Machine Intelligence

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<svg class="glue-icon glue-icon--24px" role="presentation" aria-hidden="true"> <use href="/gr/static/assets/icons/glue-icons.svg#close"></use> </svg> </button> </div> </div> </header> <main id="page-content"> <div class="research-area-detail"> <section class="basic-hero bhoig --theme-dark " data-gt-id="basic_hero" data-gt-component-name=""> <div class="glue-page"> <div class="glue-grid"> <div class="bhoig__image-wrapper glue-grid__col--span-2 glue-grid__col--span-3-md glue-grid__col--span-2-lg"> <div class="bhoig__image-bg" style="background-color: var(--glue-grey-900); "> <span class="icon icon--machine-intelligence" style="" ></span> </div> </div> <div class="bhoig__breadcrumb-wrapper glue-grid__col--span-10 glue-grid__col--span-9-md glue-grid__col--span-10-lg"> <nav class="glue-breadcrumbs" aria-label="Breadcrumbs"> <ol class="glue-breadcrumbs__list"> <li class="glue-breadcrumbs__item"> <a class="glue-breadcrumbs__link attribution" href="/">Home</a> <svg role="presentation" aria-hidden="true" class="glue-icon "> <use href="/gr/static/assets/icons/glue-icons.svg#chevron-right"></use> </svg> </li> <li class="glue-breadcrumbs__item"> <a class="glue-breadcrumbs__link attribution" href="/research-areas/">Research areas</a> <svg role="presentation" aria-hidden="true" class="glue-icon "> <use href="/gr/static/assets/icons/glue-icons.svg#chevron-right"></use> </svg> </li> </ol> </nav> </div> <h1 class="headline-1 bhoig__headline glue-grid__col--span-10 glue-grid__col--span-9-md glue-grid__col--span-10-lg">Machine Intelligence</h1> <div class="basic-hero__description bhoig__description glue-grid__col--span-10 glue-grid__col--span-9-md glue-grid__col--span-10-lg"> <p data-block-key="srum7">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.</p><p data-block-key="5klf5">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.</p> </div> <div class="bhoig__cta glue-grid__col--span-10 glue-grid__col--span-9-md glue-grid__col--span-10-lg"> </div> </div> </div> </section> <section class="offset-two-up"> <div class="glue-page glue-grid"> <div class="offset-two-up__left-col glue-grid__col glue-grid__col--span-4-sm glue-grid__col--span-12-md glue-grid__col--span-3-lg"> <h3 class="offset-two-up__headline headline-3">Recent Publications</h3> <div class="offset-two-up__cta-below-description"> <a class="glue-button glue-button--medium-emphasis" href="http://research.google/pubs/?category=machine-intelligence" > <span class="js-gt-item-id">See More</span> </a> </div> </div> <div class="glue-grid__col glue-grid__col--span-4-sm glue-grid__col--span-12-md glue-grid__col--span-9-lg"> <div class="publications-list --theme- row-card-list" data-hot-swap="pub-list" data-gt-id="publications_list" data-gt-component-name=""> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/artificial-intelligence-as-a-second-reader-for-screening-mammography/ > Artificial intelligence as a second reader for screening mammography </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> Etsuji Nakai </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Alessandro Scoccia Pappagallo </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Hiroki Kayama </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Lin Yang </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Shawn Xu </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Christopher Kelly </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Timo Kohlberger </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Daniel Golden </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Akib Uddin </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/107467/"> Joe Ledsam </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Radiology Advances, 1(2) (2024) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentbackground-artificial-intelligence tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentbackground-artificial-intelligence" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">Background Artificial intelligence (AI) has shown promise in mammography interpretation, and its use as a second reader in breast cancer screening may reduce the burden on health care systems. Purpose To evaluate the performance differences between routine double read and an AI as a second reader workflow (AISR), where the second reader is replaced with AI. Materials and Methods A cohort of patients undergoing routine breast cancer screening at a single center with mammography was retrospectively collected between 2005 and 2021. A model developed on US and UK data was fine-tuned on Japanese data. We subsequently performed a reader study with 10 qualified readers with varied experience (5 reader pairs), comparing routine double read to an AISR workflow. Results A “test set” of 4,059 women (mean age, 56 ± 14 years; 157 positive, 3,902 negative) was collected, with 278 (mean age 55 ± 13 years; 90 positive, 188 negative) evaluated for the reader study. We demonstrate an area under the curve =.84 (95% confidence interval [CI], 0.805-0.881) on the test set, with no significant difference to decisions made in clinical practice (P = .32). Compared with routine double reading, in the AISR arm, sensitivity improved by 7.6% (95% CI, 3.80-11.4; P = .00004) and specificity decreased 3.4% (1.42-5.43; P = .0016), with 71% (212/298) of scans no longer requiring input from a second reader. Variation in recall decision between reader pairs improved from a Cohen kappa of κ = .65 (96% CI, 0.61-0.68) to κ = .74 (96% CI, 0.71-0.77) in the AISR arm.