If you’ve opened an account at a bank, applied for a government benefit, or provided a proof of age document on an ecommerce website, chances are you’ve had to share a physical or digital copy of a Driver’s License or a passport as proof of your identity. For businesses or public sector organizations that need this information to provide services, processing images of identity documents has long been a time- and resource-intensive process that requires extensive human intervention. Solutions exist to help digitally capture the data, but they require extensive human intervention that impacts the speed and cost of processing and ultimately the time to service customers.

The Google Cloud Document AI family of solutions has been designed to help solve some of the hardest problems for data capture at scale by extracting structured data from unstructured documents to help reduce processing costs and improve business speed and efficiency. Today, we’re announcing the general availability of identity parsers that bring the power of Document AI to customer verification, KYC, and other identity-based workflows.

With Document AI for Identity, businesses can leverage automation to extract information from identity documents with a high degree of accuracy, without having to bear the cost and turnaround time of manual tasks by a service provider. Document AI for Identity leverages artificial Intelligence to provide a set of pre-trained models that can parse identity and supports US driver’s licenses (generally available), US passports (generally available), French driver’s licenses (preview) and French National ID cards (preview), with more documents to be added from around the world over the coming months.

When our customers process high-volume workloads or complex workflows, they need a high degree of accuracy, since getting the first step wrong can derail the entire workflow. The introduction of special parsers for Identity processing can help solve one of the most commonly required document processing needs that our financial services and public sector customers face.

Along with the identity parsers, Google Cloud is also offering its “Human in the Loop” service, in which verification for a subset of identity documents can be automatically assigned to a pool of humans (internal or external) for manual review, based on confidence scores.

While there are multiple industries and applications that could benefit from Document AI for Identity, we’ve seen two main kinds of applications being adopted during the solution’s preview. One is around processing ID cards uploaded as unstructured images at scale, so that enterprises can have IDs on file. The second use case is to perform advanced checks on identity documents to validate their authenticity and / or to detect fraud. Google Cloud’s fraud detector API (which is currently in preview) can complement Document AI for Identity and apply an extra layer of normalization to help validate the identity as a government-issued ID by checking for suspicious words, image manipulation, and other common issues with forged identity documents. With new versions of driver’s licenses being frequently released, Document AI for identity uses specialized models and constantly-updated training data to help make sure the parsers can offer a high degree of accuracy. For all use cases, Document AI does not retain any customer data after completing the processing request (successfully or with an error).

Check out this demo and visit the Document AI for Identity landing page for more information on how Document AI can help solve your identity processing needs, and ask your Google Cloud account team to help you integrate Identity Document AI into your workflows.

For practitioners who’re interested in trying out Identity DocAI, check out our companion practitioner blog for step by step instructions on how to get started.

This week, the Vera C. Rubin Observatory is launching the first preview of its new Rubin Science Platform (RSP) for an initial cohort of astronomers. The observatory, which is located in Chile but managed by the U.S. National Science Foundation’s NOIRLab in Tucson, AZ and SLAC in California, is jointly funded by the NSF and the U.S. Department of Energy. The platform provides an easy-to-use interface to store and analyze the massive datasets of the Legacy Survey of Space and Time (LSST), which will survey a third of the sky each night for ten years, detecting billions of stars and galaxies, and millions of supernovae, variable stars, and small bodies in our Solar System.

The LSST datasets are unprecedented in size and complexity, and will be far too large for scientists to download to their personal computers for analysis. Instead, scientists will use the RSP to process, query, visualize, and analyze the LSST data archives through a mixture of web portal, notebook, and other virtual data analysis services. An initial launch with simulated data, called Data Preview 0, builds on the Rubin Observatory’s three-year partnership with Google to develop an Interim Data Facility (IDF) on Google Cloud to prototype hosting of the massive LSST dataset. This agreement marks the first time a cloud-based data facility has been used for an astronomy application of this magnitude.

