Integrating AI With IoT Devices

The energy grid is under immense strain from extreme weather, wildfires, and rising electricity demand. As these pressures increase, so does the need for smarter, more resilient and reliable energy grids.   Utilidata, a company that is part of Microsoft's Climate Innovation Fund portfolio, is redefining energy delivery through its AI platform, Karman. This technology empowers utilities to optimize energy delivery and make better decisions about how to manage the grid by, for example, storing electricity in batteries during off-peak hours and distributing it when it's needed. As a result, electric vehicles and solar panels become flexible, valuable assets that help meet grid demand.   Embedding AI directly into the grid infrastructure helps utility decision-makers make more informed decisions and better serve customers. This innovation highlights the power of AI to modernize critical infrastructure and transform the energy sector.

2 New Digital Health Developments Using AI and AR to Support Parkinson’s and Neurological Disorders >> 🧠Rune Labs launches StrivePD Guardian, an AI-powered personalized coaching service for Parkinson’s disease, integrating expert coaching, health monitoring, and proactive risk detection 🧠 The service, validated in 2024, demonstrated significant outcomes, including a nearly 50% reduction in emergency room visits and an 80% improvement in medication adherence. 🧠 StrivePD Guardian uses FDA cleared Apple Watch monitoring and AI-driven analysis to provide personalized insights, proactive alerts, and caregiver support 👓Strolll a digital therapeutic firm, secured £10.35m in Series A funding to expand its AR-based physiotherapy solutions for neurological disorders. 👓 The AR glasses gamify physiotherapy exercises for Parkinson’s, stroke, and multiple sclerosis patients, improving mobility, balance, and rehabilitation adherence. 👓 Clinical trials in early 2024 showed full patient adherence, significant gait improvements, and a sevenfold increase in treatment capacity with 67% less staff time 👇Links to articles in comments below #DigitalHealth #AR #AI

𝗗𝗼𝗻’𝘁 𝗝𝘂𝘀𝘁 𝗥𝗲𝗮𝗱 𝗔𝗯𝗼𝘂𝘁 𝗔𝗜 𝗶𝗻 𝗠𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴. 𝗔𝗽𝗽𝗹𝘆 𝗜𝘁. The AI headlines are exciting. But if you're a founder, engineer, or educator in manufacturing, here's the question that actually matters: 𝗪𝗵𝗮𝘁 𝗰𝗮𝗻 𝘆𝗼𝘂 𝗱𝗼 𝘵𝘰𝘥𝘢𝘺 𝘁𝗼 𝘁𝘂𝗿𝗻 𝘁𝗵𝗲𝘀𝗲 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻𝘀 𝗶𝗻𝘁𝗼 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻? Let’s get tactical. 𝟭. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗔𝗜 𝗱𝗲𝗺𝗮𝗻𝗱 𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 Tool to try: Lenovo’s LeForecast A foundation model for time-series forecasting. Trained on manufacturing-specific datasets. 𝗨𝘀𝗲 𝗶𝘁 𝗶𝗳: You’re battling supply chain volatility and need better inventory planning. 👉 Tip: Start by connecting your ERP data. Don’t wait for perfect integration: small wins snowball. 𝟮. 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 𝘁𝘄𝗶𝗻 𝗯𝗲𝗳𝗼𝗿𝗲 𝗯𝘂𝘆𝗶𝗻𝗴 𝘁𝗵𝗮𝘁 𝗻𝗲𝘅𝘁 𝗿𝗼𝗯𝗼𝘁 Tools behind the scenes: NVIDIA Omniverse, Microsoft Azure Digital Twins Schaeffler + Accenture used these to simulate humanoid robots (like Agility’s Digit) inside full-scale virtual factories. 𝗨𝘀𝗲 𝗶𝘁 𝗶𝗳: You’re considering automation but can’t afford to mess up your live floor. 👉 Tip: Simulate your current workflows first. Even without a robot, you’ll find inefficiencies you didn’t know existed. 𝟯. 𝗕𝗿𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗤𝗔 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝗶𝗻𝘁𝗼 𝘁𝗵𝗲 𝟮𝟬𝟮𝟬𝘀 Example: GM uses AI to scan weld quality, detect microcracks, and spot battery defects: before they become recalls. 𝗨𝘀𝗲 𝗶𝘁 𝗶𝗳: You’re relying on spot checks or human-only inspections. 👉 Tip: Start with one defect type. Use computer vision (CV) models trained with edge devices like NVIDIA Jetson or AWS Panorama. 𝟰. 𝗘𝗱𝗴𝗲 𝗶𝘀 𝗻𝗼𝘁 𝗼𝗽𝘁𝗶𝗼𝗻𝗮𝗹 𝗮𝗻𝘆𝗺𝗼𝗿𝗲 Why it matters: If your AI system reacts in seconds instead of milliseconds, it's too late for safety-critical tasks. 𝗨𝘀𝗲 𝗶𝘁 𝗶𝗳: You're in high-speed assembly lines, robotics, or anything safety-regulated. 👉 Tip: Evaluate edge-ready AI platforms like Lenovo ThinkEdge or Honeywell’s new containerized UOC systems. 𝟱. 𝗕𝗲 𝗲𝗮𝗿𝗹𝘆 𝗼𝗻 𝗰𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 The EU AI Act is live. China is doubling down on "self-reliant AI." The U.S.? Deregulating. 𝗨𝘀𝗲 𝗶𝘁 𝗶𝗳: You're deploying GenAI, predictive models, or automation tools across borders. 👉 Tip: Start tagging your AI systems by risk level. This will save you time (and fines) later. Here are 5 actionable moves manufacturers can make today to level up with AI: pulled straight from the trenches of Hannover Messe, GM's plant floor, and what we’re building at DigiFab.ai. ✅ Forecast with tools like LeForecast ✅ Simulate before automating with digital twins ✅ Bring AI into your QA pipeline ✅ Push intelligence to the edge ✅ Get ahead of compliance rules (especially if you operate globally) 🧠 Each of these is something you can pilot now: not next quarter. Happy to share what’s worked (and what hasn’t). 👇 Save and repost. #AI #Manufacturing #DigitalTwins #EdgeAI #IndustrialAI #DigiFabAI

