google·Sep 10, 2025Original article

Smarter nucleic acid design with NucleoBench and AdaBeam (opens in new tab)

Google Research and Move37 Labs have introduced NucleoBench, a comprehensive open-source benchmark for nucleic acid design, alongside AdaBeam, a high-performing new optimization algorithm. While AI models have become highly proficient at predicting the biological properties of DNA and RNA, generating optimal sequences within massive search spaces—such as the $2 \times 10^{120}$ possible variations for a 5' UTR—remains a significant hurdle. By standardizing evaluation across 16 distinct biological tasks, this research identifies AdaBeam as a superior method that scales effectively to the large-scale models required for modern drug discovery. ## Standardizing the Optimization Pipeline The process of computational nucleic acid design typically follows a five-step workflow: data collection, training a predictive model, generating candidate sequences (the design step), wet-lab validation, and iterative retraining. NucleoBench focuses specifically on the design step, which has historically lacked standardized evaluation. * Most existing benchmarks rely on decades-old methods like simulated annealing or vanilla genetic algorithms. * Traditional algorithms often treat predictive models as "black boxes," failing to leverage internal model data to guide the search. * The vastness of genomic search spaces makes brute-force optimization impossible, necessitating more intelligent, model-aware generation strategies. ## The NucleoBench Framework NucleoBench is the first large-scale benchmark designed to compare gradient-free and gradient-based design algorithms under identical conditions. The framework encompasses over 400,000 experiments to ensure statistical rigor across diverse biological challenges. * **Algorithm Categories**: It compares gradient-free methods (like directed evolution), which are simple but ignore model internals, against gradient-based methods (like FastSeqProp), which use the model’s internal "direction of steepest improvement" to find better sequences. * **Task Diversity**: The 16 tasks include controlling gene expression in specific cell types (liver or neuronal), maximizing transcription factor binding, and improving chromatin accessibility. * **Scale**: The benchmark includes long-range DNA sequence challenges using large-scale models like Enformer, which are computationally demanding but critical for understanding complex genomic interactions. ## AdaBeam’s Hybrid Optimization Performance Drawing on insights from the NucleoBench evaluation, the researchers developed AdaBeam, a hybrid algorithm that combines the strengths of various optimization strategies. * **Success Rate**: AdaBeam outperformed existing algorithms on 11 of the 16 tasks in the benchmark. * **Efficiency and Scaling**: Unlike many gradient-based methods that struggle with computational overhead, AdaBeam demonstrates superior scaling properties as sequences become longer and predictive models grow in complexity. * **Methodology**: It functions as a hybrid approach, using sophisticated search techniques to navigate the sequence space more effectively than "vanilla" algorithms developed before the era of deep learning. The researchers have made AdaBeam and the NucleoBench repository freely available to the scientific community. By providing a standardized environment for testing, they aim to accelerate the development of next-generation treatments, including more stable mRNA vaccines and precise CRISPR gene therapies.

Machine LearningAIDeep LearningBioinformatics+5

line·Aug 29, 2025Original article

Extracting Trending Keywords from OpenChat (opens in new tab)

To enhance user engagement on the LINE OpenChat main screen, LY Corporation developed a system to extract and surface "trending keywords" from real-time message data. By shifting focus from chat room recommendations to content-driven keyword clusters, the team addresses the lack of context in individual messages while providing a more dynamic discovery experience. This approach utilizes a combination of statistical Z-tests to identify frequency spikes and MinHash clustering to eliminate near-duplicate content, ensuring that the trending topics are both relevant and diverse. **The Shift from Chat Rooms to Content-Driven Recommendations** * Traditional recommendations focus on entire chat rooms, which often require significant user effort to investigate and evaluate. * Inspired by micro-blogging services, the team aimed to surface messages as individual content pieces to increase the "main screen visit" KPI. * Because individual chat messages are often fragmented or full of typos, the system groups them by keywords to create meaningful thematic content. **Statistical Detection of Trending Keywords** * Simple frequency counts are ineffective because they capture common social fillers like greetings or expressions of gratitude rather than actual trends. * Trends are defined as keywords showing a sharp increase in frequency compared to a baseline from seven days prior. * The system uses a Z-test for two-sample proportions to assign a score to each word, filtering for terms with at least a 30% frequency growth. * A seven-day comparison window is specifically used to suppress weekly cyclical noise (e.g., mentions of "weekend") and to capture topics whose popularity peaks over several consecutive days. **MinHash-based Message Deduplication** * Redundant messages, such as copy-pasted text, are removed prior to frequency aggregation to prevent skewed results and repetitive user experiences. * The system employs MinHash, a dimensionality reduction technique, to identify near-duplicate messages based on Jaccard similarity. * The process involves "shingling" messages into sets of tokens (primarily nouns) and generating $k$-length signatures; messages with identical signatures are clustered together. * To evaluate the efficiency of these clusters without high computational costs, the team developed a "SetDiv" (Set Diversity) metric that operates in linear time complexity. By combining Z-test statistical modeling with MinHash deduplication, this methodology successfully transforms fragmented chat data into a structured discovery layer. For developers working with high-volume social data, using a rolling weekly baseline and signature-based clustering offers a scalable way to surface high-velocity trends while filtering out both routine social noise and repetitive content.

