Machine intelligence
Google is at the forefront of innovation in Machine Intelligence, with active research exploring virtually all aspects of machine learning, including deep learning and more classical algorithms. Exploring theory as well as application, much of our work on language, speech, translation, visual processing, ranking and prediction relies on Machine Intelligence. In all of those tasks and many others, we gather large volumes of direct or indirect evidence of relationships of interest, applying learning algorithms to understand and generalize.
Machine Intelligence at Google raises deep scientific and engineering challenges, allowing us to contribute to the broader academic research community through technical talks and publications in major conferences and journals. Contrary to much of current theory and practice, the statistics of the data we observe shifts rapidly, the features of interest change as well, and the volume of data often requires enormous computation capacity. When learning systems are placed at the core of interactive services in a fast changing and sometimes adversarial environment, combinations of techniques including deep learning and statistical models need to be combined with ideas from control and game theory.
Recent Publications
Google Earth AI Geospatial Reasoning Agent: Capabilities and a Systematic Evaluation
Aliza Hoffman
Gal Moshitch
Aaron Bell
Roy Lee
William Cai
Adam Boulanger
Mark Young
Utkarsh Patel
Ruth Alcantara
Zachary Luna
Sydney Lin
Ollie Guinan
Christopher Haire
Lily Mihalkova
Luke Barrington
Tim Thelin
Gil Fidel
Ronald Ho
Samapriya Roy
Shravya Shetty
Mandar Sharma
Tyler McHenry
Joydeep Paul
Aviv Slobodkin
Jamie McPike
Noam Slonim
Per Bjornsson
Reuven Sayag
Allison Berman
Niv Efron
Gia Jung
arXiv (2026)
Preview abstract
Google Earth AI is a family of geospatial AI models, together with an agent that orchestrates them. We present this agent, Google Earth AI's Geospatial Reasoning agent (GRA), a Gemini-powered research system that combines geospatial data sources, foundation models, and tools to answer questions about our planet. GRA organizes these into modular domain skills and pairs them with upfront geospatial planning to decompose complex queries into multi-stage workflows. Evaluating such systems is hard: real-world problems admit multiple valid workflows, require data discovery across large catalogs, and rely on evolving ground truth. We introduce two benchmarks and release their development splits. GeoExpertQueries poses complex analytical problems grounded in practitioner use cases and refined by geospatial experts; each is paired with a consensus golden workflow from three independent experts and scored by a human-validated, tool-agnostic autorater. GeoKTools consists of templated queries with deterministic reference solvers that systematically vary the number of required tool types. We also use two external benchmarks, CBGB and UnivEARTH; the latter we find to be saturated. To isolate the contribution of GRA's tools and its agentic orchestration, we compare four configurations, each with Gemini 3.1 Pro and 3.8 Flash: a standalone LLM, the LLM with basic tools, the LLM with GRA's skills and tools but without its upfront planning and execution guidance, and GRA itself. With at most basic tools, an advanced model recovers much of the answer on templated queries but satisfies only a small fraction of the expert workflows on open-ended problems; GRA performs well on both. The largest step comes from GRA's skills and tools; on open-ended problems, its planning and execution guidance add a further gain with Gemini 3.8 Flash, letting Flash match or exceed Pro at lower cost.
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Claim-Gated Source-Risk Auditing for Generative Search
Chuhong Xu
Mark Zhou
Iris Qian
Zhaoyi Li
2026
Preview abstract
Generative search can cite a passage accurately
while omitting a material relationship that changes how the
passage should be interpreted. This paper introduces an original
methodological framework and implementation blueprint for
auditing that gap; it is not an AI detector, classifier, or empirical
performance study. The framework combines a five-dimensional
query–source–answer record, an operational source-role taxonomy,
a derived omission endpoint, and explicit rules for uncertainty
and human review. Its central contribution is a claim-gated
evaluation protocol specifying the sampling, evidence capture,
blinded annotation, adjudication, agreement, accuracy, calibration,
error analysis, and paired intervention tests required before
detector, prevalence, or defense-effect claims are admissible. A
1,000-row synthetic workbook serves only as a specification fixture
for testing serialization and machine-checkable invariants. Its
score vectors are normalized within 5 × 10−4
and its recorded
labels match their vector argmax, but it lacks the provenance and
independent labels required for semantic validation. We therefore
make no claim about Web prevalence, detector accuracy, or
deployment benefit. The result is an implementation-oriented
audit methodology with falsifiable evidence gates for future realWeb evaluation.
