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AI Accelerates Science

Google DeepMind's AlphaFold solved a 50-year problem in physics. It predicted the shape of proteins using a large database of known shapes. This breakthrough used artificial intelligence and a lot of data. AlphaFold's success matters because it showed AI can make big discoveries. It used 170,000 known protein structures to make predictions. This database was built over 53 years and cost $21 billion. AI will change science but not as quickly as expected. New discoveries will come from AI agents. AI will keep changing science in the years to come.

AI Detects Lies Using Its Geometry

Researchers have developed a new method for detecting misinformation using the hidden patterns in AI's own thought process. Instead of checking surface-level words or searching for external evidence, this technique looks at how language models represent truth and lies within their internal systems. By analyzing the way these models process information, scientists can pinpoint a "falsehood direction" that helps identify misleading statements. This approach doesn't require fine-tuning the AI or accessing outside data-it just uses what's already inside the model. The method was tested on 11 different AI models, from small to large, and proved effective in spotting lies across three fact-checking tests. It especially helped smaller models perform better, which could be a game-changer for systems with limited resources. While it works well on some datasets, it struggles with those that rely heavily on evidence-based labels. Still, this breakthrough shows that truthfulness can be recognized as a clear pattern in the way AI thinks, offering a new tool to fight misinformation without relying on external data. Looking ahead, researchers hope this approach will lead to more reliable ways to detect lies online, especially for smaller or less powerful AI systems.

AI Breakthrough in Predicting Crystal Structures from Sparse Data

Scientists have developed a new machine learning framework called ED-CSP that can predict crystal structures using chemical composition, atom count, and sparse electron diffraction (ED) data. Unlike previous methods that rely on indexed reflections or predefined structure libraries, ED-CSP uses multi-view aggregation and a relational set encoder to generate accurate lattice parameters and atomic coordinates. The team trained the model using ED-CS, a dataset of 4.85 million simulated crystal structures. When tested on 2,075 materials from CHILI-100K, the framework achieved a structural match rate of 57.49% at the top five candidates, surpassing existing models like PXRDGen (52.92%). Scaling up the training data further improved performance to 66.27%. Importantly, the model demonstrated generative capability by achieving 53.52% accuracy on materials not seen during training. This advancement marks a significant step forward in crystallography, enabling researchers to predict structures from limited diffraction data with high accuracy. Future work could extend this approach to experimental ED data, potentially revolutionizing materials science and drug discovery.

AI Model Creates 16 New Viruses

Scientists trained an AI model to recognize patterns of DNA structure in nature and re-write them to create new viral genomes. The AI model invented 16 new bacteria-infecting viruses, which were then synthesized in a laboratory and successfully infected E. coli. This matters because it could lead to breakthroughs in treating antibiotic-resistant superbugs by allowing scientists to generate tailor-made therapies. The new viruses possessed sequence patterns distinct from anything found in nature, and the study's authors excluded human pathogen datasets from their training models, meaning the viruses it created aren't capable of infecting people. The ability to custom-design new viruses will likely continue to advance in the future.

AI Benchmarks Reach Plateau

Researchers found that nearly half of 60 language model benchmarks show saturation. This means that benchmarks are no longer useful for measuring model progress. The study looked at 14 properties related to saturation and found that expert-curation can help extend benchmark longevity. The rate of saturation increases with age, with older benchmarks more likely to be saturated. The study analyzed 60 language model benchmarks and found that saturation rates are high. This matters because it affects how we measure progress in artificial intelligence. Next year will see new approaches to benchmark design.