Machine LearningAINatural Language ProcessingStatistical Analysis+5

google·Aug 19, 2025Original article

Securing private data at scale with differentially private partition selection (opens in new tab)

Google Research has introduced a novel parallel algorithm called MaxAdaptiveDegree (MAD) to enhance differentially private (DP) partition selection, a critical process for identifying common data items in massive datasets without compromising individual privacy. By utilizing an adaptive weighting mechanism, the algorithm optimizes the utility-privacy trade-off, allowing researchers to safely release significantly more data than previous non-adaptive methods. This breakthrough enables privacy-preserving analysis on datasets containing hundreds of billions of items, scaling up to three orders of magnitude larger than existing sequential approaches. ## The Role of DP Partition Selection * DP partition selection identifies a meaningful subset of unique items from large collections based on their frequency across multiple users. * The process ensures that no single individual's data can be identified in the final list by adding controlled noise and filtering out items that are not sufficiently common. * This technique is a foundational step for various machine learning tasks, including extracting n-gram vocabularies for language models, analyzing private data streams, and increasing efficiency in private model fine-tuning. ## The Weight, Noise, and Filter Paradigm * The standard approach to private partition selection begins by computing a "weight" for each item, typically representing its frequency, while ensuring "low sensitivity" so no single user has an outsized impact. * Random Gaussian noise is added to these weights to obfuscate exact counts, preventing attackers from inferring the presence of specific individuals. * A threshold determined by DP parameters is then applied; only items whose noisy weights exceed this threshold are included in the final output. ## Improving Utility via Adaptive Weighting * Traditional non-adaptive methods often result in "wastage," where highly popular items receive significantly more weight than necessary to cross the selection threshold. * The MaxAdaptiveDegree (MAD) algorithm introduces adaptivity by identifying items with excess weight and rerouting that weight to "under-allocated" items sitting just below the threshold. * This strategic reallocation allows a larger number of less-frequent items to be safely released, significantly increasing the utility of the dataset without compromising privacy or computational efficiency. ## Scalability and Parallelization * Unlike sequential algorithms that process data one piece at a time, MAD is designed as a parallel algorithm to handle the scale of modern user-based datasets. * The algorithm can process datasets with hundreds of billions of items by breaking the problem down into smaller parts computed simultaneously across multiple processors. * Google has open-sourced the implementation on GitHub to provide the research community with a tool that maintains robust privacy guarantees even at a massive scale. Researchers and data scientists working with large-scale sensitive datasets should consider implementing the MaxAdaptiveDegree algorithm to maximize the amount of shareable data while strictly adhering to user-level differential privacy standards.

Machine LearningAIDifferential PrivacyData Privacy+4

google·Aug 6, 2025Original article

Achieving 10,000x training data reduction with high-fidelity labels (opens in new tab)

Google Ads researchers have developed a scalable active learning curation process that reduces the volume of training data required for fine-tuning LLMs by up to four orders of magnitude. By iteratively identifying the most informative and diverse examples through clustering and expert review, the method achieves significantly higher human-model alignment than traditional large-scale crowdsourced datasets. This approach effectively addresses the high costs and complexities of classifying ambiguous content, such as unsafe ads, where high-fidelity data is scarce and concept drift is frequent. ### The Iterative Curation Process * **Initial Labeling:** The process begins with a zero- or few-shot model (LLM-0) that generates a large, typically imbalanced dataset of "positive" and "benign" labels. * **Clustering and Confusion Identification:** Separate clusters are created for each label set; overlapping clusters indicate areas where the model is confused. * **Expert Sampling:** Human experts review pairs of examples located near the decision boundary of these overlapping clusters, prioritizing those that cover a larger area of the search space to ensure diversity. * **Recursive Refinement:** Expert labels are split into fine-tuning and evaluation sets; the model is retrained and the process repeats until model-human alignment plateaus or matches internal expert agreement. ### Measuring Alignment via Cohen’s Kappa * **Metric Selection:** Because ad safety is often subjective, the researchers use Cohen’s Kappa instead of precision and recall to measure how well two independent annotators align beyond chance. * **Performance Benchmarks:** A Kappa value above 0.8 is considered exceptional, while 0.4 is the minimum for acceptability. * **Goal Alignment:** The curation process aims to move model performance toward the "ceiling" of internal human agreement (which measured between 0.78 and 0.81 in these experiments). ### Experimental Results and Efficiency * **Model Scaling:** Experiments involved fine-tuning Gemini Nano-1 (1.8B parameters) and Nano-2 (3.25B parameters) on tasks of varying complexity. * **Drastic Data Reduction:** The curated method reached performance plateaus using fewer than 500 expert-labeled examples, compared to a baseline of 100,000 crowdsourced labels. * **Quality Gains:** Despite using 10,000x less data, the curated models saw up to a 65% improvement in alignment with human experts over the crowdsourced baselines. * **Class Balancing:** The process naturally corrected for production imbalances, moving from <1% positive examples in raw traffic to ~40% in the final curated sets. This curation method is a highly effective strategy for organizations managing high-stakes classification tasks where "ground truth" is subjective or data curation is prohibitively expensive. By shifting focus from data quantity to the quality and diversity of examples at the decision boundary, developers can maintain high-performing models that adapt quickly to evolving safety policies.

