Decentralized Machine Learning

Unleash untapped private data, idle processing power and crowdsourced algorithms

[Short general description]: Decentralized Machine Learning unleashes untapped private data, idle processing power and crowdsourced algorithms development by on-device machine learning, blockchain and federated learning technologies.

 

[Main problems tackled]:

1) Inaccessibility of Private Data - Nowadays, machine learning is mainly conducted through a centralized computer, which its processing power is usually limited or confined to the processors of a single machine.

2) Centralization of Processing Power - Only large corporations can afford investing huge initial capital and resources to build in-house machine learning models and algorithms, or acquire tailor-made ones from consultancy firms to apply machine learning in their own business.

3) DML advances machine learning development by returning power to all ecosystem contributors - DML has created an open source infrastructure and network which data, processing power and models/algorithms development will all be decentralized.

4) Processing Power - DML Procotol will utilize the idle processing power of the devices to perform on-device machine learning.

5) Data - All private data will be kept within the devices and only the local prediction results will be transferred. Usage of untapped private data is unleashed as a result.

6) Algorithms - The supply of algorithms will be crowdsourced in our developer community. Customer such as corporates, research institutes, governments or NGOs can simply search or request suitable machine learning algorithms in DML Algorithms Marketplace.

 

[Main contribution proposal]: To create a blockchain-based decentralized machine learning protocol and ecosystem through:

1) Utilizing untapped private data for machine learning while protecting data privacy,

2) Connecting and leveraging idle processing power of individual devices for machine learning,

3) Encouraging involvement from the periphery by creating a developer community and algorithm marketplace that promotes innovation to build machine learning algorithms that match practical utilities,

4) Improving and correcting existing machine learning algorithms and models through crowdsourced fine-tuning model trainers,

5) Creating a new DML utility token and leveraging on blockchain smart contract technology to provide a trustless and middle-man free platform that connects potential contributors in machine learning from all aspects.

 

[Innovation]:

1) Apply Decentralized Algorithm & Data for ML Prediction

2) Improve Existing ML Algorithm via Crowd Informed Fine-tuning

3) Smart Contract for Machine Learning on Untapped Data 

4) Smart Contracts for DML Marketplace Activities

5) Benefits for Different Participants -  DML Algorithm Marketplace creates a developer’s community where developers have the capacity to work as a freelancer to build and market their machine learning algorithms in return for incentives. Innovation and creativity to build machine learning algorithms are unleashed as no approval by tech giants or any other parties with vested interest is needed.

ICO Rating Analysis
Team Evaluation
5.00 / 5.00
Product
4.00 / 5.00
Token Economics
4.00 / 5.00
Business Evaluation
4.60 / 5.00
Hype and media presence
4.00 / 5.00

