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We recently sat down with Zin Thein Kyaw, head of solutions engineering here at Edge Impulse, to talk about the evolving landscape of #edgeAI and how this technology is transforming industries like laundry automation and pet care. In the discussion, Zin covers: - The role of AI in augmenting the workforce, rather than replacing jobs -Real-world customer production use cases with HP Poly and Purina -Common misconceptions about data readiness and model accuracy And lots more. Check out the full discussion: https://lnkd.in/gynVEDdT And reach out to Zin or Edge Impulse here on Linkedin or on any of our channels to talk about your use case.

If your business increasingly runs on AI, AI sovereignty isn't a philosophical question - it's a business continuity one. Our new post lays out the two necessary conditions for Sovereign AI, and why enterprises need to act now, not later.

AI data-center buildout is projected to hit $1.4 trillion a year by 2030 - seven times the peak of the dot-com telecom boom. Who controls that infrastructure determines who controls your business. Our new piece on Sovereign AI breaks down what's actually at stake.

Elula pinpoints which lost members can be won back and equips your team with the right conversations to bring balances home. Rethink your customer retention strategy. Put the power of our AI software to work for you. Discover more at elula.ai

Is your organization stuck on 'basic' ChatGPT use? Move beyond email drafting to automated, high-impact workflows that deliver measurable ROI. Stop settling for low adoption. Eliza guarantees high-impact ChatGPT ROI in just 4 weeks with a custom adoption roadmap designed for your business. The Eliza 4-Week Impact Program Includes: ➡ Use Case Discovery Workshop: Co-create & prioritize 10–20 high-impact use cases; prototype the top set for immediate ROI. ➡ Hands-On Workshops: Role-based training (HR, Finance, Sales) to master prompts, build Custom GPTs, and automate key workflows. ➡ Change Management: AI champions program, communications, and a rollout plan to drive safe, sustained adoption. ➡ Enablement Hub: A central source for Adoption KPIs, time-Saved Mmetrics, prompts, guides, and playbooks. Proven Results from Pilot Teams: ✅ 75%+ weekly active usage. ✅ High-impact use cases saving hundreds of hours/week. ✅ An up-skilled organization ready to leverage AI as a true business differentiator. Ready to unlock guaranteed AI ROI? Request a FREE Consultation for your custom ChatGPT adoption roadmap.

AI is driving unprecedented growth across MEA, LATAM, and SEA. Are you capturing your share? See how financial leaders are scaling AI to boost credit accuracy, digital payments, and market impact.

Despite AI investments set to reach US$368B by 2032, many banks still struggle to turn pilots into real revenue. The barrier isn't technology-it's operational. 🔍 Key insight: Banks generating measurable top-line growth execute AI with intent: 1. Align every AI use case to P&L objectives 2. Embed intelligence into frontline workflows 3. Treat AI as an enterprise-wide transformation, not a tech experiment 🌍 Insights from Southeast Asia, Latin America, and the Middle East highlight five revenue levers already driving impact: cross-selling, financial-inclusion lending, digital wealth, SME acquisition, and AI-augmented compliance. 📈 Bottom line: Banks that operationalise AI at scale are seeing real top-line impact.

The term recursive self-improvement (RSI) is everywhere right now, appearing in roadmaps, startup names and near-term visions of AI systems that improve themselves with little to no human input. But what does it actually take to get there? TechCrunch's Russell Brandom dug into that question in an article titled “RSI is the new AGI — and it’s just as hard to pin down,” and as part of his reporting, he spoke to our CEO Doris Xin about it. The common framing treats RSI as a problem of intelligence. As the frontier models get smarter, they’ll eventually be able to improve themselves. Jack Clark, Anthropic's co-founder recently wrote on his Substack that he expects RSI to arrive by the end of 2028. Others, including Doris, believe it will happen much sooner, as early as next year. She takes this position because she argues models already possess the intelligence for RSI, but lack the required infrastructure, and developing that infrastructure will happen much more quickly than the release of new frontier models. Brandom quotes Doris in his article saying, "I would argue, given infinite compute and infinite time horizon, we are already there. I want to make an argument that this is not a creative endeavor, really. It's just a lot of meat-and-potatoes engineering." That framing tracks with what we see in enterprise ML every day. The bottleneck is rarely the model, but rather the context that's fragmented, and the lack of agentic supervision that can help it determine the value of continuing on the current trajectory.

