Stiv Sterjo

🚀 Introducing PINNeAPPle: The Revolution in Scientific Machine Learning We are proud to develop our innovative platform that democratizes access to advanced ML techniques for scientific and engineering problems! 🎯 Working hard to launch the platform and API in early October! ✨ What makes PINNeAPPle special: 🔬 Physics-Informed Neural Networks (PINNs) – Solve complex differential equations with scientific precision 🧠 60+ Advanced Models organized into 13 main categories: 📊 AUTOENCODERS • 2D/3D Autoencoders • Dense Autoencoders • Variational Autoencoders • Koopman Autoencoders • Physics-Informed Koopman 🔬 PHYSICS-INFORMED NEURAL NETWORKS • Vanilla PINNs • PINN-LSTM • PINNsformer • Variational PINNs • Extended PINNs • Extreme Theory of Functional Connections • PIELM 🌊 NEURAL OPERATORS • DeepONet • Fourier Neural Operators • Galerkin Neural Operators • Physics-Informed Neural Operators • Universal Operator Networks 🧮 BAYESIAN MODELS • Neural ODEs • Neural SDEs • Latent ODEs • Hamiltonian Neural Networks • Neural Controlled Differential Equations • Bayesian RNNs • Deep State Space Models 🔁 RECURRENT NETWORKS • LSTM • GRU • Bidirectional LSTM/GRU • Seq2Seq RNNs • ODE-RNNs • Symplectic RNNs 🎯 TRANSFORMERS • Autoformer • Informer • FEDformer • Temporal Fusion Transformer • TimesNet • Custom Transformers 📈 TIME SERIES • ARIMA • VAR • Kalman Filters • Temporal Convolutional Networks 🕸️ GRAPH NEURAL NETWORKS • Equivariant GNNs • Spatiotemporal GNNs • Graph Neural ODEs 🌀 REDUCED ORDER MODELING • POD • Dynamic Mode Decomposition • Operator Inference • Hankel-Koopman • ROM Hybrid Models 💧 RESERVOIR COMPUTING • Echo State Networks • Extreme Learning Machines • Koopman Operators 🔲 CONVOLUTIONS • Specialized Conv1D/2D/3D for scientific data ⚡ Intuitive Interface – From data upload to final results, all in a few clicks 🎯 Use Cases: • Physical and engineering simulations • Predictive analytics in energy and utilities • Pattern detection in complex data • Industrial process optimization 📹 Check out the attached video to see how easy it is to train a PINN model for Australian electricity demand analysis – from data upload to final insights! 🚀 WANT TO BE ONE OF THE FIRST TO TEST IT? If you’re interested in accessing our pre-launch beta version and helping shape the platform’s future, leave a comment below sharing your use case or area of interest! We are selecting an exclusive group of beta testers for October. Don’t miss this opportunity! 👇 #MachineLearning #AI #PINNs #DataScience #Innovation #DeepLearning #PhysicsInformed #TechStartup #ArtificialIntelligence #ScientificComputing #LaunchingSoon #BetaTesting

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✨ Why Human-in-the-Loop (HITL) Is the Real Backbone of Reliable AI Everyone’s talking about autonomous agents and GenAI tools. But here’s what rarely gets mentioned: The best AI systems today still rely on human judgment. From moderating AI-generated content to approving critical decisions, HITL is what bridges speed and safety, automation and accountability. 🔍 What HITL Really Means in Practice ✔️ Labeling & Training Humans help create high-quality datasets through careful annotation. ✔️ Evaluation & Guardrails Whether it’s detecting bias, hallucinations, or failure cases — people review AI outputs before they go live. ✔️ Reinforcement Learning with Human Feedback (RLHF) This is how LLMs like ChatGPT actually learn to sound helpful, accurate, and aligned. ✔️ Decision Escalation AI might recommend — but humans still make the final call in high-stakes fields like healthcare, law, and finance. A Framework to Think About HITL 🧠 AI handles the repeatable 👀 Humans handle the risky 🔁 Together, they form a continuous improvement loop What part of your stack still has humans in the loop? #HumanInTheLoop #AITrust #GenAI #LLM #SystemDesign #AIAlignment #RLHF #ResponsibleAI

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99% of people are just using LLMs. The top 1%? They’re building them. 💻🧠 Sebastian Raschka, PhD just launched something big: Master and Build Large Language Models → 👉 https://lnkd.in/dzKMZi_p I’ve read all his books. I’ve gone through every course. This one is next-level. No shortcuts. No copy-paste notebooks. You build the architecture yourself. From scratch. 💥 What you’ll learn: → Build a transformer, layer by layer → Train and fine-tune real LLMs → Deploy them into production → Understand tokenization, attention, gradients—the whole thing This is not for tourists. It’s for builders who want to understand how LLMs actually work. Line by line. Block by block. If you’re tired of surface-level AI noise, here’s the signal. This course gives you the technical foundation everyone else is skipping. I’m diving in. Let’s go deeper. 🔧📘

