Virtual Training Platforms

You cannot train AI on reality alone anymore. There is not enough of it. Jensen Huang explains why NVIDIA built Cosmos, an AI world model that generates synthetic training data grounded in physics. The problem is simple. Teaching physical AI like robotics requires vast amounts of diverse interaction data. Videos exist, but not nearly enough to capture the variety of situations robots will encounter. So NVIDIA transformed compute into data. Using synthetic data generation grounded by laws of physics, they can selectively generate training scenarios that would be impossible to capture otherwise. The example Huang shows is remarkable. A basic traffic simulator output gets fed into Cosmos. What emerges is physically plausible surround video that AI can learn from. This solves a fundamental limitation. You cannot train autonomous systems on every possible scenario by recording reality. There are not enough cameras or time. But you can simulate physics accurately enough that AI trained on synthetic data generalises to real environments. This applies beyond robotics. Any AI learning physical interactions, from manufacturing to logistics to infrastructure monitoring, faces the same data scarcity problem. Synthetic data generation grounded in physics laws is how you create training sets reality cannot provide. The organisations building AI for physical systems will either master synthetic data generation or get limited by whatever reality they can record. Watch the full presentation to hear Huang explain how Cosmos generates training data for physical AI. What physical AI application needs synthetic data because reality cannot provide enough examples? #AI #SyntheticData #Robotics #NVIDIA #MachineLearning

Train a humanoid in a scan of your office, deploy with zero adaptation! 💼 A humanoid learns to navigate a real office entirely in simulation, a photorealistic 3D scan of that exact building. Then it walks the real space on RGB cameras with no fine-tuning needed. RL training needs hundreds or thousands of attempts. Real robots can't afford to crash. A misjudged gap or collision with a glass door breaks hardware and costs time resetting. So training happens in simulation. But how do you know a policy trained in a fake world will work at your specific site before the robot ever arrives? Traditionally, robots have been trained in randomized, untextured geometry because depth is easy to simulate. The robot reads only structure, blind to materials, lighting, what things actually are. RGB cameras carry all that information, but training RGB policies in generic fake worlds won’t generalize to the real world. Niantic Spatial, Inc. Scaniverse allows you to scan your deployment site with a single 360° camera walkthrough and then reconstruct it at metric scale as a photorealistic 3D Gaussian splat. It derives the collision mesh from the same reconstruction so visual and physics layers match perfectly. The resulting data loads directly into NVIDIA Robotics Isaac Sim and Lab environments without manual conversion. Flexion simulation-first approach then seamlessly enables the training of RGB-only navigation policies inside that reconstruction. With some domain randomization and the use of large-scale foundational image encoders, robust behaviors that work on real hardware can be learned in a scalable manner. Last but not least: zero-shot deployment to the real robot. The result? Policy trained entirely in simulation. No real-world fine-tuning. Deployment time drops from months of on-site adaptation to days. 🔗 Read more about it here: https://lnkd.in/dCXH6CcM ~~ ♻️ Join the weekly robotics newsletter, and never miss any news → ziegler.substack.com

💼 Are AWS courses & certifications enough to land a job? The short answer is no. Certifications are a good start, but will not guarantee you a job. The real learning begins when you start building & getting hands-on. 🚙 Have you ever learned to drive a car? Driving might seem simple in theory or through simulations, but it's a whole different experience on a busy road. Similarly, in your cloud career, hands-on practice is key to applying concepts effectively. To get started with hands-on projects, here are some resources I recommend: 🔹 AWS Workshops: https://workshops.aws/ 🔹 AWS Labs: https://github.com/awslabs 🔹 AWS Skillbuilder: https://skillbuilder.aws/ 🔹 AWS Educate: https://lnkd.in/gaFqhQG8 🔹 AWS Samples: https://lnkd.in/gmfh9Xxn 🙋♀️ I also have a beginner's step-by-step guidebook with 5 AWS Projects: https://lnkd.in/gRWNezg2 How do you get hands-on with the Cloud? Let me know in the comments ⬇️ -- 📥 For more Cloud and AI related content, subscribe to the Cloudbites newsletter: https://www.cloudbites.ai/ ♻️ Found this helpful? Feel free to repost & share with your network. #AWS #cloud #cloudcomputing #awscertified #zerotocloud