Discover Senseye Predictive Maintenance live from Transform 2024! Ryan Falcini walks us through the key elements of the Senseye Predictive Maintennace platform covering: ❓ What is Senseye?: Senseye is a cloud-based AI and machine learning tool designed to detect and alert users to potential machine failures and forecast breakdowns. It is industry-agnostic, supporting various sensors and technologies. ⚙️ Primary Use: Senseye acts as a decision support tool, guiding users on maintenance priorities through the Attention Index. This index uses a traffic light system (green, yellow, red) to indicate priority levels for asset issues. 👩🏻💻 User Interaction: Users receive detailed cases highlighting anomalies or trend detections, showing specific measures causing concern. Feedback from users helps fine-tune algorithms and improve the Senseye experience. 💻 Advanced Capabilities: Senseye employs generic AI to offer prescriptive guidance, beneficial for organizations lacking the expertise to interpret complex data. Language learning models provide actionable checklists to restore asset health. 🤓 Main Goal: The primary objective is delivering the right information to the right person at the right time, preventing unplanned downtime and reducing maintenance costs. #PredictiveMaintenance #Transform2024 #Industry40

Meet anyone in manufacturing, and for their top two concerns, you'll hear about:   1. Supply Chain Disruptions: Challenges related to inventory and supply chain management. 2. Operating Costs: Navigating economic headwinds and operational inefficiency.   Our clients in the manufacturing sector work in a fast-paced world where maintaining operational efficiency is crucial. One of our clients faced significant challenges with their Clean-In-Place (CIP) process, which directly impacted their quality check procedures. Frequent unplanned downtimes due to equipment failures were hampering productivity and throughput, highlighting the need for a more proactive maintenance approach. They needed real-time insights to make informed preventive maintenance decisions! To address their challenges, our team developed and implemented an AI-based predictive maintenance solution for the CIP equipment. Leveraging data analytics and machine learning, this solution integrated critical datasets from batch processes, sensors, and maintenance records.   By empowering our client with real-time insights through anomaly detection and a risk scoring system, we enabled them to make informed preventive maintenance decisions. This proactive approach not only improved their operational efficiency but also set a new standard for maintenance practices in the manufacturing industry.   Our client went from reactive and corrective maintenance to predictive maintenance! Would love to hear from the network on what you are seeing in this area. If you have a story, let us talk.

Customized Production Planning Develop Generative AI models for customized production planning, considering demand fluctuations, resource availability, and market trends, leading to agile and adaptive manufacturing processes. Conquer Demand Fluctuations with Generative AI Planning! The manufacturing landscape is ever-changing. Generative AI offers a powerful tool to adapt your production plans in real-time, ensuring you meet fluctuating demands and stay ahead of the curve. Imagine: AI systems that analyze market trends, resource availability, and customer demands to generate dynamic and optimized production plans. > Stay Agile in a Shifting Market: Generative AI can quickly adjust production plans based on sudden changes in demand, allowing you to capitalize on new opportunities and minimize the impact of market fluctuations. > Optimize Resource Allocation: AI considers your available materials, equipment, and workforce capacity when generating production plans, ensuring efficient resource utilization. > Reduce Inventory Waste: By accurately predicting demand, you can minimize overproduction and avoid costly inventory holding costs. The benefits of Generative AI for customized production planning are clear: * Enhanced Agility & Responsiveness: Adapt your production quickly to changing market conditions. * Improved Resource Efficiency: Optimize resource allocation and minimize waste. * Reduced Inventory Costs: Produce only what you need, when you need it. Generative AI empowers agile and adaptive manufacturing processes. Ready to transform how you plan your production? #manufacturing #generativeAI #productionplanning

