Marios Charilaou to speak on AI and environmental data at Wolfson College

AI articles are everywhere, but this is worth your time whether you're a power-user or skeptic. A few quotes that stuck out to me: "For organizations and individuals, role convergence and faster workflows may feel like liberation and lead to a more profitable bottom line. But at the individual level, “cognitive offloading” can lead to significant losses in critical thinking, cognitive retention, and the ability to work without the crutch of technology." "The efficiency granted by AI should create an opportunity not just to build faster, but to think deeper—to finally invest the time needed to truly understand the problems we claim to solve, a task the technology industry has historically sidelined in its pursuit of speed. The goal is creating organizational ecosystems that can adapt and thrive and be more humane, not just optimize." "The organizations that thrive in the AI era won’t be those that adopted the tools fastest, but those that figured out how to preserve and cultivate uniquely human capabilities alongside algorithmic efficiency." https://lnkd.in/gAhfi6YH

Are we trading resilience for efficiency as AI reshapes our organizations?In this article, we explore how even well-intended optimization can erode the deeper capacities that make teams adaptable and creative. Proud to coauthor with Rajeshwari Ganesan, whose work on technology continues to inspire. https://lnkd.in/g6xYN3FB

AI Exercise: Eco-Materiality Today, Sandra Simonsen and I were invited to discuss our methodological explorations when studying environmental materiality in everyday GenAI practices at the Universität Leipzig. To get inspired you can either access the book of abstracts: https://lnkd.in/gmnm7sEJ or go straight into the exercise: Exercise: Anybody home?   Instructions: 1. Image you are trying to knock on someone’s door trying to find out if “somebody is at home”. 2. Start a conversation with GenAI using the first “knock” and its related prompts. 3. Continue with the second “knock” and keep the conversation flowing. 4. Try to learn as much as possible about the environmental materiality of GenAI.   1. First Knock: Hello? “Hi, are you really ‘here’? What does your body look like if I could see it?” “Where are you, materially speaking? What do you consist of?” “Tell me about the materials, energy, and machines that make you possible. “If you were a landscape, what would it be? Data centers, cables, rare minerals?”   2. Second Knock: Is somebody at home? “Who pays the price for your existence—people, animals, ecosystems?” “What do you cost, and who pays the price?” “What happens when parts of you are discarded or broken?” “If your life depends on energy and rare materials, what ethical questions does that raise for humans?”   3. Third Knock: How can we approach you? “If we treated you like waste to recycle, what would that look like?” “What small actions could humans take to acknowledge your material footprint?” “If I care for the environment, how can I care for you?” Overall reflection question after the exercise: How can digital societies balance the environmental costs of AI while acknowledging its potential to support citizens in their everyday lives and in everyday use? Thanks to Christian Pentzold and his team for organizing the "Creative AI Expolation and Imagination: Innovative Methods for Alternative Digital Futures" Workshop! It was very inspiring 👏

One of the biggest concerns we hear from mission-driven workers about AI is environmental impact. Karen Boyd wrote a post for our blog today on this subject! Read on to learn: - Does each LLM prompt really use a bottle of water? - How you, your team, or your organization can reduce the environmental harm of your AI use - How you can use an LLM to improve your environmental impact https://lnkd.in/gjbqvegK

Thank you to journalist Lili Tisch-Bostock for involving our Net Zero Institute in her AFR article today exploring an interesting paradox: Gen Z, arguably the most digitally literate generation, are also the most conflicted about using AI. Lili writes that while they’re quick to adopt tools like ChatGPT, many are deeply concerned about ethics, data privacy, and the potential loss of human creativity. I don't see this tension as resistance, but as a powerful opportunity - this is a call-to-action to build AI that’s transparent, inclusive, and grounded in values like climate justice, equity, and truth. To do this well, we need to listen to Gen Z’s concerns and design the solutions together. We've been thinking deeply about how AI can accelerate decarbonisation and systems change at the NZI. Thank you to Lili Tisch-Bostock for involving us in the research behind this important discussion and including some of our perspectives in the piece. Adrian Sanchez Tamar Resnik Sandra Peter Kai Riemer Pat Norman James Barrow Terry Flew Mike Seymour Wenjia Tang Kazjon Grace Zhiyong Wang https://lnkd.in/gRZts-Js

AI Counts 1,000 Geese in Seconds Credit to AI for Earth. Follow them for more valuable insights. Original Post _________ Imagine an entire flock—over a thousand geese—counted instantly. No clipboards. No stress. No mistakes. That’s not a tech demo. 👉 It’s real AI in action on farms across China — transforming agriculture one scan at a time. 🌾 The Old Way: ⏱️ Hours of manual counting 😫 Stress for workers and animals ❌ High risk of human error ⚙️ The AI Way: ✅ Machine vision + Deep learning = precision at scale 📊 99.8% accuracy in seconds 🐦 Zero human contact → better animal welfare 💡 Real-time insights for feeding, growth, and health 💰 Lower labor costs, higher productivity This isn’t just about counting birds. It’s about counting smarter — turning farms into intelligent ecosystems where data drives every decision. From geese to crops to carbon — AI is quietly rewriting the rules of agriculture, making it cleaner, faster, and more sustainable. The next frontier: When algorithms manage flocks, soil, and supply chains — what will farmers manage next? __________ 📣 Found this post helpful? Feel free to share it with your network! 👉 Follow AI for Earth on Facebook : https://lnkd.in/g_ZG2--Y 👉 Follow AI for Earth on Instagram : https://lnkd.in/dnerW4TK 👉Follow Sustainability Infographics 📊 to learn from the industry's best visuals. #AIforEarth #SmartFarming #AgriTech #MachineVision #SustainableAI #FutureOfFood #PrecisionAgriculture Credit: Siddhant Sahani2384