The term recursive self-improvement (RSI) is everywhere right now, appearing in roadmaps, startup names and near-term visions of AI systems that improve themselves with little to no human input. But what does it actually take to get there? TechCrunch's Russell Brandom dug into that question in an article titled “RSI is the new AGI — and it’s just as hard to pin down,” and as part of his reporting, he spoke to our CEO Doris Xin about it. The common framing treats RSI as a problem of intelligence. As the frontier models get smarter, they’ll eventually be able to improve themselves. Jack Clark, Anthropic's co-founder recently wrote on his Substack that he expects RSI to arrive by the end of 2028. Others, including Doris, believe it will happen much sooner, as early as next year. She takes this position because she argues models already possess the intelligence for RSI, but lack the required infrastructure, and developing that infrastructure will happen much more quickly than the release of new frontier models. Brandom quotes Doris in his article saying, "I would argue, given infinite compute and infinite time horizon, we are already there. I want to make an argument that this is not a creative endeavor, really. It's just a lot of meat-and-potatoes engineering." That framing tracks with what we see in enterprise ML every day. The bottleneck is rarely the model, but rather the context that's fragmented, and the lack of agentic supervision that can help it determine the value of continuing on the current trajectory. Read the full TechCrunch piece here: https://lnkd.in/ewAMwrdg Where do you think the real bottleneck is on the path to self-improving AI systems? Drop your take in the comments.

DKube's AI classifies and reconciles W-2s, bank statements, and appraisals automatically — so your team doesn't.

DKube cuts document reconciliation costs by up to 80% with AI that classifies, extracts, and validates.

Renting AI is fast. But when intelligence sits inside decisioning, compliance, and customer-facing workflows, speed stops being enough — and dependency becomes a liability. Ownership means controlling the codebase, the model weights, the training data, and the outputs. Miss one, and the system is never fully yours. In our latest article, we break down what "renting" AI actually costs — and what full ownership looks like in practice. 👉 Read it here: https://lnkd.in/ein9gBRS

𝗢𝘂𝗿 𝗮𝗴𝗲𝗻𝗱𝗮 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗱𝗶𝗱𝗮 𝗰𝗼𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝟮𝟬𝟮𝟲 The fourth annual dida conference is only two weeks ahead, and we think we have our strongest program yet! You have seen the announcements of all the great speakers we have gathered for this iteration of our conference - we have now published a full schedule for the day here: https://dida.do/conference. What you need to know: 𝗪𝗵𝗲𝗻: June 19, 2026 · 09:00–18:00 CEST 𝗪𝗵𝗲𝗿𝗲: frizzforum, Berlin-Kreuzberg If you build with ML or decide where it goes next, this is a day of senior practitioners and researchers in one room - come and join us. We look forward seeing you there! Participation is capped by the venue, so register soon also at https://dida.do/conference

Every call you take starts with 10 minutes of "so what do you guys do?" What if they already knew before they got on the phone?

AI Performance Isn’t About More Compute It’s about smarter data. Discover how metadata is reshaping AI pipelines—delivering massive gains in speed, efficiency, and scalability.

Good infrastructure gets noticed. Great infrastructure makes the list. DDN has been named to the 2026 CRN AI 100 List in the AI Infrastructure and Edge Computing category! We are proud to be recognized alongside other innovators shaping the future of AI and even prouder to support the partners bringing enterprise AI to life.

