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New Open Mixture-of-Experts Models. Jamba SSM-MoE. Qwen1.5-MoE-A2.7B. DBRX 132B MoE. frankenMoEs. AI Agentic Workflows. 1-bit ML Models. OpenDevin. AgentStudio.

Posted by Lizao (Larry) Li, Software Engineer, and Rob Carver, Research Scientist, Google Research Accurate weather forecasts can have a direct impact on people’s lives, from helping make routine decisions, like what to pack for a day’s activities, to informing urgent actions, for example, protecting people in the face of hazardous weather conditions. The importance of accurate and timely weather forecasts will only increase as the climate changes. Recognizing this, we at Google have been investing in weather and climate research to help ensure that the forecasting technology of tomorrow can meet the demand for reliable weather information. Some of our recent innovations include MetNet-3, Google's high-resolution forecasts up to 24-hours into the future, and GraphCast, a weather model that can predict weather up to 10 days ahead. Weather is inherently stochastic. To quantify the uncertainty, traditional methods rely on physics-based simulation to generate an ensemble of forecasts. However, it is computationally costly to generate a large ensemble so that rare and extreme weather events can be discerned and characterized accurately. With that in mind, we are excited to announce our latest innovation designed to accelerate progress in weather forecasting, Scalable Ensemble Envelope Diffusion Sampler (SEEDS), recently published in Science Advances. SEEDS is a generative AI model that can efficiently generate ensembles of weather forecasts at scale at a small fraction of the cost of traditional physics-based forecasting models. This technology opens up novel opportunities for weather and climate science, and it represents one of the first applications to weather and climate forecasting of probabilistic diffusion models, a generative AI technology behind recent advances in media generation. The need for probabilistic forecasts: the butterfly effect American Association for the Advancement of Science meeting in Washington, D.C., MIT meteorology professor Ed Lorenz gave a talk entitled, “Does the Flap of a Butterfly's Wings in Brazil Set Off a Tornado in Texas?” which contributed to the term “butterfly effect”. He was building on his earlier, landmark 1963 paper where he examined the feasibility of “very-long-range weather prediction” and described how errors in initial conditions grow exponentially when integrated in time with numerical weather prediction models. This exponential error growth, known as chaos, results in a deterministic predictability limit that restricts the use of individual forecasts in decision making, because they do not quantify the inherent uncertainty of weather conditions. This is particularly problematic when forecasting extreme weather events, such as hurricanes, heatwaves, or floods. Recognizing the limitations of deterministic forecasts, weather agencies around the world issue probabilistic forecasts. Such forecasts are based on ensembles of deterministic forecasts, each of which is generated by including synthetic noise in the initial conditions and stochasticity in the physical processes. Leveraging the fast error growth rate in weather models, the forecasts in an ensemble are purposefully different: the initial uncertainties are tuned to generate runs that are as different as possible and the stochastic processes in the weather model introduce additional differences during the model run. The error growth is mitigated by averaging all the forecasts in the ensemble and the variability in the ensemble of forecasts quantifies the uncertainty of the weather conditions. While effective, generating these probabilistic forecasts is computationally costly. They require running highly complex numerical weather models on massive supercomputers multiple times. Consequently, many operational weather forecasts can only afford to generate ~10–50 ensemble members for each forecast cycle. This is a problem for users concerned with the likelihood of rare but high-impact weather events, which typically require much larger ensembles to assess beyond a few days. For instance, one would need a 10,000-member ensemble to forecast the likelihood of events with 1% probability of occurrence with a relative error less than 10%. Quantifying the probability of such extreme events could be useful, for example, for emergency management preparation or for energy traders. SEEDS: AI-enabled advances paper, we present the Scalable Ensemble Envelope Diffusion Sampler (SEEDS), a generative AI technology for weather forecast ensemble generation. SEEDS is based on denoising diffusion probabilistic models, a state-of-the-art generative AI method pioneered in part by Google Research. SEEDS can generate a large ensemble conditioned on as few as one or two forecasts from an operational numerical weather prediction system. The generated ensembles not only yield plausible real-weather–like forecasts but also match or exceed physics-based ensembles in skill metrics such as the rank histogram, the root-mean-squared error (RMSE), and the continuous ranked probability score (CRPS). In particular, the generated ensembles assign more accurate likelihoods to the tail of the forecast distribution, such as ±2σ and ±3σ weather events. Most importantly, the computational cost of the model is negligible when compared to the hours of computational time needed by supercomputers to make a forecast. It has a throughput of 256 ensemble members (at 2° resolution) per 3 minutes on Google Cloud TPUv3-32 instances and can easily scale to higher throughput by deploying more accelerators. SEEDS generates an order-of-magnitude more samples to in-fill distributions of weather patterns. Generating plausible weather forecasts Global Ensemble Forecast System, GEFS) for a particular date during the 2022 European heat waves. We also compare the results to the forecasts from a Gaussian model that predicts the univariate mean and standard deviation of each atmospheric field at each location, a common and computationally efficient but less sophisticated data-driven approach. This Gaussian model is meant to characterize the output of pointwise post-processing, which ignores correlations and treats each grid point as an independent random variable. In contrast, a real weather map would have detailed correlational structures. Because SEEDS directly models the joint distribution of the atmospheric state, it realistically captures both the spatial