Machine LearningAILarge Language ModelsGemini+4

google·Aug 5, 2025Original article

Insulin resistance prediction from wearables and routine blood biomarkers (opens in new tab)

Researchers at Google have developed a novel machine learning approach to predict insulin resistance (IR) by integrating wearable device data with routine blood biomarkers. This method aims to provide a scalable, less invasive alternative to traditional "gold standard" tests like the euglycemic insulin clamp or specialized HOMA-IR assessments. The study demonstrates that combining digital biomarkers with common laboratory results can effectively identify individuals at risk for type 2 diabetes, particularly within high-risk populations. ## Barriers to Early Diabetes Screening * Insulin resistance is a primary precursor to approximately 70% of type 2 diabetes cases, yet it often remains undetected until the disease has progressed. * Current diagnostic standards are frequently omitted from routine check-ups due to high costs, invasiveness, and the requirement for specific insulin blood tests that are not standard practice. * Early detection is vital because insulin resistance is often reversible through lifestyle modifications, making accessible screening tools a high priority for preventative medicine. ## The WEAR-ME Multimodal Dataset * The research utilized the "WEAR-ME" study, which collected data from 1,165 remote participants across the U.S. via the Google Health Studies app. * Digital biomarkers were gathered from Fitbit and Google Pixel Watch devices, tracking metrics such as resting heart rate, step counts, and sleep patterns. * Clinical data was provided through a partnership with Quest Diagnostics, focusing on routine blood biomarkers like fasting glucose and lipid panels, supplemented by participant surveys on diet, fitness, and demographics. ## Predictive Modeling and Performance * Deep neural network models were trained to estimate HOMA-IR scores by analyzing different combinations of the collected data streams. * While models using only wearables and demographics achieved an area under the receiver operating characteristic curve (auROC) of 0.70, adding fasting glucose data boosted the auROC to 0.78. * The most comprehensive models, which combined wearables, demographics, and full routine blood panels, achieved the highest accuracy across the study population. * Performance was notably strong in high-risk sub-groups, specifically individuals with obesity or sedentary lifestyles. ## AI-Driven Interpretation and Literacy * To assist with data translation, the researchers developed a prototype "Insulin Resistance Literacy and Understanding Agent" built on the Gemini family of large language models. * The agent is designed to help users interpret their IR risk predictions and provide personalized, research-backed educational content. * This AI integration aims to facilitate better communication between the data results and actionable health strategies, though it is currently intended for informational and research purposes. By utilizing ubiquitous wearable technology and existing clinical infrastructure, this approach offers a path toward proactive metabolic health monitoring. Integrating these models into consumer or clinical platforms could lower the barrier to early diabetes intervention and enable more personalized preventative care.

Machine LearningAILarge Language ModelsGemini+4

google·Jul 16, 2025Original article

Measuring heart rate with consumer ultra-wideband radar (opens in new tab)

