Tag Archives: AI

Advancing genomics to better understand and treat disease

Genome sequencing can help us better understand, diagnose and treat disease. For example, healthcare providers are increasingly using genome sequencing to diagnose rare genetic diseases, such as elevated risk for breast cancer or pulmonary arterial hypertension, which are estimated to affect roughly 8% of the population.

At Google Health, we’re applying our technology and expertise to the field of genomics. Here are recent research and industry developments we’ve made to help quickly identify genetic disease and foster the equity of genomic tests across ancestries. This includes an exciting new partnership with Pacific Biosciences to further advance genomic technologies in research and the clinic.

Helping identify life-threatening disease when minutes matter

Genetic diseases can cause critical illness, and in many cases, a timely identification of the underlying issue can allow for life-saving intervention. This is especially true in the case of newborns. Genetic or congenital conditions affect nearly 6% of births, but clinical sequencing tests to identify these conditions typically take days or weeks to complete.

We recently worked with the University of California Santa Cruz Genomics Institute to build a method – called PEPPER-Margin-DeepVariant – that can analyze data for Oxford Nanopore sequencers, one of the fastest commercial sequencing technologies used today. This week, the New England Journal of Medicine published a study led by the Stanford University School of Medicine detailing the use of this method to identify suspected disease-causing variants in five critical newborn intensive care unit (NICU) cases.

In the fastest cases, a likely disease-causing variant was identified less than 8 hours after sequencing began, compared to the prior fastest time of 13.5 hours. In five cases, the method influenced patient care. For example, the team quickly turned around a diagnosis of Poirier–Bienvenu neurodevelopmental disorder for one infant, allowing for timely, disease-specific treatment.

Time required to sequence and analyze individuals in the pilot study. Disease-causing variants were identified in patient IDs 1, 2, 8, 9, and 11.

Applying machine learning to maximize the potential in sequencing data

Looking forward, new sequencing instruments can lead to dramatic breakthroughs in the field. We believe machine learning (ML) can further unlock the potential of these instruments. Our new research partnership with Pacific Biosciences (PacBio), a developer of genomic sequence platforms, is a great example of how Google’s machine learning and algorithm development tools can help researchers unlock more information from sequencing data.

PacBio’s long-read HiFi sequencing provides the most comprehensive view of genomes, transcriptomes and epigenomes. Using PacBio’s technology in combination with DeepVariant, our award-winning variant detection method, researchers have been able to accurately identify diseases that are otherwise difficult to diagnose with alternative methods.

Additionally, we developed a new open source method called DeepConsensus that, in combination with PacBio’s sequencing platforms, creates more accurate reads of sequencing data. This boost in accuracy will help researchers apply PacBio’s technology to more challenges, such as the final completion of the Human Genome and assembling the genomes of all vertebrate species.

Supporting more equitable genomics resources and methods

Like other areas of health and medicine, the genomics field grapples with health equity issues that, if not addressed, could exclude certain populations. For example, the overwhelming majority of participants in genomic studies have historically been of European ancestry. As a result, the genomics resources that scientists and clinicians use to identify and filter genetic variants and to interpret the significance of these variants are not equally powerful across individuals of all ancestries.

In the past year, we’ve supported two initiatives aimed at improving methods and genomics resources for under-represented populations. We collaborated with 23andMe to develop an improved resource for individuals of African ancestry, and we worked with the UCSC Genomics Institute to develop pangenome methods with this work recently published in Science.

In addition, we recently published two open-source methods that improve genetic discovery by more accurately identifying disease labels and improving the use of health measurements in genetic association studies.

We hope that our work developing and sharing these methods with those in the field of genomics will improve overall health and the understanding of biology for everyone. Working together with our collaborators, we can apply this work to real-world applications.

Google Research: Themes from 2021 and Beyond

Over the last several decades, I've witnessed a lot of change in the fields of machine learning (ML) and computer science. Early approaches, which often fell short, eventually gave rise to modern approaches that have been very successful. Following that long-arc pattern of progress, I think we'll see a number of exciting advances over the next several years, advances that will ultimately benefit the lives of billions of people with greater impact than ever before. In this post, I’ll highlight five areas where ML is poised to have such impact. For each, I’ll discuss related research (mostly from 2021) and the directions and progress we’ll likely see in the next few years.

 · Trend 1: More Capable, General-Purpose ML Models
 · Trend 2: Continued Efficiency Improvements for ML
 · Trend 3: ML Is Becoming More Personally and Communally Beneficial
 · Trend 4: Growing Benefits of ML in Science, Health and Sustainability
 · Trend 5: Deeper and Broader Understanding of ML

Trend 1: More Capable, General-Purpose ML Models
Researchers are training larger, more capable machine learning models than ever before. For example, just in the last couple of years models in the language domain have grown from billions of parameters trained on tens of billions of tokens of data (e.g., the 11B parameter T5 model), to hundreds of billions or trillions of parameters trained on trillions of tokens of data (e.g., dense models such as OpenAI’s 175B parameter GPT-3 model and DeepMind’s 280B parameter Gopher model, and sparse models such as Google’s 600B parameter GShard model and 1.2T parameter GLaM model). These increases in dataset and model size have led to significant increases in accuracy for a wide variety of language tasks, as shown by across-the-board improvements on standard natural language processing (NLP) benchmark tasks (as predicted by work on neural scaling laws for language models and machine translation models).

Many of these advanced models are focused on the single but important modality of written language and have shown state-of-the-art results in language understanding benchmarks and open-ended conversational abilities, even across multiple tasks in a domain. They have also shown exciting capabilities to generalize to new language tasks with relatively little training data, in some cases, with few to no training examples for a new task. A couple of examples include improved long-form question answering, zero-label learning in NLP, and our LaMDA model, which demonstrates a sophisticated ability to carry on open-ended conversations that maintain significant context across multiple turns of dialog.

A dialog with LaMDA mimicking a Weddell seal with the preset grounding prompt, “Hi I’m a weddell seal. Do you have any questions for me?” The model largely holds down a dialog in character.
(Weddell Seal image cropped from Wikimedia CC licensed image.)

Transformer models are also having a major impact in image, video, and speech models, all of which also benefit significantly from scale, as predicted by work on scaling laws for visual transformer models. Transformers for image recognition and for video classification are achieving state-of-the-art results on many benchmarks, and we’ve also demonstrated that co-training models on both image data and video data can improve performance on video tasks compared with video data alone. We’ve developed sparse, axial attention mechanisms for image and video transformers that use computation more efficiently, found better ways of tokenizing images for visual transformer models, and improved our understanding of visual transformer methods by examining how they operate compared with convolutional neural networks. Combining transformer models with convolutional operations has shown significant benefits in visual as well as speech recognition tasks.

The outputs of generative models are also substantially improving. This is most apparent in generative models for images, which have made significant strides over the last few years. For example, recent models have demonstrated the ability to create realistic images given just a category (e.g., "irish setter" or "streetcar", if you desire), can "fill in" a low-resolution image to create a natural-looking high-resolution counterpart ("computer, enhance!"), and can even create natural-looking aerial nature scenes of arbitrary length. As another example, images can be converted to a sequence of discrete tokens that can then be synthesized at high fidelity with an autoregressive generative model.

Example of a cascade diffusion models that generate novel images from a given category and then use those as the seed to create high-resolution examples: the first model generates a low resolution image, and the rest perform upsampling to the final high resolution image.
The SR3 super-resolution diffusion model takes as input a low-resolution image, and builds a corresponding high resolution image from pure noise.

Because these are powerful capabilities that come with great responsibility, we carefully vet potential applications of these sorts of models against our AI Principles.

Beyond advanced single-modality models, we are also starting to see large-scale multi-modal models. These are some of the most advanced models to date because they can accept multiple different input modalities (e.g., language, images, speech, video) and, in some cases, produce different output modalities, for example, generating images from descriptive sentences or paragraphs, or describing the visual content of images in human languages. This is an exciting direction because like the real world, some things are easier to learn in data that is multimodal (e.g., reading about something and seeing a demonstration is more useful than just reading about it). As such, pairing images and text can help with multi-lingual retrieval tasks, and better understanding of how to pair text and image inputs can yield improved results for image captioning tasks. Similarly, jointly training on visual and textual data can also help improve accuracy and robustness on visual classification tasks, while co-training on image, video, and audio tasks improves generalization performance for all modalities. There are also tantalizing hints that natural language can be used as an input for image manipulation, telling robots how to interact with the world and controlling other software systems, portending potential changes to how user interfaces are developed. Modalities handled by these models will include speech, sounds, images, video, and languages, and may even extend to structured data, knowledge graphs, and time series data.

Example of a vision-based robotic manipulation system that is able to generalize to novel tasks. Left: The robot is performing a task described in natural language to the robot as “place grapes in ceramic bowl”, without the model being trained on that specific task. Right: As on the left, but with the novel task description of “place bottle in tray”.

Often these models are trained using self-supervised learning approaches, where the model learns from observations of “raw” data that has not been curated or labeled, e.g., language models used in GPT-3 and GLaM, the self-supervised speech model BigSSL, the visual contrastive learning model SimCLR, and the multimodal contrastive model VATT. Self-supervised learning allows a large speech recognition model to match the previous Voice Search automatic speech recognition (ASR) benchmark accuracy while using only 3% of the annotated training data. These trends are exciting because they can substantially reduce the effort required to enable ML for a particular task, and because they make it easier (though by no means trivial) to train models on more representative data that better reflects different subpopulations, regions, languages, or other important dimensions of representation.

All of these trends are pointing in the direction of training highly capable general-purpose models that can handle multiple modalities of data and solve thousands or millions of tasks. By building in sparsity, so that the only parts of a model that are activated for a given task are those that have been optimized for it, these multimodal models can be made highly efficient. Over the next few years, we are pursuing this vision in a next-generation architecture and umbrella effort called Pathways. We expect to see substantial progress in this area, as we combine together many ideas that to date have been pursued relatively independently.

Pathways: a depiction of a single model we are working towards that can generalize across millions of tasks.

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Trend 2: Continued Efficiency Improvements for ML
Improvements in efficiency — arising from advances in computer hardware design as well as ML algorithms and meta-learning research — are driving greater capabilities in ML models. Many aspects of the ML pipeline, from the hardware on which a model is trained and executed to individual components of the ML architecture, can be optimized for efficiency while maintaining or improving on state-of-the-art performance overall. Each of these different threads can improve efficiency by a significant multiplicative factor, and taken together, can reduce computational costs, including CO2 equivalent emissions (CO2e), by orders of magnitude compared to just a few years ago. This greater efficiency has enabled a number of critical advances that will continue to dramatically improve the efficiency of machine learning, enabling larger, higher quality ML models to be developed cost effectively and further democratizing access. I’m very excited about these directions of research!

