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أطلقت شركة Databricks مسرعات الشركاء عبر الصناعات التي تركز على الذكاء الاصطناعي الوكالي وGenAI وLLMOps، مما يبرز الإمكانات التحويلية للذكاء الاصطناعي التوليدي في الأعمال. تمثل هذه المبادرة خطوة مهمة في دمج تقنيات الذكاء الاصطناعي المتقدمة في مختلف الصناعات، مع التأكيد على أهميتها وتأثيرها الحالي.

Databricks ha lanzado Aceleradores de Socios Interindustriales centrados en IA Agentica, GenAI y LLMOps, destacando el potencial transformador de la IA generativa en los negocios. Esta iniciativa representa un paso significativo en la integración de tecnologías avanzadas de IA en diversas industrias, enfatizando su relevancia e impacto actuales.

Databricks a lancé des Accélérateurs de Partenaires Intersectoriels axés sur l'IA Agentique, GenAI et LLMOps, soulignant le potentiel transformateur de l'IA générative dans les affaires. Cette initiative représente une avancée significative dans l'intégration des technologies avancées de l'IA dans divers secteurs, mettant en avant leur pertinence et leur impact actuels.

Databricks has launched Cross-Industry Partner Accelerators focusing on Agentic AI, GenAI, and LLMOps, highlighting the transformative potential of generative AI in business. This initiative marks a significant step in integrating advanced AI technologies into various industries, emphasizing their current relevance and impact.

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<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

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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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The race to deploy <a href="https://venturebeat.com/ai/6-proven-lessons-from-the-ai-projects-that-broke-before-they-scaled">agentic AI is on</a>. Across the enterprise, systems that can plan, take actions and collaborate across business applications promise unprecedented efficiency. But in the rush to automate, a critical component is being overlooked: Scalable security. We are building a workforce of digital employees without giving them a secure way to log in, access data and do their jobs without creating catastrophic risk.The fundamental problem is that traditional identity and access management (IAM) designed for humans breaks at agentic scale. Controls like static roles, long-lived passwords and one-time approvals are useless when non-human identities can outnumber human ones by 10 to one. To harness the power of agentic AI, identity must evolve from a simple login gatekeeper into the dynamic control plane for your entire AI operation.“The fastest path to responsible AI is to avoid real data. Use synthetic data to prove value, then earn the right to touch the real thing.” — Shawn Kanungo, keynote speaker and innovation strategist; bestselling author of The Bold Ones<h2>Why your human-centric IAM is a sitting duck</h2><a href="https://venturebeat.com/ai/what-could-possibly-go-wrong-if-an-enterprise-replaces-all-its-engineers">Agentic AI</a> does not just use software; it behaves like a user. It authenticates to systems, assumes roles and calls APIs. If you treat these agents as mere features of an application, you invite invisible privilege creep and untraceable actions. A single over-permissioned agent can exfiltrate data or trigger erroneous business processes at machine speed, with no one the wiser until it is too late.The static nature of legacy IAM is the core vulnerability. You cannot pre-define a fixed role for an agent whose tasks and required data access might change daily. The only way to keep access decisions accurate is to move policy enforcement from a one-time grant to a continuous, runtime evaluation.Prove value before production dataKanungo’s guidance offers a practical on-ramp. Start with synthetic or masked datasets to validate agent workflows, scopes and guardrails. Once your policies, logs and break-glass paths hold up in this sandbox, you can graduate agents to real data with confidence and clear audit evidence.Building an identity-centric operating model for AISecuring this new workforce requires a shift in mindset. <a href="https://venturebeat.com/ai/large-reasoning-models-almost-certainly-can-think">Each AI agent</a> must be treated as a first-class citizen within your identity ecosystem.First, every agent needs a unique, verifiable identity. This is not just a technical ID; it must be linked to a human owner, a specific business use case and a software bill of materials (SBOM). The era of shared service accounts is over; they are the equivalent of giving a master key to a faceless crowd.Second, replace set-and-forget roles with session-based, risk-aware permissions. Access should be granted just in time, scoped to the immediate task and the minimum necessary dataset, then automatically revoked when the job is complete. Think of it as giving an agent a key to a single room for one meeting, not the master key to the entire building.<h2>Three pillars of a scalable agent security architecture</h2>Context-aware authorization at the core. Authorization can no longer be a simple yes or no at the door. It must be a continuous conversation. Systems should evaluate context in real time. Is the agent’s digital posture attested? Is it requesting data typical for its purpose? Is this access occurring during a normal operational window? This dynamic evaluation enables both security and speed.Purpose-bound data access at the edge. The final line of defense is the data layer itself. By embedding policy enforcement directly into the data query engine, you can enforce row-level and column-level security based on the agent’s declared purpose. A customer service agent should be automatically blocked from running a query that appears designed for financial analysis. Purpose binding ensures data is used as intended, not merely accessed by an authorized identity.Tamper-evident evidence by default. In a world of autonomous actions, auditability is non-negotiable. Every access decision, data query and API call should be immutably logged, capturing the who, what, where and why. Link logs so they are tamper evident and replayable for auditors or incident responders, providing a clear narrative of every agent’s activities.<h2>A practical roadmap to get started</h2>Begin with an identity inventory. Catalog all non-human identities and service accounts. You will likely find sharing and over-provisioning. Begin issuing unique identities for each agent workload.Pilot a just-in-time access platform. Implement a tool that grants short-lived, scoped credentials for a specific project. This proves the concept and shows the operational benefits.Mandate short-lived credentials. Issue tokens that expire in minutes, not months. Seek out and remove static API keys and secrets from code and configuration.Stand up a synthetic data sandbox. Validate agent workflows, scopes, prompts and policies on synthetic or masked data first. Promote to real data only after controls, logs and egress policies pass.Conduct an agent incident tabletop drill. Practice responses to a leaked credential, a prompt injection or a tool escalation. Prove you can revoke access, rotate credentials and isolate an agent in minutes.<h2>The bottom line</h2>You cannot manage an <a href="https://venturebeat.com/ai/from-human-clicks-to-machine-intent-preparing-the-web-for-agentic-ai">agentic, AI-driven future</a> with human-era identity tools. The organizations that will win recognize identity as the central nervous system for AI operations. Make identity the control plane, move authorization to runtime, bind data access to purpose and prove value on synthetic data before touching the real thing. Do that, and you can scale to a million agents without scaling your breach risk. Michelle Buckner is a former NASA Information System Security Officer (ISSO). Read more from our <a href="https://venturebeat.com/datadecisionmakers">guest writers</a>. Or, consider submitting a post of your own! See our <a href="https://venturebeat.com/guest-posts">guidelines here</a>.

