<A HREF="https://www.bloomberg.com/news/articles/2025-11-12/airbnb-will-test-adding-instacart-grocery-delivery-to-its-app-as-a-guest-service"><IMG VSPACE="4" HSPACE="4" BORDER="0" ALIGN="RIGHT" SRC="http://www.techmeme.com/251112/i20.jpg"></A>
<A HREF="http://www.techmeme.com/251112/p20#a251112p20" TITLE="Techmeme permalink"><IMG WIDTH=11 HEIGHT=12 SRC="http://www.techmeme.com/img/pml.png" STYLE="border:none;padding:0;margin:0;"></A> Natalie Lung / <A HREF="https://www.bloomberg.com/">Bloomberg</A>: 
<A HREF="https://www.bloomberg.com/news/articles/2025-11-12/airbnb-will-test-adding-instacart-grocery-delivery-to-its-app-as-a-guest-service">Airbnb says it will launch a three-month pilot on January 5 that lets guests order groceries via Instacart within its app in Phoenix, Orlando, and Los Angeles</A>&nbsp; &mdash;&nbsp; Airbnb Inc. is testing a new service with Instacart that will let guests at some US rentals order groceries through the home-sharing app ahead of and during their stay.

تعتزم Airbnb إطلاق برنامج تجريبي لمدة ثلاثة أشهر في 5 يناير، يسمح للضيوف في فينيكس وأورلاندو ولوس أنجلوس بطلب البقالة عبر Instacart داخل تطبيقها. تهدف هذه المبادرة إلى تحسين تجربة الضيوف من خلال توفير خيارات توصيل البقالة المريحة خلال إقامتهم.

Airbnb lanzará un programa piloto de tres meses el 5 de enero que permitirá a los huéspedes en Phoenix, Orlando y Los Ángeles pedir comestibles a través de Instacart dentro de su aplicación. Esta iniciativa busca mejorar la experiencia del huésped al ofrecer opciones convenientes de entrega de comestibles durante su estancia.

Airbnb lancera un programme pilote de trois mois le 5 janvier, permettant aux clients à Phoenix, Orlando et Los Angeles de commander des courses via Instacart dans son application. Cette initiative vise à améliorer l'expérience client en offrant des options de livraison de courses pratiques pendant leur séjour.

Airbnb is set to launch a three-month pilot program on January 5, allowing guests in Phoenix, Orlando, and Los Angeles to order groceries through Instacart within its app. This initiative aims to enhance the guest experience by providing convenient grocery delivery options during their stay.

Airbnb says it will launch a three-month pilot on January 5 that lets guests order groceries via Instacart within its app in Phoenix, Orlando, and Los Angeles (Natalie Lung/Bloomberg)

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

The most interesting aspect of the latest Even Realities glasses may be their limitations.

I've tried several AI smart glasses (including Meta Ray-Bans) in 2025 - these are the most comfortable

arXiv:2511.10846v1 Announce Type: new 
Abstract: Automated emotion detection is widely used in applications ranging from well-being monitoring to high-stakes domains like mental health and hiring. However, models often rely on annotations that reflect dominant cultural norms, limiting model ability to recognize emotional expression in dialects often excluded from training data distributions, such as African American Vernacular English (AAVE). This study examines emotion recognition model performance on AAVE compared to General American English (GAE). We analyze 2.7 million tweets geo-tagged within Los Angeles. Texts are scored for strength of AAVE using computational approximations of dialect features. Annotations of emotion presence and intensity are collected on a dataset of 875 tweets with both high and low AAVE densities. To assess model accuracy on a task as subjective as emotion perception, we calculate community-informed "silver" labels where AAVE-dense tweets are labeled by African American, AAVE-fluent (ingroup) annotators. On our labeled sample, GPT and BERT-based models exhibit false positive prediction rates of anger on AAVE more than double than on GAE. SpanEmo, a popular text-based emotion model, increases false positive rates of anger from 25 percent on GAE to 60 percent on AAVE. Additionally, a series of linear regressions reveals that models and non-ingroup annotations are significantly more correlated with profanity-based AAVE features than ingroup annotations. Linking Census tract demographics, we observe that neighborhoods with higher proportions of African American residents are associated with higher predictions of anger (Pearson's correlation r = 0.27) and lower joy (r = -0.10). These results find an emergent safety issue of emotion AI reinforcing racial stereotypes through biased emotion classification. We emphasize the need for culturally and dialect-informed affective computing systems.

تُستخدم أنظمة الكشف الآلي عن المشاعر بشكل متزايد في مجالات متنوعة، بما في ذلك الصحة النفسية والتوظيف. ومع ذلك، غالبًا ما تفشل هذه النماذج في التعرف بدقة على التعبيرات العاطفية في اللهجات مثل الإنجليزية العامية الأفريقية الأمريكية (AAVE) بسبب اعتمادها على المعايير الثقافية السائدة. وجدت دراسة تحلل 2.7 مليون تغريدة من لوس أنجلوس أن نماذج التعرف على المشاعر أظهرت معدلات إيجابية خاطئة أعلى بكثير للغضب في AAVE مقارنةً بالإنجليزية الأمريكية العامة (GAE)، مما يبرز قيود تقنيات الذكاء الاصطناعي العاطفي الحالية.