</span> <a class="glue-button glue-button--low-emphasis" href="http://research.google/pubs/artificial-intelligence-as-a-second-reader-for-screening-mammography/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/learning-model-uncertainty-as-variance-minimizing-instance-weights/ > Learning model uncertainty as variance-minimizing instance weights </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> Nishant Jain </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/karthikeyanshanmugam/"> Karthikeyan Shanmugam </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/106965/"> Pradeep Shenoy </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> ICLR 2024 (2024) (to appear) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentpredictive-uncertainty-a-models-s tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentpredictive-uncertainty-a-models-s" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">Predictive uncertainty-a model's self awareness regarding its accuracy on an input-is key for both building robust models via training interventions and for test-time applications such as selective classification. We propose a novel instance-conditioned reweighting approach that captures predictive uncertainty using an auxiliary network and unifies these train- and test-time applications. The auxiliary network is trained using a meta-objective in a bilevel optimization framework. A key contribution of our proposal is the meta-objective of minimizing the dropout variance, an approximation of Bayesian Predictive uncertainty. We show in controlled experiments that we effectively capture the diverse specific notions of uncertainty through this meta-objective, while previous approaches only capture certain aspects. These results translate to significant gains in real-world settings-selective classification, label noise, domain adaptation, calibration-and across datasets-Imagenet, Cifar100, diabetic retinopathy, Camelyon, WILDs, Imagenet-C,-A,-R, Clothing1M, etc. For Diabetic Retinopathy, we see upto 3.4%/3.3% accuracy and AUC gains over SOTA in selective classification. We also improve upon large-scale pretrained models such as PLEX.</span> <a class="glue-button glue-button--low-emphasis" href="http://research.google/pubs/learning-model-uncertainty-as-variance-minimizing-instance-weights/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/improved-communication-privacy-trade-offs-in-l2-mean-estimation-under-streaming-differential-privacy/ > Improved Communication-Privacy Trade-offs in L2 Mean Estimation under Streaming Differential Privacy </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> Wei-Ning Chen </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/berivanisik/"> Berivan Isik </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/peterkairouz/"> Peter Kairouz </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Albert No </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Sewoong Oh </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/106689/"> Zheng Xu </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> International Conference on Machine Learning (ICML) (2024) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-content-we-study-l_2-mean-estimation tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-content-we-study-l_2-mean-estimation" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body"> We study $L_2$ mean estimation under central differential privacy and communication constraints, and address two key challenges: firstly, existing mean estimation schemes that simultaneously handle both constraints are usually optimized for $L_\infty$ geometry and rely on random rotation or Kashin's representation to adapt to $L_2$ geometry, resulting in suboptimal leading constants in mean square errors (MSEs); secondly, schemes achieving order-optimal communication-privacy trade-offs do not extend seamlessly to streaming differential privacy (DP) settings (e.g., tree aggregation or matrix factorization), rendering them incompatible with DP-FTRL type optimizers. In this work, we tackle these issues by introducing a novel privacy accounting method for the sparsified Gaussian mechanism that incorporates the randomness inherent in sparsification into the DP noise. Unlike previous approaches, our accounting algorithm directly operates in $L_2$ geometry, yielding MSEs that fast converge to those of the uncompressed Gaussian mechanism. Additionally, we extend the sparsification scheme to the matrix factorization framework under streaming DP and provide a precise accountant tailored for DP-FTRL type optimizers. Empirically, our method demonstrates at least a 100x improvement of compression for DP-SGD across various FL tasks. </span> <a class="glue-button glue-button--low-emphasis" href="http://research.google/pubs/improved-communication-privacy-trade-offs-in-l2-mean-estimation-under-streaming-differential-privacy/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/binaural-angular-separation-network/ > Binaural Angular Separation Network </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/yangyang/"> Yang Yang </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/georgesung/"> George Sung </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/108445/"> Shao-Fu Shih </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/106904/"> Hakan Erdogan </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/kevin/"> Kevin Lee </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/author38919/"> Matthias Grundmann </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> ICASSP 2024 (2024) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentwe-propose-a-neural-network-model tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentwe-propose-a-neural-network-model" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">We propose a neural network model that can separate target speech sources from interfering sources at different angular regions using two microphones. The model is trained with simulated room impulse responses (RIRs) using omni-directional microphones without needing to collect real RIRs. By relying on specific angular regions and multiple room simulations, the model utilizes consistent time difference of arrival (TDOA) cues, or what we call delay contrast, to separate target and interference sources while remaining robust in various reverberation environments. We demonstrate the model is not only generalizable to a commercially available device with a slightly different microphone geometry, but also outperforms our previous work which uses one additional microphone on the same device. The model runs in real-time on-device and is suitable for low-latency streaming applications such as telephony and video conferencing.