Continued Improvements in ML Accelerator Performance

Each generation of ML accelerator improves on previous generations, enabling faster performance per chip, and often increasing the scale of the overall systems. Last year, we announced our TPUv4 systems, the fourth generation of Google’s Tensor Processing Unit, which demonstrated a 2.7x improvement over comparable TPUv3 results in the MLPerf benchmarks. Each TPUv4 chip has ~2x the peak performance per chip versus the TPUv3 chip, and the scale of each TPUv4 pod is 4096 chips (4x that of TPUv3 pods), yielding a performance of approximately 1.1 exaflops per pod (versus ~100 petaflops per TPUv3 pod). Having pods with larger numbers of chips that are connected together with high speed networks improves efficiency for larger models.

ML capabilities on mobile devices are also increasing significantly. The Pixel 6 phone features a brand new Google Tensor processor that integrates a powerful ML accelerator to better support important on-device features.

Left: TPUv4 board; Center: Part of a TPUv4 pod; Right: Google Tensor chip found in Pixel 6 phones.

Our use of ML to accelerate the design of computer chips of all kinds (more on this below) is also paying dividends, particularly to produce better ML accelerators.

Continued Improvements in ML Compilation and Optimization of ML Workloads

Even when the hardware is unchanged, improvements in compilers and other optimizations in system software for machine learning accelerators can lead to significant improvements in efficiency. For example, “A Flexible Approach to Autotuning Multi-pass Machine Learning Compilers” shows how to use machine learning to perform auto-tuning of compilation settings to get across-the-board performance improvements of 5-15% (and sometimes as much as 2.4x improvement) for a suite of ML programs on the same underlying hardware. GSPMD describes an automatic parallelization system based on the XLA compiler that is capable of scaling most deep learning network architectures beyond the memory capacity of an accelerator and has been applied to many large models, such as GShard-M4, LaMDA, BigSSL, ViT, MetNet-2, and GLaM, leading to state-of-the-art results across several domains.

End-to-end model speedups from using ML-based compiler autotuning on 150 ML models. Included are models that achieve improvements of 5% or more. Bar colors represent relative improvement from optimizing different model components.

Human-Creativity–Driven Discovery of More Efficient Model Architectures

Continued improvements in model architectures give substantial reductions in the amount of computation needed to achieve a given level of accuracy for many problems. For example, the Transformer architecture, which we developed in 2017, was able to improve the state of the art on several NLP and translation benchmarks while simultaneously using 10x to 100x less computation to achieve these results than a variety of other prevalent methods, such as LSTMs and other recurrent architectures. Similarly, the Vision Transformer was able to show improved state-of-the-art results on a number of different image classification tasks despite using 4x to 10x less computation than convolutional neural networks.

Machine-Driven Discovery of More Efficient Model Architectures

Neural architecture search (NAS) can automatically discover new ML architectures that are more efficient for a given problem domain. A primary advantage of NAS is that it can greatly reduce the effort needed for algorithm development, because NAS requires only a one-time effort per search space and problem domain combination. In addition, while the initial effort to perform NAS can be computationally expensive, the resulting models can greatly reduce computation in downstream research and production settings, resulting in greatly reduced resource requirements overall. For example, the one-time search to discover the Evolved Transformer generated only 3.2 tons of CO2e (much less than the 284t CO2e reported elsewhere; see Appendix C and D in this joint Google/UC Berkeley preprint), but yielded a model for use by anyone in the NLP community that is 15-20% more efficient than the plain Transformer model. A more recent use of NAS discovered an even more efficient architecture called Primer (that has also been open-sourced), which reduces training costs by 4x compared to a plain Transformer model. In this way, the discovery costs of NAS searches are often recouped from the use of the more-efficient model architectures that are discovered, even if they are applied to only a handful of downstream uses (and many NAS results are reused thousands of times).

The Primer architecture discovered by NAS is 4x as efficient compared with a plain Transformer model. This image shows (in red) the two main modifications that give Primer most of its gains: depthwise convolution added to attention multi-head projections and squared ReLU activations (blue indicates portions of the original Transformer).

NAS has also been used to discover more efficient models in the vision domain. The EfficientNetV2 model architecture is the result of a neural architecture search that jointly optimizes for model accuracy, model size, and training speed. On the ImageNet benchmark, EfficientNetV2 improves training speed by 5–11x while substantially reducing model size over previous state-of-the-art models. The CoAtNet model architecture was created with an architecture search that uses ideas from the Vision Transformer and convolutional networks to create a hybrid model architecture that trains 4x faster than the Vision Transformer and achieves a new ImageNet state of the art.

EfficientNetV2 achieves much better training efficiency than prior models for ImageNet classification.

The broad use of search to help improve ML model architectures and algorithms, including the use of reinforcement learning and evolutionary techniques, has inspired other researchers to apply this approach to different domains. To aid others in creating their own model searches, we have open-sourced Model Search, a platform that enables others to explore model search for their domains of interest. In addition to model architectures, automated search can also be used to find new, more efficient reinforcement learning algorithms, building on the earlier AutoML-Zero work that demonstrated this approach for automating supervised learning algorithm discovery.

Use of Sparsity

Sparsity, where a model has a very large capacity, but only some parts of the model are activated for a given task, example or token, is another important algorithmic advance that can greatly improve efficiency. In 2017, we introduced the sparsely-gated mixture-of-experts layer, which demonstrated better results on a variety of translation benchmarks while using 10x less computation than previous state-of-the-art dense LSTM models. More recently, Switch Transformers, which pair a mixture-of-experts–style architecture with the Transformer model architecture, demonstrated a 7x speedup in training time and efficiency over the dense T5-Base Transformer model. The GLaM model showed that transformers and mixture-of-expert–style layers can be combined to produce a model that exceeds the accuracy of the GPT-3 model on average across 29 benchmarks using 3x less energy for training and 2x less computation for inference. The notion of sparsity can also be applied to reduce the cost of the attention mechanism in the core Transformer architecture.

The BigBird sparse attention model consists of global tokens that attend to all parts of an input sequence, local tokens, and a set of random tokens. Theoretically, this can be interpreted as adding a few global tokens on a Watts-Strogatz graph.

The use of sparsity in models is clearly an approach with very high potential payoff in terms of computational efficiency, and we are only scratching the surface in terms of research ideas to be tried in this direction.

Each of these approaches for improved efficiency can be combined together so that equivalent-accuracy language models trained today in efficient data centers are ~100 times more energy efficient and produce ~650 times less CO2e emissions, compared to a baseline Transformer model trained using P100 GPUs in an average U.S. datacenter using an average U.S. energy mix. And this doesn’t even account for Google’s carbon-neutral, 100% renewable energy offsets. We’ll have a more detailed blog post analyzing the carbon emissions trends of NLP models soon.

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Trend 3: ML Is Becoming More Personally and Communally Beneficial
A host of new experiences are made possible as innovation in ML and silicon hardware (like the Google Tensor processor on the Pixel 6) enable mobile devices to be more capable of continuously and efficiently sensing their surrounding context and environment. These advances have improved accessibility and ease of use, while also boosting computational power, which is critical for popular features like mobile photography, live translation and more. Remarkably, recent technological advances also provide users with a more customized experience while strengthening privacy safeguards.

More people than ever rely on their phone cameras to record their daily lives and for artistic expression. The clever application of ML to computational photography has continued to advance the capabilities of phone cameras, making them easier to use, improving performance, and resulting in higher-quality images. Advances, such as improved HDR+, the ability to take pictures in very low light, better handling of portraits, and efforts to make cameras more inclusive so they work for all skin tones, yield better photos that are more true to the photographer’s vision and to their subjects. Such photos can be further improved using the powerful ML-based tools now available in Google Photos, like cinematic photos, noise and blur reduction, and the Magic Eraser.

HDR+ starts from a burst of full-resolution raw images, each underexposed by the same amount (left). The merged image has reduced noise and increased dynamic range, leading to a higher quality final result (right).

In addition to using their phones for creative expression, many people rely on them to help communicate with others across languages and modalities in real-time using Live Translate in messaging apps and Live Caption for phone calls. Speech recognition accuracy has continued to make substantial improvements thanks to techniques like self-supervised learning and noisy student training, with marked improvements for accented speech, noisy conditions or environments with overlapping speech, and across many languages. Building on advances in text-to-speech synthesis, people can listen to web pages and articles using our Read Aloud technology on a growing number of platforms, making information more available across barriers of modality and languages. Live speech translations in the Google Translate app have become significantly better by stabilizing the translations that are generated on-the-fly, and high quality, robust and responsible direct speech-to-speech translation provides a much better user experience in communicating with people speaking a different language. New work on combining ML with traditional codec approaches in the Lyra speech codec and the more general SoundStream audio codec enables higher fidelity speech, music, and other sounds to be communicated reliably at much lower bitrate.

Everyday interactions are becoming much more natural with features like automatic call screening and ML agents that will wait on hold for you, thanks to advances in Duplex. Even short tasks that users may perform frequently have been improved with tools such as Smart Text Selection, which automatically selects entities like phone numbers or addresses for easy copy and pasting, and grammar correction as you type on Pixel 6 phones. In addition, Screen Attention prevents the phone screen from dimming when you are looking at it and improvements in gaze recognition are opening up new use cases for accessibility and for improved wellness and health. ML is also enabling new methods for ensuring the safety of people and communities. For example, Suspicious Message Alerts warn against possible phishing attacks and Safer Routing detects hard-braking events to suggest alternate routes.

Recent work demonstrates the ability of gaze recognition as an important biomarker of mental fatigue.

Given the potentially sensitive nature of the data that underlies these new capabilities, it is essential that they are designed to be private by default. Many of them run inside of Android's Private Compute Core — an open source, secure environment isolated from the rest of the operating system. Android ensures that data processed in the Private Compute Core is not shared to any apps without the user taking an action. Android also prevents any feature inside the Private Compute Core from having direct access to the network. Instead, features communicate over a small set of open-source APIs to Private Compute Services, which strips out identifying information and makes use of privacy technologies, including federated learning, federated analytics, and private information retrieval, enabling learning while simultaneously ensuring privacy.

Federated Reconstruction is a novel partially local federated learning technique in which models are partitioned into global and local parameters. For each round of Federated Reconstruction training: (1) The server sends the current global parameters g to each user i; (2) Each user i freezes g and reconstructs their local parameters li; (3) Each user i freezes li and updates g to produce gi; (4) Users’ gi are averaged to produce the global parameters for the next round.

