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$4.3M in annual media waste reduction surfaced inside Unilever B&W. Media decisions made on 90-day-lagging ROI now move in 4 to 7 days, on the same data.

Phase 1 surfaced a $180M opportunity in one market. The next conversation is category expansion or the US Connections build. 30 minutes to scope which.

Join us live (virtually) for a technical deep dive into how Arize engineers actually built Alyx 2.0 — from architecture to production. Unlike most AI assistants that follow a single decision tree, Alyx breaks down complex tasks, reasons across multi-step workflows, and acts autonomously across your entire AI lifecycle: ↳ Error analysis ↳ Prompt experimentation ↳ Trace debugging

Bank of America designed Erica as a platform that could leverage multiple models — from BERT and OpenNLP to the latest foundation models. Arthur was built the same way. As your team brings in new models across the Erica architecture, Arthur monitors, evaluates, and governs every one of them through a single platform.

U.S. Bank's responsible use framework calls for better monitoring, better visibility, and predictive maintenance across AI systems. Arthur is the operational layer that delivers it — continuously evaluating every AI system in production, surfacing drift in real time, and giving second-line oversight what your framework requires.

Excited to announce that I will be attending and representing Arize AI at RAISE Summit! Looking forward to connecting with the brightest minds in AI and innovation at the Carrousel du Louvre, Paris. #RAISESummit

¿Cuánto dinero podrías ahorrar por mermas en Q1 2026? Esta y cualquier pregunta sobre tu operación la podrás responder con Arkham. Construye inteligencia comercial para tener análisis de marcas, categorías y comportamiento de mercado con datos confiables e insights accionables. Tu equipo operativo estará un paso adelante frente a la competencia con mejor segmentación, con métricas estandarizadas y sin análisis manuales. Obtén respuestas en segundos sobre tu operación con AI Generativa para optimizar mermas, pricing y tu estrategia comercial.

Arkham, el aliado estratégico que transforma días de análisis en respuestas confiables en segundos. ¿Cuánto dinero pierdes por exceso de stock, rotación lenta o mermas evitables? Con nuestra plataforma de Datos & AI, obtén respuestas confiables en segundos y habilita casos de uso que impactan tu operación: • Gestión de Inventario: Pronósticos 95% precisos que evitan stock outs, exceso y mermas • Pricing Estratégico: Descuentos optimizados para máximo margen • Control de Rotación: Alertas en tiempo real sobre productos de baja rotación Construye una ventaja competitiva con AI adaptada a tu operación.

Claude Opus 4.7 handles long-running tasks with more rigor, follows instructions more precisely, and verifies its own outputs before reporting back. You can hand off your hardest work with less supervision.

Claude Opus 4.7 is live today! I've been using it for a couple weeks and the thing I keep coming back to is how much further I can push a single prompt. Ambiguous, multi-step work that I used to break into pieces now goes in as one task, and it checks its own work before telling me it's done. That self-verification loop is the part that changes how it feels to build with. The numbers from early partners track with that. Cursor's internal bench cleared 70%, up from 58% on 4.6. Notion saw a 14% lift on their evals with a third of the tool errors. Also shipping today on the platform: higher-resolution image support (3x the pixels of prior Claude models), task budgets in public beta for guiding token spend on long runs, and a new xhigh effort level for tuning the reasoning/latency tradeoff. Let us know what you think! https://lnkd.in/gRUHa38h

I will be representing Amii (Alberta Machine Intelligence Institute) at HannoverMesse next week, along with Jon Larson and Felix Duerr. I look forward to connecting with German and European organizations interested in collaborating with one of Canada's national AI institutes to advance AI research and commercialization. Connect with us in Hall 27 in the Team Canada pavilion organized by NGen Canada, or reach out here and let's find a time to meet! See you there! Looking forward to connecting with colleagues at Canadian German Chamber of Industry and Commerce Inc. and attending the Canada German AI Symposium there as well! #aiinmanufacturing #hm26 #hannovermesse #ai #ai4manufacturing

