SlideShare a Scribd company logo
A blockchain based collaborative platform for developing
Data Science​ and ​Artificial Intelligence​ products
White Paper
November 15, 2017
Written By: Yousef Fadila
Executive Summary
● Synergy utilizes blockchain distributed trust technology to build a
smart contract powered research and development platform for
developing state-of-the-art machine learning and artificial intelligence
products. Collaborators and researchers are incentivized to improve
other fellow’s solutions in rounds and in exchange receive SNG
tokens. Therefore, driving innovation and continuous improvement.
● Synergy provides a fair and decentralized development tournament
platform that tracks and rewards all contributors to the final solution.
● Synergy organizes all developed products in a web based analytics
and modeling platform. A product can be available in two modes: (1)
As a model architecture with optimally tuned parameters to be trained
from scratch with user-specific data and (2) As a pre-built model which
is ready for deployment. The platform features building AI solutions as
a flow of drag and drop components to make them available for
non-technical users. As a result, they can quickly build state of the art
data science products and AI agents
● Synergy analytics and modeling platform features smooth deployment
of data science products and AI agents. Deployment can be done by a
simply dragging and dropping a deployment component to the flow. As
a result, the model becomes accessible through REST Queries.
● Subscriptions to Synergy analytics and modeling platform are fully
managed through smart contracts using SNG tokens. The smart
contract fairly distributes tokens to all contributors and therefore
provides perpetual rewards to all developers and contributors. This is
an extra incentive to our community of developers and data scientist to
continually participate in Synergy competitions and improve models.
Abstract
The value of data science and the power of machine learning is growing
exponentially. It is a matter of time for businesses to lose to its competitors if they
not utilizing their data efficiently. Data-mining and machine learning are
advanced sciences that can accurately predict customer behavior patterns,
logistics and distribution issues, future trends and more. Without data-mining and
machine learning technologies, a company is at a significant disadvantage. That
is, it won’t know what its competitors know about future trends and the current
market.
Data scientist has been called the “The Sexiest Job of the 21st Century” ]. There1
is no doubt that almost every business needs to integrate data science in its
decision-making process. However, due to the veracity and variety of big data2
and the need of it in different fields makes it very challenging to hire the right data
scientists. Furthermore, many companies are moving to the data based decision
making models and as a result there aren’t enough data scientists to meet the
increasing demand in the current market. The rapid innovation in the field makes
it even more challenging for companies to cope with state-of-the-art techniques
needed to evaluate their data-science models. Synergy solves that by providing a
rich data analytics and model-building platform, featuring human-in-demand
machine learning, and reusable off-the-shelf models. Synergy provides a fully
transparent and decentralized data science competition management platform
powered by public smart contracts on public blockchain. The platform is able to
track all contributors to the final solution in all rounds, encourage collaboration to
improve other solutions and to build state-of-the-art models that meet the
sponsor needs.
1
Harvard business review ​https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-cent​ury
2
4-Vs-of-big-data ​ ​http://www.ibmbigdatahub.com/infographic/four-vs-big-data
Table of Contents
1. Introduction 5
1.1. What is Data Science? 5
1.2. Why Data Science Matters? 6
1.3. Why Companies Need Data Science Tools? 6
1.4. How Synergy is Addressing the Market Needs? 7
2. Synergy Analytics and Modeling Platform Overview 9
2.1. Synergy Analytics and Modeling Platform 9
2.2. ZeroDriver artificial intelligence and machine learning modeling 10
2.2.1. Smooth Deployment of Data Science and AI Apps 12
2.3. Human-on-demand Machine Learning 13
2.3.1. Host a Competition in a Platform that Drives Innovation. 13
2.3.1.1. Submission and Reward 14
2.3.2. Perpetual Compensations 15
2.4. Summary of Platform Top Features: 16
3. Why Blockchain? 17
3.1. What Problem does a Blockchain Solve? 17
3.2. What are Smart Contracts? 17
3.3. How Synergy is utilizing Blockchain Technology? 17
4. High-level Roadmap 19
4.1. 2018-2019 Roadmap of Synergy Competition Host Platform 20
4.2. 2018-2019 Roadmap of Synergy Analytics and Modeling Platform 20
5. Token Info 22
5.1. Token Distribution 24
1. Introduction
1.1. What is Data Science?
Data science is an interdisciplinary
field about scientific methods and
processes to extract insights from data.
