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¿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.

Today at #UpperBound2026 we welcomed on-stage the Hon. Eleanor Olszewski K.C., P.C., M.P., Minister of Emergency Management and Community Resilience and Minister responsible for Prairies Economic Development Canada I Développement économique Canada pour les Prairies to announce $6.8M in federal funding through Prairies Can and the Regional Artificial Intelligence (RAII). This investment will support five Alberta-based organizations in innovating and commercializing AI projects. As Marlene McNaughton, Amii's Chief Revenue Officer shared on-stage, "Programs like this create something incredibly important for Canada's small and medium-sized businesses: proof of what's possible. Every successful project helps strengthen Canada's AI ecosystem, accelerates adoption across sectors, and reinforces the role of this region as a global leader in applied AI innovation." Read the full press release: https://lnkd.in/gweBGEmT

Today on Day 2 from the #UpperBound2026 stage, Minister Nate Glubish joined Amii CEO Cam Linke to announce the launch of the Health Innovation Lab, a transformative new initiative led and delivered by Amii. Supported by a $10M from the Government of Alberta, this three-year initiative is designed to bridge the gap between world-class research and real-world clinical application. The Health Innovation Lab will move AI from the lab to the clinic, focusing on Al solutions that empower healthcare providers and improve patient outcomes. By integrating predictive tools and automated workflows, the Lab aims to address the most pressing challenges within our healthcare system—streamlining clinical delivery and providing the critical insights needed to enhance the quality of care across the province. Read more about the Health Innovation Lab here: https://lnkd.in/gbz6bf-4

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/eCrWUCaa

KI-Infrastruktur für Universitäten: Von der Vision zur Realität Gemeinsam mit NVIDIA haben wir einen praxiserprobten Fahrplan entwickelt, der bereits eine Vielzahl an Universitäten zum Erfolg geführt hat. ✅ Bedarfsanalyse statt Bauchgefühl ✅ Skalierbare NVIDIA AI-Infrastrukturen ✅ Change Management für alle Fakultäten ✅ Die richtige Lösung für alle Institute Das Ergebnis: Hochschulen, die ihre KI-Ambitionen in messbare Forschungserfolge verwandeln. Unser kostenloses eBook “The Roadmap to Becoming an AI University” zeigt die 5 kritischen Erfolgsfaktoren. 🔗 Jetzt herunterladen: https://lnkd.in/e8zj96TV

Viele Unternehmen experimentieren mit KI. Doch echte Mehrwerte entstehen erst, wenn internes Wissen strukturiert verfügbar ist. ama verbindet Ihre bestehenden Tools zu einer sicheren KI-Plattform – gehostet in Deutschland & Schweden. Jetzt Überblick anfordern.

Manage issuing, tracking, and verification in one system. No scattered tools or manual follow-ups.

Learn to make your credentialing system audit-ready. Track, verify, and manage credentials without manual effort.

Voce, immagini e testo elaborati insieme: la multimodalità sta ridefinendo il modo in cui professionisti e cittadini interagiscono con l'IA , aprendo nuovi scenari applicativi in diversi ambiti. Un'evoluzione che non riguarda solo la tecnologia, ma il modo in cui le organizzazioni affrontano le sfide operative di oggi. Leggi il nuovo articolo del nostro blog: https://lnkd.in/dysMTWbw #mutimodalita #AI #LLM

E se l’intelligenza artificiale potesse trasformare la gestione delle richieste dei clienti? Case Automation è uno strumento che analizza e classifica i messaggi in arrivo, li smista verso i team corretti e genera bozze di risposta arricchite con dati provenienti dai sistemi interni e dalle knowledge base. L’obiettivo è ridurre il tempo necessario per rispondere a una richiesta, instradandola in modo fluido e raccogliendo lungo il percorso le informazioni rilevanti. In questo modo, l’operatore umano non deve fare altro che validare e inviare la risposta, concentrandosi sulla qualità del servizio invece che sulle attività ripetitive. Scopri di più: https://lnkd.in/dRU9MiJX

🌙 Most autonomy failures don’t happen in daylight. Low visibility, bad weather, night operations, this is where optical sensors struggle and where autonomy breaks if thermal perception isn’t validated properly. In our article, we explain how physics-based thermal camera simulation and multi-sensor fusion help teams test these conditions before costly field trials. 👉 Read it in the comments!