Five years ago, we started Upper Bound as a celebration of Alberta’s 20-year legacy in AI research. It has since evolved into a world-class gathering where Canada’s AI future is being built. Today we are excited to announce that Upper Bound is officially SOLD OUT for 2026! 🎉 In just five years, this event has welcomed over 16,000 members of the global AI community to Edmonton to spark the collisions that advance AI science, optimize business, scale startups and map national projects. As the Honourable Nate Glubish, Minister of Technology and Innovation, notes: “Alberta has long served as a global hub for artificial intelligence, driven by institutions like Amii and a thriving innovation ecosystem. The fact that Upper Bound has sold out underscores the strength of our province’s leadership in AI, our ability to attract top talent and investment, and the growing role Alberta plays in shaping the future of technology and economic growth.” We can't wait to welcome 8,000 of you to Edmonton next month to imagine—and realize—an optimistic AI future. Read more from our media release: https://hubs.ly/Q04f0RMV0

Viele Hochschulen haben bereits eine KI-Strategie - zumindest auf dem Papier. Aber wenn es um die Umsetzung einzelner Projekte geht, scheitern ein Großteil an der passenden Infrastruktur. ❌ Falsch dimensionierte Hardware-Anschaffungen ❌ Unklare Anforderungen aus den Fakultäten ❌ IT-Teams, die mit dem Betrieb von KI-Hardware unerfahren sind ❌ Angst, Budgets falsch einzusetzen Bei AMBER arbeiten wir seit Jahren mit Hochschulen und NVIDIA zusammen und wissen: Erfolgreiche KI-Implementierung beginnt mit der richtigen Strategie. Unser kostenloses eBook “The Roadmap to Becoming an AI University” zeigt die 5 kritischen Erfolgsfaktoren. 🔗 Jetzt herunterladen: https://lnkd.in/eCncun42

Viele Hochschulen haben bereits eine KI-Strategie - zumindest auf dem Papier. Aber wenn es um die Umsetzung einzelner Projekte geht, scheitern ein Großteil an der passenden Infrastruktur. ❌ Falsch dimensionierte Hardware-Anschaffungen ❌ Unklare Anforderungen aus den Fakultäten ❌ IT-Teams, die mit dem Betrieb von KI-Hardware unerfahren sind ❌ Angst, Budgets falsch einzusetzen Bei AMBER arbeiten wir seit Jahren mit Hochschulen und NVIDIA zusammen und wissen: Erfolgreiche KI-Implementierung beginnt mit der richtigen Strategie. Unser kostenloses eBook “The Roadmap to Becoming an AI University” zeigt die 5 kritischen Erfolgsfaktoren. 🔗 Jetzt herunterladen: https://lnkd.in/e8JeFQKs

E se potessimo parlare con l’IA invece di scriverle? Oggi l’uso della GenAI è ancora prevalentemente testuale: cittadini e professionisti inviano richieste scritte per tradurre documenti, sintetizzare informazioni o generare contenuti. Ma la voce può cambiare tutto. Velvet Speech 2B, il primo modello multimodale di Velvet, è compatto, versatile e progettato per interazioni dinamiche. Comprende il linguaggio parlato e permette di interagire sia tramite voce che testo, semplificando le attività anche nei contesti operativi più complessi. Una nuova frontiera per rendere le interazioni con l’IA più naturali e immediate. Leggi l’articolo per saperne di più: https://lnkd.in/d3yn67eA #VelvetSpeech2B #Velvet #LLM #Voicerecognition

Viele KI-Lösungen liefern generische Antworten. Echter Mehrwert entsteht erst, wenn Ihre eigenen Systeme angebunden sind. ama verbindet Deine Tools zu einer sicheren, zentralen KI-Plattform. Alle Details im Überblick.

Verification should not depend on trust. Make every credential provable and tamper-proof.

Avoid missing records and manual tracking. Ensure every certificate is stored and verifiable.

