google·Apr 7, 2025Original article

Geospatial Reasoning: Unlocking insights with generative AI and multiple foundation models (opens in new tab)

Google Research is introducing Geospatial Reasoning, a new framework that integrates generative AI with specialized foundation models to streamline complex geographical problem-solving. By combining large language models like Gemini with domain-specific data, the initiative seeks to make large-scale spatial analysis accessible to sectors like public health, urban development, and climate resilience. This research effort moves beyond traditional data silos, enabling agentic workflows that can interpret diverse data types—from satellite imagery to population dynamics—through natural language. ### Specialized Foundation Models for Human Activity * The Population Dynamics Foundation Model (PDFM) captures the complex interplay between human behaviors and their local environments. * A dedicated trajectory-based mobility foundation model has been developed to process and analyze movement patterns. * While initially tested in the US, experimental datasets are expanding to include the UK, Australia, Japan, Canada, and Malawi for selected partners. ### Remote Sensing and Vision Architectures * New models utilize advanced architectures including masked autoencoders, SigLIP, MaMMUT, and OWL-ViT, specifically adapted for the remote sensing domain. * Training involves high-resolution satellite and aerial imagery paired with text descriptions and bounding box annotations to enable precise object detection. * The models support zero-shot classification and retrieval, allowing users to locate specific features—such as "residential buildings with solar panels"—using flexible natural language queries. * Internal evaluations show state-of-the-art performance across multiple benchmarks, including image segmentation and post-disaster damage assessment. ### Agentic Workflows and Industry Collaboration * The Geospatial Reasoning framework utilizes LLMs like Gemini to manage complex datasets and orchestrate "agentic" workflows. * These workflows are grounded in geospatial data to ensure that the insights generated are both useful and contextually accurate. * Google is collaborating with inaugural industry partners, including Airbus, Maxar, Planet Labs, and WPP, to test these capabilities in real-world scenarios. Organizations interested in accelerating their geospatial analysis should consider applying for the trusted tester program to explore how these foundation models can be fine-tuned for specific proprietary data and use cases.

AIGenerative AIGeminiComputer Vision+5

google·Apr 2, 2025Original article

Evaluating progress of LLMs on scientific problem-solving (opens in new tab)

Current scientific benchmarks for large language models (LLMs) often focus on simple knowledge recall and multiple-choice responses, which do not reflect the complex, context-rich reasoning required in real-world research. To bridge this gap, Google Research has introduced CURIE, alongside the SPIQA and FEABench datasets, to evaluate LLMs on their ability to understand long-form documents, analyze multimodal data, and solve multi-step problems. These benchmarks aim to move AI from merely surfacing facts to actively assisting scientists in workflows involving information extraction, algebraic manipulation, and tool use. ### The CURIE Multitask Benchmark * CURIE spans six diverse scientific disciplines: materials science, condensed matter physics, quantum computing, geospatial analysis, biodiversity, and proteins. * The benchmark includes 10 challenging tasks, such as concept tracking, information aggregation, and cross-domain expertise, based on 429 full-length research documents. * The complexity of the benchmark is reflected in its scale, with input queries averaging 15,000 words and ground truth responses averaging 954 words. * Domain experts were involved in every phase of development, from sourcing papers to creating nuanced ground-truth answers in formats like JSON, LaTeX, and YAML. ### Multimodal Reasoning and Agentic Simulation * The SPIQA (Scientific Paper Image Question Answering) dataset evaluates the ability of multimodal LLMs to ground their answers in complex figures and tables found in scientific literature. * FEABench (Finite Element Analysis Benchmark) measures the ability of LLM agents to simulate and solve multiphysics, mathematics, and engineering problems. * These tools specifically test whether models can choose the correct computational tools and reason through the physical constraints of a given problem. ### Programmatic and Model-Based Evaluation * Because scientific answers are often descriptive or formatted heterogeneously, the evaluation uses programmatic metrics like ROUGE-L and Intersection-over-Union (IoU). * For free-form and complex technical generation, the framework incorporates model-based evaluations to ensure AI responses align with expert assessments. * Task difficulty is quantified by expert ratings, ensuring the benchmark measures high-level reasoning rather than just pattern matching. These new benchmarks provide a rigorous framework for developing LLMs that can act as true collaborators in the scientific process. By focusing on long-context understanding and tool-integrated reasoning, researchers can better track the progress of AI in handling the actual complexities of modern scientific discovery.

AILarge Language ModelsMultimodal AIBenchmarking+4

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.

AILarge Language ModelsMachine LearningGemini+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.