I’ve spent over two decades on both sides of healthcare training, first as a trauma nurse, then as someone who consulted on simulation lab design, launched top-selling simulators, and drove immersive tech adoption across hospitals, colleges and universities. One truth hasn’t changed: when the workforce isn’t ready, patients pay the price. Traditional training models are stretched to their breaking point. Faculty shortages, limited lab space, and rising costs make scaling competency-based education nearly impossible. We can’t keep throwing task trainers, manikins and travel budgets at a problem that demands a smarter solution. That’s where VR changes everything. With platforms like VRpatients, learners can practice anywhere, anytime, failing safely, mastering skills faster, and proving competency with hard data. Nursing programs are already seeing real results. Students at universities are practicing on custom-built VR simulations that prepare them for the NCLEX, all while reducing training costs. Upskilling the healthcare workforce isn’t optional anymore. It’s mission-critical.. The future of clinical readiness belongs to institutions that embrace immersive, scalable, evidence‑based training.And that future is already here. #HealthcareTraining #WorkforceUpskilling #VRinHealthcare #ImmersiveLearning #ClinicalEducation #XRTraining #FutureOfWorkforce #VRpatients VRpatients #VRpatients

If it works for the airline industry, why is simulation not used more in healthcare? What do you think❓ Patient safety and education comes first, and Baptist Health and Miami Cardiac & Vascular Institute are helping lead the way here! I recently toured a 38,000 sq.ft. facility outfitted with the latest in imaging, echo, robotics, angiographic and operating room simulators. ICU beds, nursing stations-- and even VR sim and training. All supported by patient actors, realistic phantoms, and sophisticated A/V. It's not just about learning how to intubate, or catheterize a vessel, or visualize a cardiac chamber. Bringing the entire care team *together* into a simulated cath lab, OR or ICU, and 'throwing curveballs' at us is how we all improve together. And at MCVI, they even have exhibition glass-walled #Azurion interventional suites-- with comfortable 'movie theater' seats allowing physicians of all disciplines to watch and learn during live endovascular procedures. As we think together on how to expand skillsets, access to care, and even new innovations, these types of technologies are extremely important!

Training a 1 Trillion Parameter LLM is hard. Serving it to 100 million users is even harder. Imagine a production system with: • 1T parameter MoE LLM • 100M Monthly Active Users • 10M Daily Active Users • Average response: 400 tokens Now ask yourself: How many GPUs do you need? A naive architecture would look like this: User ↓ API ↓ GPU ↓ Response It works for a demo. It completely breaks at internet scale. For comparison, a 70B LLM running on an NVIDIA H100 typically serves only 50-100 concurrent generation streams. That means serving 1 million simultaneous users would require roughly: 10,000-20,000 H100 GPUs ...just to keep inference running. And that's before considering retries, failover, batching, or traffic spikes. Now imagine a 1T parameter model. The challenge isn't inference. The challenge is infrastructure. Production systems therefore separate every stage: Users │ API Gateway │ Authentication │ Global Scheduler │ ─────────────── │ │ Prefill Decode Cluster Cluster │ │ Distributed KV Cache │ Memory Manager │ NVLink / RDMA / InfiniBand │ vLLM / TensorRT-LLM / SGLang │ Streaming Tokens The hidden memory problem Every active conversation stores a KV Cache. Even a few GB per conversation becomes enormous. At 1M concurrent users, only 2 GB of KV cache per session already means: ≈ 2 PB of KV Cache That is far beyond GPU memory. Modern inference engines solve this using: • PagedAttention • Hierarchical KV Cache • Prefix Caching • Cache-aware Routing • KV Compression Memory management becomes as important as the Transformer itself. Why PagedAttention changed everything Traditional inference wastes GPU memory by reserving the maximum context for every request. Typical VRAM fragmentation: 60-80% PagedAttention reduces fragmentation to: <4% The same GPU that previously served around 10 users can now support roughly 800-1000 concurrent users, making large-scale self-hosted inference economically practical. Continuous batching is another multiplier Static batching: GPU waits for the slowest request. Continuous batching: New requests join while generation is already running. Result: • GPU utilization increases from 40-50% → 90%+ • Nearly 2× throughput • Lower cost per token Even user connections become a scaling problem A single Envoy proxy can maintain 500,000+ WebSocket connections because network I/O is cheap compared to GPU computation. GPU clusters should scale based on inference queue depth, not user connection count. That's the difference between a system that survives viral traffic and one that collapses. The biggest mindset shift People think LLM serving is about matrix multiplication. At hyperscale, it's really about: • Distributed Systems • Operating Systems • Queue Scheduling • Networking • Cache Design • GPU Orchestration The infrastructure makes those tokens arrive fast enough for 100 million users. Free AI Resourses: https://lnkd.in/gTvEuNbX

Discover the backstage of our Sim-to-Real Transfer for AI #robotics. At Capgemini's #AI Robotics & Experiences Lab, we train our robots entirely in virtual environments – allowing them to master complex tasks before ever interacting with the real world. Why it matters: - #Cost efficiency – no wear-and-tear, no downtime - Accelerated development cycles – rapid iterations from testing to deployment - #Scalability – generalises across diverse real-world conditions - #Safety – especially in high-risk domains like nuclear operations or autonomous vehicles - Human-AI collaboration – robots trained in simulation to assist with tasks that require physical interaction This is how we bridge the gap between digital models and physical reality. Bringing intelligent robotics closer to everyday enterprise.