These technologies are critical to evolving next-generation computation and interaction paradigms, whereby personal or communal devices can both learn from and contribute to training a collective model of the world without compromising privacy. A federated unsupervised approach to privately learn the kinds of aforementioned general-purpose models with fine-tuning for a given task or context could unlock increasingly intelligent systems that are far more intuitive to interact with — more like a social entity than a machine. Broad and equitable access to these intelligent interfaces will only be possible with deep changes to our technology stacks, from the edge to the datacenter, so that they properly support neural computing.

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Trend 4: Growing Impact of ML in Science, Health and Sustainability
In recent years, we have seen an increasing impact of ML in the basic sciences, from physics to biology, with a number of exciting practical applications in related realms, such as renewable energy and medicine. Computer vision models have been deployed to address problems at both personal and global scales. They can assist physicians in their regular work, expand our understanding of neural physiology, and also provide better weather forecasts and streamline disaster relief efforts. Other types of ML models are proving critical in addressing climate change by discovering ways to reduce emissions and improving the output of alternative energy sources. Such models can even be leveraged as creative tools for artists! As ML becomes more robust, well-developed, and widely accessible, its potential for high-impact applications in a broad array of real-world domains continues to expand, helping to solve some of our most challenging problems.

Large-Scale Application of Computer Vision for New Insights

The advances in computer vision over the past decade have enabled computers to be used for a wide variety of tasks across different scientific domains. In neuroscience, automated reconstruction techniques can recover the neural connective structure of brain tissues from high resolution electron microscopy images of thin slices of brain tissue. In previous years, we have collaborated to create such resources for fruit fly, mouse, and songbird brains, but last year, we collaborated with the Lichtman Lab at Harvard University to analyze the largest sample of brain tissue imaged and reconstructed in this level of detail, in any species, and produced the first large-scale study of synaptic connectivity in the human cortex that spans multiple cell types across all layers of the cortex. The goal of this work is to produce a novel resource to assist neuroscientists in studying the stunning complexity of the human brain. The image below, for example, shows six neurons out of about 86 billion neurons in an adult human brain.

A single human chandelier neuron from our human cortex reconstruction, along with some of the pyramidal neurons that make a connection with that cell. Here’s an interactive version and a gallery of other interactive examples.

Computer vision technology also provides powerful tools to address challenges at much larger, even global, scales. A deep-learning–based approach to weather forecasting that uses satellite and radar imagery as inputs, combined with other atmospheric data, produces weather and precipitation forecasts that are more accurate than traditional physics-based models at forecasting times up to 12 hours. They can also produce updated forecasts much more quickly than traditional methods, which can be critical in times of extreme weather.

Comparison of 0.2 mm/hr precipitation on March 30, 2020 over Denver, Colorado. Left: Ground truth, source MRMS. Center: Probability map as predicted by MetNet-2. Right: Probability map as predicted by the physics-based HREF model. MetNet-2 is able to predict the onset of the storm earlier in the forecast than HREF as well as the storm’s starting location, whereas HREF misses the initiation location, but captures its growth phase well.

Having an accurate record of building footprints is essential for a range of applications, from population estimation and urban planning to humanitarian response and environmental science. In many parts of the world, including much of Africa, this information wasn’t previously available, but new work shows that using computer vision techniques applied to satellite imagery can help identify building boundaries at continental scales. The results of this approach have been released in the Open Buildings dataset, a new open-access data resource that contains the locations and footprints of 516 million buildings with coverage across most of the African continent. We’ve also been able to use this unique dataset in our collaboration with the World Food Programme to provide fast damage assessment after natural disasters through application of ML.

Example of segmenting buildings in satellite imagery. Left: Source image; Center: Semantic segmentation, with each pixel assigned a confidence score that it is a building vs. non-building; Right: Instance segmentation, obtained by thresholding and grouping together connected components.

A common theme across each of these cases is that ML models are able to perform specialized tasks efficiently and accurately based on analysis of available visual data, supporting high impact downstream tasks.

Automated Design Space Exploration

Another approach that has yielded excellent results across many fields is to allow an ML algorithm to explore and evaluate a problem’s design space for possible solutions in an automated way. In one application, a Transformer-based variational autoencoder learns to create aesthetically-pleasing and useful document layouts, and the same approach can be extended to explore possible furniture layouts. Another ML-driven approach automates the exploration of the huge design space of tweaks for computer game rules to improve playability and other attributes of a game, enabling human game designers to create enjoyable games more quickly.

A visualization of the Variational Transformer Network (VTN) model, which is able to extract meaningful relationships between the layout elements (paragraphs, tables, images, etc.) in order to generate realistic synthetic documents (e.g., with better alignment and margins).

Other ML algorithms have been used to evaluate the design space of computer architectural decisions for ML accelerator chips themselves. We’ve also shown that ML can be used to quickly create chip placements for ASIC designs that are better than layouts generated by human experts and can be generated in a matter of hours instead of weeks. This reduces the fixed engineering costs of chips and lowers the barrier to quickly creating specialized hardware for different applications. We’ve successfully used this automated placement approach in the design of our upcoming TPU-v5 chip.

Such exploratory ML approaches have also been applied to materials discovery. In a collaboration between Google Research and Caltech, several ML models, combined with a modified inkjet printer and a custom-built microscope, were able to rapidly search over hundreds of thousands of possible materials to hone in on 51 previously uncharacterized three-metal oxide materials with promising properties for applications in areas like battery technology and electrolysis of water.

These automated design space exploration approaches can help accelerate many scientific fields, especially when the entire experimental loop of generating the experiment and evaluating the result can all be done in an automated or mostly-automated manner. I expect to see this approach applied to good effect in many more areas in the coming years.

Application to Health

In addition to advancing basic science, ML can also drive advances in medicine and human health more broadly. The idea of leveraging advances in computer science in health is nothing new — in fact some of my own early experiences were in developing software to help analyze epidemiological data. But ML opens new doors, raises new opportunities, and yes, poses new challenges.

Take for example the field of genomics. Computing has been important to genomics since its inception, but ML adds new capabilities and disrupts old paradigms. When Google researchers began working in this area, the idea of using deep learning to help infer genetic variants from sequencer output was considered far-fetched by many experts. Today, this ML approach is considered state-of-the-art. But the future holds an even more important role for ML — genomics companies are developing new sequencing instruments that are more accurate and faster, but also present new inference challenges. Our release of open-source software DeepConsensus and, in collaboration with UCSC, PEPPER-DeepVariant, supports these new instruments with cutting-edge informatics. We hope that more rapid sequencing can lead to near term applicability with impact for real patients.

A schematic of the Transformer architecture for DeepConsensus, which corrects sequencing errors to improve yield and correctness.

There are other opportunities to use ML to accelerate our use of genomic information for personalized health outside of processing the sequencer data. Large biobanks of extensively phenotyped and sequenced individuals can revolutionize how we understand and manage genetic predisposition to disease. Our ML-based phenotyping method improves the scalability of converting large imaging and text datasets into phenotypes usable for genetic association studies, and our DeepNull method better leverages large phenotypic data for genetic discovery. We are happy to release both as open-source methods for the scientific community.

The process for generating large-scale quantification of anatomical and disease traits for combination with genomic data in Biobanks.

Just as ML helps us see hidden characteristics of genomics data, it can help us discover new information and glean new insights from other health data types as well. Diagnosis of disease is often about identifying a pattern, quantifying a correlation, or recognizing a new instance of a larger class — all tasks at which ML excels. Google researchers have used ML to tackle a wide range of such problems, but perhaps none of these has progressed farther than the applications of ML to medical imaging.

In fact, Google’s 2016 paper describing the application of deep learning to the screening for diabetic retinopathy, was selected by the editors of the Journal of the American Medical Association (JAMA) as one of the top 10 most influential papers of the decade — not just the most influential papers on ML and health, the most influential JAMA papers of the decade overall. But the strength of our research doesn’t end at contributions to the literature, but extends to our ability to build systems operating in the real world. Through our global network of deployment partners, this same program has helped screen tens of thousands of patients in India, Thailand, Germany and France who might otherwise have been untested for this vision-threatening disease.

We expect to see this same pattern of assistive ML systems deployed to improve breast cancer screening, detect lung cancer, accelerate radiotherapy treatments for cancer, flag abnormal X-rays, and stage prostate cancer biopsies. Each domain presents new opportunities to be helpful. ML-assisted colonoscopy procedures are a particularly interesting example of going beyond the basics. Colonoscopies are not just used to diagnose colon cancer — the removal of polyps during the procedure are the front line of halting disease progression and preventing serious illness. In this domain we’ve demonstrated that ML can help ensure doctors don’t miss polyps, can help detect elusive polyps, and can add new dimensions of quality assurance, like coverage mapping through the application of simultaneous localization and mapping techniques. In collaboration with Shaare Zedek Medical Center in Jerusalem, we’ve shown these systems can work in real time, detecting an average of one polyp per procedure that would have otherwise been missed, with fewer than four false alarms per procedure.

Sample chest X-rays (CXR) of true and false positives, and true and false negatives for (A) general abnormalities, (B) tuberculosis, and (C) COVID-19. On each CXR, red outlines indicate areas on which the model focused to identify abnormalities (i.e., the class activation map), and yellow outlines refer to regions of interest identified by a radiologist.

Another ambitious healthcare initiative, Care Studio, uses state-of-the-art ML and advanced NLP techniques to analyze structured data and medical notes, presenting clinicians with the most relevant information at the right time — ultimately helping them deliver more proactive and accurate care.

As important as ML may be to expanding access and improving accuracy in the clinical setting, we see a new equally important trend emerging: ML applied to help people in their daily health and well-being. Our everyday devices have powerful sensors that can help democratize health metrics and information so people can make more informed decisions about their health. We’ve already seen launches that enable a smartphone camera to assess heart rate and respiratory rate to help users without additional hardware, and Nest Hub devices that support contactless sleep sensing and allow users to better understand their nighttime wellness. We’ve seen that we can, on the one hand, significantly improve speech recognition quality for disordered speech in our own ASR systems, and on the other, use ML to help recreate the voice of those with speech impairments, empowering them to communicate in their own voice. ML enabled smartphones that help people better research emerging skin conditions or help those with limited vision go for a jog, seem to be just around the corner. These opportunities offer a future too bright to ignore.

The custom ML model for contactless sleep sensing efficiently processes a continuous stream of 3D radar tensors (summarizing activity over a range of distances, frequencies, and time) to automatically compute probabilities for the likelihood of user presence and wakefulness (awake or asleep).

ML Applications for the Climate Crisis

Another realm of paramount importance is climate change, which is an incredibly urgent threat for humanity. We need to all work together to bend the curve of harmful emissions to ensure a safe and prosperous future. Better information about the climate impact of different choices can help us tackle this challenge in a number of different ways.