تتسارع وتيرة نشر الذكاء الاصطناعي الوكالي، مما يعد بكفاءة غير مسبوقة عبر المؤسسات. ومع ذلك، تظهر مشكلة كبيرة مع أنظمة إدارة الهوية والوصول التقليدية (IAM)، التي تفشل في تأمين هؤلاء الموظفين الرقميين. حيث يمكن أن تتجاوز الهويات غير البشرية الهويات البشرية بمعدل عشرة إلى واحد، فإن الضوابط الحالية لـ IAM، مثل الأدوار الثابتة وكلمات المرور طويلة الأمد، غير كافية. لإدارة الذكاء الاصطناعي الوكالي بفعالية، يجب أن تتطور إدارة الهوية من مجرد بوابة تسجيل دخول إلى خطة تحكم ديناميكية يمكنها التعامل مع تعقيدات عمليات الذكاء الاصطناعي.

El despliegue de la IA agentiva está acelerándose, prometiendo una eficiencia sin precedentes en las empresas. Sin embargo, surge un problema significativo con los sistemas tradicionales de gestión de identidades y accesos (IAM), que están fallando en asegurar a estos empleados digitales. A medida que las identidades no humanas pueden superar a las humanas en una proporción de diez a uno, los controles IAM existentes, como los roles estáticos y las contraseñas de larga duración, son inadecuados. Para gestionar eficazmente la IA agentiva, la gestión de identidades debe evolucionar de un simple mecanismo de inicio de sesión a un plano de control dinámico que pueda manejar las complejidades de las operaciones de IA.

Le déploiement de l'IA agentique s'accélère, promettant une efficacité accrue au sein des entreprises. Cependant, un problème majeur se pose avec les systèmes traditionnels de gestion des identités et des accès (IAM), qui échouent à sécuriser ces employés numériques. Alors que les identités non humaines peuvent dépasser les identités humaines par un facteur de dix, les contrôles IAM existants, tels que les rôles statiques et les mots de passe à long terme, sont inadéquats. Pour gérer efficacement l'IA agentique, la gestion des identités doit évoluer d'un simple mécanisme de connexion à un plan de contrôle dynamique capable de gérer les complexités des opérations d'IA.

The deployment of agentic AI is accelerating, promising enhanced efficiency across enterprises. However, a significant issue arises with traditional identity and access management (IAM) systems, which are failing to secure these digital employees. As non-human identities may outnumber human ones by a factor of ten, existing IAM controls like static roles and long-lived passwords are inadequate. To effectively manage agentic AI, identity management must transition from a simple login mechanism to a dynamic control plane that can handle the complexities of AI operations.

Human-centric IAM is failing: Agentic AI requires a new identity control plane

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