Analysis

Team - Founders:
Are the founders known? Do they have relevant experience and connections?
5
  • 1. Unknown people. No serious background information available.
  • 2. Partial information available, no relevant experience.
  • 3. Background information available, no relevant experience.
  • 4. Solid, relevant background and connections available.
  • 5. Solid, well known, experienced and well connected founders.
Team - Advisors:
What level of commitment, experience and connections do the advisers bring?
5
  • 1. No reputable advisors with relevant experience.
  • 2. Few advisors with little to no relevant experience.
  • 3. Advisers with relevant experience.
  • 4. Reputable advisors with relevant experience and connections.
  • 5. High profile highly experienced, well connected and committed advisors.
Product - Technology Layer:
Is the product innovative? Does it contribute to the blockchain ecosystem?
4
  • 1. No, the product is just a clone with no contribution.
  • 2. The product is a dapp with minimal interest and little contribution to the ecosystem.
  • 3. The product is a dapp, exchange or protocol addressing a real problem or need.
  • 4. Innovative product offering a solution to a high interest problem.
  • 5. Innovative protocol tackling critical problems of highest interest.
Product - Proof of concept:
Is the proof of concept comprehensive? Does it address a real problem or need?
4
  • 1. No, incoherent concept or no need for it.
  • 2. Difficult concept to understand, hardly any need or problem to solve.
  • 3. Clear concept which addresses a real problem.
  • 4. Clear, well thought concept which addresses a real problem of high interest.
  • 5. Exceptional proof of concept addressing a critical problem.
Product - MVP:
Has the concept been tested? Is there an MVP? How far is the launch?
4
  • 1. Untested concept.
  • 2. Initial tests, no MVP.
  • 3. MVP ready, Alpha launch.
  • 4. MVP ready, Beta launch.
  • 5. Fully working initial product.
Token Economics - Token utility:
Does the token have any utility? Is it a core function to the network?
4
  • 1. No, the token has no utility.
  • 2. Token has a limited, unclear utility.
  • 3. The token has some added, but not inherent value.
  • 4. The token is embedded in the network and has inherent value.
  • 5. The token has both inherent and added value and is embedded at the core of the network.
Token Economics - Network effect:
Are strong network effects built into the system? Are incentives aligned to encourage the growth of the network?
4
  • 1. No network effects built in.
  • 2. Minimal network effects, unclear incentives.
  • 3. Network effects and incentives present.
  • 4. Solid network effects with clear incentives due to inherent utility.
  • 5. Strong network effects, aligned incentives and high utility value.
Business Evaluation - Valuation:
Is the valuation reasonable ? Sufficient but not too high for the scope of the project?
4
  • 1. No, the valuation is ludicrous, the project could do with 1/10 of the sum.
  • 2. Valuation is higher than the project would need. Likely a money grab.
  • 3. Valuation is reasonable for the scope of the project.
  • 4. Valuation is modest for the caliber of the project.
  • 5. Valuation is impressively modest relative to the high caliber of the project.
Business Evaluation - Market potential:
What is the market potential? Does the project look like it could penetrate the market and conquer the world?
5
  • 1. No clear market potential.
  • 2. Limited market potential.
  • 3. Reasonable market and growth potential.
  • 4. Solid market and growth potential.
  • 5. Exceptional market and growth potential.
Business Evaluation - Competition:
Does the project have competition? How strong does it look relative to its competition?
5
  • 1. Awful position competing with many strong players.
  • 2. Weak position facing strong competition.
  • 3. Reasonable position facing strong competition.
  • 4. Solid position facing weak competition.
  • 5. Exceptional position, facing almost no competition.
Business Evaluation - Supply sold:
Does the team distribute a reasonable amount of the tokens so as to encourage create strong incentives and network effects?
4
  • 1. Negligible supply, greedy team.
  • 2. Small supply, poor incentives.
  • 3. Modest supply, weak incentives.
  • 4. Reasonable supply, responsible team.
  • 5. Large supply, solid inventive, committed team.
Business Evaluation - Vesting:
Does the team have a sufficient stake to have aligned incentives? Do they have a vesting schedule implemented?
5
  • 1. Large stake, no vesting.
  • 2. Small stakes, no vesting.
  • 3. Modest stakes, no vesting.
  • 4. Reasonable stakes, modest vesting.
  • 5. Solid stake, healthy vesting.
Hype and media presence:
Is the project present on social media and chats? Is there interest for it?
4
  • 1. No presence, negative image.
  • 2. Modest exposure and no interest.
  • 3. Reasonable exposure and modest interest.
  • 4. Solid exposure and high interest.
  • 5. Exceptional exposure, high interest and considerable hype.
Final Score
4.38

Team

Member
Victor Cheung
Blockchain Developer
Michael Kwok
Project Lead Director
Jacky Chan
Blockchain and Software Developer
Pascal Lejolif
Machine Learning Engineer
Wilson Lau
Machine Learning Engineer
Patrick Sum
System Security Engineer

Advisors

Guillaume Huet
Big Data / Machine Learning Advisor
Michael Edesess
Machine Learning Advisor
Roderik van der Graaf
Blockchain Advisor
Kyle Wong
Machine Learning Advisor
Scott Christensen
Machine Learning Advisor
Steven Cody Reynolds
Blockchain Advisor
Matthew Slipper
Machine Learning Advisor
Jesmer Wong
Machine Learning Advisor
Eugene Tay
PR & Marketing Advisor
Eric Byron
Business Advisor
Fabrice Fischer
Business Advisor

Updates

Title
Published at
Introducing DML — Decentralized Machine Learning Protocol
5 months ago
The 4 Participants in Decentralized Machine Learning Protocol
5 months ago
Return Autonomy of AI, Machine Learning and Self-owned Data to People
5 months ago
DML Early Whitelist Program Announcement
4 months ago
DML Early Whitelist Program Announcement #2: DML Super Ambassador Route
4 months ago
DML Early Whitelist Program Update — 16 February 2018
4 months ago
DML Early Whitelist Program Update — 19 February 2018
4 months ago
DML Token Metrics Announcement and Early Whitelist Closing in 3 Days
4 months ago
DML Main Whitelist Program Announcement and Early Whitelist Closure
3 months ago
New Blockchain Advisor Joining DML Advisory Board — 27 February 2018
3 months ago
DML Prototype Demo
3 months ago
Token Generation Event on 16 April & Proof of Ecosystem #1
2 months ago
Centralized or Decentralized? Value Proposition of DML — On-Device Machine Learning
2 months ago
DML in 2 Minutes — Interview By Cryptocentral.net with Transcript
2 months ago
Multiple New Advisors and Partnerships Announcement — 9 April 2018
2 months ago
DML Main Whitelist Closing on 12 April 2018, 15:59 UTC and Whitelist Result
2 months ago
DML Token Generation Event Contribution Procedure and Announcement of TGE-Ecosystem-Bonus
2 months ago
DML Token Generation Event (TGE) Participation Quick Guide
2 months ago
DML Token Generation Event (TGE) Smart Contract Address
2 months ago