If your model was trained in July, it hasn’t met December yet. Most computer vision models fail unassumingly across seasons. The culprit is the training images that never saw the shift. Five failure modes we keep seeing based on our work with clients: 1. Leaf-off occlusion Trees lose foliage. Objects that were hidden in summer become visible in winter, changing scene geometry and detection patterns. 2. Salt and grit on roads Surface texture changes. Lane markings fade. Reflectivity shifts. Models trained on clean asphalt struggle with noisy winter roads. 3. Winter clothing Human silhouettes expand. Bulkier shapes, hoods, scarves. Pose estimation and tracking degrade fast. 4. Bare trees reshape skylines Vegetation density drops. Background structure changes. Models relying on consistent horizon features lose context. 5. Low-sun glare Winter sun sits lower. Long shadows and direct glare saturate sensors, especially in RGB. Detection confidence drops at the worst times of day. This is the domain gap in plain sight. AI Verse generates physics-based synthetic datasets that cover seasonal variation on demand. RGB, infrared, thermal. Pixel-perfect labels. 4 seconds per image on 1 GPU. We simulate the conditions your model hasn’t seen yet, before deployment forces the lesson. Contact us now if you want to test it yourself! 💬

Meet AI/R, your Agentic AI transformation partner. Accelerate software engineering and modernization with AI and add AI Agents to your workforce.

Acelere seu processo de recrutamento enquanto reduz custos com Llia, a agente conversacional multimodal que parece uma entrevistadora real.

Bring your team the simulation edge with our Starter Packs Get a guided, hands-on introduction to AI-powered simulation with preconfigured environments built to help you test, validate, and move faster in autonomous systems development. Each Starter Pack gives you everything you need to get started quickly: - Ready-to-use configuration files - Predefined scenarios for immediate testing - Simulation license key and plugins - Flexible, credit-based usage - Clear step-by-step documentation - Fixed cost Whether you’re exploring intelligent system testing, counter-drone, airport ground operations, or USV and UAV interception, Starter Packs give you a fast track into simulation, and a path to more complex, customer-specific use cases. Choose the Starter Pack that fits your needs and join the next cohort!

The challenge: data, knowledge, and applications often exist in silos, making it difficult to scale AI effectively across a global organization. The answer: Plus.AI Powered by Akkio, Plus.AI helps connect data, knowledge, and workflows across the organization, giving teams a smarter way to access and act on intelligence at scale. As Jon Reilly, Akkio CEO and Co-Founder, puts it: "Mediaplus operates across dozens of markets with data ownership distributed across teams and geographies, exactly the environment Akkio is built for. Plus. AI shows what's possible when AI infrastructure can handle that complexity." Read the full story below

We're excited to partner with Mediaplus Group on Plus. AI, a global AI steering system that brings the agency's collective intelligence across markets, brands, and client teams into a single connected environment. The challenge Mediaplus set out to solve is one the entire industry faces: data, knowledge, and applications often exist in silos, making it difficult to scale AI effectively across a global organization. Plus. AI is their answer. Powered by Akkio, Plus. AI helps connect data, knowledge, and workflows across the organization, giving teams a smarter way to access and act on intelligence at scale. As Jon Reilly, Akkio CEO and Co-Founder, puts it: "Mediaplus operates across dozens of markets with data ownership distributed across teams and geographies, exactly the environment Akkio is built for. Plus. AI shows what's possible when AI infrastructure can handle that complexity." Read the full story here below #MarketingAI #MediaAgency #MediaCompany #AIInfrastructure

