Trend 2: Google Research on Machine Learning Themes for 2022 and Beyond!
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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.
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.
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).
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.
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 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.
Home Depot’s Interconnected Retail Experience by Virtue of Google Cloud Migration for SAP Applications
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With nearly 2,300 stores, The Home Depot is the world’s largest home-improvement chain — a brand that professional contractors and DIYers alike have come to depend on. The home improvement industry continues to experience unprecedented demand and dramatic increases in online ordering accompanied by expanding consumer expectations for things like curbside pickup and same day delivery. The Home Depot’s decision to migrate to cloud-based infrastructure, including the migration of the company’s SAP applications on Google Cloud which began in 2017, has set it up for success in an increasingly digital world, and helped the company adapt to changing market conditions quickly.
Interconnected retail at scale
Building on a strong customer-first philosophy, The Home Depot aims to create what it calls interconnected retail—allowing customers to shop however, whenever, and wherever they want. “So many companies are focused on omni-channel retail,” explains Sam Moses, Vice President of Corporate Systems. “At The Home Depot, we wanted to take it to the next level. Interconnected retail puts the customer at the center of everything and enables them to shop in store, online, or both. Customers can begin a transaction online and continue in-store, or vice-versa.”
To support this strategy, the company’s SAP environment needed to be more agile. Running everything on-premises, from central finance to POS systems, meant that The Home Depot’s IT teams experienced redundancy and repetitive, manual processes. Their data warehouse needed an upgrade to process and analyze growing and increasingly diverse data sets. The Home Depot chose to migrate its SAP environment to Google Cloud to support both the velocity and scale needed for the business as well as critical analytics capabilities needed for its bold digital initiatives. “We chose Google Cloud to support our SAP implementation. Our decision had a lot to do with the relationship between Google Cloud and SAP and also for the applications and services that are offered by Google Cloud, like BigQuery, which are helping to enable data and analytics within our organization,” Moses explains.
After migrating its SAP applications—including S/4HANA, its customer activity repository (CAR), general ledger, e-commerce system, enterprise data warehouse and more to Google Cloud, the company now has the speed, scale and flexibility to tackle enormous spikes in the business, all while staying fully available for their customers. Additionally, The Home Depot was able to transform its financial systems and make them more agile to deliver critical information across multiple business functions in real time.
Maximizing data insights to support customer experiences
By migrating to Google Cloud, The Home Depot is leveraging Google Cloud analytics to build the industry’s most efficient supply chain including more robust demand forecasting, supplier lead times, estimated delivery times and more, all while maintaining better security than before. “We experienced unprecedented change in our customers’ behavior and their buying patterns, which puts a lot of pressure on our supply chain,” explains Moses. “So having the ability to leverage data and analytics gives us insights to know exactly what it is that our customers need.”
The company’s analysts now use BigQuery ML for machine learning directly against the company’s BigQuery data and use AutoML to determine the best model for predictions. The Home Depot’s engineers have also adapted BigQuery to monitor, analyze, and act on application performance data across all its stores and warehouses in real time—capabilities that were not as seamless in the on-premises environment.
With hundreds of projects on Google Cloud, The Home Depot’s cloud journey is well on track, but the company is always looking to the future. “As our customers’ needs have continued to evolve, and as technology has continued to evolve, our relationship with Google will continue to advance — to be able to innovate together, to be able to find new solutions together, to better serve our customers.”
Learn more about how The Home Depot is renovating its retail operation with SAP on Google Cloud.
Latest Features and Updates to Globally Bolster Translation Services
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Let’s face it: in the globalized world, which is now more than ever a digital demand world, you need to scale and reach your customers right where they’re at. Translation is a critical piece of that, whether you’re translating a website in multiple languages or releasing a document, a piece of software, or training materials.
Manual translation does not scale, which is why machine translation, powered by machine learning (ML), is becoming more important to our customers. Machine translation has historically been challenging because of the sheer volume and breadth of content that can add value when translated into multiple languages. Companies acquire and share content in many languages and formats, and scaling translation to meet needs is a tall order due to multiple document formats, integrations with optical character recognition (OCR), and the need to correct for domain-specific terminology.
Our goal is to simplify translation services, while enabling flexibility and control for our customers’ unique needs across industries. Read on to learn more about recent features and updates.
