Transitioning from big data to collective intelligence.
Today, cloud-based big data systems face enormous challenges of Privacy, Latency and Power Consumption
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Of existing AI Platform
Existing Big Data & AI platforms can’t learn without uploading data.
Big Data can’t be uploaded at once, making real-time computing an impossibility or slowness.
Transferring & processing big data in centralized clouds consumes massive amounts of energy.
Utilizing big data centers and huge computation resources leads costly operation and management.
Reinvent the framework of existing AI
TieSet Inc. is developing a paradigm-shifting AI platform that achieves privacy preserving, only intelligence sharing frameworks which also solve the fundamental problems of high latency and utilizing huge data centers and computation resources. Our customers will significantly benefit from our platform in many ways to realize AI-empowered applications and systems as we can enable new business areas where privacy has been a very serious issue, drastically improve communication and computation efficiencies, and reduce management and maintenance costs of big data systems.
Product & Service
STADLE – The Next Generation AI Platform
AI - data remains at local user devices
with decentralized federated learning - not collecting big data
architecture providing AI forensics
for more than 10K devices in minutes
Want to achieve 100% Data Privacy, Low Latency (1/10000 in transferred data) and No Centralized Power Consumption ?
AI Image Sensor Privacy preserving, low cost and latency, locally learned AI image sensors. No need for a big data cloud!
The AI image sensor incorporates both processing power and memory, allowing it to perform machine learning-powered computer vision tasks without extra hardware, which realizes faster, cheaper, and more secure AI cameras. The AI models to be utilized in the image sensors is only trained over the local edge servers so that customers’ sensitive camera data never leaves their sites. That way, our customers can significantly simplify the cloud-system management as well as keep all the data private.
Smart Robotics Learning Robotic manipulation from distributed robotic arm clusters using Federated Learning.
Grasping and manipulation in robots require extensive data from the real world of objects with different shapes and sizes and from various environments. In the current approach, robotic arms acquire grasping data in a single facility to train a centralized model. (e.g. Google Robotic Farm) While this approach leads to good results, acquiring data is time consuming and expensive resulting in a non-scalable process for a single company (e.g. Amazon), so this framework does not make general manipulators better. We propose to use Federated Learning to decentralized the learning of such robots.