Mission

TuringNet is a public chain that provides open and trustable platform for AI.

The platform sets future development and applications of Artificial Intelligence free from any controls of centralized organizations, thus serving the best interests of the AI community that reshapes humanity.

Problem

  • Centralized model ownership

    Today’s existing AI models are increasingly owned and controlled by tech giants who offer cloud-based products and APIs. Such ownership could result in monoplication, unfair service prices and limit participation of smaller companies to innovate the industry in the long term.

  • Wasted computational resources

    Typical AI system requires massive computation powers for model training and inference. Multiple centralized systems have built their own siloed data centers, which could lead to enormous resource waste. There is a great opportunity to organize computational resources from public in a decentralized but trustable way.

  • Lack of incentives to train AI models

    Smaller companies and ordinary users are not incentivized participate in advancing AI industry as there is no fair share of rewards and a high bar to entering such market. Tech giants are not obligated to pay contributors who want to contribute their computation powers, as they possess large data centers with much lower cost per unit power. The ecosystem then separates the public from participating, and further strengthens centralized AI systems and models.

  • Expensive operational cost

    Centralized AI systems are operated separately by their owners. Even though the cost per unit power is lower, the scales of the systems often lead to enormous cost to run.

  • Data storage and exfiltration

    Training and optimizing AI models require large amount of data. The primary sources of such training data are from user characteristics and interactions, or copied from enterprise clients by providing cloud-based solutions. But there has been an increasing risks and concerns of data sharing and exfiltration without authorization.

Solution

TuringNet is creating world’s first blockchain network with consensus, incentives, extensibility, and virtual machine features dedicated for an open and trusted AI platform.

solution
LBFT Consensus Tailored for AI

The LBFT(Learning Byzantine Fault Tolerance) consensus mechanism is the hallmark of TuringNet’s innovation. It was specifically designed for AI model training and empowers seamless integration between AI tasks on blockchain within every iteration of consensus. By voting the minimum loss value in each iteration, the consensus is able to select the best performing node and confirm it with token rewards.

solution
Building Trust with Next Generation DModel and Multi-layer Chain

TuringNet’s decentralized models (DModels) are trained in an open environment and are 100% accessible and verifiable by the public. Its next generation multi-layered architecture is optimized for fast speed and large scale data sets for a wide variety of AI tasks, both in training and inference.

TuringNet Token (TNET)

TNET Token is a sole crypto currency in TuringNet’s ecosystem, and a functional utility token in value exchange and transition network. The TNET Token will not be issued by TuringNet, but 100% distributed to miners who accomplish mining tasks on TuringNet’s platform operated by smart contract.

Team

  • Kai Wen

    Stanford Ph.D with expertise in Quantum Computing, Cryptography, Information Theory, and Optimization Algorithm. Hands-on experience at Google in AI and Machine Learning. Successful series entrepreneur that provides AI solutions to government sector and Fortune 500 companies.

    Stanford Ph.D with expertise in Quantum Computing, Cryptography, Information Theory, and Optimization Algorithm. Hands-on experience at Google in AI and Machine Learning. Successful series entrepreneur that provides AI solutions to government sector and Fortune 500 companies.
  • Jun Hong

    Former Senior Engineer at Google specializes in Machine Learning (filed multiple patents), Artificial Intelligence, Distributed Systems and GAN. Founder of Alphabet Blockchain Community and an ex-Chairman of the Open Source Software Association of Chinese Academy of Science.

    Former Senior Engineer at Google specializes in Machine Learning (filed multiple patents), Artificial Intelligence, Distributed Systems and GAN. Founder of Alphabet Blockchain Community and an ex-Chairman of the Open Source Software Association of Chinese Academy of Science.
  • Dany Houde

    Expertise in Distributed Systems and Encryption Algorithms. Former Engineer at Amazon where he drastically improved robustness of Amazon’s EDI platform by implementing key security enhancements using encryption and secure communication and storage mechanisms.

    Expertise in Distributed Systems and Encryption Algorithms. Former Engineer at Amazon where he drastically improved robustness of Amazon’s EDI platform by implementing key security enhancements using encryption and secure communication and storage mechanisms.
  • Bella Wang

    Innovative business strategist and crypto enthusiast. Over 10 years of business background from Google and Tencent in the areas of operations, business development, technical partnership and talent management.