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We compared self-hosted and API-based LLM deployments across a range of models, including quantized variants. One of the key factors that emerged was the role of utilization in determining cost-efficiency. Even with FP8 quantization and tuned hardware, the self-hosted environments didn’t come close to matching API efficiency. The gap wasn’t due to performance—latency and throughput were strong when the systems were busy. The challenge was keeping the GPUs consistently utilized. When utilization dropped, unit costs increased significantly. The API-based models were generally stable over the 7-day window. However, we did observe some short periods of degraded performance—spikes in latency and changes in throughput. These weren’t persistent, but they highlight a potential challenge in scenarios where predictable response times are required. Overall, the testing reinforced that deployment tradeoffs depend heavily on workload characteristics. Sustained, high-throughput demand makes it easier to justify self-hosting. For more variable or bursty workloads, efficiency is harder to maintain without advanced orchestration. Full research note is available here: https://lnkd.in/eeCY2Z5R

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IBM 𝗷𝘂𝘀𝘁 𝗼𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲𝗱 𝗮 𝘄𝗵𝗼𝗹𝗲 𝗲𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺 𝗼𝗳 𝗠𝗖𝗣 𝘀𝗲𝗿𝘃𝗲𝗿𝘀. ⬇️ If you care about AI Agents → this is big! Because the major blocker right now in AI agents isn’t the models. It’s the pipes and the data/tool access. 𝗛𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝗴𝗶𝘃𝗲 𝗮𝗴𝗲𝗻𝘁𝘀 𝘀𝗲𝗰𝘂𝗿𝗲 𝗮𝗰𝗰𝗲𝘀𝘀 𝘁𝗼 𝗳𝗶𝗹𝗲𝘀, 𝗔𝗣𝗜𝘀, 𝗰𝗹𝗼𝘂𝗱, 𝗶𝗻𝗳𝗿𝗮, 𝗱𝗼𝗰𝘀? ⬇️ That’s what MCP (Model Context Protocol) solves. And IBM just dropped servers for: → IBM Cloud → Instana (Observability) → MQ → watsonx.data → Decision Runtime → Storage Insights → Terraform, GraphQL, Langflow & more → Etc. Basically: an enterprise-grade toolbox for wiring agents into real systems. Kudos to Roy Derks for setting this up. In just a week this went to 17 MCP projects, and almost 150 stars on GitHub. Make sure to hit ⭐ for visibility. 🔗 Repo: github.com/IBM/mcp 𝗣.𝗦. 𝗜 𝗿𝗲𝗰𝗲𝗻𝘁𝗹𝘆 𝗹𝗮𝘂𝗻𝗰𝗵𝗲𝗱 𝗮 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿 𝘄𝗵𝗲𝗿𝗲 𝗜 𝘀𝗵𝗮𝗿𝗲 𝘁𝗵𝗲 𝗯𝗲𝘀𝘁 𝘄𝗲𝗲𝗸𝗹𝘆 𝗱𝗿𝗼𝗽𝘀 𝗮𝗯𝗼𝘂𝘁 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀, 𝗲𝗺𝗲𝗿𝗴𝗶𝗻𝗴 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀, 𝗮𝗻𝗱 𝗵𝗼𝘄 𝘁𝗼 𝘀𝘁𝗮𝘆 𝗮𝗵𝗲𝗮𝗱 𝘄𝗵𝗶𝗹𝗲 𝗼𝘁𝗵𝗲𝗿𝘀 𝘄𝗮𝘁𝗰𝗵 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝘀𝗶𝗱𝗲𝗹𝗶𝗻𝗲𝘀. 𝗜𝘁’𝘀 𝗳𝗿𝗲𝗲 — 𝗮𝗻𝗱 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗿𝗲𝗮𝗱 𝗯𝘆 𝟮𝟬,𝟬𝟬𝟬+ 𝗽𝗲𝗼𝗽𝗹𝗲. 𝗬𝗼𝘂 𝗰𝗮𝗻 𝗷𝗼𝗶𝗻 𝘁𝗵𝗲𝗺 𝗵𝗲𝗿𝗲:  https://lnkd.in/dbf74Y9E

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Stop building AI agents without a firewall! Your written compliance policy is officially obsolete. ➤ Do you know what this means for data governance? It means the governance 𝒊𝒔 𝒏𝒐𝒘 𝒊𝒏 𝒕𝒉𝒆 𝒄𝒐𝒅𝒆, not just the handbook. ⇢ This is a huge step forward with the launch of the Microsoft Agent Framework and the new capabilities in Azure AI Foundry, clearly signaling the shift from a passive compliance such as having a written policy to active runtime enforcement (the technology preventing the policy violation). 1) PII Guardrails enforce data privacy by automatically detecting and redacting sensitive PII in real-time during tool calls. 2) The Task Adherence API ensures data integrity and security by acting as an agentic policy firewall, preventing agents from making unauthorized or erroneous calls outside of their defined scope. ↳ Operationalizing data governance in the age of autonomous agents starts here. I think it’s a must-read for anyone building or governing AI systems. ↳ Check out more on the link provided below shared from Microsoft's Chief Product Officer Sarah Bird's post- Introducing Microsoft Agent Framework.

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