covariance and the correlation between mid-tropospheric geopotential and mean sea level pressure, both of which are closely related and are commonly used by weather forecasters for evaluation and verification of forecasts. Gradients in the mean sea level pressure are what drive winds at the surface, while gradients in mid-tropospheric geopotential create upper-level winds that move large-scale weather patterns. The generated samples from SEEDS shown in the figure below (frames Ca–Ch) display a geopotential trough west of Portugal with spatial structure similar to that found in the operational U.S. forecasts or the reanalysis based on observations. Although the Gaussian model predicts the marginal univariate distributions adequately, it fails to capture cross-field or spatial correlations. This hinders the assessment of the effects that these anomalies may have on hot air intrusions from North Africa, which can exacerbate heat waves over Europe. Stamp maps over Europe on 2022/07/14 at 0:00 UTC. The contours are for the mean sea level pressure (dashed lines mark isobars below 1010 hPa) while the heatmap depicts the geopotential height at the 500 hPa pressure level. (A) The ERA5 reanalysis, a proxy for real observations. (Ba-Bb) 2 members from the 7-day U.S. operational forecasts used as seeds to our model. (Ca-Ch) 8 samples drawn from SEEDS. (Da-Dh) 8 non-seeding members from the 7-day U.S. operational ensemble forecast. (Ea-Ed) 4 samples from a pointwise Gaussian model parameterized by the mean and variance of the entire U.S. operational ensemble. Covering extreme events more accurately SEEDS provides better statistical coverage of the 2022/07/14 European extreme heat event, denoted by the brown star . Each plot shows the values of the total column-integrated water vapor (TCVW) vs. temperature over a grid point near Lisbon, Portugal from 16,384 samples generated by our models, shown as green dots, conditioned on 2 seeds (blue squares) taken from the 7-day U.S. operational ensemble forecasts (denoted by the sparser brown triangles). The valid forecast time is 1:00 local time. The solid contour levels correspond to iso-proportions of the kernel density of SEEDS, with the outermost one encircling 95% of the mass and 11.875% between each level. Conclusion and future outlook Acknowledgements All SEEDS authors, Lizao Li, Rob Carver, Ignacio Lopez-Gomez, Fei Sha and John Anderson, co-authored this blog post, with Carla Bromberg as Program Lead. We also thank Tom Small who designed the animation. Our colleagues at Google Research have provided invaluable advice to the SEEDS work. Among them, we thank Leonardo Zepeda-Núñez, Zhong Yi Wan, Stephan Rasp, Stephan Hoyer, and Tapio Schneider for their inputs and useful discussion. We thank Tyler Russell for additional technical program management, as well as Alex Merose for data coordination and support. We also thank Cenk Gazen, Shreya Agrawal, and Jason Hickey for discussions in the early stage of the SEEDS work.

We’re sharing lessons from a small scale preview of Voice Engine, a model for creating custom voices.

Posted by Urs Köster, Software Engineer, Google Research Time series problems are ubiquitous, from forecasting weather and traffic patterns to understanding economic trends. Bayesian approaches start with an assumption about the data's patterns (prior probability), collecting evidence (e.g., new time series data), and continuously updating that assumption to form a posterior probability distribution. Traditional Bayesian approaches like Gaussian processes (GPs) and Structural Time Series are extensively used for modeling time series data, e.g., the commonly used Mauna Loa CO2 dataset. However, they often rely on domain experts to painstakingly select appropriate model components and may be computationally expensive. Alternatives such as neural networks lack interpretability, making it difficult to understand how they generate forecasts, and don't produce reliable confidence intervals. To that end, we introduce AutoBNN, a new open-source package written in JAX. AutoBNN automates the discovery of interpretable time series forecasting models, provides high-quality uncertainty estimates, and scales effectively for use on large datasets. We describe how AutoBNN combines the interpretability of traditional probabilistic approaches with the scalability and flexibility of neural networks. AutoBNN line of research that over the past decade has yielded improved predictive accuracy by modeling time series using GPs with learned kernel structures. The kernel function of a GP encodes assumptions about the function being modeled, such as the presence of trends, periodicity or noise. With learned GP kernels, the kernel function is defined compositionally: it is either a base kernel (such as Linear, Quadratic, Periodic, Matérn or ExponentiatedQuadratic) or a composite that combines two or more kernel functions using operators such as Addition, Multiplication, or ChangePoint. This compositional kernel structure serves two related purposes. First, it is simple enough that a user who is an expert about their data, but not necessarily about GPs, can construct a reasonable prior for their time series. Second, techniques like Sequential Monte Carlo can be used for discrete searches over small structures and can output interpretable results. Bayesian neural networks (BNNs) while retaining the compositional kernel structure. A BNN is a neural network with a probability distribution over weights rather than a fixed set of weights. This induces a distribution over outputs, capturing uncertainty in the predictions. BNNs bring the following advantages over GPs: First, training large GPs is computationally expensive, and traditional training algorithms scale as the cube of the number of data points in the time series. In contrast, for a fixed width, training a BNN will often be approximately linear in the number of data points. Second, BNNs lend themselves better to GPU and TPU hardware acceleration than GP training operations. Third, compositional BNNs can be easily combined with traditional deep BNNs, which have the ability to do feature discovery. One could imagine "hybrid" architectures, in which users specify a top-level structure of Add(Linear, Periodic, Deep), and the deep BNN is left to learn the contributions from potentially high-dimensional covariate information. How might one translate a GP with compositional kernels into a BNN then? A single layer neural network will typically converge to a GP as the number of neurons (or "width") goes to infinity. More recently, researchers