AIMachine LearningJaxGraph Learning+4

google·Mar 20, 2025Original article

Deciphering language processing in the human brain through LLM representations (opens in new tab)

Recent research by Google Research and collaborating universities indicates that Large Language Models (LLMs) process natural language through internal representations that closely mirror neural activity in the human brain. By comparing intracranial recordings from spontaneous conversations with the internal embeddings of the Whisper speech-to-text model, the study found a high degree of linear alignment between artificial and biological language processing. These findings suggest that the statistical structures learned by LLMs via next-word prediction provide a viable computational framework for understanding how humans comprehend and produce speech. ## Mapping LLM Embeddings to Brain Activity * Researchers utilized intracranial electrodes to record neural signals during real-world, free-flowing conversations. * The study compared neural activity against two distinct types of embeddings from the Transformer-based Whisper model: "speech embeddings" from the model’s encoder and "language embeddings" from the decoder. * A linear transformation was used to predict brain signals based on these embeddings, revealing that LLMs and the human brain share similar multidimensional spaces for coding linguistic information. * The alignment suggests that human language processing may rely more on statistical structures and contextual embeddings rather than traditional symbolic rules or syntactic parts of speech. ## Neural Sequences in Speech Comprehension * When a subject listens to speech, the brain follows a specific chronological sequence that aligns with model representations. * Initially, speech embeddings predict cortical activity in the superior temporal gyrus (STG), which is responsible for processing auditory speech sounds. * A few hundred milliseconds later, language embeddings predict activity in Broca’s area (located in the inferior frontal gyrus), marking the transition from sound perception to decoding meaning. ## Reversed Dynamics in Speech Production * During speech production, the neural sequence is reversed, beginning approximately 500 milliseconds before a word is articulated. * Processing starts in Broca’s area, where language embeddings predict activity as the brain plans the semantic content of the utterance. * This is followed by activity in the motor cortex (MC), aligned with speech embeddings, as the brain prepares the physical articulatory movements. * Finally, after articulation, speech embeddings predict activity back in the STG, suggesting the brain is monitoring the sound of the speaker's own voice. This research validates the use of LLMs as powerful predictive tools for neuroscience, offering a new lens through which to study the temporal and spatial dynamics of human communication. By bridging the gap between artificial intelligence and cognitive biology, researchers can better model how the brain integrates sound and meaning in real-time.

AILarge Language ModelsNatural Language ProcessingTransformer+4

google·Mar 19, 2025Original article

Loss of Pulse Detection on the Google Pixel Watch 3 (opens in new tab)

Google Research has developed a "Loss of Pulse Detection" feature for the Pixel Watch 3 to address the high mortality rates associated with unwitnessed out-of-hospital cardiac arrests (OHCA). By utilizing a multimodal algorithm that combines photoplethysmography (PPG) and accelerometer data, the device can automatically identify the transition to a pulseless state and contact emergency services. This innovation aims to transform unwitnessed medical emergencies into functionally witnessed ones, potentially increasing survival rates by ensuring timely intervention. ### The Impact of Witness Status on Survival * Unwitnessed cardiac arrests currently face a major public health challenge, with survival rates as low as 4% compared to 20% for witnessed events. * The "Chain of Survival" traditionally relies on human bystanders to activate emergency responses, leaving those alone at a significant disadvantage. * Every minute without resuscitation decreases the chance of survival by 7–10%, making rapid detection the most critical factor in prognosis. * Converting an unwitnessed event into a "functionally witnessed" one via a wearable device could equate to a number needed to treat (NNT) of only six people to save one life. ### Multimodal Detection and the Three-Gate Process * The system uses PPG sensors to measure blood pulsatility by detecting photons backscattered by tissue at green and infrared wavelengths. * To prevent false positives and errant emergency calls, the algorithm must pass three sequential "gates" before making a classification. * **Gate 1:** Detects a sudden, significant drop in the alternating current (AC) component of the green PPG signal, which suggests a transition from a pulsatile to a pulseless state, paired with physical stillness. * **Gate 2:** Employs a machine learning algorithm trained on diverse user data to quantify the probability of a true pulseless transition. * **Gate 3:** Conducts additional sensor checks using various LED and photodiode geometries, wavelengths, and gain settings to confirm the absence of even a weak pulse. ### On-Device Processing and User Verification * All data processing occurs entirely on the watch to maintain user privacy, consistent with Google’s established health data policies. * If the algorithm detects a loss of pulse, it initiates two check-in prompts involving haptic, visual, and audio notifications to assess user responsiveness. * The process can be de-escalated immediately if the user moves their arm purposefully, ensuring that emergency services are only contacted during true incapacitation. * When a user remains unresponsive, the watch automatically contacts emergency services to provide the individual's current location and medical situation. By providing a passive, opportunistic monitoring system on a mass-market wearable, this technology offers a critical safety net for individuals at risk of unwitnessed cardiac events. For the broader population, the Pixel Watch 3 serves as a life-saving tool that bridges the gap between a sudden medical emergency and the arrival of professional responders.