Google Research has demonstrated that ultra-wideband (UWB) radar technology, which is already integrated into many modern smartphones for tasks like precise location and vehicle unlocking, can be repurposed for contactless heart rate monitoring. By employing a transfer learning approach, researchers successfully applied models trained on large datasets from Frequency Modulated Continuous Wave (FMCW) radar to the newer UWB systems. This development suggests that everyday consumer electronics could soon provide accurate vital sign measurements without the need for additional specialized sensors or physical contact. ## Leveraging Existing Consumer Hardware While Google previously used Soli radar (FMCW) for sleep sensing in the Nest Hub, UWB technology represents a more widely available hardware platform in the mobile market. * UWB is currently used primarily for non-radar applications like digital car keys and item tracking (e.g., Apple AirTags). * The technology is increasingly standard in high-end mobile phones, providing a ready-made infrastructure for health sensing. * Utilizing existing UWB chips eliminates the need for manufacturers to add dedicated medical sensors to devices. ## Overcoming Signal Interference in Vital Sensing The primary challenge in radar-based heart rate monitoring is that the micro-movements of the chest wall caused by a heartbeat are significantly smaller than movements caused by breathing or general body shifts. * The system utilizes three-dimensional spatial resolution to create a "measurement zone" focused specifically on the user's torso. * High temporal resolution, sampling at speeds up to 200Hz, allows the radar to capture the rapid, subtle pulses of a heartbeat. * By isolating reflections from the chest area, the radar can ignore stationary background objects and external movements that would otherwise corrupt the data. ## Cross-Radar Transfer Learning Because the researchers possessed extensive datasets for FMCW radar but very limited data for UWB, they developed a method to transfer learned features between different radar types despite their different physical principles. * FMCW radar transmits continuous sinusoidal waves, whereas UWB radar transmits extremely short pulses (picoseconds to nanoseconds). * The study used a large 980-hour FMCW dataset to "teach" the model the characteristics of human vitals. * This pre-trained knowledge was then applied to a smaller 37.3-hour UWB dataset, proving that heart rate features are consistent enough across hardware types for effective transfer learning. ## A Novel Spatio-Temporal Deep Learning Model The researchers designed a custom neural network architecture to process the complex multidimensional data generated by radar sensors. * The framework uses a 2D ResNet to analyze the input data across two axes: time and spatial measurements. * Following the initial analysis, the model uses average pooling to collapse the spatial dimension, focusing purely on the temporal signal. * A 1D ResNet then identifies long-range periodic patterns to estimate the heart rate. * The model achieved a mean absolute error (MAE) of 0.85 beats per minute (bpm), which is a 50% reduction in error compared to previous state-of-the-art methods. This research indicates that high-precision health monitoring can be integrated into the mobile devices users already carry. By transforming smartphones into passive health sensors, UWB technology could allow for continuous heart rate tracking during routine activities, such as sitting at a desk or holding a phone in one's lap.

Machine LearningAIDeep LearningTransfer Learning+5

google·Jun 29, 2025Original article

How we created HOV-specific ETAs in Google Maps (opens in new tab)

Google Maps has enhanced its routing capabilities by introducing HOV-specific ETAs, addressing the significant speed differences between carpool and general lanes. This was achieved through a novel unsupervised learning approach that classifies historical trips into HOV or non-HOV categories without initial manual labels. The resulting system enables more precise travel predictions, helping users optimize their commutes and supporting the shift toward sustainable travel modes. ### Segment-Level Speed Distribution * The model analyzes trip segments within short, 15-minute time windows to identify patterns in aggregated, anonymized traffic data. * During peak traffic hours, researchers often observe a bimodal speed distribution where HOV lanes maintain significantly higher average speeds compared to general lanes. * The classification system distinguishes between "Scenario A," where the speed gap is dramatic (e.g., 65 mph vs. 25 mph), and "Scenario B," where HOV lanes are only marginally faster, ensuring accurate modeling even when benefits are minimal. * Individual trip points, including speed and observation time, are processed collectively to determine if a specific segment of a journey occurred in a restricted lane. ### Incorporating Lateral Distance and Soft Clustering * To refine accuracy beyond simple speed metrics, the model incorporates the estimated lateral distance of a vehicle from the center of the road. * While GPS data is inherently noisy, this spatial information helps identify lane-specific behaviors by mapping trip points to the known physical location of HOV lanes (e.g., the far-left lanes). * The system employs soft clustering techniques, calculating the probability of a point belonging to a specific cluster rather than using hard binary assignments, which better manages borderline data points. * Temporal clustering via a weighted median approach is used to prioritize more recent traffic observations, ensuring the model accounts for the most current road conditions and availability constraints. By integrating these segment-level classifications into full-trip analyses, Google Maps can train its ETA prediction models on high-fidelity, lane-specific data. This implementation provides users with a more realistic view of their travel options, encouraging the use of high-occupancy lanes to reduce individual travel time, urban congestion, and overall emissions.

