Enabling & Accelerating Machine Learning!
Privacy
Traditional data-centric AI platforms lack the capability for simultaneous learning while preserving data privacy.
Efficiency
Uploading and processing big data in its entirety is impractical, limiting the efficiency of real-time deployment of intelligence.
Scalability
Centralized training is time-consuming and struggles with increased devices, necessitating distributed methods for effective scalability.
Cost
Relying on large data centers and extensive computational resources can result in expensive operations and management.
STADLE: Scalable Traceable Adaptive Distributed Learning Platform
TieSet's STADLE, an intelligence orchestration AI platform, addresses critical challenges in data-centric systems such as privacy, latency, and the high costs linked to processing large volumes of data for continuous intelligence updates and enhancements. It achieves this through continuous, distributed, and collaborative learning, efficiently gathering, orchestrating, and managing intelligence across various distributed learning environments.
As the AI landscape evolves, industries dealing with AI will increasingly require solutions like our STADLE platform. It significantly aids users by powering AI-driven applications in sectors where privacy is crucial, substantial improvements in communication and computation efficiency are needed, or where reducing data management and maintenance costs is essential.
USE CASE
Smart Robotics
Learning Robotic manipulation from distributed robotic arm clusters using STADLE
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.
In the robotics arm use case, each arm is trained specifically to grab boxes, balls, ducks, and teddy bears, respectively. The intelligence from these arms is periodically collected and aggregated in STADLE. Ultimately, this aggregated intelligence enables each arm to precisely grab all types of objects, despite being trained for only specific ones.
USE CASE
Adaptive-LLM
Adaptive Large Language Model for Secure, private, efficient local AI solution
Adaptive-LLM, offered by TieSet, revolutionizes the use of local Large Language Models (LLMs) by providing a secure, private, and cost-effective AI solution. It enables secure on-device processing of locally trained language models, ensuring no external data exposure and allowing local customization for user-specific vocabulary and tasks. Additionally, Adaptive-LLM eliminates the costs associated with external APIs, offering a more economical and efficient approach to AI deployment.
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The platform's environmental impact is significantly reduced by using local LLMs instead of relying on data center operations. This approach decreases carbon emissions, aligning with greener business practices. Adaptive-LLM's local hosting on energy-efficient hardware reduces its carbon footprint by over 90% compared to conventional data center-dependent LLMs. The platform also features advanced file and tag management systems, ensuring secure and intelligent file segmentation by company department.
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Adaptive-LLM offers a range of services including language understanding, machine translation, information retrieval, text summarization, dialogue systems, and sentiment analysis. It provides customizable large language models that can be trained locally on specific data, enhancing responsiveness and precision. Adaptive-LLM represents a leap forward in AI technology, offering tailored intelligence, responsive and secure data handling, and a commitment to environmental sustainability.