AI-generated Slopoly malware used by Hive0163 in 2026 attacks maintained access for over a week, highlighting how AI accelerates malware development.
Category: robotics/AI – Page 2
AI-generated Slopoly malware used in Interlock ransomware attack
A new malware strain dubbed Slopoly, likely created using generative AI tools, allowed a threat actor to remain on a compromised server for more than a week and steal data in an Interlock ransomware attack.
The breach started with a ClickFix ruse, and in later stages of the attack, the hackers deployed the Slopoly backdoor as a PowerShell script acting as a client for the command-and-control (C2) framework.
IBM X-Force researchers analyzed the script and found strong indicators that it was created using a large language model (LLM), but could not determine which one.
Rates of Unbalanced Chromosome Rearrangements Associated with Pericentric and Paracentric Inversions: Analysis of Molecular Chromosome Results in Embryo Samples
PGT-SR reveals that even small pericentric and paracentric inversions carry a small but measurable reproductive risk, challenging assumptions of minimal impact in IVF outcomes.
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AI is homogenizing human expression and thought, computer scientists and psychologists say
AI chatbots are standardizing how people speak, write, and think. If this homogenization continues unchecked, it risks reducing humanity’s collective wisdom and ability to adapt, computer scientists and psychologists argue in an opinion paper published in Trends in Cognitive Sciences.
They say that AI developers should incorporate more real-world diversity into large language model (LLM) training sets, not only to help preserve human cognitive diversity, but also to improve chatbots’ reasoning abilities.
What Happens When AI Runs the Entire Economy?
What happens when AI controls prices, jobs, markets, and growth itself? Explore the future of an economy run by machines—and what it means for work, power, and humanity.
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Chapters 0:00 Intro — The Invisible Hand Becomes a Neural Network 2:58 What Does “Running the Economy” Actually Mean? 5:30 What Does “Running the Economy” Actually Mean? 10:19 Labor in an AI-Run Economy 14:41 Who Programs the Economy’s Values? 17:01 Government, Power, and Economic Sovereignty 20:21 So, Can Humans Stay in the Loop? 22:56 The Best-Case and Worst-Case Futures 24:53 Abolish Everything 25:57 The Last Economic Decisions We Ever Make.
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Human brain and AI speech recognition decode speech in similar step-by-step stages, study finds
Over the past decades, computer scientists have developed numerous artificial intelligence (AI) systems that can process human speech in different languages. The extent to which these models replicate the brain processes via which humans understand spoken language, however, has not yet been clearly determined.
Researchers at Columbia University, IBM Research and the Feinstein Institutes for Medical Research recently carried out a study aimed at comparing how automatic speech recognition (ASR) systems and the human brain decode speech. Their findings, published in Nature Machine Intelligence, suggest that activity in specific brain regions while people make sense of spoken language corresponds to specific stages in the processing of speech by AI models.
“The core mystery we wanted to solve is how the human brain performs the incredible computational feat of turning raw acoustic vibrations, the sounds of speech, into discrete linguistic meaning,” Nima Mesgarani, senior author of the paper, told Tech Xplore. “We now have AI systems that match human performance in transcribing speech, but we didn’t know if they were reaching those solutions independently or if they had converged on the same strategy as our biology.”
Brain-inspired device could lead to faster, more energy-efficient AI hardware
A team led by engineers at the University of California San Diego has developed a new brain-inspired hardware platform that could help computer hardware keep pace with the explosive growth of artificial intelligence. By combining memory and computation on the same chip—and allowing its components to interact collectively like neurons in the brain—the brain-inspired platform improved the speed, accuracy, and energy efficiency of pattern recognition in two simulated tasks: recognizing spoken digits and detecting epileptic seizures early from brain-wave recordings.
The approach could lead to the development of compact, energy-efficient hardware for smaller AI systems such as those used in wearable health monitors, smart sensors, and other autonomous devices.
The work, published on March 9 in Nature Nanotechnology, falls within the field of neuromorphic computing, which aims to build machines that mimic how the brain processes information. The researchers emphasize that the technology is brain-inspired, rather than brain-like; it draws ideas from how neural networks interact but does not attempt to replicate the brain itself.
