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Amazon's cloud 'hit by two outages caused by AI tools last year'

The Guardian

A technician works at an Amazon Web Services AI datacentre in New Carlisle, Indiana. A technician works at an Amazon Web Services AI datacentre in New Carlisle, Indiana. Amazon's cloud'hit by two outages caused by AI tools last year' Reported issues at Amazon Web Services raise questions about firm's use of artificial intelligence as it cuts staff Amazon's huge cloud computing arm reportedly experienced at least two outages caused by its own artificial intelligence tools, raising questions about the company's embrace of AI as it lays off human employees. A 13-hour interruption to Amazon Web Services' (AWS) operations in December was caused by an AI agent autonomously choosing to "delete and then recreate" a part of its environment, the Financial Times reported. AWS, which provides vital infrastructure for much of the internet, suffered several outages last year.


Atmospheric pollution caused by space junk could be a huge problem

New Scientist

After a Falcon 9 rocket stage burned up in the atmosphere, vaporised lithium and other metals drifted over Europe. A SpaceX rocket that burned up after re-entering the atmosphere unleashed a plume of vaporised metals over Europe, a type of pollution that is expected to increase as spacecraft and satellites multiply. The upper stage of a Falcon 9, which is designed to splash down in the Pacific Ocean for possible re-use, lost control due to engine failure and fell from orbit over the north Atlantic in February 2025. We're finally solving the puzzle of how clouds will affect our climate People across Europe saw fiery debris streaking through the sky, some of which crashed behind a warehouse in Poland. Seeing the news, Robin Wing at the Leibniz Institute of Atmospheric Physics in Germany and his colleagues turned on their lidar, an instrument for atmospheric sensing.


The People vs. AI

TIME - Tech

One icy morning in February, nearly 200 people gathered in a church in downtown Richmond, Va. Most had awakened before dawn and driven in from across the state. There were Republicans and Democrats from rural farms and D.C. exurbs. They shared one goal: to fight back against AI development in a region with the largest concentration of data centers in the world. "Aren't you tired of being ignored by both parties, and having your quality of life and your environment absolutely destroyed by corporate greed?" state senator Danica Roem said, to a standing ovation. The activists--wearing homemade shirts with slogans like Boondoggle: Data Center in Botetourt County--marched to the state capitol and spent the day testifying to lawmakers about their fears over data centers' impacts on electricity, water, noise pollution, and more. Some lawmakers pledged to help: "You're getting a sh-t deal," state delegate John McAuliff told activists. The phrase captured many people's feelings toward the AI industry as a whole. Not much unites Americans these days.



US president's son Eric Trump invests in drone maker with gov't contracts

Al Jazeera

Why was El Paso airspace shut down? US president's son Eric Trump invests in drone maker with gov't contracts United States President Donald Trump's son Eric is investing in an Israeli drone manufacturer, prompting renewed conflict-of-interest concerns as the Trump family expands its business holdings during its patriarch's second term as president. Eric Trump is investing in a $1.5bn merger between Israeli drone maker Xtend and Florida-based JFB Construction Holdings, a small construction company, in a deal aimed at taking Xtend public this year, JFB said in a news release on Tuesday. Drone maker Unusual Machines, which tapped Eric's brother Donald Trump Jr in November 2024 as an adviser, is also investing in the merger, JFB said. JFB builds commercial and residential properties, including multifamily communities and shopping centres.





ParaFuzz: An Interpretability-Driven Technique for Detecting Poisoned Samples in NLP

Neural Information Processing Systems

In this work, we propose an innovative test-time poisoned sample detection framework that hinges on the in-terpretability of model predictions, grounded in the semantic meaning of inputs. We contend that triggers (e.g., infrequent words) are not supposed to fundamentally alter the underlying semantic meanings of poisoned samples as they want to