Synthesis AI | April 18, 2022
OpenSynthetics, an open community for creating and using synthetic data in AI/ML and computer vision, was launched today to practitioners, researchers, academics, and the wider industry.
OpenSynthetics is the first dedicated community focused on advancing synthetic data technology with centralized access to synthetic datasets, research, papers, and code. Synthetic data, or the use of computer-generated images and simulations used to train computer vision models, is an emerging technology that was recently noted as one of the top 10 breakthrough technologies of 2022 by MIT Technology Review. The first book on Synthetic Data for Deep Learning was also published last year and has seen widespread adoption.
Through OpenSynthetics, AI/ML practitioners, regardless of experience, can share tools and techniques for creating and using synthetic data to build more capable AI models. Whether an individual or organization is beginning their synthetic data journey or fully utilizing it in production systems, they will have access to content relevant to their needs and experience. Additionally, OpenSynthetics will serve as a community hub, bringing together academics, practitioners, and researchers to collectively advance the use of synthetic data.
"Bringing together new and experienced researchers to contribute and share knowledge is an important step and an incredible milestone for the synthetic data industry. The launch of OpenSynthetics comes when synthetic data is at an inflection point and is being leveraged to build more capable and ethical AI models for autonomous vehicles, robotics, drones, the metaverse, and more. By creating a centralized hub of synthetic data resources, we hope to advance synthetic data's role in powering the next generation of computer vision."
Yashar Behzadi, CEO of Synthesis AI
Current computer vision models are powered by hand-labeled data, which is labor-intensive, costly, time-consuming, and prone to human error and bias. Additionally, the collection of images of people presents privacy concerns. Using synthetic data approaches, labels and data are available on-demand, allowing practitioners to experiment and reducing time spent collecting and annotating data. However, the democratization of synthetic datasets, papers, and resources is needed to educate the industry on this technology and power further use cases.
OpenSynthetics welcomes researchers and practitioners across academia and industry to contribute to the site. By contributing and participating, the community will build a knowledge base to help grow the understanding and adoption of this emerging technology.
About Synthesis AI
Synthesis AI, a San Francisco-based technology company, is pioneering the use of synthetic data to build more capable and ethical computer vision models. Through a proprietary combination of generative neural networks and cinematic CGI pipelines, Synthesis' platform can programmatically create vast amounts of perfectly-labeled image data at orders of magnitude increased speed and reduced cost compared to current approaches.
Vianai Systems, Inc. | April 20, 2022
Vianai Systems , the human-centered AI platform and products company, today launched the Vian H+AI™ Platform for reliable, optimized enterprise-wide deployment, management and governance of machine learning (ML) models at scale. The Vian H+AI Platform brings a human-centric approach, combined with advanced AI techniques to address and remove barriers that currently prevent widespread AI adoption into production environments. A lack of trust, transparency and governance of AI models over their entire lifecycles, exacerbated by skyrocketing infrastructure costs, has significantly slowed the time-to-value of ML models – preventing enterprises from realizing the full potential of their AI investments. Vian is on mission to change that.
The first set of capabilities available within the Vian H+AI Platform includes Vian MLOps for managing, optimizing, deploying, and governing ML models for production at scale. The Vian H+AI MLOps Platform provides a collaborative user experience across the AI workflow between data scientists, MLOps engineers, and risk auditors to operationalize and manage models as a continuous lifecycle over a long period of time. Vian Performance Optimization within the Platform dramatically accelerates model speed and throughput even on commodity hardware. In addition, the open, modular architecture of the Vian H+AI MLOps Platform enables MLOps teams to seamlessly leverage the entire end-to-end capabilities of the Platform or simply use components of it, to create a truly customized solution to fit their specific needs.
“AI plays a strategic role at Schneider Electric, in our products and services, and in business operations. We have tested Vianai's platform on our AI applications running in remote oil pumps, and observed very impressive AI model execution speed and throughput improvements on the same hardware,” said Dr. Peter Weckesser, Chief Digital Officer and Member of the Executive Committee, Schneider Electric. “Based on this outstanding performance, we are evaluating additional potential use-cases in Energy Management and Industrial Automation leveraging Vianai’s technology to enable us to deliver high-performance AI safely and scalably across our ecosystem."
“Over the last three years, we have worked with dozens of the world’s largest and most respected companies, to learn the needs of businesses that AI must serve. Our H+AI platform marries those business needs with our advanced technical work, using the power of design thinking, and the result is several breakthroughs that create a new category of AI capabilities. Capabilities that early enterprise AI tools simply do not have. Our platform delivers AI that is high-performance, low-footprint and cloud-agonistic, and at the same time, transparent, reliable and manageable across the lifespan of data, business activities and models, at global scale. We are now bringing this new class of AI capabilities, via our powerful platform and the extraordinary business benefits that our early customers have seen, to all enterprises.”
Dr. Vishal Sikka, Founder & CEO, Vianai Systems
With the Vian H+AI MLOps Platform, enterprises can now build a foundation for human-centered AI systems. The Platform combines several open-source tools, Vian-proprietary techniques and optimizations, and a human-centric approach to bring AI to the enterprise at scale across diverse landscapes. With the Vian H+AI MLOps Platform, enterprises have maximum flexibility in model-building tools used by data scientists, can dramatically accelerate model performance without cost-performance tradeoffs, and have a central place to manage all models including end-to-end lifecycle management and monitoring for risks such as drift, uncertainty, bias and explainability, seamlessly retraining models when and if needed. The Platform delivers these unique capabilities via its:
Unified User Experience: MLOps engineers can now quickly operationalize models, regardless of the tools used by data scientists to create those models. There is no longer a need to restrict data scientists to using specific proprietary tools, which currently slows the process of moving AI models into production.