Machine LearningAITraffic AnalysisUnsupervised Learning+5

google·Jun 23, 2025Original article

From research to climate resilience (opens in new tab)

Google Research is leveraging advanced artificial intelligence to transform climate science from theoretical exploration into scalable, real-world resilience tools. By developing sophisticated models for floods, cyclones, and hyper-local weather, the initiative provides critical lead times that empower communities to protect lives and livelihoods against increasingly frequent environmental threats. This transition from "impossible" research to global implementation highlights AI's capacity to bridge data gaps in the world's most vulnerable regions. ## AI-Powered Global Flood Forecasting * Google developed a global hydrological AI model, recently published in *Nature*, which enables riverine flood forecasts up to seven days in advance. * The system utilizes "virtual gauges" to analyze historical data and provide predictions in regions where physical water-monitoring infrastructure is non-existent. * The Flood Hub platform now covers over 100 countries and 700 million people, providing an expert data layer and API access for local governments and researchers. ## Cyclone Tracking and Intensity Prediction * Collaborative research between Google DeepMind and Google Research has produced models that predict storm existence, track, intensity, and size up to 15 days in advance. * The AI generates up to 50 different possible scenarios for each storm, providing a more nuanced view of potential impact than traditional physics-based supercomputer simulations. * Through the new Weather Lab website, these experimental models are being shared with the US National Hurricane Center to assist in forecasting during the Atlantic hurricane season. ## Global Nowcasting with MetNet-3 * The MetNet-3 state-of-the-art neural weather model provides hyper-local precipitation forecasts with a 5km resolution, updated every 15 minutes. * By utilizing satellite observations instead of traditional ground-based radar, the system delivers reliable weather data to regions like Africa that lack extensive physical infrastructure. * These 12-hour "nowcasting" windows are integrated directly into Google Search, specifically helping agricultural communities react to changing conditions to improve crop yields and reduce waste. These advancements demonstrate that the "art of the possible" is rapidly expanding, offering a future where data-scarce regions can access the same life-saving predictive capabilities as developed nations through global partnerships and satellite-based modeling.

Machine LearningAINeural NetworksWeather Forecasting+4

google·May 12, 2025Original article

Differential privacy on trust graphs (opens in new tab)

Researchers from Google have introduced Trust Graph Differential Privacy (TGDP), a framework that models privacy based on varying trust relationships between users represented as vertices in a graph. By allowing users to share data with trusted neighbors who then aggregate and privatize the information, TGDP bridges the gap between the highly accurate central DP model and the high-privacy local DP model. This approach enables more practical and accurate data analysis in scenarios where users exhibit nuanced privacy preferences rather than binary trust assumptions. ## Defining Trust Graph DP * The model represents users as vertices and mutual trust as edges, ensuring that a user’s data remains statistically indistinguishable to any party they do not trust. * This guarantee holds even if non-trusted parties pool their data or collaborate with a user's trusted neighbors to attempt re-identification. * TGDP serves as a mathematical interpolation: a "star graph" topology corresponds to the central DP model, while a fully unconnected graph corresponds to the local DP model. ## Private Aggregation and Error Metrics * The research evaluates TGDP through the fundamental task of private aggregation, where the goal is to estimate the sum of all users' private values ($\Sigma x_i$). * Accuracy is quantified using mean-squared error, allowing researchers to establish theoretical upper and lower bounds for algorithm performance. * These bounds demonstrate that the utility of a privacy-preserving algorithm is directly tied to the specific structure of the trust relationships within the network. ## The Dominating Set Algorithm * The proposed algorithm utilizes the concept of a "dominating set"—a subset of users $T$ such that every user in the graph is either in $T$ or adjacent to someone in $T$. * In this mechanism, each user sends their raw data to a trusted neighbor within the dominating set. * The members of the dominating set aggregate the data they receive and add specific statistical noise to satisfy differential privacy before sharing the results. * This method reduces the total noise required compared to the local model, as the number of noise-adding entities is limited to the size of the dominating set rather than the entire population. By leveraging existing trust networks, TGDP provides a rigorous way to optimize the trade-off between privacy and utility. This framework suggests that identifying small dominating sets within a community can significantly improve the accuracy of data analytics and machine learning without requiring a single, universally trusted central curator.

Machine LearningAIDifferential PrivacyGraph Theory+5

google·May 6, 2025Original article

A new light on neural connections (opens in new tab)

