AI in Sustainable Technology

♻️ Recycling, reimagined. I came across Ameru’s AI Smart Bin — and it made me realize something we rarely talk about in sustainability: We don’t fail to recycle because we don’t care. We fail because the friction is too high. This bin doesn’t just collect waste. It sees what you throw, sorts it automatically, and even gives you real-time feedback. The results? ✅ 95%+ sorting accuracy ✅ Analytics that show you how to reduce waste ✅ ROI in under 2 years 👉 Here’s the hidden insight: Let’s be honest: recycling is broken. Most of us want to recycle, but the system is designed for failure — too much friction, too many rules. The real innovation isn’t in AI or edge computing. It’s in making sustainability invisible. No guilt, no extra steps — just default behavior upgraded. 💡 Actionable thought: Whether you’re building tech, a product, or even a habit, ask yourself — how can I make the right choice feel effortless? Because effort scales linearly. But effortlessness? That scales exponentially. PS: Imagine when every trash bin becomes a data point in the circular economy. 👉 Do you think this kind of “invisible innovation” could transform how we recycle at home and at work? #GreenTech #AI #Innovation #Sustainability #CircularEconomy

Robots are starting to reshape the installation of solar panels. Chinese company Leapting recently rolled out its AI-controlled robot in Australia for its first commercial deployment — installing 10,000 panels at Neoen’s Culcairn solar farm in NSW. It makes a lot of sense. The largest solar farms have over a million panels, each weighing around 30 kg and requiring 3-4 people to handle. This translates to an installation rate of about 100 panels per 8-hour day. By comparison, Leapting says its robot can install 3-5x as many, at an average rate of 60 modules an hour. As well as the sheer scale of the work involved, large scale solar farms are often located in remote areas with harsh construction environments and strong sunlight, not to mention the heat in countries like Australia. Leapting hopes the use of robots will help address worker shortages and reduce the amount of downtime due to injury. The robot itself consists of a 2.5m high robotic arm mounted on a self-guided and self-propelled crawler. It has its own navigation system, uses visual recognition to adapt to different terrain, and multimodal sensors ensure each panel goes in the right place. Next up, Leapting will deploy this robot and several others to another solar farm in Australia, where together they will install half a million panels. And Leapting isn’t alone — many other companies are exploring the use of robots to speed up solar module installations. Expect to see a lot more of this in the coming years. Video credit: Leapting #energy #renewables #energytransition

Research has highlighted the environmental impact of generative AI, particularly as it relates to the energy demands of data centers. A recent Morgan Stanley report predicts that AI-related industries could emit up to 2.5 billion tons of greenhouse gases by 2030, largely due to the growing need for data centers to support AI workloads. The Green Software Foundation(GSF) Software Carbon Intensity (SCI) Specification provides a practical framework for addressing these concerns. While SCI is applicable to all software, its core principles are particularly impactful in reducing the carbon footprint of AI systems, with the goal being to reduce emissions actively, not just offset them: 1️⃣ Energy Efficiency: Optimizing AI models to use less energy is critical. Techniques like model pruning and distillation help make AI models more efficient by reducing the number of parameters and complexity without sacrificing performance, thus cutting down the energy required for training and deployment. 2️⃣ Hardware Efficiency: Using energy-efficient chipsets and maximizing hardware utilization can help reduce emissions from AI workloads. This involves developing hardware that can handle AI computations more efficiently and extending the lifecycle of existing hardware to reduce the need for frequent replacements, which contribute to emissions during production and disposal. 3️⃣ Carbon Awareness: AI systems can be made carbon-aware, meaning workloads are scheduled to run when energy grids are powered by cleaner, renewable energy. This minimizes the reliance on carbon-intensive power sources and reduces the overall environmental impact. For meaningful progress, policymakers must implement robust regulatory frameworks that support these efforts. Regulations that enforce carbon reporting for AI systems, incentivize the use of renewable energy, and establish standards for emissions will be key to aligning the AI industry with global sustainability goals. By integrating SCI principles with strong policy support, the AI industry can make substantial strides in reducing emissions while continuing to innovate responsibly. (Link - https://lnkd.in/drMQhDEY) #greenai #sustainability #genai

Goodbye herbicides. 👋 Hello LaserWeeder. 🤖🌱 For decades, farmers had only two choices: ❌ Chemicals that pollute soil, water, and food ❌ Massive, costly hand crews Now there’s a third option. Carbon Robotics built AI-powered machines that zap weeds with lasers. No chemicals. No hand crews. Why it’s a game changer: ✅ Kills 450,000 weeds/hour with sub-millimeter precision ✅ Cuts weeding costs by 80% ✅ Leaves zero chemical residues in crops ✅ Protects rivers, lakes & biodiversity ✅ Healthier food + a healthier planet 🌍 Already used on 100+ crops across 12 countries. This isn’t the future, it’s happening in fields today. AI + Robotics are redefining how we grow food. 👉 What do you think: could AI-powered robots replace herbicides worldwide? 💚 Follow me for more stories on sustainability & climate tech. #AgriTech #AI #Sustainability #ClimateTech #Robotics