To this end, we recently rolled out eco-friendly routing in Google Maps, which we estimate will save about 1 million tons of CO2 emissions per year (the equivalent of removing more than 200,000 cars from the road). A recent case study shows that using Google Maps directions in Salt Lake City results in both faster and more emissions-friendly routing, which saves 1.7% of CO2 emissions and 6.5% travel time. In addition, making our Maps products smarter about electric vehicles can help alleviate range anxiety, encouraging people to switch to emissions-free vehicles. We are also working with multiple municipalities around the world to use aggregated historical traffic data to help suggest improved traffic light timing settings, with an early pilot study in Israel and Brazil showing a 10-20% reduction in fuel consumption and delay time at the examined intersections.

With eco-friendly routing, Google Maps will show you the fastest route and the one that’s most fuel-efficient — so you can choose whichever one works best for you.

On a longer time scale, fusion holds promise as a game-changing renewable energy source. In a long-standing collaboration with TAE Technologies, we have used ML to help maintain stable plasmas in their fusion reactor by suggesting settings of the more than 1000 relevant control parameters. With our collaboration, TAE achieved their major goals for their Norman reactor, which brings us a step closer to the goal of breakeven fusion. The machine maintains a stable plasma at 30 million Kelvin (don’t touch!) for 30 milliseconds, which is the extent of available power to its systems. They have completed a design for an even more powerful machine, which they hope will demonstrate the conditions necessary for breakeven fusion before the end of the decade.

We’re also expanding our efforts to address wildfires and floods, which are becoming more common (like millions of Californians, I’m having to adapt to having a regular “fire season”). Last year, we launched a wildfire boundary map powered by satellite data to help people in the U.S. easily understand the approximate size and location of a fire — right from their device. Building on this, we’re now bringing all of Google’s wildfire information together and launching it globally with a new layer on Google Maps. We have been applying graph optimization algorithms to help optimize fire evacuation routes to help keep people safe in the presence of rapidly advancing fires. In 2021, our Flood Forecasting Initiative expanded its operational warning systems to cover 360 million people, and sent more than 115 million notifications directly to the mobile devices of people at risk from flooding, more than triple our outreach in the previous year. We also deployed our LSTM-based forecast models and the new Manifold inundation model in real-world systems for the first time, and shared a detailed description of all components of our systems.

The wildfire layer in Google Maps provides people with critical, up-to-date information in an emergency.

We’re also working hard on our own set of sustainability initiatives. Google was the first major company to become carbon neutral in 2007. We were also the first major company to match our energy use with 100 percent renewable energy in 2017. We operate the cleanest global cloud in the industry, and we’re the world’s largest corporate purchaser of renewable energy. Further, in 2020 we became the first major company to make a commitment to operate on 24/7 carbon-free energy in all our data centers and campuses worldwide. This is far more challenging than the traditional approach of matching energy usage with renewable energy, but we’re working to get this done by 2030. Carbon emission from ML model training is a concern for the ML community, and we have shown that making good choices about model architecture, datacenter, and ML accelerator type can reduce the carbon footprint of training by ~100-1000x.

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Trend 5: Deeper and Broader Understanding of ML
As ML is used more broadly across technology products and society more generally, it is imperative that we continue to develop new techniques to ensure that it is applied fairly and equitably, and that it benefits all people and not just select subsets. This is a major focus for our Responsible AI and Human-Centered Technology research group and an area in which we conduct research on a variety of responsibility-related topics.

One area of focus is recommendation systems that are based on user activity in online products. Because these recommendation systems are often composed of multiple distinct components, understanding their fairness properties often requires insight into individual components as well as how the individual components behave when combined together. Recent work has helped to better understand these relationships, revealing ways to improve the fairness of both individual components and the overall recommendation system. In addition, when learning from implicit user activity, it is also important for recommendation systems to learn in an unbiased manner, since the straightforward approach of learning from items that were shown to previous users exhibits well-known forms of bias. Without correcting for such biases, for example, items that were shown in more prominent positions to users tend to get recommended to future users more often.

As in recommendation systems, surrounding context is important in machine translation. Because most machine translation systems translate individual sentences in isolation, without additional surrounding context, they can often reinforce biases related to gender, age or other areas. In an effort to address some of these issues, we have a long-standing line of research on reducing gender bias in our translation systems, and to help the entire translation community, last year we released a dataset to study gender bias in translation based on translations of Wikipedia biographies.

Another common problem in deploying machine learning models is distributional shift: if the statistical distribution of data on which the model was trained is not the same as that of the data the model is given as input, the model’s behavior can sometimes be unpredictable. In recent work, we employ the Deep Bootstrap framework to compare the real world, where there is finite training data, to an "ideal world", where there is infinite data. Better understanding of how a model behaves in these two regimes (real vs. ideal) can help us develop models that generalize better to new settings and exhibit less bias towards fixed training datasets.

Although work on ML algorithms and model development gets significant attention, data collection and dataset curation often gets less. But this is an important area, because the data on which an ML model is trained can be a potential source of bias and fairness issues in downstream applications. Analyzing such data cascades in ML can help identify the many places in the lifecycle of an ML project that can have substantial influence on the outcomes. This research on data cascades has led to evidence-backed guidelines for data collection and evaluation in the revised PAIR Guidebook, aimed at ML developers and designers.

Arrows of different color indicate various types of data cascades, each of which typically originate upstream, compound over the ML development process, and manifest downstream.

The general goal of better understanding data is an important part of ML research. One thing that can help is finding and investigating anomalous data. We have developed methods to better understand the influence that particular training examples can have on an ML model, since mislabeled data or other similar issues can have outsized impact on the overall model behavior. We have also built the Know Your Data tool to help ML researchers and practitioners better understand properties of their datasets, and last year we created a case study of how to use the Know Your Data tool to explore issues like gender bias and age bias in a dataset.

A screenshot from Know Your Data showing the relationship between words that describe attractiveness and gendered words. For example, “attractive” and “male/man/boy” co-occur 12 times, but we expect ~60 times by chance (the ratio is 0.2x). On the other hand, “attractive” and “female/woman/girl” co-occur 2.62 times more than chance.

Understanding dynamics of benchmark dataset usage is also important, given the central role they play in the organization of ML as a field. Although studies of individual datasets have become increasingly common, the dynamics of dataset usage across the field have remained underexplored. In recent work, we published the first large scale empirical analysis of dynamics of dataset creation, adoption, and reuse. This work offers insights into pathways to enable more rigorous evaluations, as well as more equitable and socially informed research.

Creating public datasets that are more inclusive and less biased is an important way to help improve the field of ML for everyone. In 2016, we released the Open Images dataset, a collection of ~9 million images annotated with image labels spanning thousands of object categories and bounding box annotations for 600 classes. Last year, we introduced the More Inclusive Annotations for People (MIAP) dataset in the Open Images Extended collection. The collection contains more complete bounding box annotations for the person class hierarchy, and each annotation is labeled with fairness-related attributes, including perceived gender presentation and perceived age range. With the increasing focus on reducing unfair bias as part of responsible AI research, we hope these annotations will encourage researchers already leveraging the Open Images dataset to incorporate fairness analysis in their research.

Because we also know that our teams are not the only ones creating datasets that can improve machine learning, we have built Dataset Search to help users discover new and useful datasets, wherever they might be on the Web.

Tackling various forms of abusive behavior online, such as toxic language, hate speech, and misinformation, is a core priority for Google. Being able to detect such forms of abuse reliably, efficiently, and at scale is of critical importance both to ensure that our platforms are safe and also to avoid the risk of reproducing such negative traits through language technologies that learn from online discourse in an unsupervised fashion. Google has pioneered work in this space through the Perspective API tool, but the nuances involved in detecting toxicity at scale remains a complex problem. In recent work, in collaboration with various academic partners, we introduced a comprehensive taxonomy to reason about the changing landscape of online hate and harassment. We also investigated how to detect covert forms of toxicity, such as microaggressions, that are often ignored in online abuse interventions, studied how conventional approaches to deal with disagreements in data annotations of such subjective concepts might marginalize minority perspectives, and proposed a new disaggregated modeling approach that uses a multi-task framework to tackle this issue. Furthermore, through qualitative research and network-level content analysis, Google’s Jigsaw team, in collaboration with researchers at George Washington University, studied how hate clusters spread disinformation across social media platforms.

Another potential concern is that ML language understanding and generation models can sometimes also produce results that are not properly supported by evidence. To confront this problem in question answering, summarization, and dialog, we developed a new framework for measuring whether results can be attributed to specific sources. We released annotation guidelines and demonstrated that they can be reliably used in evaluating candidate models.

Interactive analysis and debugging of models remains key to responsible use of ML. We have updated our Language Interpretability Tool with new capabilities and techniques to advance this line of work, including support for image and tabular data, a variety of features carried over from our previous work on the What-If Tool, and built-in support for fairness analysis through the technique of Testing with Concept Activation Vectors. Interpretability and explainability of ML systems more generally is also a key part of our Responsible AI vision; in collaboration with DeepMind, we made headway in understanding the acquisition of human chess concepts in the self-trained AlphaZero chess system.

Explore what AlphaZero might have learned about playing chess using this online tool.

We are also working hard to broaden the perspective of Responsible AI beyond western contexts. Our recent research examines how various assumptions of conventional algorithmic fairness frameworks based on Western institutions and infrastructures may fail in non-Western contexts and offers a pathway for recontextualizing fairness research in India along several directions. We are actively conducting survey research across several continents to better understand perceptions of and preferences regarding AI. Western framing of algorithmic fairness research tends to focus on only a handful of attributes, thus biases concerning non-Western contexts are largely ignored and empirically under-studied. To address this gap, in collaboration with the University of Michigan, we developed a weakly supervised method to robustly detect lexical biases in broader geo-cultural contexts in NLP models that reflect human judgments of offensive and inoffensive language in those geographic contexts.

Furthermore, we have explored applications of ML to contexts valued in the Global South, including developing a proposal for farmer-centered ML research. Through this work, we hope to encourage the field to be thoughtful about how to bring ML-enabled solutions to smallholder farmers in ways that will improve their lives and their communities.

Involving community stakeholders at all stages of the ML pipeline is key to our efforts to develop and deploy ML responsibly and keep us focused on tackling the problems that matter most. In this vein, we held a Health Equity Research Summit among external faculty, non-profit organization leads, government and NGO representatives, and other subject matter experts to discuss how to bring more equity into the entire ML ecosystem, from the way we approach problem-solving to how we assess the impact of our efforts.

Community-based research methods have also informed our approach to designing for digital wellbeing and addressing racial equity issues in ML systems, including improving our understanding of the experience of Black Americans using ASR systems. We are also listening to the public more broadly to learn how sociotechnical ML systems could help during major life events, such as by supporting family caregiving.

