AI researchers and engineers need better data to enable better AI solutions. The quality of an AI solution is determined by both the learning algorithm (such as a deep-neural network model) and the datasets used to train and evaluate that algorithm. Historically, AI research has focused much more on algorithms than datasets, despite their vital importance. As a result, many algorithms are freely available as starting points, but many important problems lack large, high-quality open datasets. Further, creating new datasets is expensive and error-prone.

Recently, the data-centric AI movement has emerged, which aims to develop new methodologies and tools for constructing better datasets to fix this problem. Conferences, workshops,  challenges, and platforms are being launched to support improving data quality and to foster data excellence. Thought leaders such as Andrew Ng at Landing.AI and Chris Re at Stanford University are encouraging AI developers to focus more on iterative data engineering than they do tuning their learning algorithms. Our CHI-best-paper-award-winning paper, “Everyone wants to do the model work, not the data work” highlighted the significance of data quality in the practice of ML. 

At Google, we are excited to contribute to data-centric AI. Today, Google Cloud is adding a new high value dataset to the Public Dataset Program, and Google researchers are announcing DataPerf, a new multi-organizational effort to develop benchmarks for data quality and data centric algorithms.

Google Cloud is committed to helping users improve their data quality, starting with supporting better public data. The Public Datasets program provides high quality datasets pre-configured on GCP for easy access. Google Cloud is adding a new high-value dataset developed by the MLCommons™ Association (which Google co-founded) to the Public Datasets program: The Multilingual Spoken Words Corpus: a rich audio speech dataset with more than 340,000 keywords in 50 languages with upwards of 23.4 million examples.

This new public dataset is aligned with the MLCommons Association vision for “open” datasets – accessible by all – that are “living” – continually being improved to raise quality and increase representation and diversity.

Google researchers, in collaboration with multiple organizations, are announcing the DataPerf effort at the NeurIPS Data-Centric AI workshop today, to develop benchmarks to improve data quality. Much like the the MLPerf™ benchmarking effort which is now the industry standard for machine learning hardware/software speed, DataPerf brings together the originators of prior efforts including: CATS4ML, Data-Centric AI Competition, DCBench, Dynabench, and the MLPerf benchmarks to define clear metrics that catalyze rapid innovation. DataPerf will measure the utility of training and test data for common problems, and algorithms for working with datasets such as: selecting core sets, correcting errors, identifying under-optimized data slices, and valuing datasets prior to labeling.

Together, supporting open, living datasets for core ML tasks, and the development of benchmarks to direct the rapid evolution of those datasets will empower the researchers and engineers who use Google Cloud to do even more amazing things – and we can’t wait to see what they create!

Acknowledgements: In collaboration with Lora Aroyo and Praveen Paritosh.

Organizations are currently modernizing their businesses in order to meet the increasing complexity of today’s business landscape. In effect, business leaders must evaluate the best way to mitigate the challenges which plague their cloud operations, all while meeting customers’ growing expectations around digital experience (DX) through agility, automation, and proactive incident avoidance. 

In this commissioned study, “Modernize With AIOps To Maximize Your Impact”, Forrester Consulting surveyed organizations worldwide to better understand how they’re approaching artificial intelligence for IT operations (AIOps) in their cloud environments, and what kind of benefits they’re seeing. 

Within this July 2021 study, you’ll see that AIOps systems and principles are here to help. It covers how AIOps increases efficiency and productivity across day-to-day operations, and how businesses are taking note. In fact, 91% of respondents have implemented AIOps to address at least one cloud operations issue, and expansion is set to skyrocket. Those that wait to act, risk losing out on the efficacy of their cloud investment and falling behind their more efficient competitors.

As you can see in the image above, there is a plethora of great information in this complimentary study. So, if you’re looking to enhance your cloud operations and/or adopt AIOps within your organization, be sure to download this free study today.

Lung cancer is the leading cause of cancer death in the United States and like any cancer, early detection is crucial to survival. Screening at-risk populations is an important part of reducing mortality, and if concerning nodules are found on imaging, further testing may be required. Today, we’ll share how Northwell Health uses Google Cloud products such as Cloud Healthcare APIs and BigQuery to increase caregiver productivity and deliver better care for patients with findings that indicate potential development of lung cancer.

