ThinkLabs AI, Inc.’s Post

This was one of the first articles written on ThinkLabs. The complexities and pace of change on the grid is calling for the urgent need of a Grid Copilot. I'm not referring to a back office copilot for work productivity (though that's much needed as well), but one who understands how electrons flow, how the grid is operated, and how decisions are made. Imagine a Google Map with driving assistance like experience for the grid: - continuous real time power flow/state estimation (traffic patterns) - optimization of grid devices and DER fleets (routing) - generation of dispatch commands (turn-by-turn instructions) - congestion detection and management (traffic jams and auto-reroute) - voltage violation and mitigation (lane departure and auto-steer) - outage mitigation (collision avoidance) Not more long term studies, conservative buffers, system overbuilds, and delays in interconnections. Orchestrate, not build, set and forget. To achieve that we need trustworthy AI for mission critical grid infrastructure, by using physics-informed AI. Just like AI representing complex climate models, we can use all the physics and engineering of the grid to train an AI surrogate model of the grid, so that AI mirrors the physics. NVIDIA's Earth 2 is a great example. (https://lnkd.in/gWWVtYRR). This is the future (really, now) of industrial and infrastructure AI. This physics-informed AI "foundation" model of the grid can be used broadly across the utility wherever grid simulations and related decision making are required - interconnections, non-wires solutions, operational planning, congestion management, voltage and loss optimization, real time orchestration, and edge intelligence. Thank you Maeve Allsup and Latitude Media. https://lnkd.in/ghac6_qS

🌟 Exploring Hybrid Electric Systems with AI-Driven Models 🌟 We are thrilled to share our ongoing research into hybrid electric systems! Together with my partner Anuj Srinivas, we developed an AI-driven model using **Reinforcement Learning (PPO)** to optimize energy efficiency, noise reduction, and turbine-motor coordination in hybrid electric vehicles. Our efforts have yielded "impressive results", with a "final episode score of 123", showcasing the model's ability to adapt and perform effectively. The consistently "increasing performance graph" highlights the potential of our approach for real-world applications. Additionally, we explored traditional ML techniques like "Linear Regression" and "Random Forest", further validating our outcomes with strong metrics. 📊 Key Achievements:  - Successfully trained the PPO agent for noise reduction and energy efficiency optimization.  - Predicted next-state transitions with promising accuracy and reliability.  - Achieved episode scores that demonstrate significant progress, including the final score of 123.62  - Evaluated model accuracy with metrics like R-squared, MSE, and Explained Variance. 🚀 Next Steps:  This is just the beginning! We are actively working to:  1. Enhance robustness with Random Forest and Deep Learning models.  2. Validate the model using real-world datasets to ensure practical applicability.  3. Make the model fit for "government organizations", advancing sustainable and energy-efficient solutions. This project reflects our dedication to leveraging AI for impactful solutions in the energy sector. Open to collaborations and discussions to push the boundaries of innovation! #ReinforcementLearning #MachineLearning #HybridElectricSystems #AIResearch #Sustainability #Innovation

🌟 Exploring Hybrid Electric Systems with AI-Driven Models 🌟 We are thrilled to share our ongoing research into hybrid electric systems! Together with my partner Ali Asgar Jaipuri, we developed an AI-driven model using "Reinforcement Learning (PPO)" to optimize energy efficiency, noise reduction, and turbine-motor coordination in hybrid electric vehicles. Our efforts have yielded *impressive results, with a "final episode score of 123", showcasing the model's ability to adapt and perform effectively. The consistently increasing performance graph highlights the potential of our approach for real-world applications. Additionally, we explored traditional ML techniques like Linear Regression and Random Forest, further validating our outcomes with strong metrics. 📊 Key Achievements: - Successfully trained the PPO agent for noise reduction and energy efficiency optimization.  - Predicted next-state transitions with promising accuracy and reliability.  - Achieved "episode scores that demonstrate significant progress", including the final score of 123.62  - Evaluated model accuracy with metrics like R-squared, MSE, and Explained Variance. 🚀 Next Steps:  This is just the beginning! We are actively working to:  1. Enhance robustness with "Random Forest and Deep Learning" models.  2. Validate the model using real-world datasets to ensure practical applicability.  3. Make the model fit for government organizations, advancing sustainable and energy-efficient solutions. This project reflects our dedication to leveraging AI for impactful solutions in the energy sector. Open to collaborations and discussions to push the boundaries of innovation! #ReinforcementLearning #MachineLearning #HybridElectricSystems #AIResearch #Sustainability #Innovation

🚀 Ever wondered how batteries🔋 perform when powering flying vehicles 🚁 ? Do they behave the same as on the ground? What kind of loads do they experience in mid-air? How can we monitor their performance when they are far from any station? Can machine learning predict their remaining capacity and useful life while in use? And is live data transfer really safe? 💡 Our latest research dives deep into these challenges! We’ve designed a lightweight Battery Management System (BMS) that flies alongside the vehicle, collects critical battery data, and sends it back to a ground station in a secure, encrypted way where a live prediction on the life of Batteries is made using a machine learning algorithm CD-Net. Check out our newest publication, where we explore these cutting-edge solutions to ensure efficient and safe battery operation for electric propulsion 🚁🔋 Big shout-out to the team at Purdue University ( Jaya Vikeswara Rao Vajja , Alexey Serov , Meghana Sudarshan , Vikas Tomar ) for making this possible! Link: https://lnkd.in/gDuc9yAt Stay tuned for more! #ElectricPropulsion #Batteries #Innovation #MachineLearning #BMS #Research #Drone