As ML models become more capable and have impact in many domains, the protection of the private information used in ML continues to be an important focus for research. Along these lines, some of our recent work addresses privacy in large models, both highlighting that training data can sometimes be extracted from large models and pointing to how privacy can be achieved in large models, e.g., as in differentially private BERT. In addition to the work on federated learning and analytics, mentioned above, we have also been enhancing our toolbox with other principled and practical ML techniques for ensuring differential privacy, for example private clustering, private personalization, private matrix completion, private weighted sampling, private quantiles, private robust learning of halfspaces, and in general, sample-efficient private PAC learning. Moreover, we have been expanding the set of privacy notions that can be tailored to different applications and threat models, including label privacy and user versus item level privacy.

A visual illustration of the differentially private clustering algorithm.

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Datasets
Recognizing the value of open datasets to the general advancement of ML and related fields of research, we continue to grow our collection of open source datasets and resources and expand our global index of open datasets in Google Dataset Search. This year, we have released a number of datasets and tools across a range of research areas:

Datasets & Tools Description
AIST++ 3D keypoints with corresponding images for dance motions covering 10 dance genres
AutoFlow 40k image pairs with ground truth optical flow
C4_200M A 200 million sentence synthetic dataset for grammatical error correction
CIFAR-5M Dataset of ~6 million synthetic CIFAR-10–like images (RGB 32 x 32 pix)
Crisscrossed Captions Set of semantic similarity ratings for the MS-COCO dataset
Disfl-QA Dataset of contextual disfluencies for information seeking
Distilled Datasets Distilled datasets from CIFAR-10, CIFAR-100, MNIST, Fashion-MNIST, and SVHN
EvolvingRL 1000 top performing RL algorithms discovered through algorithm evolution
GoEmotions A human-annotated dataset of 58k Reddit comments labeled with 27 emotion categories
H01 Dataset 1.4 petabyte browsable reconstruction of the human cortex
Know Your Data Tool for understanding biases in a dataset
Lens Flare 5000 high-quality RGB images of typical lens flare
More Inclusive Annotations for People (MIAP) Improved bounding box annotations for a subset of the person class in the Open Images dataset
Mostly Basic Python Problems 1000 Python programming problems, incl. task description, code solution & test cases
NIH ChestX-ray14 dataset labels Expert labels for a subset of the NIH ChestX-ray14 dataset
Open Buildings Locations and footprints of 516 million buildings with coverage across most of Africa
Optical Polarization from Curie 5GB of optical polarization data from the Curie submarine cable
Readability Scroll Scroll interactions of ~600 participants reading texts from the OneStopEnglish corpus
RLDS Tools to store, retrieve & manipulate episodic data for reinforcement learning
Room-Across-Room (RxR) Multilingual dataset for vision-and-language navigation in English, Hindi and Telugu
Soft Attributes ~6k sets of movie titles annotated with single English soft attributes
TimeDial Dataset of multiple choice span-filling tasks for temporal commonsense reasoning in dialog
ToTTo English table-to-text generation dataset with a controlled text generation task
Translated Wikipedia Biographies Dataset for analysis of common gender errors in NMT for English, Spanish and German
UI Understanding Data for UIBert Datasets for two UI understanding tasks, AppSim & RefExp
WikiFact Wikipedia & WikiData–based dataset to train relationship classifiers and fact extraction models
WIT Wikipedia-based Image Text dataset for multimodal multilingual ML

Research Community Interaction
To realize our goal for a more robust and comprehensive understanding of ML and related technologies, we actively engage with the broader research community. In 2021, we published over 750 papers, nearly 600 of which were presented at leading research conferences. Google Research sponsored over 150 conferences, and Google researchers contributed directly by serving on program committees and organizing workshops, tutorials and numerous other activities aimed at collectively advancing the field. To learn more about our contributions to some of the larger research conferences this year, please see our recent conference blog posts. In addition, we hosted 19 virtual workshops (like the 2021 Quantum Summer Symposium), which allowed us to further engage with the academic community by generating new ideas and directions for the research field and advancing research initiatives.

In 2021, Google Research also directly supported external research with $59M in funding, including $23M through Research programs to faculty and students, and $20M in university partnerships and outreach. This past year, we introduced new funding and collaboration programs that support academics all over the world who are doing high impact research. We funded 86 early career faculty through our Research Scholar Program to support general advancements in science, and funded 34 faculty through our Award for Inclusion Research Program who are doing research in areas like accessibility, algorithmic fairness, higher education and collaboration, and participatory ML. In addition to the research we are funding, we welcomed 85 faculty and post-docs, globally, through our Visiting Researcher program, to come to Google and partner with us on exciting ideas and shared research challenges. We also selected a group of 74 incredibly talented PhD student researchers to receive Google PhD Fellowships and mentorship as they conduct their research.

As part of our ongoing racial equity commitments, making computer science (CS) research more inclusive continues to be a top priority for us. In 2021, we continued expanding our efforts to increase the diversity of Ph.D. graduates in computing. For example, the CS Research Mentorship Program (CSRMP), an initiative by Google Research to support students from historically marginalized groups (HMGs) in computing research pathways, graduated 590 mentees, 83% of whom self-identified as part of an HMG, who were supported by 194 Google mentors — our largest group to date! In October, we welcomed 35 institutions globally leading the way to engage 3,400+ students in computing research as part of the 2021 exploreCSR cohort. Since 2018, this program has provided faculty with funding, community, evaluation and connections to Google researchers in order to introduce students from HMGs to the world of CS research. We are excited to expand this program to more international locations in 2022.

We also continued our efforts to fund and partner with organizations to develop and support new pathways and approaches to broadening participation in computing research at scale. From working with alliances like the Computing Alliance of Hispanic-Serving Institutions (CAHSI) and CMD-IT Diversifying LEAdership in the Professoriate (LEAP) Alliance to partnering with university initiatives like UMBC’s Meyerhoff Scholars, Cornell University’s CSMore, Northeastern University’s Center for Inclusive Computing, and MIT’s MEnTorEd Opportunities in Research (METEOR), we are taking a community-based approach to materially increase the representation of marginalized groups in computing research.

Other Work
In writing these retrospectives, I try to focus on new research work that has happened (mostly) in the past year while also looking ahead. In past years’ retrospectives, I’ve tried to be more comprehensive, but this time I thought it could be more interesting to focus on just a few themes. We’ve also done great  work in many other research areas that don’t fit neatly into these themes. If you’re interested, I encourage you to check out our research publications by area below or by year (and if you’re interested in quantum computing, our Quantum team recently wrote a retrospective of their work in 2021):

Algorithms and Theory Machine Perception
Data Management Machine Translation
Data Mining Mobile Systems
Distributed Systems & Parallel Computing Natural Language Processing
Economics & Electronic Commerce Networking
Education Innovation Quantum Computing
General Science Responsible AI
Health and Bioscience Robotics
Hardware and Architecture Security, Privacy and Abuse Prevention
Human-Computer Interaction and Visualization Software Engineering
Information Retrieval and the Web Software Systems
Machine Intelligence Speech Processing

Conclusion
Research is often a multi-year journey to real-world impact. Early stage research work that happened a few years ago is now having a dramatic impact on Google’s products and across the world. Investments in ML hardware accelerators like TPUs and in software frameworks like TensorFlow and JAX have borne fruit. ML models are increasingly prevalent in many different products and features at Google because their power and ease of expression streamline experimentation and productionization of ML models in performance-critical environments. Research into model architectures to create Seq2Seq, Inception, EfficientNet, and Transformer or algorithmic research like batch normalization and distillation is driving progress in the fields of language understanding, vision, speech, and others. Basic capabilities like better language and visual understanding and speech recognition can be transformational, and as a result, these sorts of models are widely deployed for a wide variety of problems in many of our products including Search, Assistant, Ads, Cloud, Gmail, Maps, YouTube, Workspace, Android, Pixel, Nest, and Translate.

These are truly exciting times in machine learning and computer science. Continued improvement in computers’ ability to understand and interact with the world around them through language, vision, and sound opens up entire new frontiers of how computers can help people accomplish things in the world. The many examples of progress along the five themes outlined in this post are waypoints in a long-term journey!

Acknowledgements
Thanks to Alison Carroll, Alison Lentz, Andrew Carroll, Andrew Tomkins, Avinatan Hassidim, Azalia Mirhoseini, Barak Turovsky, Been Kim, Blaise Aguera y Arcas, Brennan Saeta, Brian Rakowski, Charina Chou, Christian Howard, Claire Cui, Corinna Cortes, Courtney Heldreth, David Patterson, Dipanjan Das, Ed Chi, Eli Collins, Emily Denton, Fernando Pereira, Genevieve Park, Greg Corrado, Ian Tenney, Iz Conroy, James Wexler, Jason Freidenfelds, John Platt, Katherine Chou, Kathy Meier-Hellstern, Kyle Vandenberg, Lauren Wilcox, Lizzie Dorfman, Marian Croak, Martin Abadi, Matthew Flegal, Meredith Morris, Natasha Noy, Negar Saei, Neha Arora, Paul Muret, Paul Natsev, Quoc Le, Ravi Kumar, Rina Panigrahy, Sanjiv Kumar, Sella Nevo, Slav Petrov, Sreenivas Gollapudi, Tom Duerig, Tom Small, Vidhya Navalpakkam, Vincent Vanhoucke, Vinodkumar Prabhakaran, Viren Jain, Yonghui Wu, Yossi Matias, and Zoubin Ghahramani for helpful feedback and contributions to this post, and to the entire Research and Health communities at Google for everyone’s contributions towards this work.

Source: Google AI Blog


How Abigail Annkah is using AI to improve maps in Africa

As a university student, Abigail Annkah fell in love with mathematics, which soon led to her interest in artificial intelligence. After graduating from the African Institute for Mathematical Sciences, Abigail made it through the competitive process to become an AI resident at Google Research, Accra. After her residency, Google offered her a job and she’s now a research software engineer working on several high-profile projects.

As Google grows its presence in Accra, we spoke to Abigail about the mapping project that motivates her, starting a new job while becoming a mother and the importance of inspiring young girls to enter STEM careers.


How did your science background lead you to Google?

I did my undergraduate degree in Bachelor of Science Statistics at the University of Ghana, finishing with a combined major in Mathematics and Statistics. During the second year of study, I stumbled upon Computational Maths, leading to my first taste of coding. I started taking extra credit courses, which really kickstarted my entry into AI. Then I joined the first cohort of the African Masters of Machine Intelligence program at African Institute for Mathematical Sciences with the support of Google and Facebook. The program intends to bridge the AI education gap in Africa and strengthen the growing data science ecosystem in the region — this was my first exposure to the world of Machine Learning.