Climate Week, New York- Day 2: The growth of AI requires energy consumption, and that’s a challenge. But AI can also help us solve for climate and community needs at incredible speed. Is this a dilemma? It depends on how you look at it - and I’m grateful to Ben Gemen from Axios for our thoughtful conversation about the big picture. At Amazon we’re taking a holistic approach to both make sure we support the growth of AI sustainably, and simultaneously harness it to help tackle critical climate challenges. Here’s just a few of the ways we’re using the technology (that may surprise you): 📌 After Hurricane Helene, Amazon’s Disaster Relief team used drones to capture 32,000 images across 28 miles of dangerous rivers and rough terrain. AI analyzed these images in seconds (vs. days for humans) and created detailed maps to prioritize search and rescue areas. The information enhanced safety for first responders and cut down on agonizing wait times for worried families. 🌊 Amazon Web Services (AWS) has partnered with The Ocean Cleanup to create an AI-powered "navigation system" that will help identify, track, and predict where plastic is floating in the Great Pacific Garbage Patch. With an estimated 1.8 trillion pieces spanning millions of square miles, this technology will be critical in optimizing cleanup operations. 💧 In Mississippi, we’re collaborating with Arable and MSU on AI-powered sensors to help farmers make smarter irrigation decisions. The sensors analyze real-time soil moisture, weather conditions, and crop water requirements, then AI processes historical patterns to deliver clear, actionable recommendations through a mobile app. This is expected to save 150 million gallons of water annually—enough for 1,600 households! 🍎 Our Amazon Fresh stores in India use machine vision to identify produce with minor imperfections in crates and on shelves, so the items can be redirected for sale at reduced prices rather than going to waste. These are just a few examples of how cutting-edge technology can be used for good! We’re just beginning to scratch the surface, but there’s no doubt AI can be a tool to catalyze meaningful change.

Within DP World's sustainability endeavours, I've been deeply immersed in the intersection of technology and environmental consciousness, particularly in the realm of artificial intelligence (AI). The discourse around responsible and sustainable AI is not just timely but imperative in today's rapidly evolving digital landscape, especially as AI continues to grow and is poised for even greater expansion in 2024. This article aptly highlights four crucial paths that companies can take to ensure their AI initiatives align with environmental goals while driving innovation. Efficiency emerges as a central theme, urging companies to adopt specialised AI models tailored to specific use cases rather than opting for resource-intensive, general-purpose models. This approach not only minimises energy consumption but also fosters a culture of innovation by leveraging the vast potential of open-source resources. By using less data, we can better optimise AI algorithms for reduced computational overhead while still maintaining performance and achieving results. The integration of renewable energy sources into AI infrastructure represents a significant step forward in mitigating the environmental impact of AI operations. By hosting AI functions in data centers powered by renewable energy, companies can significantly reduce their carbon footprint while driving sustainable growth. However, as highlighted in the article, challenges such as tracking energy consumption and fostering transparency remain paramount. As we navigate these challenges, it's crucial to prioritise ethical considerations and long-term sustainability in AI development. For us at DP World, as we look to tap into the potential of AI, we take into consideration these sustainable approaches to ensure that our technological advancements align with our environmental objectives and foster a greener future. A concrete example is our multi-programme software suite, CARGOES, which is an AI-driven solution automating every terminal process, from staff rostering to streamlining customs inspections—an infamously arduous process. With AI managing the basics, our Jafza teams can focus on upskilling and handling specialist shipments, thereby expanding our capabilities beyond mere throughput increase. Through the integration of AI technologies like CARGOES into our operations, we not only enhance efficiency and productivity but also reduce our environmental footprint by optimising processes and resource usage. By embracing responsible AI practices and leveraging technology as a catalyst for positive change, we can create a more sustainable future where innovation and societal well-being go hand in hand. https://lnkd.in/dugjCDMq 

🔍 New Research: How Integrating AI with Sustainability Triples Impact At Google, we see how technology can be a catalyst for solving complex challenges. A new report from the Project Management Institute shows that organisations integrating AI into their sustainability strategies are achieving outcomes that are three times stronger than those treating the two separately. Key findings: 📉 Emissions reduction: Firms combining AI and sustainability strategies achieve a 26% cut in carbon emissions, compared to 3% for others ⚙️ Operational improvements: AI is being used to optimise energy efficiency, predict maintenance needs, and streamline supply chains 🔄 Compounding progress: Early sustainability gains from AI adoption are creating a positive cycle of reinvestment and greater impact 📊 Success factors: The biggest differentiators are not just technology — but strong data foundations, cross-functional collaboration, and leadership alignment 🚀 Strategic advantage: Organisations embedding AI into sustainability are three times more likely to deliver successful outcomes The integration of AI and sustainability is not a future trend — it’s already differentiating leaders today. The challenge now is to embed it thoughtfully, responsibly, and at scale. Check out the full report: https://lnkd.in/et8c9aBp

