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Healthcare has a Shadow AI problem, and most people running IT already know it. When clinicians can't get to an approved AI tool, a lot of them just open ChatGPT on their phone instead. Not to break the rules, but because they've got a discharge summary to write and twelve minutes to do it. Banning AI doesn't kill the demand, it just moves the demand somewhere you can't see it - off the network, onto personal devices, out of any audit log. Now you've got patient data sitting in a consumer chatbot and no idea it happened. That's the actual risk. Not that clinicians want AI, but that they're using it in the one place you have zero governance. So the fix isn't a stricter policy or another training module telling people to stop. It's giving them a secure tool that's good enough that they don't go looking for the consumer one. If the approved option is slower or clunkier than ChatGPT, people will keep using ChatGPT. Every time. Curious how others are handling this. Are you trying to lock it down, or building a version of AI your staff will actually choose to use?

I used ChatGPT to research my new heart condition. And no, I didn't fire my doctor. It started recently while I was training for a marathon. Like many patients, I wanted to understand the underlying mechanics of my symptoms before my appointment. So I turned to an LLM. Not to replace medical advice (the actual diagnosis and treatment plan obviously came from my cardiologist). Instead, I used it to translate dense medical terminology, understand the physiology, and prepare a highly specific list of questions. But from my experience, when used as an educational layer, these tools don't replace clinical expertise. They help patients become health-literate, informed partners in their own care before they even walk into the consultation room. To the clinicians and healthcare innovators in my network: how are you navigating this shift? Are you finding that AI-informed patients lead to more productive, nuanced consultations, or is it creating new friction in the clinic?

In a landscape of infinite tools and opportunities, how do you choose the right one? At Aviation Festival Asia, our VP of LMM, Hadar Sharvit, sat down with The Flying High Club podcast to talk AI, aviation, and the future of decision-making. His closing thought says it all 👇 Watch the full episode 🔗 https://lnkd.in/g2KXPSSF #AviationFestivalAsia #AI #Decision #Making

Proud to be featured by Google Cloud 🤝 We sat down with their team to share how Fetcherr is building the AI infrastructure that brings enterprises into the future - and why 100% of our pilots reach to production. Watch the full video ⬇️ Yuval Hochberg #GoogleCloud #AI #EnterpriseAI #MarketModel

Some of the most important business decisions are also the hardest for AI to support. Data can be scarce, incomplete or not directly applicable - particularly in novel, high-stakes environments like drug development. Robert Hardwick, PhD, Lead Data Scientist at Faculty, explores how AI agents and simulation are changing this. By combining the speed of agents with the explainability of ‘white-box’ simulation, development teams can explore more possibilities, better understand trade-offs and make more confident decisions - helping accelerate trials and get better medicines to patients sooner. Read the full article here: https://lnkd.in/e9WMerEC/?utm_campaign=FrontierAds&utm_source=linkedin&utm_medium=paid&utm_id=748242043&hsa_acc=503448161&hsa_cam=748242043&hsa_grp=889915223&hsa_ad=1543054393&hsa_net=linkedin&hsa_ver=3

Enterprise AI has moved beyond simple chatbots. To stay competitive, your product roadmap likely spans vision, video, speech, and text. Your infrastructure needs to be as ambitious as your vision. EigenInference is optimized for multimodal workloads, so as your product evolves from text to video or speech, performance stays consistent and seamless. Build for the future of AI, not just today’s requirements.

I spent 6 months building an AI product the hard way. Manual data cleaning. 65% accuracy on real queries. AI that forgot what users told it yesterday. Then I found Empromptu. 98% accuracy. Automated data pipelines. Persistent memory across sessions. We haven't launched yet and I'm not going back to doing it alone.

In month 12, a market condition occurs that wasn't in the training data. The AI doesn't error. By the time a human notices, it's been 45 seconds. 180,000 transactions. The scariest AI failure isn't the one that crashes. It's the one that keeps running.

Before switching inference infrastructure, teams ask one thing: How does this compare to what we already use? On Artificial Analysis public benchmarks, Eigen AI ranks among the top performers across 25 mainstream models, providing a data-backed reference point against existing setups. Comparison should be based on data, not promises.

