénierie.

A new AI agent has been developed that can create 3D objects from 2D sketches using computer-aided design (CAD) software. CAD is essential for engineers to design physical products, but it is often complex and time-consuming to master. This advancement could simplify the design process and enhance productivity in various engineering fields.

Dangers to consider as AI gets smarter, more rapidly adopted

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