Google and AI Researchers Work towards Building Data-centric AI

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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.

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How Apna is using data and AI to drive the gig economy in India
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.
Google Cloud Partnership Fuels ListenField’s Agriculture Revolution

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When I was growing up in Thailand, I witnessed the challenges facing farmers including rising food demand, shortage of labor, and uneven crop yields caused by climate change. As a result, many smallholder farmers found themselves trapped in a vicious circle, unable to reduce food insecurity due to low yields, but lacking the resources to invest for a more profitable future.
I was determined to make a difference. After earning my master’s degree in Information Management I joined a research project with the University of Tokyo where we used sensors to monitor spinach fields in Thailand. The results of this early experiment in precision farming were impressive. We proved that it was possible to grow organic crops with minimal use of fertilizers, while consumers benefited from higher quality spinach grown in Thailand.
This inspired me to found ListenField in 2017. Our mission is to transform farm management by collecting data from multiple sources, including field sensors, soil scanning, weather data, seasonal forecasts, and satellite imagery. By modeling this data, we provide farmers with insights that enable them to optimize production ‘from soil to harvest’.
A bumper crop of farming data
Artificial intelligence and machine learning play a central role in our prediction platform, combining crop health monitoring, growth prediction, and soil nutrition analysis. Farmers can apply real-time insights to their schedule from our FarmAI Mobile App, while our FarmAI Dashboard enables agri-food businesses to collaborate with agronomists and farmers so that all parties benefit from higher profit margins and more sustainable growing strategies.
Another important feature of our business model is AgroAPI, which makes our analytics available to third parties. Clients can embed deep analytics in their applications, including crop growth prediction and remote sensing analysis, without needing to develop complicated algorithms and data pipelines by themselves.
We are also excited about our research into genomic prediction in collaboration with the Japanese government and several research companies. Using our Data-Driven Breeding Platform, breeders and seed companies can upload their genomic data and gain practical insights that help accelerate the reproduction of high-quality seeds and plants.
Today, more than 30,000 farmers use our technology especially in Vietnam and Thailand where it is used to improve rice, cassava, and sugar cane yields. The technology is also being rolled out for orange and mango farmers enabling them to monitor individual trees and adjust irrigation to improve the sweetness of the fruit at harvest time.
Responding fast to changing conditions
We were using another cloud provider to run our business, but one of the ListenField team members drew our attention to Google Cloud. As well as the technology, we were also attracted by the Google for Startups Cloud Program which provides us with Google Cloud credits that cover our first and second years of Google Cloud usage. We also met our Account Representative, who provides us with training, business and tech support, and Google-wide discounts. It’s great to have a point of contact that can help us on our startup journey and make the most of Google’s resources.
By reducing the pressure on our finances and human resources, we were able to experiment and adapt in response to early experiments. This flexibility also enabled us to demonstrate a compelling business case to new and existing investors.
Our Google Cloud Platform Partner, Navagis, gave us additional momentum thanks to their expertise in mapping and geospatial data. They also played a crucial role in the integration of Google Earth Engine, which we use to map agricultural areas.
We also use Firebase for application development and Google Workspace for team collaboration. Colab and Vertex AI enable us to build, deploy, and scale our machine learning models quickly, ensuring that we remain competitive and attractive to new customers.
Giving female entrepreneurs the opportunity to flourish
Both the Google Cloud team and our colleagues at Navagis helped us to navigate the challenges many early-stage startups face. My background is in science and academia, so I appreciated the business mindset offered by both organizations to help us continue to grow and scale.
Being a female entrepreneur leading a startup can also be tough, but Google Cloud and Navagis helped me to build a strong network, access funding, and make my voice heard. Today, ListenField has several female executives, while 50% of our researchers and many of the farmers on our platform are women.
Above all, Google Cloud helps us to power an agriculture revolution in south-east Asia. Smallholder farmers can transition from analog to digital farming, improving their yields and reducing waste. The benefits to the economy are also significant including greater food security and reducing the use of industrial fertilizers that generate potent greenhouse gasses.
And that’s just the beginning of what we can do. Our next milestone is to reach 50,000 farmers and cut one million tonnes of greenhouse gas emissions. It sounds ambitious, but with the Google Cloud and Navagis teams behind us, I’m confident that we will reach these targets.