Los sistemas automatizados de detección de emociones se utilizan cada vez más en diversas áreas, incluida la salud mental y la contratación. Sin embargo, estos modelos a menudo no logran reconocer con precisión las expresiones emocionales en dialectos como el inglés vernáculo afroamericano (AAVE) debido a su dependencia de normas culturales dominantes. Un estudio que analizó 2.7 millones de tweets de Los Ángeles encontró que los modelos de reconocimiento de emociones mostraron tasas de falsos positivos significativamente más altas para la ira en AAVE en comparación con el inglés americano gene…

Les systèmes de détection des émotions automatisés sont de plus en plus utilisés dans divers domaines, y compris la santé mentale et le recrutement. Cependant, ces modèles échouent souvent à reconnaître avec précision les expressions émotionnelles dans des dialectes comme l'anglais vernaculaire afro-américain (AAVE) en raison de leur dépendance à des normes culturelles dominantes. Une étude analysant 2,7 millions de tweets de Los Angeles a révélé que les modèles de reconnaissance des émotions présentaient des taux de faux positifs pour la colère significativement plus élevés pour l'AAVE par ra…

Automated emotion detection systems are increasingly utilized in various fields, including mental health and hiring. However, these models often fail to accurately recognize emotional expressions in dialects like African American Vernacular English (AAVE) due to reliance on dominant cultural norms. A study analyzing 2.7 million tweets from Los Angeles found that emotion recognition models exhibited significantly higher false positive rates for anger in AAVE compared to General American English (GAE), highlighting the limitations of current emotion AI technologies.

Reinforcing Stereotypes of Anger: Emotion AI on African American Vernacular English

<A HREF="https://www.anthropic.com/news/claude-in-microsoft-foundry"><IMG VSPACE="4" HSPACE="4" BORDER="0" ALIGN="RIGHT" SRC="http://www.techmeme.com/251118/i55.jpg"></A>
<A HREF="http://www.techmeme.com/251118/p55#a251118p55" TITLE="Techmeme permalink"><IMG WIDTH=11 HEIGHT=12 SRC="http://www.techmeme.com/img/pml.png" STYLE="border:none;padding:0;margin:0;"></A> <A HREF="https://www.anthropic.com/">Anthropic</A>: 
<A HREF="https://www.anthropic.com/news/claude-in-microsoft-foundry">Anthropic's Claude Sonnet 4.5, Haiku 4.5, and Opus 4.1 are now available in public preview in Microsoft Foundry for Azure customers</A>&nbsp; &mdash;&nbsp; Today we announced that Microsoft and Anthropic are expanding our partnership.&nbsp; As part of the partnership, Claude Sonnet 4.5, Haiku 4.5 &hellip;

Anthropic's Claude Sonnet 4.5, Haiku 4.5, and Opus 4.1 are now available in public preview in Microsoft Foundry for Azure customers (Anthropic)

<A HREF="https://www.bloomberg.com/news/articles/2025-11-18/trump-urges-congress-to-block-state-level-ai-regulation"><IMG VSPACE="4" HSPACE="4" BORDER="0" ALIGN="RIGHT" SRC="http://www.techmeme.com/251118/i54.jpg"></A>
<A HREF="http://www.techmeme.com/251118/p54#a251118p54" TITLE="Techmeme permalink"><IMG WIDTH=11 HEIGHT=12 SRC="http://www.techmeme.com/img/pml.png" STYLE="border:none;padding:0;margin:0;"></A> Kate Sullivan / <A HREF="https://www.bloomberg.com/">Bloomberg</A>: 
<A HREF="https://www.bloomberg.com/news/articles/2025-11-18/trump-urges-congress-to-block-state-level-ai-regulation">Trump says the US needs a &ldquo;Federal Standard&rdquo; governing oversight of AI instead of &ldquo;a patchwork of 50 State Regulatory Regimes&rdquo;, otherwise China will catch up</A>&nbsp; &mdash;&nbsp; President Donald Trump called on Congress to pass a federal standard governing oversight of artificial intelligence &hellip;

Trump says the US needs a "Federal Standard" governing oversight of AI instead of "a patchwork of 50 State Regulatory Regimes", otherwise China will catch up (Kate Sullivan/Bloomberg)

<A HREF="https://www.businessinsider.com/meta-soumith-chintala-mira-murati-thinking-machines-lab-pytorch-ai-2025-11"><IMG VSPACE="4" HSPACE="4" BORDER="0" ALIGN="RIGHT" SRC="http://www.techmeme.com/251118/i53.jpg"></A>
<A HREF="http://www.techmeme.com/251118/p53#a251118p53" TITLE="Techmeme permalink"><IMG WIDTH=11 HEIGHT=12 SRC="http://www.techmeme.com/img/pml.png" STYLE="border:none;padding:0;margin:0;"></A> Pranav Dixit / <A HREF="https://www.businessinsider.com/">Business Insider</A>: 
<A HREF="https://www.businessinsider.com/meta-soumith-chintala-mira-murati-thinking-machines-lab-pytorch-ai-2025-11">Soumith Chintala, who co-created the PyTorch ML framework at Meta and left the company this month, has joined Mira Murati's Thinking Machines Lab</A>&nbsp; &mdash;&nbsp; - Soumith Chintala, the creator of PyTorch, has joined Mira Murati's AI startup Thinking Machines Lab.&nbsp; &mdash; Chintala's move comes amid major restructuring inside Meta's AI ranks.

Soumith Chintala, who co-created the PyTorch ML framework at Meta and left the company this month, has joined Mira Murati's Thinking Machines Lab (Pranav Dixit/Business Insider)

Airbnb says it will launch a three-month pilot on January 5 that lets guests order groceries via Instacart within its app in Phoenix, Orlando, and Los Angeles (Natalie Lung/Bloomberg)

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