Northwell is New York’s largest healthcare provider

Northwell Health is New York’s largest healthcare provider with 23 hospitals and nearly 800 outpatient facilities. Northwell’s nearly 4,000 doctors care for millions of patients each year, and at this scale, there is an immense amount of healthcare data to manage. To better manage and leverage this data, Northwell Health partnered with Google Cloud starting in 2018.

Enabling caregivers to spend more time with patients

Nic Lorenzen, the lead developer of Northwell Emerging Technology and Innovation team, has a mission to put together data for caregivers in a way that makes sense. It is no secret that inefficient electronic health records systems have a negative impact on a physician’s ability to deliver quality care. Traditional EHRs have information distributed across many tabs, which forces caregivers to spend considerable time at the computer trying to find information. Moreover, speed of care matters. If care is delayed, patients may have to spend more time in the hospital and may suffer worse health outcomes.

To solve this problem, Nic’s team focused on giving caregivers the most relevant pieces of data at the right time by developing an intelligent clinician rounding app. The data needed to derive these insights can depend on the caregiver’s role–a nurse cares about different things than a cardiologist. This system aggregates multiple data sources, and provides patient-specific insights to caregivers.

This system would not have been possible before with traditional EHRs and data warehouses that have proprietary data models and rarely sync data in real time. Now with data easily accessible through Google Cloud’s Healthcare solutions, Nic’s team can deliver the right clinical information to the right people instantly. These days, Nic says, “instead of spending 75% of our time dealing with architecting the underlying platforms, we spend 75% of our time focused on  higher value use cases for clinicians and patients. Google Cloud’s Healthcare solutions have greatly improved our developer productivity and time to value.”

Caregivers have found this new system to be a game changer.Before the implementation of this system, caregivers would spend, on average, seven to nine minutes finding the data needed to make medical decisions for one patient. Now, that aggregated information is delivered to a caregiver’s mobile device in less than a second.

Ensuring patients get the right care with the power of AI

There are a number of reasons why patients might not get the care that they need. For example, patients today can go to multiple hospitals and clinics settings, and coordinating care across multiple facilities is complex. Regional hospitals and clinics have their own siloed view of their data, so pertinent information gathered by one clinic might not be seen by another. These gaps in clinical data lead to gaps in patient care.

When a patient gets radiologic imaging, they may have findings unrelated to the reason they initially got the imaging. For example, a chest CT for a car accident might reveal an incidental lung nodule that could be cancerous. Unfortunately, research shows that a large portion of patients do not get follow up for these incidental findings because it isn’t the primary reason why the patient is seeing a doctor. Moreover, social determinants of health are a factor that affects which patients receive follow-up care. Identifying these patients and providing the necessary follow up care prevents adverse events related to delayed detection of cancer.

With Cloud Healthcare solutions, Northwell built an AI model to identify these patients so that oncologists can appropriately follow up with patients who have findings suspicious for lung cancer. The AI model detects incidental pulmonary nodules in radiology reports so that doctors can then contact the patients that need follow-up care. Nic says his team was able to build this system in a week: “Google Cloud did a lot of heavy-lifting for us and allowed us to get to the AI applications much faster. It allowed us to build a platform that just works.” 

Healthcare systems can now rapidly generate healthcare insights with one end-to-end solution, Google Cloud Healthcare Data Engine. It builds on and extends the core capabilities of the Google Cloud Healthcare API to make healthcare data more immediately useful by enabling an interoperable, longitudinal record of patient data. Northwell Health uses Google Cloud as the core of their platform, enabling their developers to create solutions to the most pressing healthcare problems.

Special thanks to Kalyan Pamarthy, Product Management Lead on Cloud Healthcare and Natural Language APIs for contributing to this blog post.

Investing in Artificial Intelligence (AI) can bring a competitive advantage to your organization. If you’re in charge of an AI or Data Science team, you’ll want to measure and maximize the value that you’re providing. Here is some advice from our years of experience in the field. 

