<p>If this post is useful to you I kindly ask a minimal donation on Buy Me a Coffee. It shall be used to continue my Open Source efforts.<br />
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<strong>Continue reading</strong>: <a href="https://www.r-bloggers.com/2025/11/creating-a-london-population-map-with-d3po/">Creating a London Population Map with D3po</a>

يتناول المقال إنشاء خريطة سكانية لمدينة لندن باستخدام D3po، وهو أداة مفتوحة المصدر. يشجع المؤلف القراء على دعم جهوده من خلال تبرعات بسيطة على Buy Me a Coffee. يهدف المنشور إلى تقديم رؤى حول تقنيات تصور البيانات وأهمية المساهمات المفتوحة المصدر.

El artículo trata sobre la creación de un mapa de población de Londres utilizando D3po, una herramienta de código abierto. El autor anima a los lectores a apoyar sus esfuerzos mediante donaciones mínimas en Buy Me a Coffee. La publicación tiene como objetivo proporcionar información sobre técnicas de visualización de datos y la importancia de las contribuciones de código abierto.

Cet article traite de la création d'une carte de la population de Londres à l'aide de D3po, un outil open-source. L'auteur encourage les lecteurs à soutenir ses efforts par des dons minimes sur Buy Me a Coffee. Le post vise à fournir des informations sur les techniques de visualisation des données et l'importance des contributions open-source.

The article discusses the creation of a population map of London using D3po, an open-source tool. The author encourages readers to support their efforts through minimal donations on Buy Me a Coffee. The post aims to provide insights into data visualization techniques and the importance of open-source contributions.

Creating a London Population Map with D3po

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>

<p>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?</p>

<p>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.</p>

<p>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.</p>

<p><strong>Benefits of Selective Forgetting:</strong></p>

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

<p><strong>Practical Tip:</strong> 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.</p>

<p>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.</p>

<p><strong>Related Keywords:</strong> 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</p>

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

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<p>Are you ready to stay ahead in the fast-evolving world of data? Join Jumping Rivers for our free<br />
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gain practical insights, sharpen your skills, and ta...</p></div>
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<strong>Continue reading</strong>: <a href="https://www.r-bloggers.com/2025/11/elevate-your-skills-and-boost-your-career-free-jumping-rivers-webinar-on-20th-november/">Elevate Your Skills and Boost Your Career – Free Jumping Rivers Webinar on 20th November!</a>

Elevate Your Skills and Boost Your Career – Free Jumping Rivers Webinar on 20th November!

<div style = "width:60%; display: inline-block; float:left; "> Running a marathon is a big deal. It takes a lot of time to train to run a good time, and it takes a while to recover. So, if you’re chasing a marathon PB (personal best) time, you need to choose which Marathon to target wisely. How can we ...</div>
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<strong>Continue reading</strong>: <a href="https://www.r-bloggers.com/2025/11/choose-your-fighter-data-driven-selection-of-the-best-marathon/">Choose Your Fighter: data-driven selection of the best marathon</a>

Choose Your Fighter: data-driven selection of the best marathon

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<p>Heatmap of the french population</p>
<p>Day 17 of 30DayMapChallenge: « A new tool » (previously).<br />
Testing Maplibre with {mapgl}.</p>
<p>library(dplyr)<br />
library(mapgl)<br />
library(sf)</p>
<p>Data<br />
Using french communes population.</p>
<p>pop<br />
  st_centroid() &#124;__<br />
  select(population)</p>
<p>Map</p>
<p>maplibre(center = c(5, 45),<br />
         zoom = 6) &#124;__<br />
  add_heatmap_layer(<br />
    id = "pop",<br />
    source = pop,<br />
    heatmap_weight = interpolate(<br />
      column = "population",<br />
      values = ...</p></div>
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<strong>Continue reading</strong>: <a href="https://www.r-bloggers.com/2025/11/maplibre/">Maplibre</a>

Creating a London Population Map with D3po

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