have discovered a correspondence in the other direction — many popular GP kernels (such as Matern, ExponentiatedQuadratic, Polynomial or Periodic) can be obtained as infinite-width BNNs with appropriately chosen activation functions and weight distributions. Furthermore, these BNNs remain close to the corresponding GP even when the width is very much less than infinite. For example, the figures below show the difference in the covariance between pairs of observations, and regression results of the true GPs and their corresponding width-10 neural network versions. Comparison of Gram matrices between true GP kernels (top row) and their width 10 neural network approximations (bottom row). Comparison of regression results between true GP kernels (top row) and their width 10 neural network approximations (bottom row). BNN analogues of the Addition and Multiplication operators over GPs, and input warping to produce periodic kernels. BNN addition is straightforwardly given by adding the outputs of the component BNNs. BNN multiplication is achieved by multiplying the activations of the hidden layers of the BNNs and then applying a shared dense layer. We are therefore limited to only multiplying BNNs with the same hidden width. Using AutoBNN package is available within Tensorflow Probability. It is implemented in JAX and uses the flax.linen neural network library. It implements all of the base kernels and operators discussed so far (Linear, Quadratic, Matern, ExponentiatedQuadratic, Periodic, Addition, Multiplication) plus one new kernel and three new operators: a OneLayer kernel, a single hidden layer ReLU BNN, a ChangePoint operator that allows smoothly switching between two kernels, a LearnableChangePoint operator which is the same as ChangePoint except position and slope are given prior distributions and can be learnt from the data, and a WeightedSum operator. WeightedSum combines two or more BNNs with learnable mixing weights, where the learnable weights follow a Dirichlet prior. By default, a flat Dirichlet distribution with concentration 1.0 is used. WeightedSums allow a "soft" version of structure discovery, i.e., training a linear combination of many possible models at once. In contrast to structure discovery with discrete structures, such as in AutoGP, this allows us to use standard gradient methods to learn structures, rather than using expensive discrete optimization. Instead of evaluating potential combinatorial structures in series, WeightedSum allows us to evaluate them in parallel. To easily enable exploration, AutoBNN defines a number of model structures that contain either top-level or internal WeightedSums. The names of these models can be used as the first parameter in any of the estimator constructors, and include things like sum_of_stumps (the WeightedSum over all the base kernels) and sum_of_shallow (which adds all possible combinations of base kernels with all operators). Illustration of the sum_of_stumps model. The bars in the top row show the amount by which each base kernel contributes, and the bottom row shows the function represented by the base kernel. The resulting weighted sum is shown on the right. M3 dataset. The six base structures were ExponentiatedQuadratic (which is the same as the Radial Basis Function kernel, or RBF for short), Matern, Linear, Quadratic, OneLayer and Periodic kernels. The figure shows the MAP estimates of their weights over an ensemble of 32 particles. All of the high likelihood particles gave a large weight to the Periodic component, low weights to Linear, Quadratic and OneLayer, and a large weight to either RBF or Matern. Parallel coordinates plot of the MAP estimates of the base kernel weights over 32 particles. The sum_of_stumps model was trained on the N374 series from the M3 dataset (insert in blue). Darker lines correspond to particles with higher likelihoods. WeightedSums as the inputs to other operators, it is possible to express rich combinatorial structures, while keeping models compact and the number of learnable weights small. As an example, we include the sum_of_products model (illustrated in the figure below) which first creates a pairwise product of two WeightedSums, and then a sum of the two products. By setting some of the weights to zero, we can create many different discrete structures. The total number of possible structures in this model is 216, since there are 16 base kernels that can be turned on or off. All these structures are explored implicitly by training just this one model. Illustration of the "sum_of_products" model. Each of the four WeightedSums have the same structure as the "sum_of_stumps" model. Periodic and either the Matern or ExponentiatedQuadratic) lead to overfitting on many datasets. To prevent this, we have defined model classes like sum_of_safe_shallow that exclude such products when performing structure discovery with WeightedSums. For training, AutoBNN provides AutoBnnMapEstimator and AutoBnnMCMCEstimator to perform MAP and MCMC inference, respectively. Either estimator can be combined with any of the six likelihood functions, including four based on normal distributions with different noise characteristics for continuous data and two based on the negative binomial distribution for count data. Result from running AutoBNN on the Mauna Loa CO2 dataset in our example colab. The model captures the trend and seasonal component in the data. Extrapolating into the future, the mean prediction slightly underestimates the actual trend, while the 95% confidence interval gradually increases. scikit-learn–inspired estimator interface: import autobnn as ab model = ab.operators.Add( bnns=(ab.kernels.PeriodicBNN(width=50), ab.kernels.LinearBNN(width=50), ab.kernels.MaternBNN(width=50))) estimator = ab.estimators.AutoBnnMapEstimator( model, 'normal_likelihood_logistic_noise', jax.random.PRNGKey(42), periods=[12]) estimator.fit(my_training_data_xs, my_training_data_ys) low, mid, high = estimator.predict_quantiles(my_training_data_xs) Conclusion AutoBNN provides a powerful and flexible framework for building sophisticated time series prediction models. By combining the strengths of BNNs and GPs with compositional kernels, AutoBNN opens a world of possibilities for understanding and forecasting complex data. We invite the community to try the colab, and leverage this library to innovate and solve real-world challenges. Acknowledgements AutoBNN was written by Colin Carroll, Thomas Colthurst, Urs Köster and Srinivas Vasudevan. We would like to thank Kevin Murphy, Brian Patton and Feras Saad for their advice and feedback.