Formatting matters: Document Translation is now GA
In many cases, the layout of a document dictates how it should be interpreted—e.g., readers navigate text and discern meaning based on formatting, like bold or italicized text, or markups for headers, paragraphs, and columns. Previously, to automate translation of documents, text needed to be separated from these layout attributes, meaning the document’s structure was either lost or needed to be recreated later in the developer pipeline, after the text had been translated. This required translation teams to do a lot of extra work and maintain a lot of additional code. But now, those steps are unnecessary. Formatting can be retained throughout the translation process, handled directly by the Translation API Advanced.
This feature lets customers translate documents in 100+ languages and supports document types such as Docx, PPTx, XLSx, and PDF while preserving document formatting.
And if your needs go beyond Document Translation, we can help you translate audio as well. For real-time streaming translation, check out the Media Translation API, and for offline transcription translation, combine the Translation API with the Video Intelligence API.
Real-Time translation when you need it, Batch when you don’t
One of the biggest differentiators for Translation API Advanced’s document translation capabilities is the ability to do real-time, synchronous processing for a single file.
For example, if you are translating a business document such as HR documentation, online translation provides flexibility for smaller files and provides faster results. You can easily integrate with our APIs via REST or gRPC with mobile or browser applications, with instant access to 100+ language pairs so that content can be understandable in any supported language.
Meanwhile, batch translation allows customers to translate multiple files into multiple languages in a single request. For each request, customers can send up to 100 files with a total content size of up to 1 GB or 100 million Unicode codepoints, whichever limit is hit first.
State of the Art (SOTA) accuracy, with flexibility for customization
In order to achieve the highest level of accuracy for your translation, we now support multiple options:
- Use Google’s SOTA translation models: Each year, Google heavily invests to improve the quality of our translations across Apps, Cloud APIs, and Chrome, as well to enable multilanguage answers in Search. A popular metric for automatic quality evaluation of Machine translation systems is the BLEU score, which is based on the similarity between machine translation and the reference translations that were generated by people. While we push out incremental improvements for individual models on a monthly cadence, there are also times where we make significant leaps. In the releases since 2019, we have improved our average BLEU score by 5pts on average across 100+ languages and 7pts on low resource languages.
- Leverage glossaries for specific terms and phrases: Glossary is our terminology control feature. It allows you to import source content to define preferred translations, such as product names or department names. Then, when calling the glossary in the API request, your preferred translations will be enforced. This will work for words as well as phrase translation.
- Pick a pre-trained model with model selection: If you create custom models for machine translation, we don’t think you should have multiple client libraries and multiple APIs to maintain in order for you to use the best model for your needs. Translation API Advanced now supports Model Selection. Pick your pretrained model or pick your custom ML model built on AutoML for any language pair you’ve created and use the same API and the same client library.
- Build custom translation models with AutoML: AutoML Translation is a suite of ML products that enable you to build high quality models for your own use case or data, with limited-to-no ML expertise or coding required. Bring your past human-validated translations to improve translation specificity for your domain.
Keep localization local with Regional Endpoints
If you are a customer operating in the EU, we recently launched an endpoint specifically for EU regionalization. This is a configurable endpoint for customers to store and perform machine translation processing of customer data only in the EU multi region. For now, this only supports our pretrained translation models and glossary, but batch translations will be coming soon.
How Eli Lilly uses Cloud Translation to translate content globally
Historically, translations at Eli Lilly have been complicated: numerous translation vendors have been needed for different languages and organizations, all with their own processes and expectations. On top of that, translations have been costly and slow.
To solve this, Eli Lilly took a codified approach to enable users and systems to spend less time and resources to safely generate quality translations.
Learn more, and even catch a demo, from Thomas Griffin, Translation Tech Lead & Global Regulatory Architect for Eli Lilly.
Learn more
- To get started using Cloud Translation – Advanced, complete the setup and then try the Translate text (Advanced edition) quickstart.
- Document Translation is priced per page. For more information, see pricing.
