STADLE Intelligence-Centric Platform enables data to stay local, while users' models are shared
Federated & Transfer Learning
User-friendly GUI & APIs
User AI Model Database
The Economy Impact
In Transferred Data Low Latency
Enabling New Business
Privacy-preserving AI is the only way to create AI diagnosis business in Medical Record Learning
Personal home robots need privacy-preserving AI to learn and improve capabilities
Before: Upload 1GB per second, 10 TB per day
After: Upload only AI models 500MB per hour, 1.5 GB per day
About 6000 times efficient than current cloud-based solution
Saving on Cloud Data Center Cost
Investment: $200K STADLE License vs. $1M for 1,000 sq. ft. datacenter
Return: Annually save $800K on datacenter + network only for 1,000 sq. ft.
Enable New Business, Improve Efficiency, and Save Money
Technologies in Place
Federated Learning (FL) has gained worldwide recognition after Google Research released a mobile application where all the training happens at mobile devices of users. The private data of users will not leave from distributed devices, and the local AI models are aggregated to provide collective intelligence. The cost to maintain big data is significantly reduced by FL, while the level of intelligence is not compromised. FL can be applied not only to mobile services but also to all services where customers’ privacy comes into the picture. TieSet has succeeded in developing the world’s first fully decentralized federated learning technology.
With the rapid growth of Artificial Intelligence technologies, concerns over consumer privacy have been increased to a large extent, especially in areas such as healthcare, home appliances, private pictures, and videos that user’s privacy is essential. To address privacy concerns, privacy communities must bring their knowledge to the machine learning field. Many privacy-enhancing techniques concentrated on allowing multiple users to collaboratively train ML models without exchanging local data. TieSet will lead the area of distributed AI so that a wide variety of people could benefit from the true power of AI.
Artificial Intelligence models have been designed and created in a static way in big data systems. However, intelligence is not a product of single-shot learning but needs to continuously grow with dynamic environment.
STADLE assists users to create dynamic distributed learning environments where the constant change and trend of data and behaviors can be absorbed with collaborative training processes.
When data is limited, Transfer Learning (TL) aims at improving performance in the accuracy or training time of an AI model in a target domain by using knowledge contained in a different but related source domain.
With TL we can deploy your AI solution faster and more efficiently by reusing previously generated models. Additionally, a system can learn a set of completely new tasks from the combination of previously acquired models by using a proprietary model synthesis engine.