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Posted by Atilla Kiraly, Software Engineer, and Rory Pilgrim, Product Manager, Google Research Lung cancer is the leading cause of cancer-related deaths globally with 1.8 million deaths reported in 2020. Late diagnosis dramatically reduces the chances of survival. Lung cancer screening via computed tomography (CT), which provides a detailed 3D image of the lungs, has been shown to reduce mortality in high-risk populations by at least 20% by detecting potential signs of cancers earlier. In the US, screening involves annual scans, with some countries or cases recommending more or less frequent scans. The United States Preventive Services Task Force recently expanded lung cancer screening recommendations by roughly 80%, which is expected to increase screening access for women and racial and ethnic minority groups. However, false positives (i.e., incorrectly reporting a potential cancer in a cancer-free patient) can cause anxiety and lead to unnecessary procedures for patients while increasing costs for the healthcare system. Moreover, efficiency in screening a large number of individuals can be challenging depending on healthcare infrastructure and radiologist availability. At Google we have previously developed machine learning (ML) models for lung cancer detection, and have evaluated their ability to automatically detect and classify regions that show signs of potential cancer. Performance has been shown to be comparable to that of specialists in detecting possible cancer. While they have achieved high performance, effectively communicating findings in realistic environments is necessary to realize their full potential. To that end, in “Assistive AI in Lung Cancer Screening: A Retrospective Multinational Study in the US and Japan”, published in Radiology AI, we investigate how ML models can effectively communicate findings to radiologists. We also introduce a generalizable user-centric interface to help radiologists leverage such models for lung cancer screening. The system takes CT imaging as input and outputs a cancer suspicion rating using four categories (no suspicion, probably benign, suspicious, highly suspicious) along with the corresponding regions of interest. We evaluate the system’s utility in improving clinician performance through randomized reader studies in both the US and Japan, using the local cancer scoring systems (Lung-RADSs V1.1 and Sendai Score) and image viewers that mimic realistic settings. We found that reader specificity increases with model assistance in both reader studies. To accelerate progress in conducting similar studies with ML models, we have open-sourced code to process CT images and generate images compatible with the picture archiving and communication system (PACS) used by radiologists. Developing an interface to communicate model results alpha-numeric score to indicate the lung cancer risk and follow-up recommendations. When assessing patients, radiologists load the CT in their workstation to read the case, find lung nodules or lesions, and apply set guidelines to determine follow-up decisions. Our first step was to improve the previously developed ML models through additional training data and architectural improvements, including self-attention. Then, instead of targeting specific guidelines, we experimented with a complementary way of communicating AI results independent of guidelines or their particular versions. Specifically, the system output offers a suspicion rating and localization (regions of interest) for the user to consider in conjunction with their own specific guidelines. The interface produces output images directly associated with the CT study, requiring no changes to the user’s workstation. The radiologist only needs to review a small set of additional images. There is no other change to their system or interaction with the system. Example of the assistive lung cancer screening system outputs. Results for the radiologist’s evaluation are visualized on the location of the CT volume where the suspicious lesion is found. The overall suspicion is displayed at the top of the CT images. Circles highlight the suspicious lesions while squares show a rendering of the same lesion from a different perspective, called a sagittal view. prior work. The models coordinate with each other to first segment the lungs, obtain an overall assessment, locate three suspicious regions, then use the information to assign a suspicion rating to each region. The system was deployed on Google Cloud using a Google Kubernetes Engine (GKE) that pulled the images, ran the ML models, and provided results. This allows scalability and directly connects to servers where the images are stored in DICOM stores. Outline of the Google Cloud deployment of the assistive lung cancer screening system and the directional calling flow for the individual components that serve the images and compute results. Images are served to the viewer and to the system using Google Cloud services. The system is run on a Google Kubernetes Engine that pulls the images, processes them, and writes them back into the DICOM store. Reader studies area under the ROC curve (AUC) values. These were compared with and without assistance. A multi-case multi-reader study involves each case being reviewed by each reader twice, once with ML system assistance and once without. In this visualization one reader first reviews Set A without assistance (blue) and then with assistance (orange) after a wash-out period. A second reader group follows the opposite path by reading the same set of cases Set A with assistance first. Readers are randomized to these groups to remove the effect of ordering. specificity) by an absolute 5–7% compared to when they didn’t use the assistive system. This potentially means that for every 15–20 patients screened, one may be able to avoid unnecessary follow-up procedures, thus reducing their anxiety and the burden on the health care system. This can, in turn, help improve the sustainability of lung cancer screening programs, particularly as more people become eligible for screening. Reader specificity increases with ML model assistance in both the US-based and Japan-based reader studies. Specificity values were derived from reader scores from actionable findings (something suspicious was found) versus no actionable findings, compared against the true cancer outcome of the individual. Under model assistance, readers flagged fewer cancer-negative individuals for follow-up visits. Sensitivity for cancer positive individuals remained the same. Translating this into real-world impact through partnership DeepHealth, a leading AI-powered health informatics provider; and Apollo Radiology International a leading provider of Radiology services in India to explore paths for incorporating this system into future products. In addition, we are looking to help other researchers studying how best to integrate ML model results into clinical workflows by open sourcing code used for the reader study and incorporating the insights described in this blog. We hope that this will help accelerate medical imaging researchers looking to conduct reader studies for their AI models, and catalyze translational research in the field. Acknowledgements Key contributors to this project include Corbin Cunningham, Zaid Nabulsi, Ryan Najafi, Jie Yang, Charles Lau, Joseph R. Ledsam, Wenxing Ye, Diego Ardila, Scott M. McKinney, Rory Pilgrim, Hiroaki Saito, Yasuteru Shimamura, Mozziyar Etemadi, Yun Liu, David Melnick, Sunny Jansen, Nadia Harhen, David P. Nadich, Mikhail Fomitchev, Ziyad Helali, Shabir Adeel, Greg S. Corrado, Lily Peng, Daniel Tse, Shravya Shetty, Shruthi Prabhakara, Neeral Beladia, and Krish Eswaran. Thanks to Arnav Agharwal and Andrew Sellergren for their open sourcing support and Vivek Natarajan and Michael D. Howell for their feedback. Sincere appreciation also goes to the radiologists who enabled this work with their image interpretation and annotation efforts throughout the study, and Jonny Wong and Carli Sampson for coordinating the reader studies.