It employs techniques and theories
drawn from many fields within the
broad areas of mathematics, statistics,
information science, and computer
science, in particular from the
subdomains of machine learning,
classification, cluster analysis, data
mining, databases, and visualization. Data scientists usually have advanced
degrees and training in statistics, mathematics, business, computer science and
information management.
Today, a lot more organizations are opening up their doors to big data and
unlocking its power. The advancement in big data technologies that brings low
cost storage and cheap computing power makes it feasible for businesses to
collect big data for either external entities such as competitors and market trends
or internal use such as company’s operations. However, data without the right
people to process it or without the right tools to extract insight from it is worth
nothing, the real business value lies in processing and analyzing data – and that
is where, a data scientist steps into the spotlight.
1.2. Why Data Science Matters?
Data science can add significant
value to businesses by the addition
of statistics and insights across all
business processes. It helps in
making better decisions, through
measuring, tracking and modeling
performance metrics. Furthermore,
Data science supports forecasting
future trends, identifying
opportunities, discovering current
flaws and outliers, predicting future
behavioral patterns, providing personalized experience through recommendation
systems and identifying target audiences.
1.3. Why Companies Need Data Science Tools?
The number of devices sending data is growing
rapidly while the cost of storing data continues to
decline. Companies today collect tremendous
amounts of data. However, they still struggle in the
process to extract business values of their data.
There is no doubt that traditional tools are becoming
obsolete because they can’t handle the current
scale of data. In the current rapid growth market,
there is a need to continually improve current tools and develop new ones to add
automation and to maximize the benefits that data can bring to companies.
Luckily the rapid innovation in the field brought up many tools to fit the new scale
and allow quick modeling and prototyping. But that also means that new talent
and skills are required to efficiently run these tools and build better models.
Today, the role of a data scientist is one of the most in-demand positions. This
means that data scientists can be difficult to find and also expensive to hire and
retain. At Synergy we try to solve this by providing two things: (1) a platform to
outsource data science and artificial intelligence research and development -
human-on-demand machine learning (2) a Zero-code, model-building and
model-tuning(selecting optimal parameters) platform. This innovation replaces
the skills that data scientists bring in to develop machine learning models, such
as feature selection, model selection and model tuning for specific datasets.
1.4. How Synergy is Addressing the Market Needs?
In current market, there is a growing demand for data scientists and AI products
that is likely to continue in the foreseeable future. Almost no company could
survive without integrating data science products and AI. For some companies,
off-the-shelf products could be sufficient while for many others it may not. In
addition, many companies may not be able to hire enough engineers to build
complete AI products internally. Synergy addresses all of these needs by offering
(1) off-the-shelf products through Synergy analytical and modeling platform and
(2) human-on-demand machine learning for companies who don’t want to use
off-the-shelf products.
Synergy platform deploys human-on-demand developed products to the
analytical and modeling platform, therefore, allowing non-technical users and
companies to access state-of-the-art models that are developed from talented
data scientist.
In addition to addressing the market need of organizations, Synergy also meets
the market need of developers, freelance data scientists and researchers to
monetize their work with assurance to track back the credit to them. Synergy is
able to provide a fair reward to all contributors in the final solution by tracking all
forks and improvements to the solution in all rounds. Not only that but Synergy
also guarantees perpetual reward for contributors after their models are deployed
in Synergy analytical and modeling platform. By using blockchain distributed trust
network and the power of smart contracts, Synergy is able to both (1) incentivize
researchers and developers without having trust concerns and (2) guarantees
the competition’s sponsor that its minimum acceptance quality is met before the
promised reward is distributed. This happens in fully transparent mode, thanks to
the blockchain technology.
2. Synergy Analytics and Modeling Platform Overview
Value extraction from business data is a crucial mission for every company: data
mining and machine learning can accurately predict customer behavior patterns,
product-matching, logistics and inventory needs. However, the shortage of data
scientists in the market and the high demand that makes hiring an experienced
data scientist very expensive cause many businesses to lag behind. Forcing
them to use outdated analytics tools or simple machine learning models that
simply cannot maximize the value from business data.
