أعلنت سوني رسميًا عن شاشة بلاي ستيشن جديدة بمعدل تحديث 240 هرتز مع خطاف شحن مدمج لجهاز التحكم DualSense. يهدف هذا المنتج إلى تحسين تجربة اللعب من خلال تقديم معدلات تحديث عالية وخيارات شحن مريحة، مما يجعله إضافة مهمة لمستخدمي بلاي ستيشن.

Sony ha anunciado oficialmente un nuevo monitor PlayStation de 240Hz que cuenta con un gancho de carga integrado para el controlador DualSense. Este producto busca mejorar la experiencia de juego al ofrecer altas tasas de refresco y opciones de carga convenientes, convirtiéndose en una adición significativa para los usuarios de PlayStation.

Sony a annoncé officiellement un nouveau moniteur PlayStation 240Hz avec un crochet de chargement intégré pour la manette DualSense. Ce produit vise à améliorer l'expérience de jeu en offrant des taux de rafraîchissement élevés et des options de chargement pratiques, ce qui en fait un ajout significatif pour les utilisateurs de PlayStation.

Sony has officially announced a new 240Hz PlayStation monitor featuring a built-in charging hook for the DualSense controller. This product aims to enhance the gaming experience by providing high refresh rates and convenient charging options, making it a significant addition for PlayStation users.

Sony made an official 240Hz PlayStation monitor with a built-in DualSense charging hook

Here's everything you need to know about the latest smart ring by Oura, based on a wearable expert's real-world usage.

Is the $500 Oura Ring 4 Ceramic worth it? I wore one for a month, and here's my advice

Samsung's QN90F QLED delivers great streaming and gaming performance, making it a strong holiday value.

Why I recommend this Samsung QLED TV over pricier OLED models in 2025 - and don't regret it

<h1>
 
 
 AI Amnesia: Erasing Knowledge Without a Trace
</h1>

Imagine your AI model accidentally learned something it shouldn't have – sensitive customer data, for example. Current methods for deleting this information often require retraining the entire model, an expensive and time-consuming process. What if we could surgically remove that knowledge without starting from scratch?

The key lies in a novel approach: creating artificial "forgetting cues." We're teaching the model to unlearn specific data patterns by exposing it to carefully crafted synthetic examples. These examples are designed to strongly contradict the information we want the model to forget, effectively overwriting the problematic associations in its memory. This works even if you don’t have access to the original data you need to erase.

Think of it like this: you're trying to forget a bad song stuck in your head. Instead of trying to actively suppress it (which rarely works), you blast an even more catchy song. The new song overwrites the old one, effectively erasing it from your mental playlist.

Benefits of Selective Forgetting:

<ul>
<li> Enhanced Data Privacy: Remove sensitive data without compromising the overall model's performance.</li>
<li> Reduced Retraining Costs: Avoid full model retraining, saving significant time and resources.</li>
<li> Improved Model Security: Eliminate vulnerabilities introduced by unintentionally learned patterns.</li>
<li> Adaptable Learning: Enables continuous refinement of AI models based on evolving data landscapes.</li>
<li> Compliance Ready: Supports compliance with data privacy regulations like GDPR.</li>
<li> Scalable Solutions: Works efficiently even with limited access to training data.</li>
</ul>

Practical Tip: One challenge is ensuring the synthetic data accurately targets the information you want to remove without negatively impacting the model's ability to generalize. Rigorous testing and validation with holdout datasets are crucial.

The promise of AI that can truly 'forget' opens exciting possibilities for responsible AI development. By enabling precise data deletion, we pave the way for more secure, compliant, and adaptable machine learning systems. Imagine AI models that can adapt to changing ethical guidelines or quickly unlearn incorrect information, all without massive retraining efforts. This is a crucial step towards trustworthy and responsible AI that respects data privacy and aligns with societal values. Future exploration could include extending this to different data modalities and model architectures.