Start-up Paves Way for More Inclusive Clinical Research: Honoring Black Founders of Acclinate with Google Cloud
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Editor’s note: February is Black History Month—a time for us to come together to celebrate the diverse set of experiences, perspectives and identities that make up the Black experience. Over the next few weeks, we will highlight Black-led startups and how they use Google Cloud to grow their businesses. Today’s feature highlights Acclinate and its founders, Del and Tiffany.
As patients, as caregivers, and as parents taking our own children to the doctor, we want recommended medications to be safe and effective. It’s a right everyone deserves.
It’s known that certain medications don’t work in the same way in all populations. For example, Albuterol, a medication often prescribed for asthma, is less effective in 67% of all Puerto Ricans and 47% of Black Americans. These problems—which can have deadly consequences—result from historically limited diversity in pharmaceutical clinical trials.
We founded our startup Acclinate to integrate culture and technology to achieve more inclusive clinical research. Help pharmaceutical companies and healthcare organizations access and engage communities of color so research is more inclusive.
Bridging the health equity gap by building trust
It’s important that health research organizations access and engage communities of color so their efforts reflect all the people they serve. Take a disease like diabetes, which affects a significantly higher proportion of Black Americans. When you look at even recent clinical trials for diabetic drugs, the representation of Black Americans among participants is only in the low single digits, despite comprising 13 percent of the U.S. population and more than 40 percent of diabetes patients in this country. Industry leaders have been aware of the lack of diversity issue, but some have chosen to ignore it or brush it aside. The biggest problem, in our opinion, is that there have been no penalties for not achieving higher diversity figures in clinical trials, and only minor financial repercussions to pharma/biotech companies when their treatments either do not work across all groups once approved, or there is a lack of uptake by all groups due to the lack of testing in those groups. The lack of clinical trial diversity has adversely impacted the reputation of the industry and the ability to recruit diverse populations in the future.
Acclinate integrates culture and technology to promote diverse patient representation in medical research. Our approach is not transactional. We build trust through our #NOWINCLUDED community, which is an ongoing, ever-expanding digital platform that educates and engages with communities of color on health issues.
#NOWINCLUDED includes a website app, and social media presence where members can learn information about diseases, particularly those with greater negative impacts on people of color, such as cancer, diabetes, and cardiovascular diseases. Members can share stories and ask questions. By providing access to trusted resources about these health issues and the latest clinical research, we empower Black people to take control of their health and consider participating in research that is shaping the future of healthcare.
For healthcare-related organizations, we offer the opportunity to better understand the attitudes, aspirations, and unmet needs of underrepresented minority communities. Data from #NOWINCLUDED feeds our HIPAA-compliant SaaS platform, e-DICT™ (Enhanced Diversity in Clinical Trials), which uses predictive analytics and machine learning to identify individuals matching the requirements and most likely to be receptive to participation in a particular clinical trial.
Acclinate scales its platform with Google Cloud
We rely on Google Cloud services, including Vertex AI, to know whom to ask, when to ask, and how to ask for clinical trial participation. With Vertex AI, we enjoy a unified platform for developing our artificial intelligence models, including tools for preparing and storing our datasets. We can easily train and compare models using AutoML, which requires minimal ML expertise or effort with its intuitive graphical interface. This allows us to leverage more than ten ordinal and categorical data points to determine in real time a community member’s likelihood to enroll, which we call our Participation Probability Index (PPI). Our models evolve in an iterative process the more we interact with, and learn about, our community members.
We follow the pay-per-use Google Cloud Platform architecture model using serverless technology, which helps reduce infrastructure management costs and lets us focus on product development and engaging with communities across the U.S.
CloudSQL, a fully managed relational database service, integrates easily with BigQuery so we can glean insights for our clients in real time, all with Google Cloud’s robust security, governance, and reliability controls. Virtual Private Cloud (VPC) gives us scalable and flexible networking for our cloud-based resources and services. We also use Identity and Access Management (IAM) to simplify oversight of Google Cloud resource permissions for different user groups and roles, with appropriate security protections.
API Gateway manages our APIs using Cloud Functions, which both use consumption-based pricing, plus give our developers consistent and highly secure access to our services through a well-defined REST API. We use Memorystore for Redis to reduce platform latency. This is done with a fully managed service powered by the Redis in-memory data store, which builds application caches for fast data access. All of this comes together to provide an outstanding experience for our platform’s users and contributors.