A checklist to embark on a project: 

As you embark on projects we’ve found it’s good to have the following areas covered: 

• Have a customer. It’s important to have a customer for your work, and that they  agree with what you’re trying to achieve. Be sure to know what value you’re delivering to them. 
• Have a business case.  This will rely on estimates and assumptions, and may take no more than a few minute’s work.  You should revise this, but always know what justifies your team’s effort, and what you (and your customer) expect to get in return. 
• Know what process you will change or create. You’ll want to put your work in production, so you have to be clear about what business operations are changing or created around your work and who needs to be involved to make it happen
• Have a measurement plan. You’ll want to show that ongoing work is impacting some relevant business indicator. Measure and show incremental value. The goal of these measurements is to establish what has changed because of your project that would otherwise not have changed. Be sure to account for other factors like seasonality or other business changes that may affect your measurements.
• Use all the above to get your organization’s support for your team and your work. 

What measures to use?

As you start the work, what measures and indicators can you use to show that your team’s work is useful for your organization?

How many decisions you make. A major function of ML is to automate and optimize decisions: which product to recommend, which route to follow, etc. Use logs to track how many decisions your systems are making. 

Changes to revenue or costs. Better and quicker decisions often lead to increased revenue or savings. If possible, measure it directly, otherwise estimate it (for example fuel costs saved from less distance traveled, or increased purchases from personalized offers). 

As an example, the Illinois Department of Employment Security is using Contact Center AI to rapidly deploy virtual agents to help more than 1 million citizens file unemployment claims. To measure success the team tracked the two outcomes:  (1) the number of web inquiries and voice calls they were able to handle, and (2) the overall cost of the call center after the implementation. Post implementation, they were able to observe more than 140,000 phone and web inquiries per day and over 40,000 after-hours calls per night. They  also anticipate an estimated annual cost savings of $100M based on an initial analysis of IDES’s virtual agent data (see more in the link to case study).

Implementation costs. The other side of increased revenue or savings, is to put your achievements in the context of how much they cost. Show the technology costs that your team incurs and, ideally, how you can deliver more value, more efficiently. 

How much time was saved.  If the team built a routing system then it saved travel time, if it built an email classifier then it saved reading time, etc. Quantify how many hours were given back to the organization thanks to the efficiency of your system. 

In the medical field, quicker diagnostics matter. Johns Hopkins University’s Brain Injury Outcomes (BIOS) Division has focused on studying brain hemorrhage aiming to improve medical outcomes. The team identified the time to insights as a key metric in measuring business success. They experimented with a range of cloud computing solutions like Dataflow, Cloud Healthcare API, Compute Engine, and AI Platform for distributed training to accelerate iterations. As a result, in their recent work they were able to accelerate insights from scans from approximately 500 patients from 2,500 hours to 90 minutes.

How many applications your team supports. Some of your organization’s operations don’t use ML (say reconciling financial ledgers) but others do. Know how many parts of your organization benefit from the optimization and automation your team builds.

User experience. You may be able to measure your customer’s experience: fewer complaints, better reviews, reduced latency, more interactions, etc. This is valid both for internal and external stakeholders. At Google we measure usage and regularly ask for feedback on any internal system or process.

One of our customers, The City of Memphis, is using VisionAI and ML to tackle a common but very challenging issue: identifying and addressing potholes.  The implementation team identified the percentage increase of potholes identified as one of the key metrics along with accuracy and cost savings. The solution captures video footage from it’s public vehicles and leverages Google Cloud capabilities like Compute Engine, AI Platform, and BigQuery to automate the review of videos.  The project increased  pothole detection by 75% with over 90% accuracy. By measuring and demonstrating these outcomes, the team proved the viability of a cost-effective, cloud-based machine learning model and is looking into new applications of AI and ML that will further improve city services and help it build a better future for its 652,000 residents. 

Acknowledgements

Filipe and Payam would like to thank our colleague and co-author Mona Mona (AI/ML Customer Engineer, Healthcare and lifesciences) who contributed equally to the writing.