The most asked question in 2025: “I’m a UiPath developer , how can I scale and stay competitive in this market?” Here’s my honest advice.I believe there are 3 tracks you can go. You need to choose the right track based on your experience, location, and what you’re comfortable doing. Track 1: Upgrade within UiPath If you’re still doing classic attended/unattended bots only, this is your first risk. The market is clearly moving toward: UiPath AI Professional UiPath Agentic Process Automation AI-powered workflows, not rule-based automation only Most current job openings are no longer asking for “RPA Developer” They want someone who understands AI + automation + orchestration. Stay in UiPath, but move forward. Track 2: Expand to another platforms (high demand) This is one of the safest and most practical moves right now. I strongly recommend Microsoft Power Platform, especially: Power Automate , Power Apps , Power BI Why? Massive enterprise adoption Strong integration with automation and AI Many companies are running UiPath and Power Platform together This combo alone can double your job opportunities. Track 3: Build AI Engineer fundamentals You don’t need to become ML researcher , but you must understand: AI concepts , How models work How AI integrates with automation use cases Automation and AI are now deeply intersecting. For RPA developers, I highly recommend: DataCamp ,Associate AI Engineer https://lnkd.in/g3jvPH94 Very suitable if you already have a technical background Don’t try to do everything at once. Pick one main track, then add the others gradually. All the best to everyone building their next move  #AI #automation #uipath Sarah Ghanem

18 Platforms That Simulate Real-World Cyber Attacks Most security teams don't get breached because they lack security tools. They get breached because they don't know which defenses actually work. The strongest security leaders I've followed all have one habit in common. They continuously test their security as if a real attacker already had a foothold. Here are 18 Cyber Attack Simulation Tools worth knowing: 1. SafeBreach ✦ Simulates sophisticated attacks to continuously validate security controls. 2. AttackIQ ✦ Tests detections and response against real-world attack scenarios. 3. Cymulate ✦ Measures security posture through continuous breach and attack simulations. 4. XM Cyber ✦ Maps attack paths to uncover and prioritize critical exposures. 5. Picus Security ✦ Continuously validates security controls and integrates with DevSecOps. 6. Foreseeti ✦ Models attacker behavior to identify exploitable weaknesses. 7. Infection Monkey (Open Source) ✦ Automates adversary simulations across Active Directory environments. 8. CALDERA (Open Source) ✦ Emulates real attacker behavior using automated post-exploitation techniques. 9. Randori ✦ Discovers external attack surfaces and validates exploitable risks. 10. Scythe ✦ Automates adversary emulation for realistic red team exercises. 11. Horizon3.ai ✦ Continuously identifies exploitable attack paths with autonomous pentesting. 12. Pentera ✦ Safely validates security controls through automated penetration testing. 13. Qualys ✦ Combines vulnerability management with continuous security validation. 14. FireMon ✦ Validates firewall policies and network security effectiveness. 15. Akamai Guardicore ✦ Simulates lateral movement to strengthen microsegmentation strategies. 16. Mandiant ✦ Delivers advanced attack simulations backed by real-world threat intelligence. 17. NetSPI ✦ Identifies exploitable vulnerabilities through expert-led offensive security testing. 18. Skybox Security ✦ Prioritizes cyber risk using attack path analysis and exposure management. The best security teams don't wait for attackers to expose weaknesses. They expose their own weaknesses first. Which attack simulation tool would you add to this list? ♻️ If you found this useful, repost it to help your network. 📌 Follow Marcel Velica for more cybersecurity tools, frameworks, and security insights.

Extended Reality training helps employees practice real tasks before they face them at work. By using realistic simulations, companies can improve onboarding, reduce training gaps, and prepare teams for safer execution. In practice: - Realistic scenarios make training easier to connect with daily work. - Hands-on practice helps people build confidence before handling critical tasks. - Short sessions make learning easier to repeat and adapt across teams. - High-risk activities can be simulated early, reducing exposure during real operations. - Standardized scenarios support consistent training across different locations. - Early onboarding with XR can shorten the path from instruction to competence. XR creates value when training design, business processes, and measurable learning outcomes are aligned from the start. #XRTraining #FutureOfWork #ExtendedReality