With each Wolfram Technology Conference comes the Wolfram Innovator Awards, where we seek out a new round of computational trailblazers across all disciplines. Every year we are in awe seeing all of the new ways of applying concentrated computational creativity that break the boundaries of technology and remind us all to never stop pushing. Without [&#8230;]

تم الإعلان عن الفائزين بجوائز مبتكري وولفرام في مؤتمر وولفرام للتكنولوجيا، احتفالاً بالإبداع والابتكار الحاسوبي. هذا الحدث السنوي يكرم الأفراد الذين يدفعون حدود التكنولوجيا من خلال أعمالهم الرائدة، مما يلهم الآخرين لمواصلة استكشاف تطبيقات جديدة للطرق الحاسوبية.

Se anunciaron los ganadores de los Wolfram Innovator Awards en la Wolfram Technology Conference, celebrando la creatividad e innovación computacional. Este evento anual reconoce a individuos que empujan los límites de la tecnología a través de su trabajo innovador, inspirando a otros a seguir explorando nuevas aplicaciones de métodos computacionales.

Les lauréats des Wolfram Innovator Awards ont été annoncés lors de la Wolfram Technology Conference, célébrant la créativité et l'innovation computationnelles. Cet événement annuel reconnaît les individus qui repoussent les limites de la technologie grâce à leur travail révolutionnaire, inspirant les autres à explorer de nouvelles applications des méthodes computationnelles.

The Wolfram Innovator Awards were announced at the Wolfram Technology Conference, celebrating computational creativity and innovation. This annual event recognizes individuals who push the boundaries of technology through their groundbreaking work, inspiring others to continue exploring new applications of computational methods.

Announcing the 2025 Innovator Award Winners

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 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

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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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Huge congratulations to John Clarke, Michel H. Devoret and John M. Martinis on the 2025 Nobel Prize in Physics “for the discovery of macroscopic quantum mechanical tunneling and energy quantization in an electric circuit.” Their superconducting Josephson-circuit experiments made quantum effects unmistakably visible at circuit scale, discrete, anharmonic energy levels and coherent tunneling between macroscopically distinct states, laying key groundwork for modern superconducting qubits. In this short computational essay, we’ll walk through compact simulations that reproduce those signatures: a spectroscopy-style level map for the Cooper-pair box/transmon, and time-domain tunneling dynamics with realistic decoherence to mirror the original observations.

تم منح جون كلارك وميشيل إتش. ديفوريت وجون م. مارتينيس جائزة نوبل في الفيزياء لعام 2025 لعملهم الرائد في مجال النفق الكمي الماكروسكوبي وتكميم الطاقة في الدوائر الكهربائية. جعلت تجاربهم مع دوائر جوزيفسون الفائقة التوصيل التأثيرات الكمية واضحة تمامًا على نطاق الدائرة، كاشفة عن مستويات طاقة منفصلة وغير متناسقة ونفق متماسك بين حالات متميزة ماكروسكوبية. يعد هذا العمل أساسياً لتطوير الكيوبتات الفائقة التوصيل الحديثة.

John Clarke, Michel H. Devoret y John M. Martinis han sido galardonados con el Premio Nobel de Física 2025 por su trabajo innovador sobre el túnel cuántico macroscópico y la cuantización de la energía en circuitos eléctricos. Sus experimentos con circuitos Josephson superconductores han hecho que los efectos cuánticos sean claramente observables a escala de circuito, revelando niveles de energía discretos, anharmónicos y un túnel coherente entre estados macroscópicamente distintos. Este trabajo es fundamental para el desarrollo de qubits superconductores modernos.

John Clarke, Michel H. Devoret et John M. Martinis ont reçu le Prix Nobel de Physique 2025 pour leur travail révolutionnaire sur le tunnel quantique macroscopique et la quantification de l'énergie dans les circuits électriques. Leurs expériences avec des circuits Josephson supraconducteurs ont rendu les effets quantiques clairement observables à l'échelle du circuit, révélant des niveaux d'énergie discrets, anharmoniques et un tunnel cohérent entre des états macroscopiquement distincts. Ce travail est fondamental pour le développement des qubits supraconducteurs modernes.

John Clarke, Michel H. Devoret, and John M. Martinis have been awarded the 2025 Nobel Prize in Physics for their groundbreaking work on macroscopic quantum mechanical tunneling and energy quantization in electric circuits. Their experiments with superconducting Josephson circuits have made quantum effects clearly observable at the circuit scale, revealing discrete, anharmonic energy levels and coherent tunneling between macroscopically distinct states. This work is foundational for the development of modern superconducting qubits.

Announcing the 2025 Innovator Award Winners

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