🚀 AI-Powered Industrial Revolution: How Rockwell Automation is Shaping the Future of Smart Manufacturing Artificial Intelligence and Generative AI are transforming industrial automation, and Rockwell Automation is at the forefront of this revolution. By embedding AI into manufacturing execution systems (MES), digital twins, industrial IoT, and supply chain optimization, Rockwell is unlocking new levels of efficiency, productivity, and resilience in industrial operations. 💡 Key AI Innovations by Rockwell Automation: ✅ Predictive Maintenance – AI-driven analytics reduce machine downtime and optimize performance. ✅ Generative AI for Industrial Design – AI automates engineering workflows, system design, and PLC programming. ✅ AI-Powered Industrial IoT (IIoT) – FactoryTalk InnovationSuite provides real-time monitoring and predictive insights. ✅ AI in Supply Chain Management – Intelligent forecasting, risk assessment, and logistics optimization. 🌍 The Bigger Picture: AI is driving autonomous manufacturing, edge computing, and human-machine collaboration, making industrial automation smarter, faster, and more resilient. Competitors like Siemens, ABB, Schneider Electric, and Honeywell are also investing in AI, but Rockwell’s integrated approach to AI-powered automation gives it a competitive edge. ⚠️ Challenges & Considerations: 🔹 AI model accuracy and reliability in critical industrial processes. 🔹 Cybersecurity risks in AI-driven industrial control systems. 🔹 Regulatory compliance with NIST, ISO, and the EU AI Act for AI governance. The future of industrial automation is AI-driven, autonomous, and adaptive. Rockwell Automation is shaping that future by blending AI, IoT, and automation to build the factories of tomorrow. 💬 What do you think about AI’s role in industrial automation? How do you see AI transforming manufacturing in the next decade? Drop your thoughts below! ⬇️ #AI #Automation #Industry40 #SmartManufacturing #RockwellAutomation #IndustrialAI

𝐑𝐞𝐧𝐞𝐰𝐚𝐛𝐥𝐞𝐬 𝐚𝐫𝐞 𝐨𝐧 𝐭𝐡𝐞 𝐫𝐢𝐬𝐞 𝐚𝐧𝐝 𝐭𝐡𝐞 𝐠𝐫𝐢𝐝 𝐬𝐮𝐟𝐟𝐞𝐫𝐬. 𝐂𝐚𝐧 𝐀𝐈 𝐡𝐞𝐥𝐩 𝐮𝐬 𝐫𝐞𝐬𝐨𝐥𝐯𝐞 𝐭𝐡𝐞 𝐢𝐬𝐬𝐮𝐞𝐬 𝐭𝐡𝐚𝐭 𝐩𝐚𝐫𝐭𝐢𝐚𝐥𝐥𝐲 -𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐯𝐚𝐬𝐭 𝐩𝐨𝐰𝐞𝐫 𝐜𝐨𝐧𝐬𝐮𝐦𝐩𝐭𝐢𝐨𝐧- 𝐜𝐫𝐞𝐚𝐭𝐞𝐬? EMBER says wind and solar outpaced EU fossil fuel production in H1 2024. For the first time, wind and solar generated 30% of EU electricity, surpassing fossil fuels. However, power infrastructure constraints limit Europe's wind and solar energy growth. Electricity grids waste #renewable energy. Transmission networks supply most data for centralized, stable electrical grids without analysis or prediction. Utility companies rarely gather real-time windspeed, line temperature, voltage, and frequency data, hindering renewable energy integration. Estimate peak or #solarpower generation by tracking network-wide wind and temperature. Some grids feature extensive blind spots. Traffic and blind spots waste #energygrid capacity, so #utilities cannot swap excess capacity or use all renewables during peak hours. Instead of monitoring line temperatures and local weather in real time, many utilities set safe capacity limitations using crude, overcautious calculations, which may underutilize the system. Flexible networks are needed to connect intermittent renewable power sources with power capacity awareness. European and US PV and wind rates update every few minutes. Accurate system capacity, generation, and transmission linkages will lower power prices. With multi-sensing (#IoT) grid monitoring systems, old grids can become AI-enhanced systems that detect multi-point electrical, physical, and environmental phenomena like voltage, frequency, harmonics, cable ampacity, temperature, and wind speed. ML uses this extensive data set to adapt network capacity and renewable power sources to the weather set. Innovative technologies boost renewables and cut power loss. Weather and cable temperatures assist #ML systems in anticipating network safety months ahead. Network operators can securely add capacity and renewable energy at night or in better mountainous locations. Parallel lines share loads to boost capacity and predict demand. The new wave of #AI may boost renewables. Weather-related renewable power sensor data, mostly scattered, could anticipate capacity increases. #Utility operators can forecast solar and wind peak production and use cheap, clean #power. Power theft and loss decrease with renewables. AI-based location-based fault detection systems could secure networks and conserve clean #electricity by detecting power leaks and theft. Data-driven network designs boost capacity, save electricity, and integrate renewable #energy early for security. Machine learning algorithms may recommend new wire cooling, capacity, or energy-conducting materials network areas. AIs predict power-saving network designs and locations, boosting #cybersecurity.

