Global AI News Aggregator
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LaserWeeder G2 Is Transforming Farming Across 7 Countries
— Ronald van Loon (@Ronald_vanLoon) 7 avril 2026
by @carbon_robotics#AgriTech #TechForGood #Innovation #Tech #Technology pic.twitter.com/wcpugvBsjY
LaserWeeder G2 Is Transforming Farming Across 7 Countries by @carbon_robotics #AgriTech #TechForGood #Innovation #Tech #Technology
→ View original post on X — @ronald_vanloon, 2026-04-07 21:50 UTC
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Algorithmic Greenwashing: Lessons from Building an AI Agent! #BigData #Analytics #AI #MachineLearning #DataScience #IoT #IIoT #Python #RStats #TensorFlow #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode geni.us/Algorithmic-Green
→ View original post on X — @gp_pulipaka, 2026-04-07 06:26 UTC
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China has launched self-driving electric scooters with features like auto-balance.
— Ronald van Loon (@Ronald_vanLoon) 6 avril 2026
by @XueJia24682#SelfDrivingCars #AutonomousVehicles #EV #Automation #Automotive #Transport pic.twitter.com/m4MXrVAEvl
China has launched self-driving electric scooters with features like auto-balance. by @XueJia24682 #SelfDrivingCars #AutonomousVehicles #EV #Automation #Automotive #Transport
→ View original post on X — @ronald_vanloon, 2026-04-06 21:50 UTC
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5/ If neuro-symbolic AI scales beyond robotics into language models and search, we could see a future where powerful AI no longer requires a power plant behind it. The paper drops in May at ICRA Vienna. The efficient AI era might be closer than we think.
→ View original post on X — @kimmonismus, 2026-04-06 20:42 UTC
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On a planning puzzle, the hybrid system hit 95% accuracy vs. 34% for standard models. Training took 34 minutes instead of 36 hours. Energy use dropped to 1% during training. It didn't just use less power, it got dramatically smarter. [Translated from EN to English]
→ View original post on X — @kimmonismus, 2026-04-06 20:42 UTC
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2/ AI is devouring electricity. Data centers already consume over 10% of U.S. power, and demand is set to double by 2030. Meanwhile, these systems still hallucinate, although its getting more and more correct. However: We're burning city-sized energy budgets.
→ View original post on X — @kimmonismus, 2026-04-06 20:42 UTC
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1/ Huge: Researchers just built an AI system that uses 100x less energy than current models – and it's actually more accurate. This could rewrite the rules of the entire AI industry. Lets dig into this 🧵
→ View original post on X — @kimmonismus, 2026-04-06 20:42 UTC
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Jedsy X #Drone Revolutionizes Medical Deliveries with Smart, All-Weather #Logistics
— Ronald van Loon (@Ronald_vanLoon) 6 avril 2026
via @WevolverApp#MedTech #HealthTech #TechForGood #Tech pic.twitter.com/lzfmUqxUcT
Jedsy X #Drone Revolutionizes Medical Deliveries with Smart, All-Weather #Logistics via @WevolverApp #MedTech #HealthTech #TechForGood #Tech
→ View original post on X — @ronald_vanloon, 2026-04-06 18:56 UTC
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WSJ obtained confidential financials from both OpenAI and Anthropic ahead of their expected IPOs later this year. The core tension: revenue is exploding, but training costs are exploding faster. OpenAI projects $121 billion in compute spending by 2028, resulting in $85 billion in losses that year alone, even after nearly doubling revenue. Strip out training costs and both companies are near profitability now; add them back and OpenAI doesn't break even until the 2030s. Anthropic expects to get there sooner. Inference costs still eat over half of revenue at both labs, though that share is declining.
→ View original post on X — @kimmonismus, 2026-04-06 17:18 UTC
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Google's new AI can predict flash floods 24 hours before they strike.
— Rowan Cheung (@rowancheung) 6 avril 2026
How it works:
> Uses Gemini to extract confirmed flood locations and times from global news
> Builds a dataset of past events that never formally existed.
> That dataset feeds a neural network
> The neural… pic.twitter.com/wR0d43Qpsc
Google's new AI can predict flash floods 24 hours before they strike. How it works: > Uses Gemini to extract confirmed flood locations and times from global news > Builds a dataset of past events that never formally existed. > That dataset feeds a neural network > The neural network combines real-time weather forecasts with local terrain, soil absorption rates, and urban density > Can flag at-risk areas with results that match the accuracy of America's National Weather Service > Can do this in countries that have almost no flood monitoring infrastructure at all Flash floods kill more than 5,000 people every year, but most victims never see them coming. A 12-hour warning alone can reduce flash flood damage by 60%. …For billions of people without any early warning system, this could be the difference between life and death. [Translated from EN to English]
→ View original post on X — @rowancheung, 2026-04-06 15:13 UTC