From Months to Minutes: VersAI™ Sets New Standard for AI Training Speed

Verseon’s novel general-purpose AI technology shows dramatically faster training speeds compared to Deep Learning technologies like Google’s AutoML

FREMONT, Calif., Dec. 19, 2024 /PRNewswire/ — Whenever AI is used, training new predictive models is the most expensive and time-consuming step. Verseon International Corporation‘s newly posted results on ArXiv demonstrate that its VersAI™ technology has achieved 3,000 times faster training times than Google AutoML, the current state-of-the-art, when creating models based on a large variety of standard benchmark datasets. VersAI™ is the company’s patent-pending AI designed to handle small, sparse datasets typical of most real-world scenarios. It is a general-purpose AI technology that can make a huge impact on training speed in any context that would benefit from utilizing AI.

Ed Ratner, Verseon’s Head of Machine Learning, stated, “Not only does our AI technology thrive in the sparse-data scenarios that cause Deep Learning frameworks to struggle, but it is also much more computationally efficient. Verseon’s different approach to AI can train predictive models far more rapidly than other methods.”

Fundamental differences in network architecture give rise to VersAI’s faster training speeds. “Unlike Deep Learning AI, VersAI uses a completely novel and proprietary neural ‘network of networks’ architecture with a distinct advantage in speed when generating new predictive models,” said Ratner.

Verseon continues to steadily build its AI technologies. CEO Adityo Prakash said, “We found that Deep Learning, the culmination of the Big Data approach to AI, does not work well in the context of small, sparse datasets typical of novel drug discovery. That problem has spurred us to develop advances in fundamental AI technologies with applications far beyond just drug discovery.”

Peer-reviewed benchmark test results for this novel AI technology have been published before in Proceedings of ELM. In scenarios that take Deep Learning technologies months to generate models, VersAI™ takes mere minutes. “Since we first released our peer-reviewed model generation times, we’ve continued development and improved on prior speeds, as shown in our latest preprint on ArXiv,” added Ratner.

VersAI’s faster training times are beneficial in any context where AI is applied. For example, rapid AI training is critical for dynamic applications with high-velocity data throughput, like e-commerce.

Superior speed also translates into lower capital-equipment and operating costs. Attaining manageable training times for Deep Learning models usually entails large numbers of servers equipped with the latest – and most expensive – hardware. This equipment in turn consumes a great deal of electricity and drives other ongoing operational costs. In contrast, VersAI™ achieves its superior training speeds even on readily available consumer-grade CPUs and GPUs, thereby dramatically reducing the costs and environmental footprint associated with training AI. It is yet another way that VersAI™ can enhance AI’s usefulness in our everyday lives.

About Verseon

Verseon International Corporation (www.verseon.com) is a clinical-stage, technology-driven pharmaceutical company transforming the delay, prevention, and treatment of disease. Using its Deep Quantum Modeling + AI platform, Verseon is rolling out a steady stream of life-changing medicines. Each of the company’s drug programs features multiple novel candidates with unique therapeutic properties. None of these candidates can be found by other current methods. Verseon’s fast-growing pipeline addresses major human diseases in the areas of cardiometabolic disorders and cancers. The company’s supporters and advisors include multiple Nobel laureates, former heads of R&D of major pharmaceutical companies, and various key opinion leaders in medicine.

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SOURCE Verseon International Corporation