Engineering

The future of batteries may not need lithium. ⚡🧂 For years, lithium has powered the clean energy revolution. Electric vehicles. Home batteries. Grid storage. But lithium isn't the only path forward. Researchers are now exploring battery chemistries built from materials as common as salt and oxygen from the air. Why does that matter? Because today's lithium batteries come with real challenges: ⛏️ Intensive mining 🌍 Concentrated global supply chains 💰 Rising costs 🔋 Performance that gradually degrades over time Salt-based battery technologies aim to tackle many of those issues. Potential advantages include: 🧂 Abundant, widely available materials 🔥 Improved safety ⏳ Longer operational lifetimes ⚡ Lower costs for large-scale energy storage The biggest opportunity isn't necessarily replacing lithium in electric cars. It's storing renewable electricity on the grid. Solar and wind need affordable storage to deliver power when the sun isn't shining and the wind isn't blowing. If these next-generation batteries can scale commercially, they could reduce dependence on critical minerals while making clean energy more resilient and accessible. The next breakthrough may not come from digging deeper. It may come from using materials we've had all along. 📌PS: Found this helpful? Join 19,000+ founders and investors leveling up their game to fix this climate mess. 👉 Subscribe to my newsletter and get actionable insights delivered weekly! 🌍🚀 https://lnkd.in/ektv3C_s #BatteryTechnology #EnergyStorage #CleanEnergy #ClimateTech #RenewableEnergy #Innovation #FutureEnergy

We’re planting trees — but losing biodiversity. Global efforts to restore forests are gathering pace, driven by promises of combating climate change, conserving biodiversity, and improving livelihoods. Yet a recent paper published in Nature Reviews Biodiversity warns that the biodiversity gains from these initiatives are often overstated — and sometimes absent altogether. Forest restoration is at the heart of Target 2 of the Kunming-Montreal Global Biodiversity Framework, which aims to place 30% of degraded ecosystems under effective restoration by 2030. But the gap between ambition and outcome is wide. "Biodiversity will remain a vague buzzword rather than an actual outcome" unless projects explicitly prioritize it, the authors caution. Restoration has typically prioritized utilitarian goals such as timber production, carbon sequestration, or erosion control. This bias is reflected in the widespread use of monoculture plantations or low-diversity agroforests. Nearly half of the Bonn Challenge’s forest commitments consist of commercial plantations of exotic species — a trend that risks undermining biodiversity rather than enhancing it. Scientific evidence shows that restoring biodiversity requires more than planting trees. Methods like natural regeneration — allowing forests to recover on their own — can often yield superior biodiversity outcomes, though they face social and economic barriers. By contrast, planting a few fast-growing species may sequester carbon quickly but offers little for threatened plants and animals. Biodiversity recovery is influenced by many factors: the intensity of prior land use, the surrounding landscape, and the species chosen for restoration. Recovery is slow — often measured in decades — and tends to lag for rare and specialist species. Alarmingly, most projects stop monitoring after just a few years, long before ecosystems stabilize. However, the authors say there are reasons for optimism. Biodiversity markets, including emerging biodiversity credit schemes and carbon credits with biodiversity safeguards, could mobilize new financing. Meanwhile, technologies like environmental DNA sampling, bioacoustics, and remote sensing promise to improve monitoring at scale. To turn good intentions into reality, the paper argues, projects must define explicit biodiversity goals, select suitable methods, and commit to long-term monitoring. Social equity must also be central. "Improving biodiversity outcomes of forest restoration… could contribute to mitigating power asymmetries and inequalities," the authors write, citing examples from Madagascar and Brazil. If designed well, forest restoration could help address the twin crises of biodiversity loss and climate change. But without a deliberate shift, billions of dollars risk being spent on projects that plant trees — and little else. 🔬 Brancalion et al (2025): https://lnkd.in/gG6X36WP