We, at Synergy, believe that for many companies, especially small and medium
sized companies, better technology can be affordable. As companies prefer to
invest less for more, we are proposing a rich data analytics platform combined
with a ZeroDriver machine learning modeling platform which makes machine
learning accessible to non-expert users. We do this by auto-tuning the process
from data cleaning and features selection to model evaluation. The platform is
backed by a community of data scientists who are incentivized to build reusable
off-the-shelf models in a collaborative manner, improving each others’ solution.
To summarize Synergy Platform consists of:
1) Component-Rich data analytics platform combined with ZeroDriver
artificial intelligence and machine learning modeling
2) Fully transparent and decentralize human-on-demand machine learning
development platform with rewards, powered by smart contracts on a
public blockchain.
2.1. Synergy Analytics and Modeling Platform
Synergy aims to build a component-Rich data analytics web platform backed with
community of highly talented data scientists and data engineers who are
incentivized to continuously improve these components. Synergy aims to bring
predictive intelligence to judgments made by decision makers by offering a wide
range of techniques and algorithms. All without writing a single line of code.
Synergy relies on several open-source platforms to act as infrastructure for the
Analytics and Modeling Platform. The modeling platform PoC (proof of concept)
was built on Apache Zeppelin but we still consider other candidate for the final
solution. Apache Zeppelin is an open-source award winning web-based notebook
that enables data-driven and interactive data analytics in the browser. The
following is a screenshot of advanced analytics page of Synergy’s PoC for
illustration purpose only.
A screenshots of Synergy’s proof of concept - advanced analytics page.
2.2. ZeroDriver artificial intelligence and machine learning modeling
In the ZeroDriver artificial intelligence approach, the user defines a flow by
building components that start with data connection and ends with the model
evaluation component. In addition, the user defines a measurement factor
(evaluation metric) such as Accuracy, Precision, F1 measure or any other
custom measure that could be calculated in the model evaluation component.
Synergy platform will tune the model parameters to maximize the measurement
value. (optimally tuning)
A screenshot of Synergy’s proof of concept- build a flow page. The right side shows samples of
configurable components that can be used to build a flow.
2.2.1. Smooth Deployment of Data Science and AI Apps
Synergy’s artificial intelligence and machine learning modeling platform enables
a smooth deployment of pre-built or user developed models as web apps. Users
can easily initiate the deployment process by adding an API access component
to the flow, and then connect it to a responding model. Synergy allows full
customization of the flow by allowing users to add input filtering components or
output customizing components at any stage in the flow.
The platform enables many advanced features, among them, are the bagging
and aggregation features. The aggregation feature allows users to build AI apps
that are composed of multiple models rather than one model. This is achieved by
enabling the user to build a flow that forwards the API queries to multiple models.
For example, a user wants to deploy an image classification model to distinguish
cats from dogs. Let’s assume that there is a pre-built model in Synergy's catalog
for such task and the user has created an additional two models using different
architectures for the same purpose. In many cases, there is no one model that is
always more accurate than others in all inputs. In such case, a user can consider
a multiple models approach to build a more accurate app. Therefore, instead of
building an app that consists of one model, the user can decide to deploy all
three models to respond to API queries with a policy, such as a majority vote, to
agree on the final answer. Synergy enables building such scenario by using
drag-and-drop components without writing a single line of code.
2.3. Human-on-demand Machine Learning
2.3.1. Host a Competition in a Platform that Drives Innovation.
Building state-of-the-art machine learning models and data science products
can’t be done without collaboration between mathematicians, scientists, and
domain-specific researchers. Synergy incentivizes collaboration on a
decentralized iteration process to build better data science and artificial
intelligence models. Synergy is composed of a research and development
environment with a reward system and evaluation platform where developed
models could be evaluated on new datasets. Synergy features multi-stages,
multi-round, fully transparent, smart contract powered competitions that
encourage collaborators to build and expand upon each other’s work. Synergy
platform tracks all contributions in all stages to the final solution.
A screenshot of Synergy’s PoC - host a competition page. The proof of concept features a user
friendly interface to build and deploy contracts on the blockchain
2.3.1.1. Submission and Reward
Let’s look at this example to illustrate how it works:
⇒ A new competition is published to develop artificial intelligence based trading
algorithm for cryptocurrencies.
Rewards Rules ​- (all rules are set in a smart contract that escrows all tokens):
1) 1000 SNG tokens to the final solution.
2) 1000 SNG tokens for top two solutions in each round.