A picture of Abigail and lots of people outside the entrance to The African Institute for Mathematical Sciences

Abigail and her cohort at The African Institute for Mathematical Sciences

I quickly developed an interest in using data-driven approaches to solving pressing societal challenges, leading me to work on biochemical image segmentation for my master’s thesis. I then joined the Google AI center in Ghana as an AI resident and after two years gained a full-time role as a research software engineer. There, I used my expertise in computer vision to help build better image segmentation models that led to significant improvement of Google maps. This project created new possibilities for using improved satellite imagery analysis tools for purposes like disaster response or census planning.


Is there a specific project you’re especially proud to have worked on?

The aforementioned Google maps project — also known as the Open Buildings open-access dataset project — is close to my heart as an African. Open Buildings uses AI to provide a digital footprint of building locations and geometry across most of Africa. Our aim is to map Africa's built environment using satellite imagery, and I dedicated almost all my residency to contributing to that work.

Cities in Africa aren't constructed the same as in other parts of the world. For example, AI models in a U.S. city won't be as useful here but the problem is actually bigger than just one product. Many large-scale digital maps today are usually missing that AI context. It was exciting to see the potential and unanticipated use cases that helped us refine the dataset, and we saw it make an impact on local communities. For example, the data we collected about buildings can also be used to analyze the density of the built landscape for environmental science purposes.

After identifying and adding millions of previously unmapped buildings to our dataset, we decided to open source the dataset, making it available for anyone to download.


How do you hope your work inspires the next generation of young scientists in STEM?

That’s a funny question because sometimes I think I haven't gone that far in my career — but that’s only because I want to achieve so much more. When I’ve spoken to students they always ask about my journey to Google, especially starting a new role as a new mother. I want them to look at me and think if she did it, then I can do it too! It’s really important to me that my work reaches people so that they in turn can reach out to others when they achieve career success.

I’m very pleased there are more programs today encouraging girls and women to get into STEM. I was fortunate enough to participate in one of these programs early on, and it helped me get where I am today. Currently, the Accra team is launching Mind the Gap in Ghana and I get to interact with young students to inspire them to pursue STEM along with other members of the team.


How did you balance motherhood with your new position at Google?

Having a newborn at home while start my residency was stressful, especially following a difficult pregnancy. I was anxious about how much of myself I could give to my work, but I was able to make valuable contributions to the work and still be a trusted member of the team. When I became a full-time researcher, I thought to myself that if I can succeed as a working mother, then I should have confidence that I had earned this position. I also had a great maternity package and a super supportive team. I had a support system where I could ask colleagues, “How did you get through this? What did you do?” I didn't have to figure out everything on my own.


Who are your heroes in real life?

I think the younger me is my greatest hero! I've had so many amazing people pushing me, but whenever I hit a roadblock, she’s the one who inspires me and reminds me that yes I can.

2021 Year in Review: Google Quantum AI

Google’s Quantum AI team has had a productive 2021. Despite ongoing global challenges, we’ve made significant progress in our effort to build a fully error-corrected quantum computer, working towards our next hardware milestone of building an error-corrected quantum bit (qubit) prototype. At the same time, we have continued our commitment to realizing the potential of quantum computers in various applications. That's why we published results in top journals, collaborated with researchers across academia and industry, and expanded our team to bring on new talent and expertise.

An update on hardware

The Quantum AI team is determined to build an error-corrected quantum computer within the next decade, and to simultaneously use what we learn along the way to deliver helpful—and even transformational—quantum computing applications. This long-term commitment is expanded broadly into three key questions for our quantum hardware:

  1. Can we demonstrate that quantum computers can outperform the classical supercomputers of today in a specific task? We demonstrated beyond-classical computation in 2019.
  2. Can we build a prototype of an error-corrected qubit? In order to use quantum computers to their full potential, we will need to realize quantum error correction to overcome the noise that is present during our computations. As a key step in this direction, we aim to realize the primitives of quantum error correction by redundantly encoding quantum information across several physical qubits, demonstrating that such redundancy leads to an improvement over using individual physical qubits. This is our current target.
  3. Can we build a logical qubit which does not have errors for an arbitrarily long time? Logical qubits encode information redundantly across several physical qubits, and are able to reduce the impact of noise on the overall quantum computation. Putting together a few thousand logical qubits would allow us to realize the full potential of quantum computers for various applications.

Progress toward building an error-corrected qubit prototype

The distance between the noisy quantum computers of today and the fully error-corrected quantum computers of the future is vast. In 2021, we made significant progress in closing this gap by working toward building a prototype logical qubit whose errors are smaller than those of the physical qubits on our chips.

This work requires improvements across the entire quantum computing stack. We have made chips with better qubits, improved the methods that we use to package these chips to better connect them with our control electronics, and developed techniques to calibrate large chips with several dozens of qubits simultaneously.

These improvements culminated in two key results. First, we are now able to reset our qubits with high fidelity, allowing us to reuse qubits in quantum computations. Second, we have realized mid-circuit measurement that allows us to keep track of computation within quantum circuits. Together, the high-fidelity resets and mid-circuit measurements were used in our recent demonstration of exponential suppression of bit and phase flip errors using repetition codes, resulting in 100x suppression of these errors as the size of the code grows from 5 to 21 qubits.

Chart chronicling repetition code

Suppression of logical errors as the number of qubits in the repetition code is increased. As we increase the code size from 5 to 21 qubits, we see 100x reduction in logical. Image acknowledgement: Kevin Satzinger/Google Quantum AI

Repetition codes, an error correction tool, enable us to trade-off between resources (more qubits) and performance (lower error) which will be central in guiding our hardware research and development going forward. This year we showed how error decreases as we increase the number of included qubits for a 1-dimensional code. We are currently running experiments to extend these results to two-dimensional surface codes which will correct errors more comprehensively.

Applications of quantum computation

In addition to building quantum hardware, our team is also looking for clear margins of quantum advantage in real world applications. With our collaborators in academia and industry, we are exploring fields where quantum computers can provide significant speedups, with realistic expectations that error-corrected quantum computers will likely require better than quadratic speedups for meaningful improvements.

As always, our collaborations with academic and industry partners were invaluable in 2021. One notable collaboration with Caltech showed that, under certain conditions, quantum machines can learn about physical systems from exponentially fewer experiments than what is conventionally required. This novel method was validated experimentally using 40 qubits and 1300 quantum operations, demonstrating a substantial quantum advantage even with the noisy quantum processors we have today. This paves the way to more innovation in quantum machine learning and quantum sensing, with potential near-term use cases.

In collaboration with researchers at Columbia University, we combined one of the most powerful techniques for chemical simulation, Quantum Monte Carlo, with quantum computation. This approach surpasses previous methods as a promising quantum approach to ground state many-electron calculations, which are critical in creating new materials and understanding their chemical properties. When we run a component of this technique on a real quantum computer, we are able to double the size of prior calculations without sacrificing accuracy of the measurements, even in the presence of noise on a device with up to 16 qubits. The resilience of this method to noise is an indication of its potential for scalability even on today’s quantum computers.

We continue to study how quantum computers can be used to simulate quantum physical phenomena—as was most recently reflected in our experimental observation of a time crystal on a quantum processor (Ask a Techspert: What exactly is a time crystal?). This was a great moment for theorists, who’ve pondered the possibility of time crystals for nearly a century. In other work, we also explored the emergence of quantum chaotic dynamics by experimentally measuring out-of-time-ordered correlations on one of our quantum computers, which was done jointly with collaborators at the NASA Ames Research Center; and experimentally measuring the entanglement entropy of the ground state of the Toric code Hamiltonian by creating its eigenstates using shallow quantum circuits with collaborators at the Technical University of Munich.

Our collaborators contributed to, and even inspired, some of our most impactful research in 2021. Quantum AI remains committed to discovering and realizing meaningful quantum applications in collaboration with scientists and researchers from across the world in 2022 and beyond as we continue our focus on machine learning, chemistry, and many-body quantum physics.

You can find a list of all our publications here.

Continuing investment in the quantum computing ecosystem

This year, at Google’s annual developer conference, Google I/O, we reaffirmed our commitment to the roadmap and investments required to make a useful quantum computer within the decade. While we were busy growing in Santa Barbara, we also continue to support the enablement of researchers in the quantum community through our open source software. Our quantum programming framework, Cirq, continues to improve with contributions from the community. 2021 also saw the release of specialized tools in collaboration with partners in the ecosystem. Two examples of these are:

  • The release of a new Fermionic Quantum Simulator for quantum chemistry applications in collaboration with QSimulate, taking advantage of the symmetry in quantum chemistry problems to provide efficient simulations.
  • A significant upgrade to qsim which allows for simulation of noisy quantum circuits on high performance processors such as GPUs via Google Cloud, and qsim integration with NVIDIA’s cuQuantum SDK to enable qsim users to make the most of NVIDIA GPUs when developing quantum algorithms and applications.

We also released an open-source tool called stim, which provides a 10000x speedup when simulating error correction circuits.

You can access our portfolio of open-source software here.

Looking toward 2022

Resident quantum scientist Qubit the Dog taking part in a holiday sing-along.

Resident quantum scientist Qubit the Dog taking part in a holiday sing-along led by team members Jimmy Chen and Ofer Naaman.

Through teamwork, collaboration, and some innovative science, we are excited about the progress that we have seen in 2021. We have big expectations for 2022 as we focus on progressing through our hardware milestones, the discovery of new quantum algorithms, and the realization of quantum applications on the quantum processors of today. To tackle our difficult mission, we are growing our team, building on our existing network of collaborators, and expanding our Santa Barbara campus. Together with the broader quantum community, we are excited to see the progress that quantum computing makes in 2022 and beyond.

This archaeologist fights tomb raiders with Google Earth

In the summer, Dr. Gino Caspari’s day starts at 5:30 a.m. in Siberia, where he studies the ancient Scythians with the Swiss National Science Foundation. There, he looks for burial places of these nomadic warriors who rode through Asia 2,500 years ago. The work isn’t easy, from dealing with extreme temperatures, to swamps covered with mosquitos. But the biggest challenge is staying one step ahead of tomb raiders.

It’s believed that more than 90% of the tombs — called kurgans — have already been destroyed by raiders looking to profit off what they find, but Gino is looking for the thousands he believes remain scattered across Russia, Mongolia and Western China. To track his progress, he began mapping these burial sites using Google Earth. “There’s a plethora of open data sources out there, but most of them don’t have the resolution necessary to detect individual archaeological structures,” Dr. Caspari says, pointing out that getting quality data is also very expensive. “Google Earth updates high-res data across the globe, and, especially in remote regions, it was a windfall for archaeologists. Google Earth expanded our possibilities to plan surveys and understand cultural heritage on a broader geographic scale.”