Exciting updates on Project GR00T! We discover a systematic way to scale up robot data, tackling the most painful pain point in robotics. The idea is simple: human collects demonstration on a real robot, and we multiply that data 1000x or more in simulation. Let’s break it down: 1. We use Apple Vision Pro (yes!!) to give the human operator first person control of the humanoid. Vision Pro parses human hand pose and retargets the motion to the robot hand, all in real time. From the human’s point of view, they are immersed in another body like the Avatar. Teleoperation is slow and time-consuming, but we can afford to collect a small amount of data.  2. We use RoboCasa, a generative simulation framework, to multiply the demonstration data by varying the visual appearance and layout of the environment. In Jensen’s keynote video below, the humanoid is now placing the cup in hundreds of kitchens with a huge diversity of textures, furniture, and object placement. We only have 1 physical kitchen at the GEAR Lab in NVIDIA HQ, but we can conjure up infinite ones in simulation. 3. Finally, we apply MimicGen, a technique to multiply the above data even more by varying the *motion* of the robot. MimicGen generates vast number of new action trajectories based on the original human data, and filters out failed ones (e.g. those that drop the cup) to form a much larger dataset. To sum up, given 1 human trajectory with Vision Pro  -> RoboCasa produces N (varying visuals)  -> MimicGen further augments to NxM (varying motions). This is the way to trade compute for expensive human data by GPU-accelerated simulation. A while ago, I mentioned that teleoperation is fundamentally not scalable, because we are always limited by 24 hrs/robot/day in the world of atoms. Our new GR00T synthetic data pipeline breaks this barrier in the world of bits. Scaling has been so much fun for LLMs, and it's finally our turn to have fun in robotics! We are creating tools to enable everyone in the ecosystem to scale up with us: - RoboCasa: our generative simulation framework (Yuke Zhu). It's fully open-source! Here you go: http://robocasa.ai - MimicGen: our generative action framework (Ajay Mandlekar). The code is open-source for robot arms, but we will have another version for humanoid and 5-finger hands: https://lnkd.in/gsRArQXy - We are building a state-of-the-art Apple Vision Pro -> humanoid robot "Avatar" stack. Xiaolong Wang group’s open-source libraries laid the foundation: https://lnkd.in/gUYye7yt - Watch Jensen's keynote yesterday. He cannot hide his excitement about Project GR00T and robot foundation models! https://lnkd.in/g3hZteCG Finally, GEAR lab is hiring! We want the best roboticists in the world to join us on this moon-landing mission to solve physical AGI: https://lnkd.in/gTancpNK

Last week, I described four design patterns for AI agentic workflows that I believe will drive significant progress: Reflection, Tool use, Planning and Multi-agent collaboration. Instead of having an LLM generate its final output directly, an agentic workflow prompts the LLM multiple times, giving it opportunities to build step by step to higher-quality output. Here, I'd like to discuss Reflection. It's relatively quick to implement, and I've seen it lead to surprising performance gains. You may have had the experience of prompting ChatGPT/Claude/Gemini, receiving unsatisfactory output, delivering critical feedback to help the LLM improve its response, and then getting a better response. What if you automate the step of delivering critical feedback, so the model automatically criticizes its own output and improves its response? This is the crux of Reflection. Take the task of asking an LLM to write code. We can prompt it to generate the desired code directly to carry out some task X. Then, we can prompt it to reflect on its own output, perhaps as follows: Here’s code intended for task X: [previously generated code] Check the code carefully for correctness, style, and efficiency, and give constructive criticism for how to improve it. Sometimes this causes the LLM to spot problems and come up with constructive suggestions. Next, we can prompt the LLM with context including (i) the previously generated code and (ii) the constructive feedback, and ask it to use the feedback to rewrite the code. This can lead to a better response. Repeating the criticism/rewrite process might yield further improvements. This self-reflection process allows the LLM to spot gaps and improve its output on a variety of tasks including producing code, writing text, and answering questions. And we can go beyond self-reflection by giving the LLM tools that help evaluate its output; for example, running its code through a few unit tests to check whether it generates correct results on test cases or searching the web to double-check text output. Then it can reflect on any errors it found and come up with ideas for improvement. Further, we can implement Reflection using a multi-agent framework. I've found it convenient to create two agents, one prompted to generate good outputs and the other prompted to give constructive criticism of the first agent's output. The resulting discussion between the two agents leads to improved responses. Reflection is a relatively basic type of agentic workflow, but I've been delighted by how much it improved my applications’ results. If you’re interested in learning more about reflection, I recommend: - Self-Refine: Iterative Refinement with Self-Feedback, by Madaan et al. (2023) - Reflexion: Language Agents with Verbal Reinforcement Learning, by Shinn et al. (2023) - CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing, by Gou et al. (2024) [Original text: https://lnkd.in/g4bTuWtU ]

