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01Discovery · Google DeepMind
In what can be called a major breakthrough for synthetic biology and AI-powered biosecurity, Google DeepMind, Google’s AI lab, has developed a new method to watermark AI-designed proteins, establishing a permanent proof of origin directly inside biological code. Published in the journal Nature, the proof-of-concept technology, which is named SynthID Bio, solves a long-standing technical challenge: embedding an non-noticeable, verifiable digital identifier into biological sequences and predicted molecular structures without degrading their physical real-world utility in laboratory tests. The rollout comes amid rapid advancements in biological machine learning, where systems like AlphaFold 3, AlphaProteo and ProteinMPNN allow scientists to predict complex folding patterns and engineer functional binders. Yet these generative capabilities have heightened global biosecurity risks. Unfamiliar, machine-generated sequences can slip past conventional gene-synthesis filters, while unverified synthetic structural data risks contaminating open-access scientific repositories. ‘An urgent challenge for the AI era’ Google DeepMind Chief AI Scientist Demis Hassabis emphasised that guarding against biological misuse must be treated as a frontline safety priority for frontier AI: “Biosecurity is one of the most urgent challenges for the AI era. Bringing SynthID to biology so AI-generated proteins can be watermarked is a critical step - and we’re open sourcing SynthID Bio tools so the research community can build on this work. Published in @Nature today, congrats to the team!” Pushmeet Kohli, Vice President of Science at Google DeepMind and Chief Scientist at Google Cloud, celebrated the practical synthesis of these molecules: “Very happy to announce that our team at Google DeepMind has pushed the boundaries of generative biology, achieving the successful synthesis of AI-designed proteins that are both functional and watermarked. This proof-of-concept watermarking of the building blocks of life is enabled by SynthID Bio, our new protein watermarking method. It is designed to safeguard the new era of AI-powered generative biology and strengthen global biosecurity.” How SynthID Bio operates SynthID Bio functions as a modular toolkit tailored across two primary dimensions of structural biology: SynthIDBio-sequence that subtly steers the selection of amino acid building blocks du…
Earth Species Project · Blog
Image 1. A matriarch leads her family across the dry lake bed in Amboseli, Kenya. Such a movement is typically preceded by single and chorused Let’s go and Cadenced rumbles as individuals initiate and negotiate a plan of action. Image Credit: ElephantVoices . Key Takeaways Around 60% of elephant rumbles occur in overlapping choruses , yet these multi-caller exchanges have been far less studied than isolated calls. In a new study published in Scientific Reports and authored in collaboration with ElephantVoices , we analyzed elephant choruses as they naturally occur, without needing to separate each call . We could predict broad behavioral context from the choruses much better than chance. We also identified vocal convergence during some coordinated interactions, with some rumbles becoming progressively more acoustically similar as an exchange unfolded. Elephants face habitat loss and human-wildlife conflict across their range. Understanding how elephants communicate, make decisions, and coordinate their movements could help protect key habitats and corridors , ease human-elephant conflict , and support people and elephants living alongside one another. The Challenge of Studying Elephant Choruses In many social contexts, including when elephants greet each other after time apart, several animals may rumble at once. Some of those low-frequency, overlapping sounds even extend into the infrasonic range below human hearing. Roughly 60% of elephant rumbles occur inside choruses of two or more voices. Yet these exchanges have received far less scientific attention than isolated calls because they are difficult to analyze. That means a large part of elephants’ natural communication has not been studied. Instead of trying to pull each individual voice apart, we wanted to know whether we could predict behavior from choruses as a whole. In a new study published in Scientific Reports , working with ElephantVoices and Amboseli Trust for Elephants , we used machine learning to extract behavioral information directly from complex, overlapping elephant choruses. Listening to the Whole Chorus We analyzed 903 rumbles from adult females recorded between 1986 and 2022. The recordings come from one of the most closely studied elephant populations in the world. Since the 1970s, researchers from the Amboseli Elephant Research Project have followed these families across generations…
Nature Portfolio · Scientific Data
Abstract Robotic processing of shoe uppers requires accurate segmentation of thin processing boundaries in 3D point clouds. However, acquiring and annotating large collections of real-world scans is costly. We present a multimodal synthetic 3D point cloud dataset of shoe uppers for robotic processing boundary segmentation. The dataset contains 1,067 samples, including 1,000 synthetic point clouds and 67 real-world test scans for independent evaluation. The synthetic data generation pipeline includes texture and appearance augmentation, region-aware geometric deformation, and sensor degradation simulation. Both branches provide three-dimensional coordinates, RGB attributes, and pointwise binary processing boundary labels. The synthetic branch additionally includes panel identifiers, while the real-world labels were manually refined. Technical validation with standard point cloud segmentation backbones assesses dataset usability under a controlled synthetic-to-real evaluation protocol. The released point clouds form a complete, directly usable dataset. The accompanying code and assets support regeneration and adaptation of synthetic subsets.