While Google Earth helped Dr. Caspari plan his expeditions, he still couldn’t stay ahead of the looters. He needed to get there faster. That’s when he met data scientist Pablo Crespo and started using another Google tool, TensorFlow.

“Since I started my PhD in 2013, I have been interested in automatic detection of archaeological sites from remote sensing data,” Gino says. “It was clear we needed to look at landscapes and human environmental interaction to understand past cultures. The problem was that our view was obscured by a lack of data and a focus on individual sites.” Back then, he tried some simple automatization processes to detect the places he needed for his research with the available technology, but only got limited results. In 2020, though, Gino and Pablo created a machine learning model using TensorFlow that could analyze satellite images they pulled from Google Earth. This model would look for places on the images that had the characteristics of a Scythian tomb.

The progress in the field of machine learning has been insanely fast, improving the quality of classification and detection to a point where it has become much more than just a theoretical possibility. Google’s freely available technologies have help

This technology sped up the discovery process for Gino, giving him an advantage over looters and even deterioration caused by climate change.

“Frankly, I think that without these tools, I probably wouldn’t have gotten this far in my understanding of technology and what it can do to make a difference in the study of our shared human past,” Gino says. “As a young scholar, I just lack the funds to access a lot of the resources I need. Working with Pablo and others has widened my perspective on what is possible and where we can go.”

Technology solutions have given Dr. Caspari’s work a new set of capabilities, supercharging what he’s able to do. And it’s also made him appreciate the importance of the human touch. “The deeper we dive into our past with the help of technology, the more apparent it becomes how patchy and incomplete our knowledge really is,” he says. “Technology often serves as an extension of our senses and mitigates our reality. Weaving the fabric of our reality will remain the task of the storyteller in us.”

This archaeologist fights tomb raiders with Google Earth

In the summer, Dr. Gino Caspari’s day starts at 5:30 a.m. in Siberia, where he studies the ancient Scythians with the Swiss National Science Foundation. There, he looks for burial places of these nomadic warriors who rode through Asia 2,500 years ago. The work isn’t easy, from dealing with extreme temperatures, to swamps covered with mosquitos. But the biggest challenge is staying one step ahead of tomb raiders.

It’s believed that more than 90% of the tombs — called kurgans — have already been destroyed by raiders looking to profit off what they find, but Gino is looking for the thousands he believes remain scattered across Russia, Mongolia and Western China. To track his progress, he began mapping these burial sites using Google Earth. “There’s a plethora of open data sources out there, but most of them don’t have the resolution necessary to detect individual archaeological structures,” Dr. Caspari says, pointing out that getting quality data is also very expensive. “Google Earth updates high-res data across the globe, and, especially in remote regions, it was a windfall for archaeologists. Google Earth expanded our possibilities to plan surveys and understand cultural heritage on a broader geographic scale.”

While Google Earth helped Dr. Caspari plan his expeditions, he still couldn’t stay ahead of the looters. He needed to get there faster. That’s when he met data scientist Pablo Crespo and started using another Google tool, TensorFlow.

“Since I started my PhD in 2013, I have been interested in automatic detection of archaeological sites from remote sensing data,” Gino says. “It was clear we needed to look at landscapes and human environmental interaction to understand past cultures. The problem was that our view was obscured by a lack of data and a focus on individual sites.” Back then, he tried some simple automatization processes to detect the places he needed for his research with the available technology, but only got limited results. In 2020, though, Gino and Pablo created a machine learning model using TensorFlow that could analyze satellite images they pulled from Google Earth. This model would look for places on the images that had the characteristics of a Scythian tomb.

The progress in the field of machine learning has been insanely fast, improving the quality of classification and detection to a point where it has become much more than just a theoretical possibility. Google’s freely available technologies have help

This technology sped up the discovery process for Gino, giving him an advantage over looters and even deterioration caused by climate change.

“Frankly, I think that without these tools, I probably wouldn’t have gotten this far in my understanding of technology and what it can do to make a difference in the study of our shared human past,” Gino says. “As a young scholar, I just lack the funds to access a lot of the resources I need. Working with Pablo and others has widened my perspective on what is possible and where we can go.”

Technology solutions have given Dr. Caspari’s work a new set of capabilities, supercharging what he’s able to do. And it’s also made him appreciate the importance of the human touch. “The deeper we dive into our past with the help of technology, the more apparent it becomes how patchy and incomplete our knowledge really is,” he says. “Technology often serves as an extension of our senses and mitigates our reality. Weaving the fabric of our reality will remain the task of the storyteller in us.”

General and Scalable Parallelization for Neural Networks

Scaling neural networks, whether it be the amount of training data used, the model size or the computation being utilized, has been critical for improving model quality in many real-world machine learning applications, such as computer vision, language understanding and neural machine translation. This, in turn, has motivated recent studies to scrutinize the factors that play a critical role in the success of scaling a neural model. Although increasing model capacity can be a sound approach to improve model quality, doing so presents a number of systems and software engineering challenges that must be overcome. For instance, in order to train large models that exceed the memory capacity of an accelerator, it becomes necessary to partition the weights and the computation of the model across multiple accelerators. This process of parallelization increases the network communication overhead and can result in device under-utilization. Moreover, a given algorithm for parallelization, which typically requires a significant amount of engineering effort, may not work with different model architectures.

To address these scaling challenges, we present “GSPMD: General and Scalable Parallelization for ML Computation Graphs”, in which we describe an open-source automatic parallelization system based on the XLA compiler. GSPMD is capable of scaling most deep learning network architectures and has already been applied to many deep learning models, such as GShard-M4, LaMDA, BigSSL, ViT, and MetNet-2, leading to state-of-the-art-results across several domains. GSPMD has also been integrated into multiple ML frameworks, including TensorFlow and JAX, which use XLA as a shared compiler.

Overview
GSPMD separates the task of programming an ML model from the challenge of parallelization. It allows model developers to write programs as if they were run on a single device with very high memory and computation capacity — the user simply needs to add a few lines of annotation code to a subset of critical tensors in the model code to indicate how to partition the tensors. For example, to train a large model-parallel Transformer, one may only need to annotate fewer than 10 tensors (less than 1% of all tensors in the entire computation graph), one line of additional code per tensor. Then GSPMD runs a compiler pass that determines the entire graph's parallelization plan, and transforms it into a mathematically equivalent, parallelized computation that can be executed on each device. This allows users to focus on model building instead of parallelization implementation, and enables easy porting of existing single-device programs to run at a much larger scale.

The separation of model programming and parallelism also allows developers to minimize code duplication. With GSPMD, developers may employ different parallelism algorithms for different use cases without the need to reimplement the model. For example, the model code that powered the GShard-M4 and LaMDA models can apply a variety of parallelization strategies appropriate for different models and cluster sizes with the same model implementation. Similarly, by applying GSPMD, the BigSSL large speech models can share the same implementation with previous smaller models.

Generality and Flexibility
Because different model architectures may be better suited to different parallelization strategies, GSPMD is designed to support a large variety of parallelism algorithms appropriate for different use cases. For example, with smaller models that fit within the memory of a single accelerator, data parallelism is preferred, in which devices train the same model using different input data. In contrast, models that are larger than a single accelerator’s memory capacity are better suited for a pipelining algorithm (like that employed by GPipe) that partitions the model into multiple, sequential stages, or operator-level parallelism (e.g., Mesh-TensorFlow), in which individual computation operators in the model are split into smaller, parallel operators.

GSPMD supports all the above parallelization algorithms with a uniform abstraction and implementation. Moreover, GSPMD supports nested patterns of parallelism. For example, it can be used to partition models into individual pipeline stages, each of which can be further partitioned using operator-level parallelism.

GSPMD also facilitates innovation on parallelism algorithms by allowing performance experts to focus on algorithms that best utilize the hardware, instead of the implementation that involves lots of cross-device communications. For example, for large Transformer models, we found a novel operator-level parallelism algorithm that partitions multiple dimensions of tensors on a 2D mesh of devices. It reduces peak accelerator memory usage linearly with the number of training devices, while maintaining a high utilization of accelerator compute due to its balanced data distribution over multiple dimensions.

To illustrate this, consider a simplified feedforward layer in a Transformer model that has been annotated in the above way. To execute the first matrix multiply on fully partitioned input data, GSPMD applies an MPI-style AllGather communication operator to partially merge with partitioned data from another device. It then executes the matrix multiply locally and produces a partitioned result. Before the second matrix multiply, GSPMD adds another AllGather on the right-hand side input, and executes the matrix multiply locally, yielding intermediate results that will then need to be combined and partitioned. For this, GSPMD adds an MPI-style ReduceScatter communication operator that accumulates and partitions these intermediate results. While the tensors generated with the AllGather operator at each stage are larger than the original partition size, they are short-lived and the corresponding memory buffers will be freed after use, which does not affect peak memory usage in training.

Left: A simplified feedforward layer of a Transformer model. Blue rectangles represent tensors with dashed red & blue lines overlaid representing the desired partitioning across a 2x2 mesh of devices. Right: A single partition, after GSPMD has been applied.

A Transformer Example with Nested Parallelism
As a shared, robust mechanism for different parallelism modes, GSPMD allows users to conveniently switch between modes in different parts of a model. This is particularly valuable for models that may have different components with distinct performance characteristics, for example, multimodal models that handle both images and audio. Consider a model with the Transformer encoder-decoder architecture, which has an embedding layer, an encoder stack with Mixture-of-Expert layers, a decoder stack with dense feedforward layers, and a final softmax layer. In GSPMD, a complex combination of several parallelism modes that treats each layer separately can be achieved with simple configurations.

In the figure below, we show a partitioning strategy over 16 devices organized as a logical 4x4 mesh. Blue represents partitioning along the first mesh dimension X, and yellow represents partitioning along the second mesh dimension Y. X and Y are repurposed for different model components to achieve different parallelism modes. For example, the X dimension is used for data parallelism in the embedding and softmax layers, but used for pipeline parallelism in the encoder and decoder. The Y dimension is also used in different ways to partition the vocabulary, batch or model expert dimensions.

Computation Efficiency
GSPMD provides industry-leading performance in large model training. Parallel models require extra communication to coordinate multiple devices to do the computation. So parallel model efficiency can be estimated by examining the fraction of time spent on communication overhead — the higher percentage utilization and the less time spent on communication, the better. In the recent MLPerf set of performance benchmarks, a BERT-like encoder-only model with ~500 billion parameters to which we applied GSPMD for parallelization over 2048 TPU-V4 chips yielded highly competitive results (see table below), utilizing up to 63% of the peak FLOPS that the TPU-V4s offer. We also provide efficiency benchmarks for some representative large models in the table below. These example model configs are open sourced in the Lingvo framework along with instructions to run them on Google Cloud. More benchmark results can be found in the experiment section of our paper.

