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With the Global Engagement Center shut down, the State Department is now set to investigate whether past US efforts against foreign propaganda amounted to censorship of Americans.
A team of Lehigh University researchers has successfully predicted abnormal grain growth in simulated polycrystalline materials for the first time—a development that could lead to the creation of stronger, more reliable materials for high-stress environments, such as combustion engines. A paper describing their novel machine learning method was recently published in Nature Computational Materials.
Not all AI scaling strategies are equal. Longer reasoning chains are not sign of higher intelligence. More compute isn’t always the answer.Read More
A lawsuit over the Trump administration’s infamous Houthi Signal group chat has revealed what steps departments took to preserve the messages—and how little they actually saved.
A team has developed an explainable AI model for automatic collision avoidance between ships.
Penn Engineers have developed the first programmable chip that can train nonlinear neural networks using light—a breakthrough that could dramatically speed up AI training, reduce energy use and even pave the way for fully light-powered computers.
Amex GBT CISO David Levin is accelerating AI security, cutting false positives and speeding SOC response to anticipate and shut down threats.Read More
The abrupt firing of Xiaofeng Wang and his wife from Indiana University last month shocked the academic community and is stoking fears that Chinese-born scholars are being targeted.
A groundbreaking open-source computer program uses artificial intelligence to analyze videos of patients with Parkinson’s disease and other movement disorders. The tool, called VisionMD, helps doctors more accurately monitor subtle motor changes, improving patient care and advancing clinical research.
A small team of AI researchers from Carnegie Mellon University, Stanford University, Harvard University and Princeton University, all in the U.S., has found that if large language models are over-trained, it might make them harder to fine-tune. In their paper posted on the arXiv preprint server, the group compared the impact of different amounts of …
Read more “Over-training large language models may make them harder to fine-tune”