Discovery · Google DeepMind
Introducing SynthID Bio: Google DeepMind Puts Watermarks Inside AI-Designed Proteins Google DeepMind is introducing SynthID Bio after laboratory tests produced the first watermarked protein binders that retained their intended biological function. The September 30 announcement moves AI watermarking beyond images, text, audio, and video. Its signature can remain detectable in a synthesized physical protein, not merely in a digital design file. That makes the work more consequential than another content-labeling experiment. The central conflict is provenance versus control. SynthID Bio can identify outputs from participating models, but it cannot establish that every unmarked protein is natural or safe. Researchers also found that determined users can remove its sequence watermark by redesigning the protein. The result is a credible proof of concept with a demanding path to deployment. It pressures model developers, biological databases, and DNA synthesis providers to decide whether shared provenance standards belong in the infrastructure of AI-assisted biology. Introducing SynthID Bio as a Physical Provenance Test Google DeepMind has shown that an AI-generated protein can carry a detectable signature without losing the function researchers designed it to perform. SynthID Bio is a family of methods for watermarking protein sequences and predicted biomolecular structures. A watermark is a hidden statistical signal that authorized software can detect later. It indicates an output came from a compatible AI system. The project has two distinct components. SynthIDBio-sequence influences which amino acids a sequence-generation model selects. SynthIDBio-structure introduces small, detectable patterns into the atomic coordinates predicted by a folding model. That distinction matters because a protein sequence and a predicted structure represent different things. The sequence specifies an ordered chain of amino acids. The structure describes how atoms in that chain are arranged in three dimensions. For the sequence test, researchers combined AlphaProteo-designed backbones with a modified version of ProteinMPNN. ProteinMPNN generates amino acid sequences expected to fold into a supplied protein structure. The team evaluated binders against three targets: VEGF-A, PD-L1, and the receptor-binding domain of the SARS-CoV-2 spike protein. Protein binders are molecules design…
Hugging Face Daily Papers
What makes great scientists great? Even as AI systems start to make progress on open problems, scientists remain far ahead of them at sensing which prior idea, buried in an ever-growing archive of research, a new problem needs. To study this skill, we draw on researchers who know firsthand which earlier work advanced their completed projects, with papers serving as pointers to the ideas within. Using our automated pipeline that makes author annotation scalable, we build ScholarCatalyst by having 184 lead authors of 207 recent computer science papers label which candidates did or could have advanced their project, each with a detailed rationale. We introduce a retrieval task with author-provided judgments: given an initial research question, retrieve these papers from only the literature available when the project began. Agentic search does no better than embedding retrieval (0.42 vs. 0.48 Recall@20) despite calling that same retriever as a tool. Even an agent built on Claude Fable 5.1, which may have seen the completed papers during training, reaches only 0.51 R@20. These results highlight the need for new training recipes that equip models with expert intuition for searching broad corpora. We envision ScholarCatalyst as a step toward scientific agents that can take a half-formed idea and point to the prior research it needs.