The evolution of underground parking systems over the years has been marked by advancements in technology, design, sustainability, and user experience. How would you rate this one? Automated Parking Systems: In recent years, there has been a rise in automated or robotic parking systems. These systems use robotics and technology to park and retrieve vehicles automatically, optimizing space and increasing efficiency. Smart Parking Solutions: Integration of smart technologies has become prominent. This includes features such as real-time parking availability tracking, mobile apps for parking space reservations, and sensors to guide drivers to available spaces. Green Parking Initiatives: There's a growing emphasis on sustainable and eco-friendly parking solutions. This involves incorporating green roofs, energy-efficient lighting, and the use of permeable materials to manage stormwater. Efficient Space Utilization: Modern underground parking designs focus on maximizing space utilization. This includes compact parking spaces, stackers, and innovative layouts to accommodate more vehicles in limited space. User-Friendly Design: The design of underground parking facilities has evolved to prioritize user experience. This involves well-lit spaces, clear wayfinding signage, security features, and convenient pedestrian access points. Integration with Urban Planning: Underground parking is increasingly integrated into urban planning initiatives. Cities are incorporating underground parking as part of mixed-use developments, contributing to more efficient land use and reducing the visual impact of parking structures. Electric Vehicle Charging Infrastructure: With the rise of electric vehicles, underground parking facilities are integrating charging infrastructure. This includes the installation of electric vehicle charging stations to support the growing demand for sustainable transportation. Improved Security Measures: Enhanced security features, such as surveillance systems, access control, and emergency response plans, have become standard in modern underground parking facilities to ensure the safety of both vehicles and users. Innovations in Construction Materials: The use of advanced construction materials, including stronger and more durable concrete, is contributing to the longevity and stability of underground parking structures. Adaptive Lighting Systems: Energy-efficient and adaptive lighting systems are being implemented to improve visibility while minimizing energy consumption. These systems often use sensors to adjust lighting levels based on occupancy and natural light conditions. Urban Density Considerations: As urban areas become more densely populated, the design of underground parking systems is evolving to address the unique challenges of accommodating a larger number of vehicles in limited space. #innovation 🎥: Adriano Bagni/FB #parking