How Simulation-in-the-Loop (SiL) Saves Time and Money? Real-world testing for autonomous systems is slow, expensive, and often risky. With Simulation-in-the-Loop, teams can run their full autonomy stack inside a simulated environment, test thousands of scenarios, and validate edge cases before going to the field. Read the full the article (Link in the comments) 👇

È possibile coniugare intelligenza artificiale e massima protezione dei dati? L’edge AI permette di elaborare i dati direttamente sul dispositivo o su server locali, garantendo rapidità, continuità dei servizi e maggiore rispetto della protezione della privacy. Dagli assistenti clinici alla guida autonoma dei mezzi, l’edge AI offre vantaggi concreti in diversi scenari. Per rispondere a esigenze applicative sempre più diversificate, la famiglia Velvet di Almawave include, accanto ai modelli linguistici di grandi dimensioni, soluzioni leggere come Velvet 2B e Velvet Speech 2B, ottimizzate per funzionare anche in ambienti edge. Leggi l'articolo del nostro blog per saperne di più: https://lnkd.in/dRyprSNw #edgeAI #ai #LLM

Validating autonomous systems is no longer just about scale but about relevance. AILiveSim provides a powerful simulation platform for robotics testing, validation, and synthetic data generation in physics-based virtual environments. With Intelligent System Testing (IST), teams can move beyond brute-force testing by adopting an adaptive validation approach, focusing on the scenarios that matter most and accelerating learning cycles. IST integrates seamlessly with existing simulation workflows, enabling developers to strengthen their testing pipelines and validate complex autonomous behaviors with greater confidence. 📌 Our latest Technical Brief explores this approach in detail, including a real-world auto-docking use case. 👉 Download the full brief: link in the comments

Sunny Paris. Foggy London. Same model. Different outcomes. We see this gap often. A model performs well in controlled environments, then degrades when conditions shift. This is usually not a model issue. It is a data coverage issue. Here are five ways weather affects perception in defense scenarios: 1. Rain on EO sensors creates fast-moving streaks that act as occlusions and false edges, lowering detection confidence for vehicles and personnel at distance. 2. Fog reduces contrast over distance, pushing objects below detection thresholds sooner than expected, especially for appearance-based models. 3. Snow and ground glare introduce noise that hides useful features, increasing false positives in threat classification. 4. Wet surfaces create reflections that mimic shapes and thermal signatures, leading to false detections and unstable tracking. 5. Wind-driven clutter like dust and vegetation disrupts temporal consistency, which video-based models rely on for motion cues. We tested this with a client. The same detector ran on clear and light rain footage. Precision stayed stable. Recall dropped. The model depended heavily on clean visual features, and partial occlusions reduced the signal it needed. For ISR, autonomous airborne and ground systems, and perimeter monitoring, weather can degrade model performance. In our work, we treat image distortion as a core variable in the data. This means collecting data across real conditions and applying physics-based rules and lens effects. In our work, we treat image distortion as a core variable in the data. If you are looking to close data gaps and improve how your models perform in real conditions, we are here to help.

If your model was trained in July, it hasn’t met December yet. We see this all the time. Models don’t fail loudly. They drift when the season changes. Not because the model is weak. Because the data never covered the shift. A few patterns keep coming up: 1️⃣ Leaf-off occlusion Objects hidden in summer suddenly appear. Scene geometry changes and detections follow. 2️⃣ Salt and grit on roads Texture changes. Lane markings fade. Reflection behaves differently. Clean-road training doesn’t hold up. 3️⃣ Winter clothing Silhouettes get bulkier. Hoods, scarves, layers. Tracking and pose estimation lose precision. 4️⃣ Bare trees reshape skylines Less vegetation means a different background. Models that rely on stable horizons lose context. 5️⃣ Low sun glare Sun sits lower. Long shadows and glare hit sensors directly. Confidence drops at the worst time. This is the domain gap in plain sight. At AI Verse, we generate physics-based synthetic data to cover these seasonal shifts on demand. RGB, infrared, thermal. Pixel-level labels. About 4 seconds per image on a single GPU. You can train on conditions your model hasn’t seen yet, before it faces them in production. Happy to show how this looks in practice. DM me 💬!