Hello! ¡Hola! 你好! नमस्ते! Bonjour! Being greeted in your own language can instantly put a smile on your face, and organizations around the world recognize that messages become more inclusive and powerful when translated into many languages.

As the Head of Product Management for Translation AI at Google Cloud, I am always looking to make our user’s lives easier when it comes to language tools—like Google Cloud’s Document Translation API, which has been used to translate over 2.5 billion documents across Google Translate and our Cloud customers.

But during my time working on translation products, I have seen enterprise users making compromises, like sacrificing cost and speed of translation for quality, or paying much more for high-quality translations and making peace with the slow turnaround times and high costs. That’s why we are excited to see momentum building behind Translation Hub, which launched earlier this year. Built to address organizations’ localization and translation challenges and delight users with an incredibly intuitive and administratively simple offering, Translation Hub makes it easy to translate content at scale—users just need to select the files to be translated, choose the output languages, and then they’re off and running.

In this article, we’ll take a closer look at Translation Hub’s powerful features, and the ways it is helping customers do more with their content.

Enterprise translation, reimagined: fast, lean, and intuitive

Translation Hub is a fully-managed, self-serve translation offering, powered by Google AI and built for the enterprise. With Translation Hub, businesses can instantaneously translate content into 135 languages with a single click, via an intuitive interface that integrates human reviews (i.e., a “human in the loop”) where required.

Translation Hub is designed to be both easy to manage and use. Each organization’s Translation Hub administrator uses the Google Cloud console to onboard and manage business users, typically by adding their email, which triggers an invite to the business user. Once a business user is added, they can sign in and start requesting translations and post-editing reviews.

With advanced features like “Glossary” and “Custom Translation Models (AutoML Translation),” Translation Hub makes it easy for enterprises to control the terminology and inject domain-specific context to meet their unique needs. Furthermore, Translation Hub ensures that organizations own their Translation Memory, for use in future translations or to leverage as a rich, human-validated source of training data for custom translation models (e.g., AutoML Translation). For example, when a user edits a phrase or segment through Translation Hub, this is immediately captured and used in the next translation for all future users who initiate translation where the same content or segment appears.

One of my favorite features is that Translation Hub preserves the layout and format of documents, saving both time and money. And while Translation Hub works with Google Drive (you can ingest and export documents to Drive) and is compatible with all our Google Workspace content sources (e.g., Slides, Docs etc.), it also supports many of the most commonly used formats, like documents and presentations created in Microsoft Office, and scanned and native PDFs.

Organizations need to be able to share the output of AI-powered translation with localization teams or agencies, for review. They need to save time by leveraging glossaries or customer machine learning (ML) models. They need these capabilities in one cohesive platform—and they need all of this to be user-friendly for all kinds of business users. It’s the difference between being able to automate translations in a few scenarios and being able to translate at scale. Google Cloud’s mission is to accelerate every organization’s ability to digitally transform, and with Translation Hub, our customers can leverage the best of AI for their content translations without sacrificing speed, quality or cost.

All of Google’s Translation Innovations within a single product

Translation Hub uses Google Cloud’s Translation API, AutoML Translation, and innovations in Neural Machine Translation Quality Prediction and Translation Memory—all technologies we’ve put to good use inside Google. For example, our localization teams have realized more than $80 million in cost avoidance and savings since 2021, using Google Cloud AutoML Translation and localization expertise.

“In just three months of using Translation Hub and AutoML translation models, we saw our translated page count go up by 700% and translation cost reduced by 90%,” said Murali Nathan, digital innovation and employee experience lead, at materials science company Avery Dennison. “Beyond numbers, Google’s enterprise translation technology is driving a feeling of inclusion among our employees. Every Avery Dennison employee has access to on-demand, general purpose, and company-specific translations. English language fluency is no longer a barrier, and our employees are beginning to broadly express themselves right in their native language.”

To get started with Translation Hub, visit our solution page, and for a deep dive, check out this Google Next ‘22 session, including more details about Avery Dennison’s use case.