Posted by Yossi Matias, VP Engineering & Research, and Grey Nearing, Research Scientist, Google Research Floods are the most common natural disaster, and are responsible for roughly $50 billion in annual financial damages worldwide. The rate of flood-related disasters has more than doubled since the year 2000 partly due to climate change. Nearly 1.5 billion people, making up 19% of the world’s population, are exposed to substantial risks from severe flood events. Upgrading early warning systems to make accurate and timely information accessible to these populations can save thousands of lives per year. Driven by the potential impact of reliable flood forecasting on people’s lives globally, we started our flood forecasting effort in 2017. Through this multi-year journey, we advanced research over the years hand-in-hand with building a real-time operational flood forecasting system that provides alerts on Google Search, Maps, Android notifications and through the Flood Hub. However, in order to scale globally, especially in places where accurate local data is not available, more research advances were required. In “Global prediction of extreme floods in ungauged watersheds”, published in Nature, we demonstrate how machine learning (ML) technologies can significantly improve global-scale flood forecasting relative to the current state-of-the-art for countries where flood-related data is scarce. With these AI-based technologies we extended the reliability of currently-available global nowcasts, on average, from zero to five days, and improved forecasts across regions in Africa and Asia to be similar to what are currently available in Europe. The evaluation of the models was conducted in collaboration with the European Center for Medium Range Weather Forecasting (ECMWF). These technologies also enable Flood Hub to provide real-time river forecasts up to seven days in advance, covering river reaches across over 80 countries. This information can be used by people, communities, governments and international organizations to take anticipatory action to help protect vulnerable populations. Flood forecasting at Google launched a pilot early warning system in the Ganges-Brahmaputra river basin in India, with the hypothesis that ML could help address the challenging problem of reliable flood forecasting at scale. The pilot was further expanded the following year via the combination of an inundation model, real-time water level measurements, the creation of an elevation map and hydrologic modeling. In collaboration with academics, and, in particular, with the JKU Institute for Machine Learning we explored ML-based hydrologic models, showing that LSTM-based models could produce more accurate simulations than traditional conceptual and physics-based hydrology models. This research led to flood forecasting improvements that enabled the expansion of our forecasting coverage to include all of India and Bangladesh. We also worked with researchers at Yale University to test technological interventions that increase the reach and impact of flood warnings. Our hydrological models predict river floods by processing publicly available weather data like precipitation and physical watershed information. Such models must be calibrated to long data records from streamflow gauging stations in individual rivers. A low percentage of global river watersheds (basins) have streamflow gauges, which are expensive but necessary to supply relevant data, and it’s challenging for hydrological simulation and forecasting to provide predictions in basins that lack this infrastructure. Lower gross domestic product (GDP) is correlated with increased vulnerability to flood risks, and there is an inverse correlation between national GDP and the amount of publicly available data in a country. ML helps to address this problem by allowing a single model to be trained on all available river data and to be applied to ungauged basins where no data are available. In this way, models can be trained globally, and can make predictions for any river location. There is an inverse (log-log) correlation between the amount of publicly available streamflow data in a country and national GDP. Streamflow data from the Global Runoff Data Center. estimate uncertainty in river forecasts and showed how ML river forecast models synthesize information from multiple data sources. They demonstrated that these models can simulate extreme events reliably, even when those events are not part of the training data. In an effort to contribute to open science, in 2023 we open-sourced a community-driven dataset for large-sample hydrology in Nature Scientific Data. The river forecast model LSTMs perform well on the task of river forecasting. A diagram of the LSTM, which is a neural network that operates sequentially in time. An accessible primer can be found here. mixture density networks to produce a probabilistic forecast (i.e., predicted parameters of a probability distribution over streamflow). Specifically, the model predicts the parameters of a mixture of heavy-tailed probability density functions, called asymmetric Laplacian distributions, at each forecast time step. The result is a mixture density function, called a Countable Mixture of Asymmetric Laplacians (CMAL) distribution, which represents a probabilistic prediction of the volumetric flow rate in a particular river at a particular time. LSTM-based river forecast model architecture. Two LSTMs are applied in sequence, one ingesting historical weather data and one ingesting forecasted weather data. The model outputs are the parameters of a probability distribution over streamflow at each forecasted timestep. Input and training data Static watershed attributes representing geographical and geophysical variables: From the HydroATLAS project, including data like long-term climate indexes (precipitation, temperature, snow fractions), land cover, and anthropogenic attributes (e.g., a nighttime lights index as a proxy for human development). Historical meteorological time-series data: Used to spin up the model for one year prior to the issue time of a forecast. The data comes from NASA IMERG, NOAA CPC Global Unified Gauge-Based Analysis of Daily Precipitation, and the ECMWF ERA5-land reanalysis. Variables include daily total precipitation, air temperature, solar and thermal radiation, snowfall, and surface pressure. Forecasted meteorological time series over a seven-day forecast horizon: Used as input for the forecast LSTM. These data are the same meteorological variables listed above, and come from the ECMWF HRES atmospheric model. Training data are daily streamflow values from the Global Runoff Data Center over the time period 1980 - 2023. A single streamflow forecast model is trained using data from 5,680 diverse watershed streamflow gauges (shown below) to improve accuracy. Location of 5,680 streamflow gauges that supply training data for the river forecast model from the Global Runoff Data Center. Improving on the current state-of-the-art GloFAS version 4, the current state-of-the-art global flood forecasting system. These experiments showed that ML can provide accurate warnings earlier and over larger and more impactful events. The figure below shows the distribution of F1 scores when predicting different severity events at river locations around the world, with plus or minus 1 day accuracy. F1 scores are an average of precision and recall and event severity is measured by return period. For example, a 2-year return period event is a volume of streamflow that is expected to be exceeded on average once every two years. Our model achieves reliability scores at up to 4-day or 5-day lead times that are similar to or better, on average, than the reliability of GloFAS nowcasts (0-day lead time). Distributions of F1 scores over 2-year return period events in 2,092 watersheds globally during the time period 2014-2023 from GloFAS (blue) and our model (orange) at different lead times. On average, our model is statistically as accurate as GloFAS nowcasts (0–day lead time) up to 5 days in advance over 2-year (shown) and 1-year, 5-year, and 10-year events (not shown). paper for more information. Looking into the future Adaptation and Resilience efforts and reflects Google's commitment to address climate change while helping global communities become more resilient. We believe that AI and ML will continue to play a critical role in helping advance science and research towards climate action. We actively collaborate with several international aid organizations (e.g., the Centre for Humanitarian Data and the Red Cross) to provide actionable flood forecasts. Additionally, in an ongoing collaboration with the World Meteorological Organization (WMO) to support early warning systems for climate hazards, we are conducting a study to help understand how AI can help address real-world challenges faced by national flood forecasting agencies. While the work presented here demonstrates a significant step forward in flood forecasting, future work is needed to further expand flood forecasting coverage to more locations globally and other types of flood-related events and disasters, including flash floods and urban floods. We are looking forward to continuing collaborations with our partners in the academic and expert communities, local governments and the industry to reach these goals.

