ICO Token: DML
Token Type: ERC20
ICO Token price: 1 ETH = 3,780 DML (0.00026ETH)
Hard cap: 28,000ETH
Token Generation event: April 16,2018
Token supply: 330 mn
PROJECT OVERVIEW: Decentralised Machine learning Protocol.
With the DML Application, data owners can authorize specific types of datasets, such as photos in the album or text messages etc., for machine learning algorithms to be run. Only with the consent of the data owners, the algorithms can be run on certain authorized types of data in the devices. Since the algorithms are run directly in the device, the data will be kept within the devices without transferring to any third parties or being stored in the cloud server. Furthermore, non-public data located in other applications such as social media and messenger can be used to run machine learning algorithm too with the aid of API and users’ authorization. Therefore, DML protocol is not only a gateway to connect private data stored inside the device for machine learning, but also a mean to access massive amount of non-public data stored within the existing networks.
After the machine learning algorithms are run directly on the authorized dataset within the devices, only the analytical conclusion in the form of local prediction results will be encrypted and transmitted to a federated node via a secured network. All individual raw data will be retained in the data owners’ devices without sharing to the nodes. Therefore, concerns over data privacy are resolved as data owners can stay anonymous and their private data will not be obtained by any third parties including developers or customers.
After various federated nodes aggregate and average the local encrypted prediction results of respective connected individual devices, they will send the encrypted aggregated results to the Report Node. The report node will further average the encrypted results as processed by individual federated nodes, of which an encrypted final report will then be generated and stored in a distributed file system such as IPFS. If the total number of devices that contribute to the final results are less than a certain amount, the result will not be shared to avoid inaccurate predictions due to small sample sizes and data privacy protection by eliminating the possibility of reverse-engineering
Once the report node shares and stores the final report in IPFS, the customers can retrieve by decrypting the report. At the same time, the report node will automatically update the smart contract once it stores the final report in IPFS. The smart contract will be automatically executed by rewarding DML tokens to different participants in performing their roles according to the criteria and terms as established in the smart contract. The contributing participants include developers, data owners and decentralized nodes such as distributing nodes, federated nodes and report node.
TOKEN UTILITY:
1.Customers, who request for machine learning service, to pay DML tokens to run machine learning algorithm on data owners’ authorized dataset in their individual devices. The decentralized nodes will facilitate by distributing algorithms and collecting, aggregating and averaging the local prediction results to produce the final prediction report;
2.Machine learning developers, who sell their algorithm on the DML MarketPlace, to get paid by customers in DML tokens; and
3.DML for Model trainers, who correct and improve the machine learning model, to be rewarded, by the customers or machine learning developers, who solely want to improve their algorithm for further use;
TEAM, ADVISORS AND VC:
Team and advisors are presented strong but are not really an all star, with previous startups not coming too successful.
Jacky Chan: Blockchain developer Founding engineer at blockchain consultancy firm Kyokan Labs. Remaining are the members of Kyokan Labs
Star advisor is Steven Cody Reynolds who facilitated Binance’s rapid rise, managed over 7,000% growth in users to this fastest growing cryptocurrency exchange. His tenure was during July 2017 to Dec 2017.
All Other Advisors are not coming really strong.
VC: No information provided
TOKENOMICS: Market cap calculated at 0.10 USD
Team and advisor tokens are vested for 3 years.
The Green Portion are public tokens i.e. 54.2%
ROADMAP: Roadmap has milestones but not well described.
The team also talks about future plans of Flexibility to Upgrade and Multi-Blockchain Compatibility, Promote DML Protocol Upgrade ,Scalability and Off-chain Transactions, Cross-chain Compatibility and Interoperability.
Which is not convincing as not enough information is provided on implementation.
GITHUB: Prototype available
, Demo Video
MARKETING:
Twitter followers: 1.6k
Telegram:10k
Reddit:110
Medium: 317
PROS/CONS:
PROS:
White paper , one pager are all detailed and properly presented.
CONCERNS:
Talks about big future plans on scalablilty and Cross-chain Compatibility and Interoperability but not convinced on implementation.
Doubt on team efforts to achieve roadmap Milestones.
COMPETITION: DML already has a working prototype and is in the process of building an open source machine learning community, but there are already tech giants innovating in the same space. Competition is strong in this space.
Not raising enough money to compete with giants.
CONCLUSION:
With not so strong team and big competing tech giants already working in the domain. Increases the risk of investment,
Our rating for this ICO would be 3/10.
We, at Ledgerfund run one of the biggest Crypto Hedge Funds in the world and give returns in bitcoins. To know more about how you can invest your bitcoins in this fund, refer to our website: https://ledgerfund.io
The ICO Analysis was published on LedgerFund's Medium publication : https://medium.com/ledgerfund/decentralised-machine-learning-dml-ico-analysis-aeca7479e6b
