A new photonics chip that generates multicolored laser beams could supercharge data center technology and ease the strain of AI’s surging data demands.
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Mother’s voice seems to boost language development in premature babies
Babies born too soon seem to have stronger connections in one of the major brain areas that supports language processing if they regularly heard their mother read them a story while in intensive care
Disease-associated radial glia-like cells with epigenetically dysregulated interferon response in MS
Li et al. report that Edwardsiella piscicida employs HigA, an anti-toxin protein, to facilitate the diversion of tryptophan metabolism to the kynurenine pathway, rather than the serotonin pathway, by directly activating IDO1 in a T6SS-dependent manner as a cross-kingdom effector. The serotonin-level fluctuation modulates host intestinal histological damage and bacterial infection.
How To Track And Optimize Biomarkers: Blood Test #6 in 2025
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Size doesn’t matter: Just a small number of malicious files can corrupt LLMs of any size
Large language models (LLMs), which power sophisticated AI chatbots, are more vulnerable than previously thought. According to research by Anthropic, the UK AI Security Institute and the Alan Turing Institute, it only takes 250 malicious documents to compromise even the largest models.
The vast majority of data used to train LLMs is scraped from the public internet. While this helps them to build knowledge and generate natural responses, it also puts them at risk from data poisoning attacks. It had been thought that as models grew, the risk was minimized because the percentage of poisoned data had to remain the same. In other words, it would need massive amounts of data to corrupt the largest models. But in this study, which is published on the arXiv preprint server, researchers showed that an attacker only needs a small number of poisoned documents to potentially wreak havoc.
To assess the ease of compromising large AI models, the researchers built several LLMs from scratch, ranging from small systems (600 million parameters) to very large (13 billion parameters). Each model was trained on vast amounts of clean public data, but the team inserted a fixed number of malicious files (100 to 500) into each one.
Capturing a Rainy Day in Islamabad with Unreal Engine 5
Zubair Tanoli explained to us how he created The Faisal Avenue project, focusing on the rain and the lightning that helped him achieve a cinematic view of the daily life in Islamabad.