Digital Transformation for Sustainability 🌎 Digital transformation is becoming a central strategy for advancing sustainability goals. By embedding digital tools into core operations, organizations can improve efficiency, reduce environmental impact, and generate more reliable data for decision-making. However, to ensure that these efforts deliver meaningful results, a structured and purpose-driven approach is required. The process begins with a thorough assessment of current IT systems, energy consumption, emissions, and digital capabilities. This baseline helps identify areas of inefficiency and sets the stage for defining clear sustainability targets. Measurable goals — such as reducing IT energy use or emissions from data centers — provide direction and enable progress tracking. Once objectives are in place, the next step is to identify key internal and external stakeholders who will lead and support implementation. With alignment secured, organizations can move forward by selecting the right enabling technologies. Tools like cloud computing, IoT, and automation can support efforts to streamline operations and minimize resource consumption. To generate real impact, operational processes must be redesigned. Digital workflows can reduce material use, energy demand, and waste generation. At the same time, robust data tracking systems — including dashboards and performance tools — are essential to monitor progress, inform decisions, and guide course corrections when needed. Effective transformation also depends on building internal capacity. Training and support help teams adopt new tools and sustain improvements over time. Monitoring results, adjusting strategies, and scaling successful initiatives ensure that digital transformation delivers both environmental and business value. When executed with clarity and structure, it becomes a key enabler of long-term sustainability performance. I’ve created this post in partnership with Voiz Academy as part of the 'Sustainability Simplified: A Biweekly Educational Series. #sustainability #sustainable #business #climatechange #digital

# HPE Chief Technologist's Five-Point Plan to Cut AI Infrastructure Emissions TLDR; Sustainability for AI needs to be planned from the outset and consider the full stack, not bolted on later. Great to see our own John Frey, Senior Director and Chief Technologist for Sustainable Transformation at HPE, interviewed in this article for Capacity Media - a techoraco brand this week. John runs through the five levers of efficiency, and here's my take on them: 1. Equipment efficiency: We typically overprovision and underutilise IT equipment, so consider how to maximise utilisation of the assets you have before adding more capacity 2. Energy efficiency: Maximise performance per Watt of energy consumed, and make use of low power states when resources are idle 3. Resource efficiency: Advanced cooling options like DTC and fanless liquid cooling are more energy efficient than air cooling for power dense workloads. Consider heat recovery to convert waste heat into an asset that can decarbonise other forms of heating 4. Software efficiency: In AI, Python is popular for notebooks and experimentation but as a high-level interpreted language it's also the least energy efficient. Particularly when deploying to production, consider compiled alternatives like Rust or C++ to minimise processor cycles. The Green Software Foundation's Software Carbon Index (SCI) is a useful tool for calculating the carbon impact of software in meaningful terms like number of concurrent users, prompts or tokens 5. Data efficiency: Data exists everywhere and it is inherently messy, it resists our attempts to constrain it into neat boxes. Data strategies need to consider the energy cost of data movement - embracing a hybrid, distributed approach to data management and bringing the AI to the data can significantly reduce unnecessary data movement, loading and duplication. Check out the full interview with John here: https://lnkd.in/eimVfv9d HPE has a long history of building some of the world's most energy efficient AI computers, making use of technical and energy innovations to optimise performance per watt. Now that AI is becoming part of everyone's IT portfolio, efficiency is more important than ever. #sustainableIT #livingprogress #fiveleversofefficiency #ITefficiency

Addressing the Carbon Footprint of Foundation Models Training LLMs is extremely energy-intensive, with a single session capable of emitting up to 626,000 pounds of carbon dioxide equivalent. The energy demands extend beyond training. As AI becomes integrated into everyday applications like web search, energy consumption can skyrocket, sometimes increasing usage by more than tenfold. Creating a more sustainable AI future is not just necessary; it’s imperative. Companies are increasingly acknowledging the environmental impact of foundation models and are actively working to reduce their carbon footprint. Key strategies include: 1️⃣ Optimize AI Software and Hardware Efficiency - Fine-tune AI algorithms for maximum efficiency to reduce computing power needs. - Use approaches like Quantization and Speculative Decoding - Deploy AI on energy-efficient hardware. - Foster collaboration between sustainability and IT teams for AI deployment. 2️⃣ Use Renewable Energy for AI Computing - Power AI operations with renewable sources like solar and wind. - Place AI data centers in regions rich in renewable energy. 3️⃣ Carefully Select and Manage AI Training Data - Choose high-quality, relevant data for training AI models. - Avoid unnecessary data that increases computational demands. 4️⃣ Integrate AI into Existing Decarbonization Efforts - Use AI to optimize and automate sustainability initiatives. - Employ AI for real-time monitoring and optimization of energy use, emissions, and resource consumption. - Redesign business models and production systems with AI to minimize environmental impact. 5️⃣ Prioritize AI Use Cases with High Emissions Reduction Potential - Focus AI efforts on areas with the highest potential for emissions reduction. - Enhance logistics, supply chains, and transportation with AI. - Utilize AI for climate modeling, prediction, and decision support. Together, let's drive a greener future with AI! 🌍💡 #IBM #IBMiX #AI #genAI #generativeAI