Open, Modular Architecture: Vian MLOps is open source-based, built from scratch to deliver a flexible, componentized architecture. MLOps engineers can quickly and easily plug-and-play components and make decisions on the best cloud, database, repositories, and other components to use, without changing the API. In addition, the modular architecture enables MLOps teams to leverage the entire Platform end-to-end or seamlessly bring specific components of the Platform into their AI workflows.
Performance Optimization: With Vian Performance Optimization, a key capability within the Vian H+AI Platform, customers can dramatically accelerate the execution speed and throughput of models without costly hardware upgrades.
Risk Monitoring: Vian MLOps provides a rich set of model risk monitoring capabilities for data quality and integrity, drift, uncertainty, bias, explainability, and more. Leveraging integrated open-source as well as Vian proprietary technologies, each model can follow a customized risk monitoring plan before and after deployment to production.
Vian MLOps delivers the foundational starting point for building human-centered AI systems and reflects the company’s unique approach to creating solutions that address real business needs, based on empathy and understanding of the customer. In its strategic engagements with customers across a variety of industries, Vian saw a clear need for a more human-centered approach to AI, in particular, the need for a platform to manage and operationalize reliable, explainable, observable, and trustworthy ML models. Going forward, the Vian H+AI Platform will deliver a wide range of advanced AI tools and techniques, to help enterprises pursue their most strategic opportunities by leveraging human-centered AI.
Vianai Systems, Inc. is a Human-Centered AI platform and products company launched in 2019 to address the unfulfilled promise of enterprise AI. Vian's customers include many of the largest and most respected businesses in the world, to which it delivers AI, ML and data science platforms and products. Vian helps its customers amplify the transformational potential within their organizations using its H+AI Platform and a variety of advanced AI and ML tools with a distinct approach in how it thoughtfully brings together humans with technology. This human-centered approach differentiates Vian from other platform and product companies and enables its customers to fulfill AI's true promise for the benefit of humanity.
Blueprint Technologies | August 05, 2020
At Blueprint, one of our key Practice Areas is helping clients establish their Modern Data Estate. However, many are not familiar with the term nor do they understand the benefits of having a modernized data pipeline. We interviewed our Solution Architect, Jeba Selvaraj, to help answer some of the most asked questions. The world is changing. Previously, companies built platforms architecturally and it took years and years to build something that allowed for advanced consumption and analysis of data. Now, everybody wants everything in a very short time frame. Companies don’t want to spend years building a platform and only then begin to explore and utilize the data they already have; they want to be working in the data now. Cloud platforms provide that speed as one of the higher-end platforms we use to make all an organization’s relevant data consumable. Simply put, a Modern Data Estate is the infrastructure that enables a company to systematically manage and consume corporate data in near real-time.
Zest AI | March 02, 2022
Zest AI, a leader in software for AI-driven lending, today announced the launch of Zest Race Predictor (ZRP). This open-source machine-learning algorithm estimates the race/ethnicity of an individual using only their full name and home address as inputs.
ZRP can be used to analyze racial equity and outcomes in critical spheres such as health care, financial services, criminal justice, or anywhere there's a need to attribute the race or ethnicity of a population dataset when race/ethnicity data is missing. The financial services industry, for example, has struggled for years to achieve more equitable outcomes amid charges of discrimination in lending practices. A better yardstick can help reverse this legacy of bias.
ZRP improves upon the most widely used racial and ethnic proxying method, Bayesian Improved Surname Geocoding (BISG), developed by RAND Corporation in 2009. In multiple tests against BISG, ZRP was able to identify African-Americans correctly 25% more often, identify 35% fewer African-Americans as non-African American, and 60% fewer Whites as non-White.
Zest AI began developing ZRP in 2020 to improve the accuracy of our clients' fair lending analyses by using more data and better math, We believe ZRP can significantly improve our understanding of the disparate impact and disparate treatment of protected-status borrowers."
Mike de Vere, CEO of Zest AI.
I've employed the ZRP output myself and found that it provided results consistent with our predictions, in the context of predicting the race of PPP borrower firm owners, Getting race estimates right is key to facilitating fair lending practices in America, and by making their tool open-source and freely available, Zest's application is an important step towards that goal."
Sabrina Howell, Assistant Professor of Finance at NYU Stern.
We have known since our 2014 study that BISG leaves much room for improvement, We are thrilled Zest took the initiative to apply modern data science methods to develop a better race estimator, and we are looking forward to further validating this work."
Dr. Marsha J. Courchane, Vice President and Financial Economics Practice Leader, Charles River Associates.
More accurate race prediction will help the entire lending ecosystem:
Lenders will be better able to identify unfair outcomes to improve models.
Regulators will have a better tool to enforce fair lending rules that drive equity in access to credit products that could help people of color earn better credit scores.
Borrowers will benefit by knowing their race and ethnicity are more accurately reflected alongside their credit history.
About Zest AI
Zest AI software helps lenders make better decisions and better loans—increasing revenue, reducing risk, and automating compliance. Since 2009, it has made fair and transparent credit available to everyone and is now the leader in software for more inclusive underwriting. The company is headquartered in Los Angeles, California.