3) 5000 SNG tokens for all contributors in the chain lead to the final solution.
4) Number of rounds: three
5) Evaluation metric: Return on investment in a cryptocurrency data.
6) Minimum accepted quality: the minimum value of the assigned metric
required by the final solution in order to accept it and distribute rewards.
In round 1​, submitter of A and B will
share 1000 tokens reward which will be
In round 2, all solutions should be
forked from round 1 solutions with
enhancement. Submitter of ​E and F
solutions will share the 2nd round
reward of 1000 tokens.
In round 3 (final)​, Submitter of solution
K​ will be rewarded with 1000 tokens.
5000 tokens ​for all contributors in the
chain lead to the final solution.B, F and
K will share these 5000.
⇒ Tokens are distributed from the smart contract of the competition to the
submitters’ SNG wallets.
2.3.2. Perpetual Compensations
At the end of each competition, if the solution can be reused for different
problems, Synergy or a contributor will wrap the solution with Synergy catalog
API and add it for later usage by Synergy analytics and modeling platform. In
addition, a smart contract is created to define all contributors to the final solution
for future rewards. The smart contract will allow receipt of tokens and
automatically distribute them to all contributors according to the predefined rules.
The contributors will gain perpetual tokens based on their model’s usage rate.
To illustrate that, let’s continue with previous example, assume the winner,
solution K, has the potential to be applied for different commodity trading. Let’s
assume that the rules of perpetual compensations are:
1. 60% equally to the chain lead to the final solution(B and F in the example).
2. 30% for the submitter of the final solution.
3. 10% for the contributor who worked on the API wrapping.
The deployed smart contract has an attached ERC20 payment address that
automatically distributes 60% of the recipient SNG tokens to the submitter of B
and F, 30% to the submitter of K, and 10% to the submitter of the solution to the
catalog (or to Synergy company in case the wrapping and submitting was done
by an internal employee). To learn more how it works, see section 3.3.How
Synergy is utilizing Blockchain Technology?
2.4. Summary of Platform Top Features:
Synergy Analytics and Modeling
Platform
● Automated multiple modelling
● Support multiple data sources
● Multiple visualization method
● Multiple analytics methods
● Rich data pre-processing library
● Build a flow throw drag and
drop components.
● Code-less, optimally tuning
mode
● Pre-build statistical models
● Pre-build classical machine
learning models.
● Acquire state-of-the-art models
from Synergy's competitions
submitted models.
Synergy Competition Host Platform
● Human-on-demand: Host a
competition or challenge in a
simple click
● Competition host can set a
minimum acceptance quality
criteria that the proposed
solutions have to meet in order
to be rewarded.
● Multi-round, multi-stage
competitions to maximize
proposed model performance.
● Bases on distributed trust
blockchain technology. Foster
data scientists to participate
without trust concerns.
● The winning models can
generate perpetual
compensations for contributors.
3. Why Blockchain?
3.1. What Problem does a Blockchain Solve?
Blockchain technology allows exchange of digital assets without an intermediary
trust entity. This technology enables a distributed trust network with no single
trusted arbiter to verify trust and the transfer of value. It transfers power and
control from one entity to the many entities, enabling safe, fast and cheaper
transactions despite the fact that we may not know the entities we are dealing
with. A blockchain lets us agree on the state of the system, even if we all don’t
trust each other.
3.2. What are Smart Contracts?
Smart contracts are self-executing pieces of logic that run on the blockchain. The
smart contract contains logic that two parties agree on that can’t be altered by
any of the parties. Smart contracts can also act as payment recipient with a
defined logic about when and how to distribute received assets.
3.3. How Synergy is utilizing Blockchain Technology?
Synergy platform consists of two major services:
(1) Human-on-Demand machine learning development platform with rewards.
(2) Component-Rich data analytics and modeling platform.
First, Synergy leverages blockchain to provide a fully transparent incentivized
research and development platform. This enables trustworthy transactions in a
trustless world. Collaborators and developers can expand on each others
solutions without fear of losing credit or manipulation. Collaborators do not need
to trust the sponsor of the challenge as the SNG tokens are escrowed into the
smart contract. The sponsor of the challenge do not need to trust the developers
as the contract will not execute the payment if the final solution does not meet
the minimum accepted quality which is the minimum value of the assigned metric
required by the final solution in order to accept it and distribute rewards.
