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Stripping polite corporate filters from leading artificial intelligence models makes their predictions far more fatalistic. Surprisingly, humans strongly agree with these dark visions once they read them.
A new machine learning model predicts whether an electrical fault will spark a fire and calculates the exact split-second of ignition down to milliseconds.
Using local large language models, researchers harmonised messy details across 4.2 million biological records and linked 99% of the extracted facts straight back to their sources.
A robotic system uses mechanical kneading on a semi-solid paste to run chemical reactions with minimal solvent, skipping messy liquid clean-ups.
Researchers trained artificial intelligence models on roughly 190,000 images, achieving over 99% accuracy when identifying roasted coffee varieties.
A newly designed system pairs a specialised language model with reinforcement learning to spot complex website security flaws that regular scanners miss. By practising over time, the tool learned to link subtle weaknesses together like moves on a chessboard.
Researchers found that a compact model called MobileNetV2 reliably handles dog breed identification entirely offline on a smartphone in under a tenth of a second.
A new deep learning model predicts airplane hard landings from flight recorder data while clearly showing which moments and instruments triggered the alert.
Scientists have shown that patterns inside an engineered magnetic material can behave like distinct, interacting life-like creatures. The team proved these magnetic creatures can coexist in physical hardware without needing biological cells or computer code.
A microscopic siloxane coating protects sulfide-based solid battery electrolytes from moisture, halting toxic gas release while allowing lithium ions to pass.