CIOs are leading a transformation focused on strategic, long-term value rather than just adopting the latest tech. 🌍 Lenovo’s Global CIO Study shows 96% of CIOs plan to boost tech investments, focusing on AI and security. From my conversations, it’s clear they’re also thinking about sustainability and future-proofing in a rapidly evolving tech landscape. 💡 However, 61% of CIOs face challenges in proving ROI from these investments, highlighting the need to not only innovate but to deliver measurable outcomes. Here are four strategies to tackle this challenge: 1️⃣ Align Tech Investments with Business Goals Tie each technology decision directly to business outcomes. Whether it’s enhancing customer experience, increasing revenue, or improving operational efficiency, measurable goals make the case for ROI clearer. 2️⃣ Build Cross-functional Alignment Involve key business leaders in the early stages of technology planning. Demonstrating how investments benefit various departments, from marketing to operations, builds stronger support for technology initiatives and ensures alignment with broader company objectives. 3️⃣ Prioritize Long-term Value Creation While short-term wins are important, CIOs must invest in technology that continues to deliver value over time. AI, for instance, plays a pivotal role in future-proofing organizations in a rapidly changing digital landscape. 4️⃣ Leverage Sustainability and Future-of-Work Strategies New growth areas, like sustainability and adapting to the future of work, are top-of-mind for CIOs. AI is central to addressing these trends, from optimizing energy use to enabling more productive environments - key factors in demonstrating ROI over the long term. For me, leading through this transformation isn’t just about adopting AI or new tools. It’s about building a roadmap that is thoughtful and strategic, building a solid foundation today for tomorrow’s growth. How are you navigating your business’s tech transformation to demonstrate ROI? I’d love to hear your insights on the challenges and opportunities. 🤝 #WeAreLenovo #TechTransformation #AI

China is turning fire trucks into drone launch systems. And that is a much bigger shift than it sounds. What interests me here is not just the hardware. It is the new logic of emergency response. Instead of relying only on ladders and human entry, these systems pair fire trucks with drones that can reach high-rise fire zones quickly, fly into smoke, and send live intelligence back to crews. That is what is new. The truck is no longer just transport. It becomes a mobile aerial response base. And that matters because in dense high-rise environments, access is often the real bottleneck. To me, this is where the story gets interesting. This is not just about fighting fires better. It is about changing who gets exposed to danger first. �� drones go where ladders cannot → commanders get visibility earlier → crews make faster decisions → fewer firefighters enter blind conditions That is a serious innovation. And it opens up important use cases: → faster high-rise reconnaissance → targeted suppression from outside upper floors → better coordination in smoke-heavy environments → safer response where humans cannot reach quickly That is why I would not dismiss this as just another drone demo. It is a glimpse of what emergency response looks like when robotics, data, and frontline operations finally converge. What do you think matters more here: faster firefighting, or the fact that robots may now take the first risk instead of humans? #AI #Robotics #Drones #Firefighting #Innovation #EmergencyResponse #SmartCities #FutureOfWork #Technology

China has switched on the world’s first grid-connected 20 MW offshore wind turbine – the largest wind turbine currently operating anywhere in the world. Installed around 30 km offshore in China’s Fujian province, the turbine has a rotor diameter of 300 metres, nearly the height of the Eiffel Tower. Wind turbines have been getting steadily bigger for decades – driven by physics and economics: ✅ Power from wind scales with the square of the rotor diameter. ✅ Power also scales with the cube of wind speed, and taller turbines can access the stronger, steadier winds higher above the surface. ✅ Costs such as foundations and cables increase as turbines get larger, but energy production tends to grow faster than these costs. Offshore wind farms in particular benefit from scale because installation vessels are extremely expensive to operate. Reducing the total number of turbines - foundations, lifts and cable connections - can materially lower overall project costs. Larger turbines do introduce challenges, including more complex manufacturing and greater single-asset risk. But the economic advantages of larger turbines in offshore projects continue to outweigh these challenges, which is why turbine sizes keep increasing. Even larger 25–26 MW turbines are already under development – all from Chinese manufacturers. With the world’s largest domestic deployment pipeline and an integrated manufacturing ecosystem, China is increasingly setting the pace in the next generation of offshore wind turbines.

What's the best low-carbon way to power vehicles? A groundbreaking study in Joule examines the potential of green hydrogen to revolutionize transportation across ground, air, and marine sectors. While green hydrogen presents promise in the hard-to-abate areas of transportation, significant investments in infrastructure and technology are needed to realize its full potential. For passenger vehicles and most road transport, the most efficient use of power is electric vehicles, sometimes by a factor of 5-10x over other “emissions-free” methods including hydrogen! https://lnkd.in/e9GZjv3f #climate #energy #transportation #ev #renewables #emissions #innovation