Sensor fusion is a process that merges data from multiple sources to get a more accurate, reliable and complete understanding of a given environment.  For cars this is crucial for safe and efficient automation   Developing it is not without challenges and the last few years’ progress in deep neural nets is shifting the playing field substantially.  Listen to Veronika Nihlén and Maryam Fatemi discuss the ins and outs if sensor fusion, touching on data, computing demands, timing issues and sensor alignment. Tune in! https://lnkd.in/dXUijDth #carsafety #sensorfusion #sensors #automation #deeplearning #software #deeplearning #machinelearning

🚀 Exciting news! 🚀 As the Director of Innovation, I am thrilled to share an article that delves into the fascinating role of artificial intelligence (AI) in designing next-generation electric powertrains. Published on August 14, 2024, this article explores how AI, through machine learning algorithms and data analytics, is revolutionizing the optimization of every component of the powertrain for maximum efficiency and performance. 🤖⚡️ By sifting through vast amounts of data from simulations and real-world tests, AI can identify the perfect combination of motor size, winding configuration, and inverter topology, pushing the boundaries of what's possible in electric vehicle design. This means that AI is not just an add-on feature but the very intersection of product and system design. 🚗💡 But that's not all! AI is also transforming battery management systems by accurately predicting battery state of charge, health, and remaining useful life. This predictive capability enables optimized charging strategies, energy utilization, and proactive maintenance, ultimately enhancing battery performance and longevity. 🔄🔋 The applications of AI extend beyond the riding experience and into the realm of predictive maintenance. Algorithms can monitor everything from subtle energy output variations to changes in the vehicle's behavior, identifying faults and issues. This means that AI is not only shaping the evolution of electric mobility but also paving the way for safer and more reliable vehicles. 📊🔧 The article also highlights the importance of training employees to work effectively in the emerging human-machine interface scenario. While machines may take over more complex tasks, humans will continue to have a vital role. This collaboration between humans and AI is crucial for optimizing operations and improving productivity in the automotive industry. 🤝🏭 Overall, this article emphasizes the transformative potential of AI in shaping the future of electric mobility. It showcases the limitless possibilities that lie ahead and highlights how AI is driving innovation in the automotive industry. 🌟 I encourage you to read the full article and share your thoughts in the comments below. Let's discuss how AI is revolutionizing electric powertrains and what it means for the future of transportation! 🗣️💭 #AI #ElectricPowertrains #Innovation #FutureOfMobility #ArtificialIntelligence #AutomotiveIndustry

Andreessen Horowitz on "AI for the Physical World": "We are at a unique moment in time... many industrial use cases once thought impossible are now nearly, if not totally, possible." Some examples: "the ability to predict, simulate, produce, and test new advanced materials in a closed loop process with minimal human intervention" "robots that help install solar panels in remote desert locations 1000x faster than humans can alone" "autonomous controls that more efficiently manage energy-taxing industrial processes" "autonomous factories to reshore production of goods currently too costly to manufacture in the United States." In short, continued advances in AI could completely transform the physical world. #defensetech #defenseinnovation https://lnkd.in/e443zcwk 

Jensen Huang, CEO of Nvidia, predicts a wave of AI automation that will revolutionize $50 trillion worth of heavy industries! Here's a glimpse into what the future might hold: - Self-driving haul trucks navigating complex underground mines. - AI-powered robots handling hazardous tasks in steel mills & chemical plants. - Real-time AI monitoring preventing accidents and optimizing processes. - Virtual simulations perfecting construction plans before groundbreaking. ️ These are just a few possibilities. What are you most excited about for the future of AI in heavy industries? #AI #FutureOfWork #HeavyIndustry #Automation

The future of #EV charging is about to get a major upgrade. Get ready to witness the power of AI like never before. Stay tuned as we unveil the next big thing in electric mobility landscape. #comment below with your guesses. #follow #like #share this series for more ongoing insights into the transformative world of electric mobility. #electricvehicle #electriccar #ev #smart #intelligence #ai #revolution #emerging #hopcharge

The McKinsey report highlights #ten #transformative #technology trends shaping the mobility sector. These include advanced connectivity, applied AI, cloud and edge computing, generative AI, immersive-reality tech, industrialized machine learning, next-generation software development, quantum tech, trust architectures, and Web3. These technologies are enhancing various aspects of the industry, such as #engineering, #procurement, #manufacturing, #marketing, and #life #cycle #services. Applied #AI, in particular, is driving significant innovation and investment. For more details, you can read the full report: https://lnkd.in/ddAEUXsH