Google and the Institute of Science and Technology Austria (ISTA) have developed LICONN, the first light-microscopy-based method capable of comprehensively mapping neurons and their connections in brain tissue. This approach overcomes the traditional reliance on expensive electron microscopy by utilizing physical tissue expansion and advanced machine learning to achieve comparable resolution and accuracy. The researchers successfully validated the technique by reconstructing nearly one million cubic microns of mouse cortex, demonstrating that light microscopy can now achieve "dense" connectomics at scale. ## Overcoming Resolution and Cost Barriers * Connectomics has traditionally relied on electron microscopy (EM) because it offers nanometer-scale resolution, whereas standard light microscopy is limited by the diffraction limit of visible light. * Electron microscopes cost millions of dollars and require specialized training, restricting high-level neuroscience research to wealthy, large-scale institutions. * LICONN provides a more accessible alternative by utilizing standard light microscopy equipment already found in most life science laboratories. ## Advanced Tissue Expansion and Labeling * The project uses a specialized expansion microscopy protocol where brain tissue is embedded in hydrogels that absorb water and physically swell. * The technique employs three different hydrogels to create interweaving polymer networks that expand the tissue by 16 times in each dimension while preserving structural integrity. * A whole-protein labeling process is used to provide the necessary image contrast, allowing for the tracing of densely packed neurites and the detection of synapses. ## Automated Reconstruction and Validation * Google applied its established suite of machine learning and image analysis tools to automate the reconstruction of the expanded tissue samples. * The team verified the accuracy of the method by tracing approximately 0.5 meters of neurites within mouse hippocampus tissue, confirming results comparable to electron microscopy. * In a large-scale validation, the researchers provided an automated reconstruction of a volume of mouse cortex totaling nearly one million cubic microns. ## Integration of Molecular and Structural Data * One of LICONN’s primary advantages over electron microscopy is its ability to capture multiple light wavelengths simultaneously. * Researchers can use fluorescent markers to visualize specific proteins, neurotransmitters, and other molecules within the structural map. * This dual-layered approach allows scientists to align molecular information with physical neuronal pathways, offering new insights into how brain circuits drive behavior and cognition. LICONN represents a significant shift in neuroscience by democratizing high-resolution brain mapping. By replacing expensive hardware requirements with sophisticated chemical protocols and machine learning, this method enables a wider range of laboratories to contribute to the global effort of mapping the brain’s intricate wiring.

Machine LearningAIConnectomicsNeuroscience+3

google·Apr 23, 2025Original article

Improving brain models with ZAPBench (opens in new tab)

Google Research, in collaboration with HHMI Janelia and Harvard, has introduced ZAPBench, a first-of-its-kind whole-brain activity dataset and benchmark designed to improve the accuracy of brain activity models. Using the larval zebrafish as a model organism, the project provides single-cell resolution recordings of approximately 70,000 neurons, capturing nearly the entire vertebrate brain in action. This resource allows researchers to bridge the gap between structural connectomics and dynamic functional activity to better understand how neural wiring generates complex behavior. ## Whole-Brain Activity in Larval Zebrafish * The dataset focuses on the six-day-old larval zebrafish because it is small, transparent, and capable of complex behaviors like motor learning, hunting, and memory. * Researchers used light-sheet microscopy to scan the brain in 3D slices, recording two hours of continuous activity. * The fish were engineered with GCaMP, a genetically encoded calcium indicator that emits light when neurons fire, allowing for the visualization of real-time neural impulses. * To correlate neural activity with behavior, the fish were placed in a virtual reality environment where stimuli—such as shifting water currents and light changes—were projected around them while tail muscle activity was recorded via electrodes. ## The ZAPBench Framework * ZAPBench standardizes the evaluation of machine learning models in neuroscience, following the tradition of benchmarks in fields like computer vision and language modeling. * The benchmark provides a high-quality dataset of 70,000 neurons, whereas previous efforts in other species often covered less than 0.1% of the brain. * It challenges models to predict how neurons will respond to specific visual stimuli and behavioral patterns. * Initial results presented at ICLR 2025 demonstrate that while simple linear models provide a baseline, advanced architectures like Transformers and Convolutional Neural Networks (CNNs) significantly improve prediction accuracy. ## Integrating Structure and Function * While previous connectomics projects mapped physical neural connections, ZAPBench adds the "dynamic" layer of how those connections are used over time. * The team is currently generating a comprehensive structural connectome for the exact same specimen used in the activity recordings. * This dual approach will eventually allow scientists to investigate the direct relationship between precise physical wiring and the resulting patterns of neural activity across an entire vertebrate brain. By providing an open-source dataset and standardized benchmark, ZAPBench enables the global research community to develop and compare more sophisticated models of neural dynamics, potentially leading to breakthroughs in how we simulate and understand vertebrate cognition.

Machine LearningAIBenchmarkingConnectomics+5

google·Apr 22, 2025Original article

Introducing Mobility AI: Advancing urban transportation (opens in new tab)

