Synthesis AI Secures Funding to Fuel Product Development
Start-up exits stealth mode to address market need for high-quality machine learning training data with launch of FaceAPI, its synthetic data-as-a-service product
Synthesis AI, a pioneer in synthetic data technologies, announced $4.5 million in additional funding as they launch a synthetic data-as-a-service FaceAPI product. The round was backed by existing investors Bee Partners, PJC, iRobot Ventures, Swift Ventures, Boom Capital, Kubera VC and Leta Capital. Synthesis AI comes out of stealth mode to provide their unique approach to data generation for computer vision to a wider range of organizations and applications. The new capital will allow Synthesis AI to add to its world-class R&D teams and continue leading the industry in the development of synthetic data technologies.
With recent advances in deep learning, there are tremendous opportunities for companies to develop new and more capable AI-driven computer vision applications. However, traditional data collection and human dataset labeling approaches cannot keep pace, and enterprise companies are lacking access to high-quality and diverse machine learning training data.
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“Synthesis AI is laying the foundation to own the synthetic data category. The company is approaching data as a verb, leading to synthetic full body simulations with increasing animations, movements and behaviors interacting in diverse environments that apply across industry domains,” said Kira Noodleman, Principal at Bee and Board Observer for Synthesis AI. “Essentially, this opportunity is unbounded with an incredibly experienced founder at the helm.”
Through a proprietary solution, Synthesis AI’s platform is addressing industry needs by letting customers programmatically create vast amounts of perfectly-labeled, unbiased image data enabling the development of more capable models.
“The synthetic data market is exploding as companies look for better and faster ways to expand their data sets and improve the performance of their machine learning models,” said Rob May, Partner at PCJ. “When we researched the space, the Synthesis AI team seemed far ahead of everyone else in the enterprise market, which is why we are so excited to be part of this round.”
IMMEDIATE PRODUCT MOMENTUM
In addition to securing new funds, Synthesis AI is also launching their FaceAPI product, enabling the on-demand generation of millions of perfectly labeled human images to train more capable computer vision models. FaceAPI, which is now generally available and currently in use by major technology and handset manufacturers, will enable users to prototype, develop and test systems 100 times faster and cheaper than current approaches, while addressing ethical and privacy concerns often associated with facial computer vision. FaceAPI represents the first of many APIs to address various computer vision use-cases.
“We are excited to show this type of growth and momentum. Computer vision is set to explode as companies are currently gated by access to high-quality, diverse image data,” said Yashar Behzadi, CEO of Synthesis AI. “From smartphones to teleconferencing, most devices in our daily routines rely on computer vision, but developing these models is arduous, expensive, and inefficient. Current approaches for human-centered data also face ethical issues related to the breach of consumer privacy and model bias related to gender & ethnicity. We pride ourselves on offering an ever-growing suite of enterprise grade on-demand image and label generation APIs to address the technical, economic and ethical issues with current approaches.”
Synthesis AI’s technology also scales in the cloud, from research and development phases with small amounts of data to production requirements of terabytes of data.
“Synthetic data has become a critical component to advancing deep learning and is truly changing the state of the art in AI-based applications,” said Dr. Rana el Kaliouby, Co-Founder and CEO of Affectiva. “Our collaboration with Synthesis AI has been extremely rewarding and sparked innovation for how we train our deep learning models, ultimately reducing error rates and cost.”