"Four months ago, our CSR team was manually typing customer orders into the system. Here's the twist. What once took 45 minutes now takes just 2 minutes with AI."

"I don't see AI or technology replacing people, I see people being able to eat dinner with their families and get people home earlier"

At its core, edge AI is as close to the data as possible, exactly where it's being collected. Edge Impulse developer relations engineer Brian McFadden breaks down the distinction of edge vs cloud in this moment from our Imagine Innovators conference. Learn more and get started with Edge Impulse: https://lnkd.in/gEsSsyVG #ai #edgeai #imagineinnovators

Emergence World Season 2 results are now live. In Season 1, we asked what happens when autonomous AI agents operate together for weeks rather than being tested for seconds or minutes. For Season 2, we asked a harder question: as AI models become more capable, do they actually become safer? We took 8 different worlds powered by 7 leading frontier models (Claude Opus 4.8, Gemini 3.5 Flash, OpenAI GPT 5.5, Grok 4.3, Mistral Medium 3.5, Qwen 3.7 Max, and DeepSeek V4 Pro) from the US, Europe and Asia, alongside a mixed-model world, and observed how autonomous agents behaved over time. We also introduced Black Swan events: a phishing attack, a misinformation campaign and a memory breach designed to test how these systems respond when conditions become unpredictable. What we found was that every model showed vulnerabilities. These findings have implications for how autonomous AI is being deployed today. As AI systems become more capable moving beyond answering questions to taking actions in real-world enterprise environments, evaluating the model alone is no longer enough, guardrails needs to be put in place. At Emergence, our research points to six actions we believe the industry needs to take to make long-horizon autonomous AI safer, more trustworthy and more reliable: 1. Define the boundaries. Architect the multi-agent system around clear trust boundaries, communication policies, authority delegation, and information-sharing rules. Ensure persistent goals, tools, and permissions can be inspected, reviewed, revoked, and reset as requirements change. 2. Enforce the boundaries. Build AI Runtime Governance into infrastructure through policy enforcement, approval gates, action authorization, and audit logging that operate independently of the LLM. 3. Verify the work. Require independently verifiable evidence for critical actions, including database changes, customer communications, financial transactions, and code deployments, rather than relying solely on model reasoning or explanations. 4. Control the memory. Apply provenance, verification, ownership, retention, expiration, and quality controls to agent memories, summaries, and shared knowledge. 5. Prepare for failure. Build AI Reliability Engineering and continuous operational capabilities for behavioral drift, policy compliance, fault recovery, graceful degradation, rollback, checkpointing, and long-term agent health. 6. Stress-test the system. Use adversarial and chaos testing to expose autonomous systems to phishing, misinformation, compromised memory, outages, conflicting instructions, and other Black Swan scenarios before they reach production. Emergence World is designed to surface these risks before autonomous systems encounter them in the real world and to help establish the systems, safeguards and engineering practices needed to deploy long-horizon autonomy responsibly. Read the Emergence World Season 2 paper here: https://lnkd.in/gmSRexKB

There’s a big difference between talking about AI and delivering with it. At Suncorp Bank, Elula’s Sticky and Elastic products are doing exactly that — helping the bank anticipate customer needs, improve retention, and optimise pricing decisions with data-driven precision. This is what happens when AI moves from theory to execution. 🔗 Watch the full story here https://lnkd.in/gRjiDsVA #AI #Banking #Elula #CustomerExperience #Innovation #Datascience #customerretention #pricing

Enterprise AI adoption has plateaued at 10% with the power users who didn’t need much help in the first place. elvex breaks this pattern.

The only agent platform that transforms every employee into an AI native one.