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Assessing Global Flood Risk from Atmospheric Rivers through Physically Guided Machine Learning
Assaf Shmuel
Oleg Zlydenko
Martin Gauch
Colin Price
Scientific Reports (2026)
Preview abstract
Atmospheric rivers (ARs) are narrow corridors of concentrated moisture transport that play a crucial role in the global water cycle, delivering both beneficial rainfall and severe floods. Here, we develop a physically guided, explainable machine learning framework to predict flood occurrence during AR conditions worldwide by integrating AR characteristics with meteorological and topographic variables. The best model achieves a receiver operating characteristic area under the curve (ROC AUC) of 0.94, outperforming a logistic regression baseline at 0.81. Despite their limited footprint, we find that one third of large midlatitude floods occur under AR conditions, reflecting their disproportionate role in global flood risk. SHAP analysis highlights integrated vapor transport, precipitation, and elevation as dominant predictors. We show the non-linear amplification of flood risk under combined conditions of high soil moisture and persistent AR activity, underscoring the importance of antecedent wetness in modulating flood risk. Using consistent reanalysis inputs, we find that model-estimated high-flood-risk AR conditions increased globally by over 10% from 1980 to 2020 and shifted poleward. Validation on an independent satellite-based flood database shows comparable skill. We estimate that roughly 90% of the global population lives in regions that experience at least one AR annually, underscoring the broad societal relevance of AR dynamics. These findings highlight the value of physically guided machine learning for mapping and monitoring AR-related flood risk globally, offering actionable insights for preparedness and climate adaptation.
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Approaching the Privacy-Enhancing Technologies Adoption from a Requirements Engineering Perspective
Oleksandr Kosenkov
Danica Vranjanin
Abhinava Singh
Vadym Honcharenko
Volodymyr Spirin
2026
Preview abstract
In recent decades, Privacy Enhancing Technologies (PETs) have been gaining attention as a means to fulfill regulatory and user privacy requirements when processing personal data in software systems. Despite broad attention in research, support by regulators, contributions and adoption by large technological companies (like Google or Microsoft), and interest from software developers, adoption of PETs in practice is still challenging and lagging. For many years, existing research points to the same typical challenges to the adoption of PETs, such as complexity or absence of training; however, these challenges remain unresolved in practice despite the recent efforts.
In this industrial challenge paper, we take a deeper, practical requirements engineering-driven perspective on the challenges to PETs adoption across different stakeholders (namely, PETs developers and integrators, and PET adopters and users) and different disciplines (engineering, law, and business).
We suggest that the adoption of PETs should be facilitated with an in-depth understanding of engineering, business, and legal challenges to PETs, such as PETs' impact on software architecture, their business impact, and contribution to compliance. We suggest that requirements engineering research can play a crucial role in better exploring and modeling such viewpoints. In practice, requirements engineering could facilitate the coordination of the involved viewpoints to enable the adoption of existing and the development of new PETs.
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When “Correct” Is Not Safe: Can We Trust Functionally Correct Patches Generated by Code Agents?
Corina Pasareanu
Haizhong Zheng
Beidi Chen
Ravi Mangal
Xinyu Yang
James Song
Yibo Peng
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, San Diego, California, United States (2026), 15514–15546
Preview abstract
Code agents are increasingly trusted to autonomously fix bugs on platforms such as GitHub, yet their security evaluation focuses almost exclusively on functional correctness. In this paper, we reveal a novel type of threat to real-world code agents: Functionally Correct yet Vulnerable (FCV) patches, which pass all test cases but contain vulnerable code. With our proposed FCV-Attack, which can be deliberately crafted by malicious attackers or implicitly introduced by benign developers, we show that SOTA LLMs (e.g., ChatGPT and Claude) and agent scaffolds (e.g., SWE-agent and OpenHands) are all vulnerable to this FCV threat; across 12 agent-model combinations on SWE-Bench, the attack only requires black-box access and a single query to the code agent to perform the attack. For example, for CWE-538 (information exposure vulnerability), the FCV-Attack attains an attack success rate of 40.7% on GPT-5 Mini + OpenHands. Our results reveal an important security threat overlooked by current evaluation paradigms and urge the development of security-aware defenses for code agents.
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Preview abstract
This paper provides an overview of Google's TPUs across five generations, from TPU v2 to Ironwood, highlighting their evolution as scalable, resilient, and sustainable supercomputers for AI training. It details the TPU’s stable architecture and microarchitecture, which has surprisingly easily accommodated the rapidly changing deep neural network workloads, such as the rise of Transformers. Key advancements over eight years include 10x increase in HBM capacity and bandwidth per node, a 100x increase in peak node performance, and a 3600x increase in supercomputer performance. The paper also discusses the role of optical circuit switches and built-in self test in enhancing resilience, how TPU’s carbon footprint was reduced by improving embodied carbon emissions per floating point operation and a 30x gain in performance per Watt. It concludes by identifying six features that may well characterize the successful AI accelerators of this decade.
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