Google Research has introduced Mobility AI, a comprehensive program designed to provide transportation agencies with data-driven tools for managing urban congestion, road safety, and evolving transit patterns. By leveraging advancements in measurement, simulation, and optimization, the initiative translates decades of Google’s geospatial research into actionable technologies for infrastructure planning and real-time traffic management. The program aims to empower policymakers and engineers to mitigate gridlock and environmental impacts through high-resolution modeling and continuous monitoring of urban transportation systems. ### Measurement: Understanding Mobility Patterns The measurement pillar focuses on establishing a precise baseline of current transportation conditions using real-time and historical data. * **Congestion Functions:** Researchers utilize machine learning and floating car data to develop city-wide models that mathematically describe the relationship between vehicle volume and travel speeds, even on roads with limited data. * **Geospatial Foundation Models:** By applying self-supervised learning to movement patterns, the program creates embeddings that capture local spatial characteristics. This allows for better reasoning about urban mobility in data-sparse environments. * **Analytical Formulation:** Specific research explores how adjusting traffic signal timing influences the distribution of flow across urban networks, revealing patterns in how congestion propagates. ### Simulation: Forecasting and Scenario Analysis Mobility AI uses simulation technologies to create digital twins of cities, allowing planners to test interventions before implementing them physically. * **Traffic Simulation API:** This tool enables the modeling of complex "what-if" scenarios, such as the impact of closing a major bridge or reconfiguring lane assignments on a highway. * **High-Fidelity Calibration:** The simulations are calibrated using large-scale, real-world data to ensure that the virtual models accurately reflect local driver behavior and infrastructure constraints. * **Scalable Evaluation:** These digital environments provide a risk-free way to assess how new developments, such as the rise of autonomous vehicles or e-commerce logistics, will reshape existing traffic patterns. ### Optimization: Improving Urban Flow The optimization pillar focuses on applying AI to solve large-scale coordination problems, such as signal timing and routing efficiency. * **Project Green Light:** This initiative uses AI to provide traffic signal timing recommendations to city engineers, specifically targeting a reduction in stop-and-go traffic to lower greenhouse gas emissions. * **System-Wide Coordination:** Optimization algorithms work to balance the needs of multiple modes of transport, including public transit, cycling, and pedestrian infrastructure, rather than focusing solely on personal vehicles. * **Integration with Google Public Sector:** Research breakthroughs from this program are being integrated into Google Maps Platform and Google Public Sector tools to provide agencies with accessible, enterprise-grade optimization capabilities. Transportation agencies and researchers can leverage these foundational AI technologies to transition from reactive traffic management to proactive, data-driven policymaking. By participating in the Mobility AI program, public sector leaders can gain access to advanced simulation and measurement tools designed to build more resilient and efficient urban mobility networks.

Machine LearningAIFoundation ModelsGeospatial Analysis+4

google·Apr 17, 2025Original article

InstructPipe: Generating Visual Blocks pipelines with human instructions and LLMs (opens in new tab)

InstructPipe is a research prototype designed to simplify machine learning prototyping by generating visual programming pipelines directly from natural language instructions. By leveraging a multi-stage large language model (LLM) framework, the system automates the selection and connection of nodes to lower the barrier for novice users. The result is a streamlined workflow that transforms abstract text commands into functional, editable node-graph diagrams within the Visual Blocks for ML environment. ### Pipeline Representation and Efficiency - Visual Blocks pipelines are structured as Directed Acyclic Graphs (DAGs) and are typically stored in a verbose JSON format. - To improve LLM performance, InstructPipe utilizes a "pseudocode" intermediate representation that is highly token-efficient, compressing pipeline data from 2.8k tokens down to approximately 123 tokens. - This pseudocode defines output variables, unique node IDs, and node types while specifying arguments such as input images or text prompts (e.g., `pali_1_out:pali(image=input_image_1, prompt=input_text_1)`). ### Two-Stage LLM Refinement - The **Node Selector** module acts as a high-level filter, using brief node descriptions to identify a relevant subset of tools from the library based on the user's intent. - The **Code Writer** module receives the filtered list and uses detailed node configurations—including specific input/output data types and usage examples—to draft the actual pipeline logic. - This dual-prompting strategy mimics human developer behavior by first scanning documentation categories and then focusing on specific function requirements to ensure accurate node connections. ### Interpretation and Execution - A dedicated **Code Interpreter** parses the generated pseudocode to reconstruct the final JSON-formatted pipeline required by the visual editor. - The system renders the resulting graph in an interactive workspace, allowing users to immediately execute, modify, or extend the machine learning workflow. - Technical evaluations indicate that this approach effectively supports multimodal pipelines, such as those involving the PaLI model for vision-language tasks, while significantly reducing the learning curve for new users. InstructPipe demonstrates how LLMs can bridge the gap between high-level human intent and low-code visual programming environments. For developers and researchers, this approach mitigates the "blank canvas" problem, allowing for faster experimentation and the rapid prototyping of complex machine learning architectures through simple text-based collaboration.