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Expanding influence with the Google for Startups Accelerator for Black Founders
Three months of one-on-one Google support and mentorship as part of the most recent Google for Startups Accelerator : Black Founders cohort not only helped us build our product, but also helped us earn external credibility. People use Google every day, so whether we’re trying to engage in conversations with industry experts or with somebody in a rural community, it is helpful to have the buy-in of a globally-recognized brand as we take on a historically difficult, systemic issue with challenges around trust. Getting access to the products, best practices, and people we need to build and grow through the Accelerator program has been priceless. For example, working with the Google AdWords team helped us generate important traffic from people interested in learning more about #NOWINCLUDED or sharing their story with us. Jason Scott, who leads the Google for Startups Accelerator: Black Founders program, is still connecting us to people in his network and identifying key opportunities for us months after the program wrapped. He continues to demonstrate that he is invested in seeing us succeed.
Our company has made great progress against our goals, in part thanks to receiving capital from the Google for Startups Black Founders Fund. We received $100K in non-dilutive funding along with Google Cloud credits, Google.org Ads grants, and hands-on support. We used the funds to pay for the transition and development costs associated with moving to Google Cloud. The Google support and accountability has been incredible. After receiving the Google for Startups Black Founders Fund award, we’ve gone on to raise another $1M and moved our cloud from Salesforce to Google Cloud.
We also had the amazing opportunity to be selected as one of six companies to take part in a face-to-face web conference with Sundar Pichai, Google’s CEO. We were thrilled to hear him explain his vision around health equity and the role Google plays. Ultimately, for us, it’s not just about the funding we get, but we are also gratified to receive support from an entity that truly believes in addressing this issue. We know Google is aligned with our mission of health and racial equity.
Championing diversity in clinical trials
Our 2022 looks bright. We expanded our presence to Washington, D.C. as part of the Johnson & Johnson Innovation JLABS ecosystem. We were also selected to take part in the BLUE KNIGHT initiative created between Johnson & Johnson Innovation and the Biomedical Advanced Research and Development Authority (BARDA) under the U.S. Department of Health and Human Services.
Acclinate is also on track to have contracts with five of the top 25 largest biopharmaceutical companies in the U.S this year. They’ve taken note, as has the Food and Drug Administration (FDA), that the lack of diversity in clinical trials represents a significant health concern—to the extent that the FDA has provided strong guidance for pharmaceutical companies to diversify their clinical trials. At the same time, the industry is also responding to pressure from communities of people of color to make equitable representation a priority.
Today, we are in the fortunate but challenging position to have significant inbound opportunities coming our way. In response, we continue to recruit and hire talented people to join our team. On the technology side, we are happy to be aligned with Google Cloud to have powerful cloud infrastructure that will scale with us, as well as high-caliber champions united in partnership. With people’s lives at stake, we are passionate in our commitment to helping ensure medications do what they are supposed to do: heal and improve the quality of life for everyone who takes them.
Hear Acclinate cofounders Del Smith and Tiffany Whitlow chat with Google’s Head of Startup Developer Ecosystem Jason Scott and fellow Black Founders Fund recipient Bobby Bryant about building on Google Cloud in a recent Google for Startups Instagram Live.
If you want to learn more about how Google Cloud can help your startup, visit our page here to get more information about our program, and sign up for our communications to get a look at our community activities, digital events, special offers, and more
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Google Cloud’s AI Adoption Framework: Helping You Build a Transformative AI Capability
AI can help organizations improve the decision-making process across most business functions. However, building an effective AI capability encompasses more than just creating a technology platform.
To do this effectively requires alignment to business objectives, strong executive sponsorship, and collaboration between skilled employees and strategic partners.
Additionally, you need your initiatives to be powered by secure data management and cloud-native services to scale and automate ML workloads, and ensure all of this is underpinned by responsible AI principles.
Successfully adopting AI in your business is determined by your practices in these areas. Learn more about the AI journey and how you can gain value every step of the way.
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Generating Value with AI
Hear how enterprises leveraging the Google Cloud–across industries–are using AI to navigate uncertain times, and how they are innovating with AI to generate value moving forward.
In this video, you’ll uncover how to start using innovations from Google Cloud AI in your business today, and how customers deploy AI to transform their organizations.
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