𝐅𝐫𝐨𝐧𝐭𝐢𝐞𝐫-𝐜𝐥𝐚𝐬𝐬 𝐯𝐢𝐬𝐢𝐨𝐧-𝐥𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐦𝐨𝐝𝐞𝐥 𝐚𝐜𝐜𝐮𝐫𝐚𝐜𝐲. 𝐀𝐭 𝐮𝐩 𝐭𝐨 ~150× 𝐥𝐞𝐬𝐬 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐜𝐨𝐦𝐩𝐮𝐭𝐞. 𝐀𝐧𝐝 𝐮𝐩 𝐭𝐨 3.3× 𝐥𝐨𝐰𝐞𝐫 𝐢𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐜𝐨𝐬𝐭. Without bigger clusters. Without elaborate post-training. Just curated, high-quality training data. That's what our latest research, 20/20 Vision-Language Models, delivers. And the implications for any enterprise building multimodal AI are significant. Holding architecture and compute fixed and varying only the training data, our 2B model lands within 1.8 percentage points of Qwen3-VL-2B at ~87× less training compute. Our 4B matches Qwen3.5-2B at 2.4× lower inference cost. For companies building multimodal AI, that translates into: - 𝐁𝐞𝐭𝐭𝐞𝐫 𝐦𝐨𝐝𝐞𝐥𝐬 𝐟𝐫𝐨𝐦 𝐬𝐦𝐚𝐫𝐭𝐞𝐫 𝐝𝐚𝐭𝐚, 𝐧𝐨𝐭 𝐛𝐢𝐠𝐠𝐞𝐫 𝐜𝐥𝐮𝐬𝐭𝐞𝐫𝐬. Frontier-class accuracy without committing to hyperscaler-scale pretraining budgets. - 𝐋𝐨𝐰𝐞𝐫 𝐫𝐢𝐬𝐤 𝐨𝐧 𝐞𝐯𝐞𝐫𝐲 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐫𝐮𝐧. Per-capability seed variance drops by ~67%, making the recipe robust to the seed lottery that plagues large training runs. - 𝐋𝐨𝐰𝐞𝐫 𝐜𝐨𝐬𝐭 𝐭𝐨 𝐬𝐞𝐫𝐯𝐞, 𝐟𝐨𝐫𝐞𝐯𝐞𝐫. Up to 3.3× lower inference FLOPs than comparable models, with Pareto-dominance on response cost at every scale tested. This is capable multimodal AI that can run on edge devices, not just in a data center. 𝐇𝐨𝐰 𝐲𝐨𝐮 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐲𝐨𝐮𝐫 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐝𝐚𝐭𝐚 𝐢𝐬 𝐭𝐡𝐞 𝐦𝐨𝐚𝐭. Companies with high-quality proprietary data and the discipline to curate and engineer it well don't need to match hyperscaler budgets dollar-for-dollar to achieve amazing performance. The standard approach to competitive multimodal AI has been to throw more compute at it. The new approach is to treat training data as the strategic lever that it is. Read the full write-up: https://lnkd.in/gwn6w-Mp

Ahora está disponible para tu contact center, tu IVR y tus agentes de voz. Sin hardware. Sin contratos permanentes. Sin complicaciones técnicas. Solo una API que se integra en días y que tus clientes notarán desde la primera llamada. Deepdub no es un proveedor de TTS más. Es la plataforma que los líderes tecnológicos globales han elegido para construir el futuro de la conversación con IA🔝🔝 El playground es gratuito. Pega tu propio script, elige una voz y comprueba cómo podría sonar tu empresa. Después de eso, tú decides☝🏽

Deepdub no es un proveedor de TTS más. Es la plataforma que los líderes tecnológicos globales han elegido para construir el futuro de la conversación con IA🔝🔝 Ahora está disponible para tu contact center, tu IVR y tus agentes de voz. Sin hardware. Sin contratos permanentes. Sin complicaciones técnicas. Solo una API que se integra en días y que tus clientes notarán desde la primera llamada. El playground es gratuito. Pega tu propio script, elige una voz y comprueba cómo podría sonar tu empresa. Después de eso, tú decides☝🏽

A lot of companies talk about “doing” synthetic data, but what does it actually take to generate high-quality synthetic pretraining data at the trillion-token scale, necessary for training foundation models? The DatologyAI team answers this question with BeyondWeb, a synthetic data generation system that outperforms all publicly available methods for synthetic pretraining data generation. BeyondWeb establishes a new Pareto frontier for the trade-off between model size and quality in language model pretraining. It delivers superior performance at every scale, with our 3B model surpassing the results of most 8B-scale baselines, using the same token budget. This new frontier reshapes the economics and accessibility of high-performance AI development, enabling organizations to achieve state-of-the-art results with computational budgets that were previously insufficient for competitive model development. Read more about the impact of BeyondWeb—and what it means for the future of the AI industry—here: https://lnkd.in/g_6-3QBQ