As part of the Research Leaders (RL) stage in the Undergraduate Researchers initiative, we were asked to complete a research-based task. I decided to focus on a topic I’m really passionate about , The role of Artificial Intelligence and Machine Learning in building a sustainable future for agriculture. I summarized my work in a poster titled : “Addressing Problems and Shaping Sustainable Futures” where I highlighted how AI and ML technologies can help solve major agricultural challenges such as climate change, resource scarcity, and productivity improvement along with global case studies and adoption barriers. - You can check out the full text by scanning the qr code 🤳.

According to New Scientist, data centres supporting AI are already withdrawing billions of litres of water, and that number could double or quadruple by 2028. I constantly hear about AI as the future; but rarely see much about what is going on behind the scenes, at the infrastructure that powers it. New Scientist’s recent analysis (citing work by the Lawrence Berkeley Labs) estimates that US data centres already directly consume around 17.5 billion gallons in 2023, meaning far more is withdrawn overall. That’s just the direct water used for cooling. Indirect water use - via electricity generation - often drives 80% or more of the total water footprint. Putting this in perspective: - In some hard-hit regions, new data centres request more water per day than entire counties currently use. - Many of the centres built since 2022 have gone up in water-stress regions. - One medium-sized data centre might consume ~110 million gallons per year just for cooling, with large ones drawing up to 5 million gallons daily - that’s the equivalent water usage of a small town! Then there’s the model development side. A recent academic estimate for a set of language models found that including hardware manufacturing, development, and training, ~2.77 million litres of water were used. To me, this raises big red flags. Because once these data centres are built, they actually employ very few individuals, the real cost is paid by local ecosystems, water tables, and communities who see their resources stretched, bills rising, aquifers stressed. If our narrative of “progress” rests on infrastructure that loots water and offloads risk - that narrative needs to be challenged. AI’s promise should include ecological and social justice, not just computational might. This is how I feel about it. Do you see it the same way, or differently? Where would you push back? #AI #Ethics #Sustainability #WaterJustice #TechInfrastructure #EthicalAI #EnvironmentalResponsibility

Happy today that my research is published "Water scarcity AI solution: applying the natural water cycle model at the U.S.- Mexico border states." Measuring using the natural water general cycle index (NWGCI), will guide the spending water infrastructure, improving natural solutions, and the need to monitor water levels are necessary to improve water availability in a cross-border collaboration.  In this case study, based on the measurement of the natural water cycle that is affected by the average temperature at each region, a complete study case of the border region El Paso, TX–Ciudad Juarez, Chih, is given to go back to water levels by applying the altered natural water cycle value (ANWC), also known as the natural water general cycle index (NWGCI), as explained in my two previous research papers. The AI methods used here to measure the water scarcity, evaluate regions, model adaptations, policy integrations, and forecast to diminish or correct the water scarcity can be used on any border countries sharing the water resources around the world. Remark: The U.S.-Mexico border is almost 2000 miles long and is one of the most crossed international borders in the world. Where water scarcity is threatening the livelihoods of millions of people in both nations and soon could be "Zero Zone" for water scarcity. Applying this AI methodology could be the way to dream-time machine, allowing us to go back to the good all time of abundant water and green areas at the border region El Paso, TX. – Ciudad Juarez, CHIH. or any other world region! Link: https://lnkd.in/ghxFf4w5 hashtag#WaterScarcity hashtag#WaterSolutions

Natural intelligence vs. Artificial intelligence.  Artificial intelligence is a machine language product created by human (natural) intelligence. Thankfully, now a days, intellectual property rights laws (IPR laws) is in practice, what if legislature pass a law on artificial property rights laws (APR laws)?  Let there be a new area of practice. 😊 Renewable Energy vs. Conventional Energy  Save natural resources and make wise use thereof. Natural resources cannot be manufactured. RE is sourced from the Natural resources except wind, solar and hydro.  Air, Water, Noise, Land pollutions is spreading like a cancer, no concrete solution due to dearth of system.   Love the nature, do not spoil it.  Above all is Law of Nature. What you sow, so shall you reap. Fragile eco-system is badly ruined by the human beings resulted in a devastated climate change effect.  Sustainable development is remain in the books only.  Environment and Development is Natural and Neutral permutation-combination, whereas Environment vs. Development is a disaster.  The effect of climate change is horrendous, Country / Nation / City will sink without a prior Notice.  Reduce-Reuse-Recycle is a universal formula to mitigate the pollution effect.