ListenField team members
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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Companies everywhere are seeking to leverage the power of AI. And rightly so. The smart applications of AI enable organizations to improve, to scale, and to accelerate the decision-making process across most business functions, so as to work both more efficiently and more effectively. It can also open up new avenues and new revenue streams, providing the organization with an additional competitive edge.
In short, many believe (as we do) that the enterprises that invest in building industry-specific AI solutions today are positioning themselves to be the global economic leaders of tomorrow. But the path to building an effective AI capability is not an easy one. There are many challenges to overcome. Challenges with the technology to develop platforms and solutions. With the people who will implement and manage that technology. With the data that fuels the technology. And with the processes that govern the whole of it. How do you harness the power inherent in AI, while avoiding any potential missteps?
That’s where Google Cloud comes in. Our framework for AI adoption provides a guide to technology leaders who want to build an effective AI capability, one that enables them to leverage the power of AI to enhance and streamline their business, smoothly and smartly. The framework is informed by Google’s own evolution, innovation, and leadership in AI, including experience deploying AI in production through products such as Gmail and Google Photos. It is also inspired by many years of experience helping cloud customers — from startups to enterprises, in various industries — to solve complex challenges.
With Google Cloud’s AI Adoption Framework, you’ll be able to create and evolve your own transformative AI capability. You’ll have a map for assessing where you are in the journey and where, at the end of it, you’d like to be. You’ll have a structure for building scalable AI capabilities to create better insights from big data with powerful algorithms across the entire business.
With Google Cloud as your guide, the path to AI is considerably smoother.
Download this whitepaper to find out:
- A map for assessing where you are in your AI journey and where you want to be
- A comprehensive structure for building an effective AI capability across your entire organisation to create actionable insights from data
- A technical deep dive for technology leaders
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
Building Unique Customer Experiences with Speed & Scale: Sprinklr & Google Cloud

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Enterprises are increasingly seeking out technologies that help them create unique experiences for customers with speed and at scale. At the same time, customers want flexibility when deciding where to manage their enterprise data, particularly when it comes to business-critical applications.
That’s why I’m thrilled that Sprinklr, the unified customer experience management (Unified-CXM) platform for modern enterprises, has partnered with Google Cloud to accelerate its go-to-market strategy and grow awareness among our joint customers. Sprinklr will work closely with our global salesforce, benefitting from our deep relationships with enterprises that have chosen to build on Google Cloud.
Akin to Google Cloud’s mission to accelerate every organization’s ability to digitally transform their business through data-powered innovation, Sprinklr’s primary objective is to empower the world’s largest and most loved brands to make their customers happier by listening, learning, and taking action through insights. With this strategic partnership now in place, Sprinklr and Google Cloud will go-to-market together with the end-customer as our sole focus.
Traditionally, brands have adopted point solutions to manage segments of the customer journey. In isolation, these may work — but they rarely work collaboratively, even when vendors build “Frankenstacks” of disconnected products. These solutions can’t deliver a 360° view of the customer, and often reinforce departmental silos. All of which creates point-solution chaos.
Sprinklr’s approach is fundamentally different and is the way out of the aforementioned point-solution chaos. As the first platform purpose-built for unified customer experience management (Unified-CXM) and trusted by the enterprise, Sprinklr’s industry-leading AI and powerful Care, Marketing, Research, and Engagement solutions enable the world’s top brands to learn about their customers, understand the marketplace, and reach, engage, and serve customers on all channels to drive business growth.
Sprinklr was built from the ground up as a platform-first solution, designed to evolve and grow with the rapid expansion of digital channels and applications. The results? Faster innovation. Stronger performance. And a future-proof strategy for customer engagement on an enterprise scale.

“Sprinklr works with large, global companies that want flexibility when deciding where to manage their enterprise data and consider our platform a business-critical application,” said Doug Balut, Senior Vice President of Global Alliances, Sprinklr. “Giving our customers the opportunity to manage Sprinklr on Google Cloud empowers them to create engaging customer experiences while maintaining the high security, scalability, and performance they need to run their business.”
To learn more about this exciting partnership and the challenges we jointly solve for customers, check out the recent conversation between Google Cloud’s VP of Marketing, Sarah Kennedy, and Sprinklr’s Chief Experience Officer, Grad Conn. Or read the press release on the partnership.
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