Posted by Srinivas Sunkara and Gilles Baechler, Software Engineers, Google Research Screen user interfaces (UIs) and infographics, such as charts, diagrams and tables, play important roles in human communication and human-machine interaction as they facilitate rich and interactive user experiences. UIs and infographics share similar design principles and visual language (e.g., icons and layouts), that offer an opportunity to build a single model that can understand, reason, and interact with these interfaces. However, because of their complexity and varied presentation formats, infographics and UIs present a unique modeling challenge. To that end, we introduce “ScreenAI: A Vision-Language Model for UI and Infographics Understanding”. ScreenAI improves upon the PaLI architecture with the flexible patching strategy from pix2struct. We train ScreenAI on a unique mixture of datasets and tasks, including a novel Screen Annotation task that requires the model to identify UI element information (i.e., type, location and description) on a screen. These text annotations provide large language models (LLMs) with screen descriptions, enabling them to automatically generate question-answering (QA), UI navigation, and summarization training datasets at scale. At only 5B parameters, ScreenAI achieves state-of-the-art results on UI- and infographic-based tasks (WebSRC and MoTIF), and best-in-class performance on Chart QA, DocVQA, and InfographicVQA compared to models of similar size. We are also releasing three new datasets: Screen Annotation to evaluate the layout understanding capability of the model, as well as ScreenQA Short and Complex ScreenQA for a more comprehensive evaluation of its QA capability. ScreenAI PaLI, composed of a multimodal encoder block and an autoregressive decoder. The PaLI encoder uses a vision transformer (ViT) that creates image embeddings and a multimodal encoder that takes the concatenation of the image and text embeddings as input. This flexible architecture allows ScreenAI to solve vision tasks that can be recast as text+image-to-text problems. On top of the PaLI architecture, we employ a flexible patching strategy introduced in pix2struct. Instead of using a fixed-grid pattern, the grid dimensions are selected such that they preserve the native aspect ratio of the input image. This enables ScreenAI to work well across images of various aspect ratios. The ScreenAI model is trained in two stages: a pre-training stage followed by a fine-tuning stage. First, self-supervised learning is applied to automatically generate data labels, which are then used to train ViT and the language model. ViT is frozen during the fine-tuning stage, where most data used is manually labeled by human raters. ScreenAI model architecture. Data generation publicly accessible web pages and following the programmatic exploration approach used for the RICO dataset for mobile apps. We then apply a layout annotator, based on the DETR model, that identifies and labels a wide range of UI elements (e.g., image, pictogram, button, text) and their spatial relationships. Pictograms undergo further analysis using an icon classifier capable of distinguishing 77 different icon types. This detailed classification is essential for interpreting the subtle information conveyed through icons. For icons that are not covered by the classifier, and for infographics and images, we use the PaLI image captioning model to generate descriptive captions that provide contextual information. We also apply an optical character recognition (OCR) engine to extract and annotate textual content on screen. We combine the OCR text with the previous annotations to create a detailed description of each screen. A mobile app screenshot with generated annotations that include UI elements and their descriptions, e.g., TEXT elements also contain the text content from OCR, IMAGE elements contain image captions, LIST_ITEMs contain all their child elements. LLM-based data generation PaLM 2 to generate input-output pairs in a two-step process. First, screen annotations are generated using the technique outlined above, then we craft a prompt around this schema for the LLM to create synthetic data. This process requires prompt engineering and iterative refinement to find an effective prompt. We assess the generated data's quality through human validation against a quality threshold. You only speak JSON. Do not write text that isn’t JSON. You are given the following mobile screenshot, described in words. Can you generate 5 questions regarding the content of the screenshot as well as the corresponding short answers to them? The answer should be as short as possible, containing only the necessary information. Your answer should be structured as follows: questions: [ {{question: the question, answer: the answer }}, ... ] {THE SCREEN SCHEMA} A sample prompt for QA data generation. Question answering: The model is asked to answer questions regarding the content of the screenshots, e.g., “When does the restaurant open?” Screen navigation: The model is asked to convert a natural language utterance into an executable action on a screen, e.g., “Click the search button.” Screen summarization: The model is asked to summarize the screen content in one or two sentences. Block diagram of our workflow for generating data for QA, summarization and navigation tasks using existing ScreenAI models and LLMs. Each task uses a custom prompt to emphasize desired aspects, like questions related to counting, involving reasoning, etc. LLM-generated data. Examples for screen QA, navigation and summarization. For navigation, the action bounding box is displayed in red on the screenshot. Experiments and results ChartQA, DocVQA, Multi page DocVQA, InfographicVQA, OCR VQA, Web SRC and ScreenQA. For navigation, datasets used include Referring Expressions, MoTIF, Mug, and Android in the Wild. Finally, we use Screen2Words for screen summarization and Widget Captioning for describing specific UI elements. Along with the fine-tuning datasets, we evaluate the fine-tuned ScreenAI model using three novel benchmarks: Screen Annotation: Enables the evaluation model layout annotations and spatial understanding capabilities. ScreenQA Short: A variation of ScreenQA, where its ground truth answers have been shortened to contain only the relevant information that better aligns with other QA tasks. Complex ScreenQA: Complements ScreenQA Short with more difficult questions (counting, arithmetic, comparison, and non-answerable questions) and contains screens with various aspect ratios. The fine-tuned ScreenAI model achieves state-of-the-art results on various UI and infographic-based tasks (WebSRC and MoTIF) and best-in-class performance on Chart QA, DocVQA, and InfographicVQA compared to models of similar size. ScreenAI achieves competitive performance on Screen2Words and OCR-VQA. Additionally, we report results on the new benchmark datasets introduced to serve as a baseline for further research. Comparing model performance of ScreenAI with state-of-the-art (SOTA) models of similar size. Model performance increases with size, and the performance has not saturated even at the largest size of 5B params. Conclusion Acknowledgements This project is the result of joint work with Maria Wang, Fedir Zubach, Hassan Mansoor, Vincent Etter, Victor Carbune, Jason Lin, Jindong Chen and Abhanshu Sharma. We thank Fangyu Liu, Xi Chen, Efi Kokiopoulou, Jesse Berent, Gabriel Barcik, Lukas Zilka, Oriana Riva, Gang Li,Yang Li, Radu Soricut, and Tania Bedrax-Weiss for their insightful feedback and discussions, along with Rahul Aralikatte, Hao Cheng and Daniel Kim for their support in data preparation. We also thank Jay Yagnik, Blaise Aguera y Arcas, Ewa Dominowska, David Petrou, and Matt Sharifi for their leadership, vision and support. We are very grateful toTom Small for helping us create the animation in this post.