</span> <a class="glue-button glue-button--low-emphasis" href="http://research.google/pubs/binaural-angular-separation-network/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/delphic-offline-reinforcement-learning-under-nonidentifiable-hidden-confounding/ > Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> Alizée Pace </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Hugo Yèche </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Bernhard Schölkopf </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Gunnar Rätsch </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/108307/"> Guy Tennenholtz </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> The Twelfth International Conference on Learning Representations (2024) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contenta-prominent-challenge-of-offline-r tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contenta-prominent-challenge-of-offline-r" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">A prominent challenge of offline reinforcement learning (RL) is the issue of hidden confounding. There, unobserved variables may influence both the actions taken by the agent and the outcomes observed in the data. Hidden confounding can compromise the validity of any causal conclusion drawn from the data and presents a major obstacle to effective offline RL. In this paper, we tackle the problem of hidden confounding in the nonidentifiable setting. We propose a definition of uncertainty due to confounding bias, termed delphic uncertainty, which uses variation over compatible world models, and differentiate it from the well known epistemic and aleatoric uncertainties. We derive a practical method for estimating the three types of uncertainties, and construct a pessimistic offline RL algorithm to account for them. Our method does not assume identifiability of the unobserved confounders, and attempts to reduce the amount of confounding bias. We demonstrate through extensive experiments and ablations the efficacy of our approach on a sepsis management benchmark, as well as real electronic health records. Our results suggest that nonidentifiable confounding bias can be addressed in practice to improve offline RL solutions.</span> <a class="glue-button glue-button--low-emphasis" href="http://research.google/pubs/delphic-offline-reinforcement-learning-under-nonidentifiable-hidden-confounding/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/locality-aware-graph-rewiring-in-gnns/ > Locality-Aware Graph Rewiring in GNNs </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> Federico Barbero </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Ameya Velingker </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Amin Saberi </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Michael Bronstein </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Francesco Di Giovanni </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> ICLR 2024 </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentgraph-neural-networks-gnns-are-p tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentgraph-neural-networks-gnns-are-p" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">Graph Neural Networks (GNNs) are popular models for machine learning on graphs that typically follow the message-passing paradigm, whereby the feature of a node is updated recursively upon aggregating information over its neighbors. While exchanging messages over the input graph endows GNNs with a strong inductive bias, it can also make GNNs susceptible to \emph{over-squashing}, thereby preventing them from capturing long-range interactions in the given graph. To rectify this issue, {\em graph rewiring} techniques have been proposed as a means of improving information flow by altering the graph connectivity. In this work, we identify three desiderata for graph-rewiring: (i) reduce over-squashing, (ii) respect the locality of the graph, and (iii) preserve the sparsity of the graph. We highlight fundamental trade-offs that occur between {\em spatial} and {\em spectral} rewiring techniques; while the former often satisfy (i) and (ii) but not (iii), the latter generally satisfy (i) and (iii) at the expense of (ii). We propose a novel rewiring framework that satisfies all of (i)--(iii) through a locality-aware sequence of rewiring operations. We then discuss a specific instance of such rewiring framework and validate its effectiveness on several real-world benchmarks, showing that it either matches or significantly outperforms existing rewiring approaches.</span> <a class="glue-button glue-button--low-emphasis" href="http://research.google/pubs/locality-aware-graph-rewiring-in-gnns/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> </div> </div> </div> </section> <section class="offset-two-up --theme-dark both" data-gt-id="offset_two_up" data-gt-component-name=""> <div class="glue-page glue-grid"> <div class="offset-two-up__left-col glue-grid__col glue-grid__col--span-4-sm glue-grid__col--span-12-md glue-grid__col--span-3-lg"> <h2 class="offset-two-up__headline headline-3">Some of our teams</h2> </div> <div class="glue-grid__col glue-grid__col--span-4-sm glue-grid__col--span-12-md glue-grid__col--span-9-lg"> <ul class="card-stack--small nested-glue-grid-override" 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