Model Family Parameter Count % of model activated* No. of Experts** No. of Layers No. of TPU FLOPS utilization
Dense Decoder (LaMDA) 137B 100% 1 64 1024 TPUv3 56.5%
Dense Encoder (MLPerf-Bert) 480B 100% 1 64 2048 TPUv4 63%
Sparsely Activated Encoder-Decoder (GShard-M4) 577B 0.25% 2048 32 1024 TPUv3 46.8%
Sparsely Activated Decoder 1.2T 8% 64 64 1024 TPUv3 53.8%
*The fraction of the model activated during inference, which is a measure of model sparsity.
**Number of experts included in the Mixture of Experts layer. A value of 1 corresponds to a standard Transformer, without a Mixture of Experts layer.

Conclusion
The ongoing development and success of many useful machine learning applications, such as NLP, speech recognition, machine translation, and autonomous driving, depend on achieving the highest accuracy possible. As this often requires building larger and even more complex models, we are pleased to share the GSPMD paper and the corresponding open-source library to the broader research community, and we hope it is useful for efficient training of large-scale deep neural networks.

Acknowledgements
We wish to thank Claire Cui, Zhifeng Chen, Yonghui Wu, Naveen Kumar, Macduff Hughes, Zoubin Ghahramani and Jeff Dean for their support and invaluable input. Special thanks to our collaborators Dmitry Lepikhin, HyoukJoong Lee, Dehao Chen, Orhan Firat, Maxim Krikun, Blake Hechtman, Rahul Joshi, Andy Li, Tao Wang, Marcello Maggioni, David Majnemer, Noam Shazeer, Ankur Bapna, Sneha Kudugunta, Quoc Le, Mia Chen, Shibo Wang, Jinliang Wei, Ruoming Pang, Zongwei Zhou, David So, Yanqi Zhou, Ben Lee, Jonathan Shen, James Qin, Yu Zhang, Wei Han, Anmol Gulati, Laurent El Shafey, Andrew Dai, Kun Zhang, Nan Du, James Bradbury, Matthew Johnson, Anselm Levskaya, Skye Wanderman-Milne‎, and Qiao Zhang for helpful discussions and inspirations.

Source: Google AI Blog


Unlocking human rights information with machine learning

Human rights defenders need information from many sources to do their work effectively. But as issues evolve and new precedents are set, finding the right information to defend a particular case can be like looking for a needle in a haystack.

For example, a human rights advocate campaigning for LGBTQ rights may want to know which countries have made the most progress and what resolutions they’ve passed. To do so, they have to manually sift through thousands of pages of dense documentation covering global laws and victims’ testimonies to find what they’re looking for.

The curation and cataloging of documents makes this process much easier, but still relies on the manual work of skilled experts. To help, the non-profit organization HURIDOCS looked to machine learning. With support from Google.org Fellows and grant funding, they’ve built new tools that can automatically tag human rights documents so they are searchable — making the curation process 13 times faster.

How machine learning can make information more accessible

Typically, non-governmental organizations collect and curate large bodies of human rights information, with the goal of making these collections useful for advocates. Manually processing these documents can take several days, particularly when they’re published in unfamiliar languages or in PDF format which is difficult to search through. As a result, many NGOs face a large backlog of documents that remain to be processed, and by the time they’re added to collections new documentation often supersedes them.

Based in Geneva, HURIDOCS has been developing tools to manage and analyze collections of human rights evidence, law and research for nearly four decades. In 2016, they had an idea: What if machine learning could skim through documents, make terms extractable, and classify the content to catalog documents more quickly?

HURIDOCS took their idea to the Google AI Impact Challenge and was selected for a $1 million grant from Google.org and six months of technical support from a team of seven full-time pro bono Google.org Fellows. As one of the Fellows, I helped train AI models and make sure that the tool was useful to human rights experts, not just machine learning experts.

The curation process of human rights documents gets a boost

Since then, HURIDOCS has launched ML-powered features to improve platforms they’ve built with other NGO partners, and, earlier this year, they began integrating the technology into more of its tools, including their flagship application Uwazi. As a result, updating documents now takes one week instead of two to three months, and curators have been able to catch up on multi-year document backlogs.

In June, HURIDOCS won a CogX Award for its machine learning work, and now the organization is continuing to explore what else its machine learning models can do — from creating automatic tables of contents for documents to identifying references within text. With the power of artificial intelligence, HURIDOCS hopes to solve the trickiest challenges facing human rights defenders.

Machine learning to make sign language more accessible

Google has spent over twenty years helping to make information accessible and useful in more than 150 languages. And our work is definitely not done, because the internet changes so quickly. About 15% of searches we see are entirely new every day. And when it comes to other types of information beyond words, in many ways, technology hasn’t even begun to scratch the surface of what’s possible. Take one example: sign language.

The task is daunting. There are as many sign languages as there are spoken languages around the world. That’s why, when we started exploring how we could better support sign language, we started small by researching and experimenting with what machine learning models could recognize. We also spoke with members of the Deaf community, as well as linguistic experts. We began combining several ML models to recognize sign language as a sum of its parts — going beyond just hands to include body gestures and facial expressions.

After 14 months of testing with a database of videos for Japanese Sign Language and Hong Kong Sign Language, we launched SignTown: an interactive desktop application that works with a web browser and camera.

SignTown is an interactive web game built to help people to learn about sign language and Deaf culture. It uses machine learning to detect the user's ability to perform signs learned from the game.

Project Shuwa

SignTown is only one component of a broader effort to push the boundaries of technology for sign language and Deaf culture, named “Project Shuwa” after the Japanese word for sign language (“手話”). Future areas of development we’re exploring include building a more comprehensive dictionary across more sign and written languages, as well as collaborating with the Google Search team on surfacing these results to improve search quality for sign languages.

A woman in a black top facing the camera and making a sign with her right hand.

Advances in AI and ML now allow us to reliably detect hands, body poses and facial expressions using any camera inside a laptop or mobile phone. SignTown uses the MediaPipe Holistic model to identify keypoints from raw video frames, which we then feed into a classifier model to determine which sign is the closest match. This all runs inside of the user's browser, powered by Tensorflow.js.

A grid with separate images of four people facing the camera and making signs with their hands.

We open-sourced the core models and tools for developers and researchers to build their own custom models at Google IO 2021. That means anyone who wants to train and deploy their own sign language model has the ability to do so.

At Google, we strive to help build a more accessible world for people with disabilities through technology. Our progress depends on collaborating with the right partners and developers to shape experiments that may one day become stand-alone tools. But it’s equally important that we raise awareness in the wider community to foster diversity and inclusivity. We hope our work in this area with SignTown gets us a little closer to that goal.

AI Fest in Spain: Exploring the Potential of Artificial Intelligence in Careers, Communities, and Commerce

Posted by Alessandro Palmieri, Regional Lead for Spain Developer Communities

Google Developer Groups (GDGs) around the world are in a unique position to organize events on technology topics that community members are passionate about. That’s what happened in Spain in July 2021, where two GDG chapters decided to put on an event called AI Fest after noticing a lack of conferences dedicated exclusively to artificial intelligence. “Artificial intelligence is everywhere, although many people do not know it,” says Irene Ruiz Pozo, the organizer of GDG Murcia and GDG Cartagena. While AI has the potential to transform industries from retail to real estate with products like Dialogflow and Lending DocAI, “there are still companies falling behind,” she notes.

Image of Irene standing on stage at AI Fest Spain

Irene and her GDG team members recognized that creating a space for a diverse mix of people—students, academics, professional developers, and more—would not only enable them to share valuable knowledge about AI and its applications across sectors and industries, but it could also serve as a potential path for skill development and post-pandemic economic recovery in Spain. In addition, AI Fest would showcase GDGs in Spain as communities offering developer expertise, education, networking, and support.

Using the GDG network to find sponsors, partners, and speakers

The GDGs immediately got to work calling friends and contacts with experience in AI. “We started calling friends who were great developers and worked at various companies, we told them who we are, what we wanted to do, and what we wanted to achieve,” Irene says.

The GDG team found plenty of organizations eager to help: universities, nonprofit organizations, government entities, and private companies. The final roster included the Instituto de Fomento, the economic development agency of Spain’s Murcia region; the city council of Cartagena; Biyectiva Technology, which develops AI tools used in medicine, retail, and interactive marketing; and the Polytechnic University of Cartagena, where Irene founded and led the Google Developer Student Club in 2019 and 2020. Some partners also helped with swag and merchandising and even provided speakers. “The CEOs and different executives and developers of the companies who were speakers trusted this event from the beginning,” Irene says.

A celebration of AI and its potential

The event organizers lined up a total of 55 local and international speakers over the two-day event. Due to the ongoing COVID-10 pandemic, in-person attendance was limited to 50 people in a room at El Batel Auditorium and Conference Center in Cartagena, but sessions—speakers, roundtables, and workshops—were also live-streamed on YouTube on three channels to a thousand viewers.

Some of the most popular sessions included economics professor and technology lab co-founder Andrés Pedreño on "Competing in the era of Artificial Intelligence," a roundtable on women in technology; Intelequa software developer Elena Salcedo on "Happy plants with IoT''; and Google Developer Expert and technology firm CEO Juantomás García on "Vertex AI and AutoML: Democratizing access to AI." The sessions were also recorded for later viewing, and in less than a week after the event, there were more than 1500 views in room A, over 1100 in room B and nearly 350 views in the Workshops room.

The event made a huge impact on the developer community in Spain, setting an example of what tech-focused gatherings can look like in the COVID-19 era and how they can support more education, collaboration, and innovation across a wide range of organizations, ultimately accelerating the adoption of AI. Irene also notes that it has helped generate more interest in GDGs and GDSCs in Spain and their value as a place to learn, teach, and grow. “We’re really happy that new developers have joined the communities and entrepreneurs have decided to learn how to use Google technologies,” she says.

The effect on the GDG team was profound as well. “I have remembered why I started creating events--for people: to discover the magic of technology,” Irene says.

Taking AI Fest into the future—and more

Irene and her fellow GDG members are already planning for a second installment of AI Fest in early 2022, where they hope to be able to expect more in-person attendance. The team would also like to organize events focused on topics such as Android, Cloud, AR /VR, startups, the needs of local communities, and inclusion. Irene, who serves as a Women Techmakers Ambassador, is particularly interested in using her newly expanded network to host events that encourage women to choose technology and other STEM areas as a career.

Finally, Irene hopes that AI Fest will become an inspiration for GDGs around the world to showcase the potential of AI and other technologies. It’s a lot of work, she admits, but the result is well worth it. “My advice is to choose the area of technology that interests you the most, get organized, relax, and have a good team,” she advises.