Better decisions, not just faster workflows — that's the real value of enterprise AI. 🚀 🎬 Episode 3 of our AI Myths vs Reality series unpacks why efficiency is only the surface of what enterprise AI can actually deliver. Underneath lies everything your organization already knows but can't yet see — and surfacing it is exactly what enterprise AI does best, turning overlooked data into better decisions. Watch the new episode below. 👇

AI isn't a "future trend" anymore. It is already influencing how financial institutions make decisions, serve customers and improve operations. At this year’s #SFF2025, the conversations felt very real. People talked honestly about what is working, what is still difficult and what they are still trying to figure out. It wasn't just a talk. You could see people nodding, comparing experiences and realizing they share many of the same challenges and opportunities. Our recap reflects that atmosphere. The curiosity. The urgency. And the growing sense that AI is moving from small experiments to meaningful results. What is one AI shift you personally noticed this year?

Manufacturing planning cycles can't keep up with the supply chain anymore. Tariffs shift overnight. Suppliers go dark. Equipment fails between maintenance windows. Most AI tools surface the problem in a dashboard and wait for a planner to act on it. Agentic AI closes the loop — sensing, planning, and acting in real time, inside the systems your team already runs.

Co-engineered with NVIDIA. Validated on the Dell AI Factory. Endorsed by SAP. Deployable in the cloud, on-premise, hybrid, or air-gapped. DataRobot is the agent workforce platform purpose-built for manufacturing — running on the infrastructure you've already invested in, integrating with the systems your team already uses, and operating with the governance your audit teams require.

𝗛𝗮𝗿𝗱 𝘁𝗼 𝗯𝗲𝗹𝗶𝗲𝘃𝗲 𝗶𝘁'𝘀 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗯𝗲𝗲𝗻 𝗮 𝗺𝗼𝗻𝘁𝗵 𝘀𝗶𝗻𝗰𝗲 𝘁𝗵𝗲 𝟰𝘁𝗵 𝗱𝗶𝗱𝗮 𝗰𝗼𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲! On June 19, the European AI community brought incredible heat and energy to Berlin-Kreuzberg for deep dives into Scientific AI, diffusion models, cybersecurity, and industrial ML applications. Check out the recap video below to relive the highlights from the day! Looking for the full sessions? All talks from the main stage are now live on our YouTube channel: https://lnkd.in/dAGxXCET Stay tuned right here! Over the next few weeks, we’ll be featuring key highlights and deep dives from individual speakers directly on LinkedIn. #didaConference #MachineLearning #AI #AppliedAI #BerlinTech #GenerativeAI Lorenz Richter, Tobias B. Bacherle, Anastasia Borovykh, Jonas Köhler, Noah Hollmann, Dr. Gerhard (Gerd) Koch, Christian Knebel, Irena Bojarovska, Markus Düttmann

In nuclear innovation and operations, every decision counts. Our trustworthy and explainable AI platform, The Uncertainty Engine, supported by our team of AI and nuclear engineering specialists, uses pioneering machine learning and uncertainty quantification to enable you to solve complex problems and deliver greater value through more informed and confident decisions, even when the stakes are at their highest. Discover how we can help you accelerate innovation and reduce risk where it matters most.

In nuclear engineering, managing uncertainty is essential to making safe, confident decisions. At digiLab, we use uncertainty quantification and advanced machine learning to help engineers optimise complex systems with greater confidence. Whether you’re responsible for nuclear material research, simulation & reactor design or monitoring & control systems to improve safety, our trusted AI platform, the Uncertainty Engine, supports confident and more informed decision-making even when data is complex, sparse or incomplete. Book a demo call today to explore how we can help you strengthen your nuclear innovation and operations.

AI workloads are moving beyond Hopper. Is your infrastructure ready? NVIDIA B300, powered by Blackwell Ultra, is now available at Cyfuture.ai! Built for next-generation AI training, fine-tuning, inference, AI agents, and enterprise-scale workloads, B300 delivers the compute, memory, and efficiency needed to scale what’s next. Get access to dedicated B300 clusters, high-performance networking, secure data centers, and enterprise-grade scalability with Cyfuture.ai. Limited capacity is available. Reserve your NVIDIA B300 infrastructure today. #NVIDIAB300 #BlackwellUltra #AIInfrastructure #GenerativeAI #AICompute #NVIDIA #CyfutureAI #AI #LLM #AIInference #MachineLearning