💥 Agentic AI Unleashes the Green Revolution: How Generative Workflows Will Power the Energy Sector Generative AI is no longer just about creating stunning images or crafting compelling text. A new paradigm can alter energy industry - generative AI agentic workflows. Imagine AI not just as a tool for analysis or content creation, but as an autonomous agent, capable of generating solutions, orchestrating actions, and driving complex processes from end-to-end. Nowhere is this transformative potential more profound than in the energy sector, a domain crying out for innovation and efficiency. ✅ What exactly are these agentic workflows? They combine the creative power of generative AI with the proactive execution of intelligent agents. Think of it as AI that can not only imagine optimal energy solutions but also autonomously implement them. These workflows are designed to handle complex, multi-step processes, learn from experience, and adapt to dynamic environments, pushing automation beyond simple rule-based systems ✅ Why is this a game-changer for energy? Because the energy sector faces immense challenges: meeting growing demand, transitioning to renewables, optimizing vast and complex grids, and the like. Generative AI agentic workflows offer a powerful toolkit to tackle these head-on. Let's dive into specific examples of how this will unfold: 🛫 Hyper-Personalized Energy Savings Agents for Consumers: Forget generic energy-saving tips. Agentic AI can analyze a household’s specific energy consumption patterns, appliance usage, and even lifestyle habits. Based on this deep dive, it generates truly personalized energy-saving recommendations – and crucially, it can autonomously implement them. Imagine an AI agent that learns your preferred home temperature, analyzes energy pricing fluctuations, and then subtly adjusts your smart thermostat and appliance schedules to minimize your bill without impacting comfort. 🛫 Predictive Maintenance Agents for Energy Infrastructure: Power plants, wind turbines, and pipelines require constant maintenance to prevent costly failures. Agentic AI can continuously monitor sensor data from these assets, generate predictive maintenance schedules based on subtle anomaly detection, and even autonomously trigger maintenance workflows. This minimizes downtime, extends asset lifespan, and improves the overall reliability of energy infrastructure. ☑️ The implications are staggering: a more resilient, efficient, and sustainable energy sector, powered by AI agents working autonomously to optimize every facet of energy generation, distribution, and consumption. While challenges like data security, ethical considerations, and job displacement need careful consideration, the potential of generative AI agentic workflows to drive a transformation in the energy sector is undeniable. #slb #ai #genAI #energy #tech

The Cybersecurity and Infrastructure Security Agency together with the National Security Agency, the Federal Bureau of Investigation (FBI), the National Cyber Security Centre, and other international organizations, published this advisory providing recommendations for organizations in how to protect the integrity, confidentiality, and availability of the data used to train and operate #artificialintelligence. The advisory focuses on three main risk areas: 1. Data #supplychain threats: Including compromised third-party data, poisoning of datasets, and lack of provenance verification. 2. Maliciously modified data: Covering adversarial #machinelearning, statistical bias, metadata manipulation, and unauthorized duplication. 3. Data drift: The gradual degradation of model performance due to changes in real-world data inputs over time. The best practices recommended include: - Tracking data provenance and applying cryptographic controls such as digital signatures and secure hashes. - Encrypting data at rest, in transit, and during processing—especially sensitive or mission-critical information. - Implementing strict access controls and classification protocols based on data sensitivity. - Applying privacy-preserving techniques such as data masking, differential #privacy, and federated learning. - Regularly auditing datasets and metadata, conducting anomaly detection, and mitigating statistical bias. - Securely deleting obsolete data and continuously assessing #datasecurity risks. This is a helpful roadmap for any organization deploying #AI, especially those working with limited internal resources or relying on third-party data.