Jede Gesellschaft nutzt eine andere KI. Wer in Ihrer Gruppe kennt am Ende alle Datenflüsse? Microsoft 365 ist überall gesetzt, Copilot ist eingeführt. Doch Copilot deckt ein Modell ab und nicht jeden Bedarf der Fachabteilungen. Also greifen Teams zu eigenen Tools, und die IT prüft seit Monaten einen Wunsch nach dem nächsten. So entsteht entweder Schatten-KI ohne Standard oder ein Verbot, das die Mitarbeiter ohnehin umgehen. Das passiert nicht irgendwann. Das passiert täglich. In deutschen Unternehmensgruppen jeder Größe. DeutschlandGPT gibt allen Gesellschaften denselben sicheren Zugang, zentral steuerbar und auf Wunsch im eigenen Branding. Die kostenlose PDF gibt Ihnen die Orientierung dafür: ✔ Wie ein gemeinsamer Standard für die Gruppe aussieht, neben Microsoft 365 ✔ Wie zentrale Rollen, Rechte und Audit Logs funktionieren ✔ Wie sich die Plattform im eigenen Corporate Design ausrollen lässt ✔ Welche technischen Eckdaten die IT für die Bewertung braucht Jetzt kostenlose PDF herunterladen.

Wissen Sie, wie viele KI-Tools in Ihrer Unternehmensgruppe gerade parallel laufen? In der einen Tochtergesellschaft nutzt das Marketing ChatGPT, in der anderen testet die IT ein eigenes Tool, eine dritte hat alles verboten. Niemand hat den vollen Überblick. Firmenwissen, Kundendaten und Vertragsentwürfe landen dabei auf Servern, die niemand zentral freigegeben hat. Solange jede Gesellschaft ihr eigenes Ding macht, wächst das Datenschutzrisiko mit jeder neuen Anmeldung. Das passiert nicht irgendwann. Das passiert täglich. In deutschen Unternehmensgruppen jeder Größe. DeutschlandGPT bündelt über 34 führende Sprachmodelle in einer Plattform, gehostet in Deutschland, ISO 27001 und BSI C5 zertifiziert. Die kostenlose PDF gibt Ihnen die Orientierung, um Schatten-KI zu beenden: ✔ Welches der über 34 Modelle sich für welche Aufgabe eignet, inklusive Auswahlhilfe ✔ Wie Sie alle Tochtergesellschaften auf einen sicheren Standard bringen ✔ Wie KI direkt im Microsoft Stack läuft, neben Copilot ✔ Was Datenhoheit konkret heißt, von BSI C5 bis Zero-Data-Retention Jetzt kostenlose PDF herunterladen.

𝗦𝗽𝗲𝗮𝗸𝗲𝗿 𝗔𝗻𝗻𝗼𝘂𝗻𝗰𝗲𝗺𝗲𝗻𝘁: 𝗣𝗵𝗶𝗹𝗶𝗽𝗽 𝗧𝗿𝘂𝗻𝘀𝗰𝗵𝗸𝗲, 𝗣𝗧𝗕 We're excited to have Philipp Trunschke Ph.D., Postdoctoral Researcher at Physikalisch-Technische Bundesanstalt, PTB, speak at this year's dida conference! His talk "Tensor Trains for High-Dimensional PDEs" addresses a recurring pain point in applied scientific computing: many industrial PDE models become computationally challenging once parameters, uncertainty, or multiscale resolution enter the picture. Philipp will introduce tensor trains as a low-rank representation for PDE solutions, coefficients, and operators, offering a structured alternative to physics-informed neural networks. He will sketch the tensor train format and the basic numerical toolbox, highlight where these methods can pay off in practice, and contrast them with neural networks. 📅 June 19, 2026 · 09:00–18:00 CET 📍 frizzforum, Friedrichstraße 23, Berlin-Kreuzberg Philipp is one of many excellent speakers, the full lineup and registration are at 👉 https://dida.do/conference Spots are limited, so early registration is recommended. #MachineLearning #ScientificComputing #TensorTrains #PDEs #AppliedAI #didaconference

Most AI in manufacturing supply chains stops at the forecast — producing a prediction and waiting for a planner to act on it. The cost shows up in stockouts, supplier delays, and emergency rework that traces back to the same root cause: insight that never connects to action. Our ebook walks through how agentic AI closes that gap across the full supply chain lifecycle. Forecasting is the first domino. Agentic AI is the second.