AIMachine LearningWearable TechnologyPhotoplethysmography+5

google·Mar 19, 2025Original article

Load balancing with random job arrivals (opens in new tab)

Research from Google explores the competitive ratio of online load balancing when tasks arrive in a uniformly random order rather than an adversarial one. By analyzing a "tree balancing game" where edges must be oriented to minimize node indegree, the authors demonstrate that random arrival sequences still impose significant mathematical limitations on deterministic algorithms. The study ultimately concludes that no online algorithm can achieve a competitive ratio significantly better than $\sqrt{\log n}$, establishing new theoretical boundaries for efficient cluster management. ### The Online Load Balancing Challenge * Modern cluster management systems, such as Google’s Borg, must distribute hundreds of thousands of jobs across machines to maximize utilization and minimize the maximum load (makespan). * In the online version of this problem, jobs arrive one-by-one, and the system must assign them immediately without knowing what future jobs will look like. * Traditionally, these algorithms are evaluated using "competitive analysis," comparing the performance of an online algorithm against an optimal offline version that has full knowledge of the job sequence. ### The Tree Balancing Game * The problem is modeled as a game where an adversary presents edges of a tree (representing jobs and machines) one at a time. * For every undirected edge $(u, v)$ presented, the algorithm must choose an orientation ($u \to v$ or $v \to u$), with the goal of minimizing the maximum number of edges pointing at any single node. * In a worst-case adversarial arrival order, it has been mathematically proven since the 1990s that no deterministic algorithm can guarantee a maximum indegree of less than $\log n$, where $n$ is the number of nodes. ### Performance Under Random Arrival Orders * The research specifically investigates "random order arrivals," where every possible permutation of the job sequence is equally likely, simulating a more natural distribution than a malicious adversary. * While previous assumptions suggested that a simple "greedy algorithm" (assigning the job to the machine with the currently lower load) performed better in this model, this research proves a new, stricter lower bound. * The authors demonstrate that even with random arrivals, any online algorithm will still incur a maximum load proportional to at least $\sqrt{\log n}$. * For more general load balancing scenarios beyond simple trees, the researchers established a lower bound of $\sqrt{\log \log n}$. ### Practical Implications These findings suggest that while random job arrival provides a slight performance advantage over adversarial scenarios, system designers cannot rely on randomness alone to eliminate load imbalances. Because the maximum load grows predictably according to the $\sqrt{\log n}$ limit, large-scale systems must be architected to handle this inherent logarithmic growth in resource pressure to maintain high utilization and stability.

Load BalancingScheduling AlgorithmsGraph TheoryOnline Algorithms+3

google·Mar 17, 2025Original article

Generating synthetic data with differentially private LLM inference (opens in new tab)

Researchers at Google have developed an inference-only method for generating differentially private (DP) synthetic data that avoids the high costs and data requirements associated with private fine-tuning. By prompting off-the-shelf large language models (LLMs) with sensitive examples in parallel and aggregating their outputs, the approach can generate thousands of high-quality synthetic data points while maintaining rigorous privacy guarantees. This method allows synthetic data to serve as a secure interface for model development, enabling teams to collaborate without requiring specialized knowledge of differential privacy. ## Differentially Private Prediction and Aggregation The core of this method relies on "private prediction," where privacy is applied to the model's output rather than the model itself. * Sensitive data points are distributed across multiple independent prompts, ensuring that no single individual's record can significantly influence the final output. * The LLM generates next-token predictions for each prompt in parallel, which are then aggregated to mask individual contributions. * The researchers designed a DP token sampling algorithm that treats the standard LLM "softmax" sampling process as a version of the exponential mechanism, a mathematical framework used to select the best option from a set while maintaining privacy. ## Enhancing Efficiency via KV Caching Previous attempts at private prediction were computationally expensive because they required a fresh batch of sensitive examples for every single token generated. * A new privacy analysis allows the system to reuse a fixed batch of sensitive examples across an entire generation sequence. * By maintaining the same context for each generation step, the system becomes compatible with standard inference optimization techniques like KV (Key-Value) caching. * This improvement enables the generation of synthetic data at a scale two to three orders of magnitude larger than prior methods. ## Optimizing Privacy Spend with Public Drafters To preserve the "privacy budget"—the limited amount of information that can be released before privacy is compromised—the method introduces a public drafter model. * The drafter model predicts the next token based solely on previously generated synthetic text, without ever seeing the sensitive data. * Using the sparse vector technique, the system only consumes the privacy budget when the public drafter’s suggestion disagrees with the private aggregate of the sensitive data. * This is particularly useful for structured data, where the drafter can handle formatting and syntax tokens, saving the privacy budget for the actual content. By leveraging off-the-shelf models like Gemma, this approach provides a scalable way to transform sensitive datasets into useful synthetic versions. These synthetic datasets are high-quality enough to replace real data in downstream machine learning tasks, such as in-context learning or fine-tuning models like BERT, without the risk of leaking individual user information.

AILarge Language ModelsMachine LearningDifferential Privacy+5