Related Keywords: machine unlearning, data privacy, few-shot learning, zero-shot learning, synthetic data, model editing, catastrophic forgetting, incremental learning, continual learning, deep learning, neural networks, data security, algorithmic fairness, responsible ai, ethical ai, federated unlearning, privacy-preserving ai, model retraining, AI governance, data deletion, GDPR compliance

يتناول المقال نهجًا جديدًا لمعالجة مشكلة نسيان الذكاء الاصطناعي، حيث قد تحتفظ نماذج الذكاء الاصطناعي عن غير قصد بمعلومات حساسة. تتطلب الطرق التقليدية لحذف هذه البيانات غالبًا إعادة تدريب كاملة للنموذج، وهو ما يعد مكلفًا ويستغرق وقتًا. تتضمن التقنية الجديدة إنشاء 'إشارات نسيان' اصطناعية تساعد النموذج على نسيان أنماط بيانات معينة من خلال تعريضه لأمثلة صناعية تتناقض مع المعلومات غير المرغوب فيها، مما يسمح بإزالة المعرفة المستهدفة دون الحاجة للوصول إلى البيانات الأصلية.

El artículo aborda un nuevo enfoque para tratar el problema de la amnesia de la IA, donde los modelos de IA pueden retener inadvertidamente información sensible. Los métodos tradicionales para eliminar estos datos a menudo requieren un reentrenamiento completo del modelo, lo que es costoso y lleva tiempo. La nueva técnica implica crear 'señales de olvido' artificiales que ayudan al modelo a desaprender patrones de datos específicos al exponerlo a ejemplos sintéticos que contradicen la información no deseada, permitiendo así la eliminación selectiva de conocimientos sin necesidad de acceder a l…

L'article traite d'une nouvelle approche pour résoudre le problème de l'amnésie de l'IA, où les modèles d'IA peuvent involontairement conserver des informations sensibles. Les méthodes traditionnelles pour supprimer ces données nécessitent souvent un réentraînement complet du modèle, ce qui est coûteux et long. La nouvelle technique consiste à créer des 'cues d'oubli' artificiels qui aident le modèle à désapprendre des schémas de données spécifiques en lui présentant des exemples synthétiques qui contredisent les informations indésirables, permettant ainsi une suppression ciblée des connaissan…

The article discusses a novel approach to address the issue of AI amnesia, where AI models may inadvertently retain sensitive information. Traditional methods for deleting such data often require complete retraining of the model, which is costly and time-consuming. The new technique involves creating artificial 'forgetting cues' that help the model unlearn specific data patterns by presenting it with synthetic examples that contradict the unwanted information, allowing for targeted knowledge removal without needing access to the original data.

AI Amnesia: Erasing Knowledge Without a Trace

Baseus' Enercore CG11 travel adapter is one of the better designed ones I've tested, although its best feature isn't immediately apparent.

Traveling soon? Why this one charger is the only one you'll ever need to pack

Turning materials like wood chips, crop residues and municipal solid waste into fuels and chemicals is important for our country's energy independence.

طور الباحثون نماذج حاسوبية متقدمة تهدف إلى تحسين التنبؤات لعمليات طحن الكتلة الحيوية. تركز هذه الابتكارات على تحويل مواد مثل رقائق الخشب، بقايا المحاصيل، والنفايات الصلبة البلدية إلى وقود ومواد كيميائية قيمة، وهو أمر مهم لتعزيز استقلالية الطاقة في البلاد. من المتوقع أن تعمل النماذج على تحسين كفاءة معالجة الكتلة الحيوية، مما يساهم في حلول الطاقة المستدامة.

Investigadores han desarrollado modelos informáticos avanzados destinados a mejorar las predicciones para los procesos de molienda de biomasa. Esta innovación se centra en convertir materiales como astillas de madera, residuos de cultivos y residuos sólidos municipales en combustibles y productos químicos valiosos, lo que es crucial para mejorar la independencia energética del país. Se espera que los modelos optimicen la eficiencia del procesamiento de biomasa, contribuyendo así a soluciones energéticas sostenibles.

Des chercheurs ont développé des modèles informatiques avancés visant à améliorer les prévisions pour les processus de broyage de la biomasse. Cette innovation se concentre sur la conversion de matériaux tels que les copeaux de bois, les résidus de culture et les déchets solides municipaux en combustibles et produits chimiques précieux, ce qui est crucial pour renforcer l'indépendance énergétique du pays. Les modèles devraient optimiser l'efficacité du traitement de la biomasse, contribuant ainsi à des solutions énergétiques durables.

Researchers have developed advanced computer models aimed at improving predictions for biomass milling processes. This innovation focuses on converting materials such as wood chips, crop residues, and municipal solid waste into valuable fuels and chemicals, which is crucial for enhancing energy independence in the country. The models are expected to optimize the efficiency of biomass processing, thereby contributing to sustainable energy solutions.

