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
MoXaRt: Audio-Visual Object-Guided Sound Interaction for XR
Sieun Kim
Qianhui Zheng
Ruoyu Xu
Ravi Tejasvi
Anuva Kulkarni
Junyi Zhu
2026
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In Extended Reality (XR), complex acoustic environments often overwhelm users, compromising both scene awareness and social engagement due to entangled sound sources. We introduce MoXaRt, a real-time XR system that uses audio-visual cues to separate these sources and enable fine-grained sound interaction. MoXaRt's core is a cascaded architecture that performs coarse, audio-only separation in parallel with visual detection of sources (e.g. faces, instruments). These visual anchors then guide refinement networks to isolate individual sources, separating complex mixes of up to five concurrent sources (e.g. two voices + three instruments) with ca. 2 second processing latency. We validate MoXaRt through a technical evaluation on a new, complex dataset we collected, and a 22-participant user study. Our results demonstrate that MoXaRt significantly improves communication clarity—boosting listening comprehension in noisy conditions by 33.2% (p=0.0058)—and significantly reduces cognitive load (M=7.50 vs. M=3.36, p<0.001), paving the way for more perceptive and socially adept XR experiences.
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Large Language Models utilizing reasoning techniques improve task performance but incur significant latency and token costs due to verbose generation. Existing automatic prompt optimization(APO) frameworks target task accuracy exclusively at the expense of generating long reasoning traces. We propose Cost-Regularized Optimization of Prompts (CROP), an APO method that introduces regularization on response length by generating textual feedback in addition to standard accuracy feedback. This forces the optimization process to produce prompts that elicit concise responses containing only critical information and reasoning. We evaluate our approach on complex reasoning datasets, specifically GSM8K, LogiQA and BIG-Bench Hard. We achieved an 80.6% reduction in token consumption while maintaining competitive accuracy, seeing only a nominal decline in performance. This presents a pragmatic solution for deploying token-efficient and cost-effective agentic AI systems in production pipelines.
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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)
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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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The Ontic-Epistemic Distinction: Implications for General Intelligence
Master's Thesis (2026) (to appear)
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The current pursuit of robust machine intelligence is largely predicated on a substrate independent, computational functionalist view of cognition, where sufficiently complex computational processing is expected to eventually yield generalized reasoning. This paper explores the ontological distinctions between these computational frameworks and biological cognition, specifically how these differences impact the capacity for semantic understanding. By analyzing phenomena such as the "reversal curse" where models fail to generalize the symmetry in identity relations (A=B implies B=A), and performance on novel reasoning benchmarks (e.g., ARC-AGI), this paper examines whether current model limitations are transient artifacts of scale or indicative of a distinct architectural category. Integrating Stevan Harnad’s “symbol grounding problem” with Evan Thompson’s biological model of “intrinsic normativity,” I investigate whether robust general intelligence might require sense-making: a process distinct from information processing, whereby an agent’s internal states are causally coupled with its environment via survival or system-wide stakes which grounds symbols in meaning. Current Large Language Models (LLMs) appear to lack this intrinsic normativity, and consequently may operate primarily as epistemic instruments rather than ontic agents. By introducing the concept of “ontic grounding”, this paper presents a potential framework for distinguishing between the simulation of reasoning and true understanding, which could have implications for AI safety and governance.
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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
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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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A Framework for Interactive Machine Learning and Enhanced Conversational Systems
Jerry Young
Richard Abisla
Sanjay Batra
Mikki Phan
Nature, Springer-Verlag (2026)
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Conversational systems are increasingly prevalent, yet current versions often fail to support the full range of human speech, including variations in speed, rhythm, syntax, grammar, articulation, and resonance. This reduces their utility for individuals with dysarthria, apraxia, dysphonia, and other language and speech-related disabilities. Building on research that emphasizes the need for specialized datasets and model training tools, our study uses a scaffolded approach to understand the ideal model training and voice recording process. Our findings highlight two distinct user flows for improving model training and provide six guidelines for future conversational system-related co-design frameworks. This study offers important insights on creating more effective conversational systems by emphasizing the need to integrate interactive machine learning into training strategies.
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