    Innovative business strategist and crypto enthusiast. Over 10 years of business background from Google and Tencent in the areas of operations, business development, technical partnership and talent management.
  • Jia Pan

    6 years of extensive experience in IT industry in the areas of Business and Marketing Intelligence, Healthcare systems, Artificial Intelligence, and Big Data. Jia is also a blockchain enthusiasts and has strong entrepreneur network. Jia holds a M.S. Industrial & System Engineering at University of Southern California.

    6 years of extensive experience in IT industry in the areas of Business and Marketing Intelligence, Healthcare systems, Artificial Intelligence, and Big Data. Jia is also a blockchain enthusiasts and has strong entrepreneur network. Jia holds a M.S. Industrial & System Engineering at University of Southern California.
  • Pengcheng Song

    Ph.D. from New York University with a demonstrated history of working in the Database Architecture, IoT developing and Cryptography. Dr. Song is the CTO of factube.com and researcher in Institute of Internet Industry of Tsinghua University.

    Ph.D. from New York University with a demonstrated history of working in the Database Architecture, IoT developing and Cryptography. Dr. Song is the CTO of factube.com and researcher in Institute of Internet Industry of Tsinghua University. He was the former co-founder and CTO of Weiita LED providing smart illumination equipment for NYPD, Chicago PD and others. He conducted in-depth research on connecting smart contract of Ethereum with IoT devices and developed the protocol for such application.
  • Melissa Xu

    Stanford M.S. and a seasoned Product Marketing Manager in early-stage IT startups and B2B hardware enterprises. Melissa works relentlessly on discovering users’ true needs, delivering superior products and experience. A creative marketer and decentralized artificial intelligence believer.

    Stanford M.S. and a seasoned Product Marketing Manager in early-stage IT startups and B2B hardware enterprises. Melissa works relentlessly on discovering users’ true needs, delivering superior products and experience. A creative marketer and decentralized artificial intelligence believer.

Advisory Board

  • Andreas Weigend

    Former Chief Scientist of Amazon.com and the author of the book Data for the People (2017). Teaches "Social Data Revolution" on applications of predictive analytics and the impact of big data on individuals, business and society at the UC Berkeley and Fudan University in Shanghai. Advisor to Alibaba, Goldman Sachs, World Economic Forum and various others. Stanford Ph.D.

    Former Chief Scientist of Amazon.com and the author of the book Data for the People (2017). Teaches "Social Data Revolution" on applications of predictive analytics and the impact of big data on individuals, business and society at the UC Berkeley and Fudan University in Shanghai. Advisor to Alibaba, Goldman Sachs, World Economic Forum and various others. Stanford Ph.D.
  • Tao Xie

    Professor and Willett Faculty Scholar in the Department of Computer Science at the University of Illinois at Urbana-Champaign. ACM Distinguished Scientist and an IEEE Fellow. Received multiple awards including NSF CAREER Award, Microsoft Research Outstanding Collaborators Award, Microsoft Research Software Engineering Innovation Foundation Award, Google Faculty Research Award, IBM Jazz Innovation Award, and IBM Faculty Awards.

    Professor and Willett Faculty Scholar in the Department of Computer Science at the University of Illinois at Urbana-Champaign. ACM Distinguished Scientist and an IEEE Fellow. Received multiple awards including NSF CAREER Award, Microsoft Research Outstanding Collaborators Award, Microsoft Research Software Engineering Innovation Foundation Award, Google Faculty Research Award, IBM Jazz Innovation Award, and IBM Faculty Awards.
  • Leo Wang

    Chief Token Architect of TuringNet. Over 17 years of field experience in high tech, and an active Angel Investor of 300+ successful investment since 2011. Manages a fund of 100 Million USD focusing on AI and Blockchain Technology. Early investor of NEO, QuarkChain and Dxchain.

    Chief Token Architect of TuringNet. Over 17 years of field experience in high tech, and an active Angel Investor of 300+ successful investment since 2011. Manages a fund of 100 Million USD focusing on AI and Blockchain Technology. Early investor of NEO, QuarkChain and Dxchain.

Documents

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