Researchers develop computer models for better biomass milling predictions

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arXiv:2511.13019v1 Announce Type: cross 
Abstract: MeanFlow (MF) is a diffusion-motivated generative model that enables efficient few-step generation by learning long jumps directly from noise to data. In practice, it is often used as a latent MF by leveraging the pre-trained Stable Diffusion variational autoencoder (SD-VAE) for high-dimensional data modeling. However, MF training remains computationally demanding and is often unstable. During inference, the SD-VAE decoder dominates the generation cost, and MF depends on complex guidance hyperparameters for class-conditional generation. In this work, we develop an efficient training and sampling scheme for MF in the latent space of a Representation Autoencoder (RAE), where a pre-trained vision encoder (e.g., DINO) provides semantically rich latents paired with a lightweight decoder. We observe that naive MF training in the RAE latent space suffers from severe gradient explosion. To stabilize and accelerate training, we adopt Consistency Mid-Training for trajectory-aware initialization and use a two-stage scheme: distillation from a pre-trained flow matching teacher to speed convergence and reduce variance, followed by an optional bootstrapping stage with a one-point velocity estimator to further reduce deviation from the oracle mean flow. This design removes the need for guidance, simplifies training configurations, and reduces computation in both training and sampling. Empirically, our method achieves a 1-step FID of 2.03, outperforming vanilla MF's 3.43, while reducing sampling GFLOPS by 38% and total training cost by 83% on ImageNet 256. We further scale our approach to ImageNet 512, achieving a competitive 1-step FID of 3.23 with the lowest GFLOPS among all baselines. Code is available at https://github.com/sony/mf-rae.

MeanFlow (MF) هو نموذج توليدي مستوحى من عمليات الانتشار، مصمم لتوليد فعال في خطوات قليلة من خلال تعلم الانتقالات المباشرة من الضوضاء إلى البيانات. يُستخدم عادةً كنموذج MF كامن، مستفيدًا من مشفر التباين المدرب مسبقًا Stable Diffusion (SD-VAE) لنمذجة البيانات عالية الأبعاد. ومع ذلك، فإن تدريب MF يتطلب موارد حسابية كبيرة وغالبًا ما يكون غير مستقر. تقدم هذه الدراسة مخطط تدريب وعينة فعال لـ MF في الفضاء الكامن لمشفر التمثيل (RAE)، مع معالجة مشكلات مثل انفجار التدرجات أثناء التدريب.

MeanFlow (MF) es un modelo generativo motivado por procesos de difusión, diseñado para una generación eficiente en pocos pasos al aprender transiciones directas del ruido a los datos. Se utiliza comúnmente como un MF latente, aprovechando el autoencoder variacional Stable Diffusion (SD-VAE) preentrenado para la modelización de datos de alta dimensión. Sin embargo, el entrenamiento de MF es computacionalmente exigente y a menudo inestable. Este trabajo desarrolla un esquema de entrenamiento y muestreo eficiente para MF en el espacio latente de un Autoencoder de Representación (RAE), abordando p…

MeanFlow (MF) est un modèle génératif inspiré des processus de diffusion, conçu pour une génération efficace en quelques étapes en apprenant des transitions directes du bruit aux données. Il est couramment utilisé comme un MF latent, utilisant l'autoencodeur variationnel Stable Diffusion pré-entraîné (SD-VAE) pour la modélisation de données de haute dimension. Cependant, l'entraînement de MF est intensif en calcul et souvent instable. Cette étude introduit un schéma d'entraînement et d'échantillonnage efficace pour MF dans l'espace latent d'un autoencodeur de représentation (RAE), abordant des…

MeanFlow (MF) is a generative model inspired by diffusion processes, designed for efficient few-step generation by learning direct transitions from noise to data. It is commonly utilized as a latent MF, employing the pre-trained Stable Diffusion variational autoencoder (SD-VAE) for high-dimensional data modeling. However, MF training is computationally intensive and often unstable. This study introduces an efficient training and sampling scheme for MF in the latent space of a Representation Autoencoder (RAE), addressing issues like gradient explosion during training.

Sony made an official 240Hz PlayStation monitor with a built-in DualSense charging hook

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