Posted by Pooja Rao, Research Scientist, Google Research Health datasets play a crucial role in research and medical education, but it can be challenging to create a dataset that represents the real world. For example, dermatology conditions are diverse in their appearance and severity and manifest differently across skin tones. Yet, existing dermatology image datasets often lack representation of everyday conditions (like rashes, allergies and infections) and skew towards lighter skin tones. Furthermore, race and ethnicity information is frequently missing, hindering our ability to assess disparities or create solutions. To address these limitations, we are releasing the Skin Condition Image Network (SCIN) dataset in collaboration with physicians at Stanford Medicine. We designed SCIN to reflect the broad range of concerns that people search for online, supplementing the types of conditions typically found in clinical datasets. It contains images across various skin tones and body parts, helping to ensure that future AI tools work effectively for all. We've made the SCIN dataset freely available as an open-access resource for researchers, educators, and developers, and have taken careful steps to protect contributor privacy. Example set of images and metadata from the SCIN dataset. Dataset composition tanning propensity (self-reported Fitzpatrick Skin Type, i.e., sFST), and to describe the texture, duration and symptoms related to their concern. One to three dermatologists labeled each contribution with up to five dermatology conditions, along with a confidence score for each label. The SCIN dataset contains these individual labels, as well as an aggregated and weighted differential diagnosis derived from them that could be useful for model testing or training. These labels were assigned retrospectively and are not equivalent to a clinical diagnosis, but they allow us to compare the distribution of dermatology conditions in the SCIN dataset with existing datasets. The SCIN dataset contains largely allergic, inflammatory and infectious conditions while datasets from clinical sources focus on benign and malignant neoplasms. Monk Skin Tone (eMST) for the images. This allowed comparison of the skin condition and skin type distributions to those in existing dermatology datasets. Although we did not selectively target any skin types or skin tones, the SCIN dataset has a balanced Fitzpatrick skin type distribution (with more of Types 3, 4, 5, and 6) compared to similar datasets from clinical sources. Self-reported and dermatologist-estimated Fitzpatrick Skin Type distribution in the SCIN dataset compared with existing un-enriched dermatology datasets (Fitzpatrick17k, PH², SKINL2, and PAD-UFES-20). Fitzpatrick Skin Type scale was originally developed as a photo-typing scale to measure the response of skin types to UV radiation, and it is widely used in dermatology research. The Monk Skin Tone scale is a newer 10-shade scale that measures skin tone rather than skin phototype, capturing more nuanced differences between the darker skin tones. While neither scale was intended for retrospective estimation using images, the inclusion of these labels is intended to enable future research into skin type and tone representation in dermatology. For example, the SCIN dataset provides an initial benchmark for the distribution of these skin types and tones in the US population. The SCIN dataset has a high representation of women and younger individuals, likely reflecting a combination of factors. These could include differences in skin condition incidence, propensity to seek health information online, and variations in willingness to contribute to research across demographics. Crowdsourcing method research paper co-authored with investigators at Stanford Medicine. This approach empowers individuals to play an active role in healthcare research. It allows us to reach people at earlier stages of their health concerns, potentially before they seek formal care. Crucially, this method uses advertisements on web search result pages — the starting point for many people’s health journey — to connect with participants. Our results demonstrate that crowdsourcing can yield a high-quality dataset with a low spam rate. Over 97.5% of contributions were genuine images of skin conditions. After performing further filtering steps to exclude images that were out of scope for the SCIN dataset and to remove duplicates, we were able to release nearly 90% of the contributions received over the 8-month study period. Most images were sharp and well-exposed. Approximately half of the contributions include self-reported demographics, and 80% contain self-reported information relating to the skin condition, such as texture, duration, or other symptoms. We found that dermatologists’ ability to retrospectively assign a differential diagnosis depended more on the availability of self-reported information than on image quality. Dermatologist confidence in their labels (scale from 1-5) depended on the availability of self-reported demographic and symptom information. Data Use License prohibits attempts to re-identify contributors. We hope the SCIN dataset will be a helpful resource for those working to advance inclusive dermatology research, education, and AI tool development. By demonstrating an alternative to traditional dataset creation methods, SCIN paves the way for more representative datasets in areas where self-reported data or retrospective labeling is feasible. Acknowledgements We are grateful to all our co-authors Abbi Ward, Jimmy Li, Julie Wang, Sriram Lakshminarasimhan, Ashley Carrick, Bilson Campana, Jay Hartford, Pradeep Kumar S, Tiya Tiyasirisokchai, Sunny Virmani, Renee Wong, Yossi Matias, Greg S. Corrado, Dale R. Webster, Dawn Siegel (Stanford Medicine), Steven Lin (Stanford Medicine), Justin Ko (Stanford Medicine), Alan Karthikesalingam and Christopher Semturs. We also thank Yetunde Ibitoye, Sami Lachgar, Lisa Lehmann, Javier Perez, Margaret Ann Smith (Stanford Medicine), Rachelle Sico, Amit Talreja, Annisah Um’rani and Wayne Westerlind for their essential contributions to this work. Finally, we are grateful to Heather Cole-Lewis, Naama Hammel, Ivor Horn, Michael Howell, Yun Liu, and Eric Teasley for their insightful comments on the study design and manuscript.