Bringing the stars to the cloud

For Data Preview 0, the IDF leverages Cloud Storage, Google Kubernetes Engine (GKE), and Compute Engine to provide the Rubin Observatory user community access to simulated LSST data in an early version of the RSP. The simulated data were developed over several years by the LSST Dark Energy Science Collaboration to imitate five years of an LSST-like survey over 300 square degrees of the sky (about 1,500 times the area of the moon). The resulting images are very realistic: they have the same instrumental characteristics, such as pixel size and sensitivity to photons, that are expected from the Rubin Observatory’s LSST Camera, and they were processed with an early version of the LSST Science Pipelines that will eventually be used to process LSST data. “This will be the first time that these workloads have ever been hosted in a cloud environment. Researchers will have an opportunity to explore an early version of this platform,” says Ranpal Gill, senior manager and head of communications at the Rubin Observatory.

Broadening access for more researchers

Over 200 scientists and students with Rubin Observatory data rights were selected to participate in Data Preview 0 from a pool of applicants that represents a wide range of demographic criteria, regions, and experience level. Participants will be supported with resources such as tutorials, seminars, communication channels, and networking opportunities—and they will be free to pursue their own science at their own pace using the data in the RSP. 

“The revolutionary nature of the future LSST dataset requires a commensurately innovative system for data access and analysis paired with robust support for scientists,” says Melissa Graham, lead community scientist for the Rubin Observatory and research scientist in the astronomy department at the University of Washington. “I’m personally excited to enhance my own skills by using the RSP’s tools for big data analysis, while also helping others to learn and to pursue their LSST-related science goals during Data Preview 0.” 

At the same time, the fact that the RSP is hosted in the cloud provides researchers at smaller institutions access to state-of-the-art astronomy infrastructure that is comparable to that of the largest national research centers.

The launch benefits the observatory too: the development team can learn what researchers are interested in while also testing and debugging the platform. Graham says that “the platform is still in active development so researchers using it will be able to follow along in the progress, and provide feedback on ways that we can optimize the development of the tools.”

Next steps

The LSST aims to begin the ten-year survey in 2023-24 and expects it to include 500 petabytes of data. Through the cloud, Google aims to help make this extraordinary project scalable and accessible to researchers everywhere. To learn more about Data Preview 0, watch this video.

Want to ramp up your own research in the cloud? We offer research credits to academics using Google Cloud for qualifying projects in eligible countries. You can find our application form on Google Cloud’s website or contact our sales team.

Over the last year, COVID-19 presented unforeseen challenges for practically every type of business and organization—including schools, colleges, and universities. For educational institutions, the pandemic was an unapologetic agent of acceleration, shifting one billion learners from in-person to online learning within two months. 

The rapid transition to online learning exposed many schools’ lack of readiness for the new online learning environment. It also widened the learning equity gap for students, with fewer than 40% of students from low-income families having access to the tools required for remote learning.

For those who do have online access, today’s students expect everything from engaging and collaborative digital learning experiences to skills-based training for their roles in the future workforce. Expectations are also high for 24×7 multi-channel tech support across all learning devices, applications, and platforms.  

In these remarkable times, education technology companies have an important role to play in supporting academic institutions and students. Indeed, this is already happening, as the EdTech (Educational Technology) market is nearly tripling, with total global expenditures expected to reach $404 billion by 2025. However, the success of these EdTech companies depends on their performance in a number of areas, including:

• Content and products: How quickly can they generate new content and react to learner needs with new products to additional markets for broader adoption?
• Personalization: How effectively can they leverage artificial intelligence (AI) to provide a personalized experience to all types of learners?
• Trust and security: How trusted and secure are their services when educational organizations are suffering the highest number of data breaches since 2005?

Here are a few examples of how EdTech companies are successfully using AI and analytics to capture this opportunity and transform their businesses:

• Build better products: iSchoolConnect is an online platform that lets students explore schools, courses, and countries where they might study, and makes higher education admissions accessible to students around the globe. The company leverages AI services to help educational institutions optimize their academic operations by accelerating admission processing by greater than 90%, while saving significant costs.
• Launch in new markets faster: Classroom creativity tools provider Book Creator uses AI APIs to enhance accessibility and improve the learner experience. “The broad suite of intelligent APIs enables us to deliver richer experiences, faster and more easily, without having to be experts in machine learning, drawing recognition, map embeds, or other areas,” says VP of engineering Thom Leggett.
• Scale businesses securely: Using DevOps and CDN [content delivery network] services, Chrome browser recording extension creator Screencastify was able to support eight times growth in users overnight amid the COVID-19 pandemic, while maintaining consistent total cost of ownership. These technologies helped the company rapidly scale operations in response to the overnight increase in demand from consumers and assure student data privacy and security on a budget. “We know this is just the beginning, as more educators rely on technology to deliver richer, more interactive curricula to students,” says CEO James Francis. 
• Provide personalized learning and support: Smart analytics and AI can provide personalized support and recommendations for students, forecast demand, and predict shifts in learners’ preferences. Online learning platform Mindvalley uses cloud-based tools to understand and make decisions based on user activity and leverage machine learning to predict behavior. 

