Transitioning from big data to collective intelligence.

Today, cloud-based big data AI systems face enormous challenges of privacy, latency, power consumption, storage & computational costs.

The Problem

Of existing AI Platform

Privacy

Existing Big Data & AI platforms can’t learn simultaneously preserving privacy.

Latency

Big Data can’t be uploaded at once, making real-time computing an impossibility or slowness.

Energy

Transferring & processing big data in centralized clouds consumes massive amounts of energy.

Cost

Utilizing big data centers and huge computation resources leads costly operation and management.

The Problem

Of existing AI Platform

Data
Privacy

Big Data & AI platforms can’t learn while simultaneously preserving user privacy.

Apple Kills
IDFA

In early 2021 Apple will automatically opt users out of sharing device IDs (IDFA), disrupting the $80B mobile ad ecosystem

Google Depreciates Cookie

Chrome will join Safari and Firefox in blocking 3rd-Party Cookies in 2022, leaving the industry scrambling for options

The Solution

Reinvent the framework of existing AI

TieSet Inc. offers an intelligence-centric platform called STADLE with continuous, distributed & collaborative learning frameworks that resolves the major problems in data-centric AI systems such as privacy, latency, and high costs of utilizing huge data centers and computation resources. We achieve them by only gathering, maximizing, and sharing intelligence from users. Our customers significantly benefit from our STADLE platform in many ways to realize AI-empowered applications as we can also enable new business areas where the privacy has been a very serious bottleneck, drastic improvement in communication and computation efficiencies is needed, or management & maintenance costs of big data systems need to be reduced.

Product & Service

STADLE – Scalable Traceable Adaptive Distributed Learning Platform

The Next Generation Intelligence-Centric AI Platform

Privacy-preserving

AI - data remains at local user devices

Intelligence sharEABLE

with decentralized federated learning - not collecting big data

TRACEABLE

uploading and downloading AI models while tracking their performance

Scalable

for more than 10K devices in minutes

Low-latency

edge AI training empowered with federated learning

Want to achieve 100% Data Privacy, Low Latency (1/10000 in transferred data) and No Centralized Power CONSUMPTION?
Use Cases

Privacy-Safe Ad Targeting Intelligent Dynamic Ad Delivery for the Cookieless Future

TieSet’s patented Decentralized Federated Learning framework enables intelligent ad targeting, without dependence on identifiers like the cookie or mobile device ID. User data stays with the owner, training models on the edge device, and only resulting intelligence is shared.

Consumer sentiment is driving a privacy revolution, with regulations gaining momentum worldwide, and major platforms responding by removing identifiers or requiring users to opt-in to identification. Publishers report earning only 50-70% CPMs on anonymized traffic compared to impressions with a cookie, but by 2022, no major browsers will continue supporting this outdated technology. In the mobile ecosystem, Apple is already moving to automatically opt users out of sharing the IDFA (Identifier for Advertisers), on which app campaign targeting relies.

Such changes in the ecosystem tend to drive more dollars toward the walled gardens, but independent players can compete. Continue driving top CPMs for publishers by delivering the results advertisers expect in a sustainable, ad-supported, privacy-protected internet.

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.

WE’RE INNOVATORS IN AI & DISTRIBUTED SYSTEMS

Our Team

Kiyoshi Nakayama, Ph.D.

Founder & CEO

Anthony Maddalone

Chief Business Officer

Vibhatha Abeykoon, Ph.D.

Chief Architect & Engineer

Mei-Yu Wang, Ph.D.

Research Scientist

George Jeno

Research Engineer

Genya Ishigaki, Ph.D.

Visiting Researcher

Patrick Amalraj

Innovation Strategist

Aneel Lakhani

Funding & GTM Advisor

Badrinath Narasimhan

Business Development Advisor

Partners

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Latest Updates

May 27, 2020
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