Machine LearningAILarge Language ModelsPrototyping+5

google·Apr 1, 2025Original article

ECLeKTic: A novel benchmark for evaluating cross-lingual knowledge transfer in LLMs (opens in new tab)

ECLeKTic is a novel benchmark designed to evaluate how effectively large language models (LLMs) transfer knowledge between languages, addressing a common limitation where models possess information in a source language but fail to access it in others. By utilizing a closed-book question-answering format based on language-specific Wikipedia entries, the benchmark quantifies the gap between human-like cross-lingual understanding and current machine performance. Initial testing reveals that even state-of-the-art models have significant room for improvement, with the highest-performing model, Gemini 2.5 Pro, achieving only a 52.6% success rate. ## Methodology and Dataset Construction The researchers built the ECLeKTic dataset by focusing on "information silos" within Wikipedia to ensure the models would need to perform internal transfer rather than simply recalling translated training data. * The dataset targets 12 languages: English, French, German, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Mandarin Chinese, Portuguese, and Spanish. * Researchers selected 100 articles per language from a July 2023 Wikipedia snapshot that existed exclusively in that specific language and had no equivalent articles in the other 11 targeted languages. * This approach uses Wikipedia presence as a proxy to identify facts likely encountered by the model in only one language during its training phase. ## Human Refinement and Decontextualization To ensure the quality and portability of the questions, the team employed native speakers to refine and verify the data generated by AI. * Human annotators filtered Gemini-generated question-and-answer pairs to ensure they were answerable in a closed-book setting without referring to external context. * Annotators performed "decontextualization" by adding specific details to ambiguous terms; for example, a reference to the "Supreme Court" was clarified as the "Israeli Supreme Court" to ensure the question remained accurate after translation. * Questions were curated to focus on cultural and local salience rather than general global knowledge like science or universal current events. * The final dataset consists of 384 unique questions, which were translated and verified across all 11 target languages, resulting in 4,224 total examples. ## Benchmarking Model Performance The benchmark evaluates models using a specific metric called "overall success," which measures a model's ability to answer a question correctly in both the original source language and the target language. * The benchmark was used to test eight leading open and proprietary LLMs. * Gemini 2.0 Pro initially set a high bar with 41.6% success, which was later surpassed by Gemini 2.5 Pro at 52.6%. * The results demonstrate that while models are improving, they still struggle to maintain consistent knowledge across different linguistic contexts, representing a major hurdle for equitable global information access. The release of ECLeKTic as an open-source benchmark on Kaggle provides a vital tool for the AI community to bridge the "knowledge gap" between high-resource and low-resource languages. Developers and researchers should use this data to refine training methodologies, aiming for models that can express their internal knowledge regardless of the language used in the prompt.

Machine LearningAILarge Language ModelsGemini+4

google·Mar 30, 2025Original article

The evolution of graph learning (opens in new tab)

The evolution of graph learning has transformed from classical mathematical puzzles into a cornerstone of modern machine learning, enabling the modeling of complex relational data. By bridging the gap between discrete graph algorithms and neural networks, researchers have unlocked the ability to generate powerful embeddings that capture structural similarities. This progression, spearheaded by milestones like PageRank and DeepWalk, has established graph-based models as essential tools for solving real-world challenges ranging from traffic prediction to molecular analysis. **Foundations of Graph Theory and Classical Algorithms** * Graph theory originated in 1736 with Leonhard Euler’s analysis of the Seven Bridges of Königsberg, which established the mathematical framework for representing connections between entities. * Pre-deep learning efforts focused on structural properties, such as community detection and centrality, or solving discrete problems like shortest paths and maximum flow. * The 1996 development of PageRank by Google’s founders applied these principles at scale, treating the internet as a massive graph of nodes (pages) and edges (hyperlinks) to revolutionize information retrieval. **Bridging Graph Data and Neural Networks via DeepWalk** * A primary challenge in the field was the difficulty of integrating discrete graph structures into neural network architectures, which typically favor feature-based embeddings over relational ones. * Developed in 2014, DeepWalk became the first practical method to bridge this gap by utilizing a neural network encoder to create graph embeddings. * These embeddings convert complex relational data into numeric representations that preserve the structural similarity between objects, allowing graph data to be processed by modern machine learning pipelines. **The Rise of Graph Convolutional Networks and Message Passing** * Following the success of graph embeddings, the field moved toward Graph Convolutional Networks (GCNs) in 2016 to better handle non-Euclidean data. * Modern frameworks now utilize Message Passing Neural Networks (MPNNs), which allow nodes to aggregate information from their neighbors to learn more nuanced representations. * These advancements are supported by specialized libraries in TensorFlow and JAX, enabling the application of graph learning to diverse fields such as physics simulations, disease spread modeling, and fake news detection. To effectively model complex systems where relationships are as important as the entities themselves, practitioners should transition from traditional feature-based models to graph-aware architectures. Utilizing contemporary libraries like those available for JAX and TensorFlow allows for the integration of relational structure directly into the learning process, providing more robust insights into interconnected data.

Machine LearningAIJaxGraph Learning+4