Google Cloud is partnering with many of these leading EdTech companies, as well as industry-leading consortiums like Ed-Fi and Unizin, to standardize educational common data models and best practices for more agile and cost-effective integration of EdTech into existing environments.

The education landscape is changing rapidly, and EdTech has a major role to play as institutions adapt to the massive shift in learners’ preferences and expectations. We’re committed to empowering EdTech companies with the tools and services they need to help expand learning for everyone, anywhere. 

Watch our Spotlight session with EdTechX to learn more.

When Google announced our plan to go beyond purchasing renewable power for 100% of our energy usage and operate on 24/7 carbon-free energy by 2030, we noted that achieving this goal will require new transaction structures, advancements in clean energy policy, and innovative new technologies. Today, we’re pleased to announce that one of these new technologies—a first-of-its-kind, next-generation geothermal project—will soon begin adding carbon-free energy to the electric grid that serves our data centers and infrastructure throughout Nevada, including our Cloud region in Las Vegas.  

Google and clean-energy startup Fervo have just signed the world’s first corporate agreement to develop a next-generation geothermal power project, which will provide an “always-on” carbon-free resource that can reduce our hourly reliance on fossil fuels. In 2022, Fervo will begin adding “firm” geothermal energy to the state’s electric grid system, where Google’s commitments already include one of the world’s largest corporate solar-plus-storage power purchase agreements. 

Importantly, this collaboration also sets the stage for next-generation geothermal to play a role as a firm and flexible carbon-free energy source that can increasingly replace carbon-emitting fossil fuels—especially when aided by policies that expand and improve electricity markets; incentivize deployment of innovative technologies; and increase investments in clean energy research, development, and demonstration (RD&D). 

Next-generation geothermal technology

Traditional geothermal already provides carbon-free baseload energy to a number of power grids. But because of cost and location constraints, it accounts for a very small percentage of global clean energy production. 

That’s one reason this new approach is so exciting; by using advanced drilling, fiber-optic sensing, and analytics techniques, next-generation geothermal can unlock an entirely new class of resource. And the US Department of Energy has found that with advancements in policy, technology, and procurement, geothermal energy could provide up to 120 GW of firm, flexible generation capacity in the US by 2050. 

As part of our agreement, Google is partnering with Fervo to develop AI and machine learning that could boost the productivity of next-generation geothermal and make it more effective at responding to demand, while also filling in the gaps left by variable renewable energy sources. Although this project is still in the early stages, it shows promise.

Using fiber-optic cables inside wells, Fervo can gather real-time data on flow, temperature, and performance of the geothermal resource. This data allows Fervo to identify precisely where the best resources exist, making it possible to control flow at various depths. Coupled with the AI and machine learning development outlined above, these capabilities can increase productivity and unlock flexible geothermal power in a range of new places. 

This won’t be the first time that Google is applying software solutions to clean energy applications: we’ve just announced an update to our carbon-intelligent computing program that helps us reduce emissions associated with running applications at Google data centers. And other forms of AI and machine learning are currently being used to increase the value of wind energy. 

Already this year, Google has taken significant strides toward sourcing 24/7 carbon-free energy for all our data centers, office campuses, and Cloud regions. On Earth Day, our CEO Sundar Pichai announced that for the first time, five of our global data center sites operated near or at 90% carbon-free energy in 2020. 

Not only does this Fervo project bring our data centers in Nevada closer to round-the-clock clean energy, but it also acts as a proof-of-concept to show how firm clean energy sources such as next-generation geothermal could eventually help replace carbon-emitting power sources around the world.