🚀 QwenWork International Edition is here! Alibaba's all-in-one workplace AI agent is now in public beta for global users — web & desktop. What it does: ✅ Describe tasks in natural language — the agent does the work for you ✅ Awareness: an agent that learns how YOU work, across sessions ✅ Build & deploy live web apps in one flow — no code, no servers ✅ Native image, video & audio generation — a full multimodal toolkit ✅ Basic & Advanced model tiers, powered by leading AI models Built for fast-growing markets across Asia, the Middle East, and Latin America — with enterprise-grade support from Alibaba Cloud's global infrastructure. Try it now 👉 https://lnkd.in/eQyPARif #QwenWork #AIagent #WorkplaceAI #Productivity #AlibabaCloud #Qwen

Wan3.0 for Deep-Dive Explainers Dense, technical documents rarely get read the way they deserve to be. Wan3.0 turns a DOC, XLS, TXT, or MD file into a clear 30-second explainer, the substance intact, just easier to actually sit through. Give any kind of input, and get cinematic output. · Alibaba Cloud Model Studio: https://lnkd.in/eaDfuuFK · Qwen Cloud: https://lnkd.in/evfvF78C #LetsWanIt #AlibabaCloud #Wan #QwenCloud

Qwen-Audio-3.0-TTS is here. 🎙️ Our latest text-to-speech model, in two flavors: • Flash: real-time interaction • Plus: high-quality generation What's new: • Multilingual coverage across 16 languages • Style control in natural language • Fine-grained tags for non-verbal details • More robust voice cloning from imperfect audio Also in this release (coming soon): • A curated preset voice library • 48 kHz audio output Try it now: 📄Release Blog: https://lnkd.in/gDqs-V36 🔗API: https://lnkd.in/gyzvnk4g 📚Substack: https://lnkd.in/gsHDKEJj

Your cheap AI agent probably already found the right answer. It just didn’t pick it. That’s the thing we kept running into over the past few months, working on making AI agents better at web research. When an agent gets a question wrong, the instinct is to buy a smarter model. That works. It’s also the most expensive option on the table. But give it a few attempts and a correct answer usually shows up somewhere in the pool. It just doesn't get selected. Majority voting doesn't fix this either, because it can't overturn a consensus on a wrong answer, and cheap pools are where that happens most. So the bottleneck is selection, not generation. The real job is curating the candidate pool: dropping the incorrect candidates without discarding the correct ones. And that's a much easier problem than generating the answer in the first place. So instead of improving the answering, we added a second agent whose only job is to check: given a question and a proposed answer, go research it on the web, come back with a verdict — valid or not. It worked. State of the art on FACTS-Search, DeepMind’s web research benchmark. It was also brutally expensive: the judge re-researches every candidate, so it ended up costing more than generating the answers in the first place. So we trained our own small 8B judge. Two approaches failed: → Imitation alone — it learns to search, but the verdict comes out fuzzy. The lesson gets spread across thousands of tokens, while the actual decision lives in one. → Reinforcement learning alone — no competent starting policy to explore from, so it gets stuck and never converges. Imitation first, then RL. That worked. We kept nearly all the quality at 3.2x lower cost. How you run your models matters more than which model you run. Guy Freund Niv Granot Yuval Belfer AI21 Labs. Full write-up 👇 https://lnkd.in/dSdKeqw2

Adding a verifier elevates every agentic AI system - but frontier verifiers are expensive. The good news: you can train a dedicated verifier and serve it at a fraction of the cost. In agentic search, the right answer is usually already in the pool. The system just doesn’t pick it. Majority voting can’t fix that. It simply ratifies whatever the crowd already believes, even when the crowd is wrong. What works: an independent verifier that re-researches every candidate and vetoes the bad ones. Only the survivors get voted on. On Google DeepMind’s FACTS-Search: • Frontier verifier: 83.3 → 93.4 (past published SOTA of 89.4) • Our trained 8B verifier: 92.9 at 3.2× lower cost It also generalizes to a new benchmark and search tool it never saw during training. This is the kind of result I like most - not just throwing a bigger model at the problem, but finding a better way to decide which answer to trust. Great work by Guy Freund, Eran Goldstein, Niv Granot & Yuval Belfer. Full post + training details → https://lnkd.in/gqgUJsfx

The State of Enterprise GenAI Report surveyed over 100 companies to see how they plan to launch generative AI for business use cases.