Posted by Mark Matthews, Senior Software Engineer, and Dmitry Lagun, Research Scientist, Google Research A person's prior experience and understanding of the world generally enables them to easily infer what an object looks like in whole, even if only looking at a few 2D pictures of it. Yet the capacity for a computer to reconstruct the shape of an object in 3D given only a few images has remained a difficult algorithmic problem for years. This fundamental computer vision task has applications ranging from the creation of e-commerce 3D models to autonomous vehicle navigation. A key part of the problem is how to determine the exact positions from which images were taken, known as pose inference. If camera poses are known, a range of successful techniques — such as neural radiance fields (NeRF) or 3D Gaussian Splatting — can reconstruct an object in 3D. But if these poses are not available, then we face a difficult “chicken and egg” problem where we could determine the poses if we knew the 3D object, but we can’t reconstruct the 3D object until we know the camera poses. The problem is made harder by pseudo-symmetries — i.e., many objects look similar when viewed from different angles. For example, square objects like a chair tend to look similar every 90° rotation. Pseudo-symmetries of an object can be revealed by rendering it on a turntable from various angles and plotting its photometric self-similarity map. Self-Similarity map of a toy truck model. Left: The model is rendered on a turntable from various azimuthal angles, θ. Right: The average L2 RGB similarity of a rendering from θ with that of θ*. The pseudo-similarities are indicated by the dashed red lines. ill-posed, with naïve approaches often converging to local minima. In practice, such an approach might mistake the back view as the front view of an object, because they share a similar silhouette. Previous techniques (such as BARF or SAMURAI) side-step this problem by relying on an initial pose estimate that starts close to the global minima. But how can we approach this if those aren’t available? Methods, such as GNeRF and VMRF leverage generative adversarial networks (GANs) to overcome the problem. These techniques have the ability to artificially “amplify” a limited number of training views, aiding reconstruction. GAN techniques, however, often have complex, sometimes unstable, training processes, making robust and reliable convergence difficult to achieve in practice. A range of other successful methods, such as SparsePose or RUST, can infer poses from a limited number views, but require pre-training on a large dataset of posed images, which aren’t always available, and can suffer from “domain-gap” issues when inferring poses for different types of images. In “MELON: NeRF with Unposed Images in SO(3)”, spotlighted at 3DV 2024, we present a technique that can determine object-centric camera poses entirely from scratch while reconstructing the object in 3D. MELON (Modulo Equivalent Latent Optimization of NeRF) is one of the first techniques that can do this without initial pose camera estimates, complex training schemes or pre-training on labeled data. MELON is a relatively simple technique that can easily be integrated into existing NeRF methods. We demonstrate that MELON can reconstruct a NeRF from unposed images with state-of-the-art accuracy while requiring as few as 4–6 images of an object. MELON convolutional neural network (CNN) encoder that regresses camera poses from training images. We pass a downscaled training image to a four layer CNN that infers the camera pose. This CNN is initialized from noise and requires no pre-training. Its capacity is so small that it forces similar looking images to similar poses, providing an implicit regularization greatly aiding convergence. The second technique is a modulo loss that simultaneously considers pseudo symmetries of an object. We render the object from a fixed set of viewpoints for each training image, backpropagating the loss only through the view that best fits the training image. This effectively considers the plausibility of multiple views for each image. In practice, we find N=2 views (viewing an object from the other side) is all that’s required in most cases, but sometimes get better results with N=4 for square objects. These two techniques are integrated into standard NeRF training, except that instead of fixed camera poses, poses are inferred by the CNN and duplicated by the modulo loss. Photometric gradients back-propagate through the best-fitting cameras into the CNN. We observe that cameras generally converge quickly to globally optimal poses (see animation below). After training of the neural field, MELON can synthesize novel views using standard NeRF rendering methods. We simplify the problem by using the NeRF-Synthetic dataset, a popular benchmark for NeRF research and common in the pose-inference literature. This synthetic dataset has cameras at precisely fixed distances and a consistent “up” orientation, requiring us to infer only the polar coordinates of the camera. This is the same as an object at the center of a globe with a camera always pointing at it, moving along the surface. We then only need the latitude and longitude (2 degrees of freedom) to specify the camera pose. MELON uses a dynamically trained lightweight CNN encoder that predicts a pose for each image. Predicted poses are replicated by the modulo loss, which only penalizes the smallest L2 distance from the ground truth color. At evaluation time, the neural field can be used to generate novel views. Results peak signal-to-noise ratio (PSNR) against held out test views. We see that MELON quickly converges to the approximate poses of most cameras within the first 1,000 steps of training, and achieves a competitive PSNR of 27.5 dB after 50k steps. Convergence of MELON on a toy truck model during optimization. Left: Rendering of the NeRF. Right: Polar plot of predicted (blue x), and ground truth (red dot) cameras. Reconstruction quality comparison between ground-truth (GT) and MELON on NeRF-Synthetic scenes after 100k training steps. Noisy images novel view synthesis from extremely noisy, unposed images. We add varying amounts, σ, of white Gaussian noise to the training images. For example, the object in σ=1.0 below is impossible to make out, yet MELON can determine the pose and generate novel views of the object. Novel view synthesis from noisy unposed 128×128 images. Top: Example of noise level present in training views. Bottom: Reconstructed model from noisy training views and mean angular pose error. RawNeRF have demonstrated NeRF’s excellent de-noising capabilities with known camera poses. The fact that MELON works for noisy images of unknown camera poses so robustly was unexpected. Conclusion paper and MELON site to learn more. Acknowledgements We would like to thank our paper co-authors Axel Levy, Matan Sela, and Gordon Wetzstein, as well as Florian Schroff and Hartwig Adam for continuous help in building this technology. We also thank Matthew Brown, Ricardo Martin-Brualla and Frederic Poitevin for their helpful feedback on the paper draft. We also acknowledge the use of the computational resources at the SLAC Shared Scientific Data Facility (SDF).

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