Secondly, Synergy utilizes blockchain technology to provide a fully automatic and
transparent way to give perpetual rewards for the developers of the off-the-shelf
models and analytical components. Subscriptions to the data analytics and
modeling platform are paid to a global smart contract address that splits all
received SNG tokens between the company and model’s developers. This is
done by having a hierarchy of smart contracts with ERC20 addresses attached to
them that distributes all SNG tokens sent to its address to all contributors to
off-the-shelf models based on model usage ratios. The following figure shows
how Synergy is managing to provide perpetual rewards to all developers using
smart contract on public blockchain.
The figure illustrates Synergy’s smart contracts hierarchy that manage the subscription to
Synergy Analytics and Modeling Platform. All contracts are deployed on public blockchain
and can be audited by the public.
4. High-level Roadmap
Synergy has started in Q2 2017 by a group of Fulbright scholars and data
engineers at WPI and Brown university. The team built the first proof of concept
during Q3 2017. Afterwards, the idea has attracted a lot of interest.ater, during
Q4 2017, Synergy’s core and advisory teams were expanded with highly talented
technologists and business leaders who have accumulated 100+ years of
experience in engineering, technology management, entrepreneurship and
marketing.
During Q4 , 2017 we surveyed a group of R&D managers in our network. The
survey shows an increasing demand for a decentralized competition host
platform rather than the Synergy Analytics and Modeling Platform in businesses
with mature AI integration. One of the respondents explained that he will be
willing to outsource parts of his development or model improvement efforts to
Synergy community even if it is in beta release as it does not involve internal
bureaucracy due to its risk free nature, thanks to “pay only for a quality solution”
feature. Conversely, switching to a different cloud based analytics platform is a
complex process inside the organization that involves training efforts, risk
assessment and top management approval.
The result of the survey made us adopt an agile go-to-market strategy which
allows us to provide a quick answer to the growing market needs. We decided to
deploy the platform in two phases. In the first phase, we plan to launch Synergy
competition host platform only. That means Synergy Analytics and Modeling
Platform would have only one function; Human-on-demand machine learning
which allows to host a competition or a development challenge. This helps
Synergy in having quick access to the market and to start building a community
based on data scientists and data engineers.
We believe that being backed by strong diverse technical community will also
boost the development of Synergy Analytics and Modeling Platform. We can
outsource part of the development to our community who will be willing to
participate in the development in exchange with perpetual rewards once their
models are deployed. Furthermore, the risk-free nature of Synergy competition
host platform is likely to motivate many R&D managers to outsource part of their
model’s to our community. This will allow us to increase our initial offer of
off-the-shelf models when launching Synergy Analytics and Modeling Platform in
the second phase.
4.1. 2018-2019 Roadmap of Synergy Competition Host Platform
The Synergy team has scheduled the beta release of essential smart contracts
that powers the Synergy Competition Host Platform to the end of Q3 2018, the
smart contracts will be developed using Solidity[ ] over Ethereum testnet.3
During Q1 2019, Synergy will release a Graphical User Interface (GUI) to provide
seamless interaction with Ethereum public blockchain.
During Q2 2019, after completing testing and fixing on testnet, Synergy will
deploy the contracts to Ethereum mainnet and the GUI to a separate domain.
4.2. 2018-2019 Roadmap of Synergy Analytics and Modeling Platform
With the focus on Synergy Competition Host Platform during phase one, there
will be no releases of Synergy Analytics and Modeling Platform during 2018.
Synergy team is currently working with industrial partners to integrate their
requirements in the project roadmap. The alpha release of Synergy Analytics and
Modeling Platform is scheduled to Q3 2019, the release will feature data
connectors components that support various proprietary and open-source
databases, pre-processing components and classical machine learning models.
The following release will feature big data analytics and resource allocation for
3
​http://solidity.readthedocs.io/en/develop/index.html
the algorithms. Storage and computational power will be performed either by
cloud computing, such as AWS, or by exchanging SNG token to other ERC20
tokens to buy resources (For example, filecoin.io that provides decentralized
storage network).
Synergy adopts the agile methodology for all internal development. Feedback
from our industrial partners will be integrated in all scrums, which means that the
roadmap could be shifted to meet the future market needs and future trends.
5. Token Info
Token type: ERC-20 | Ticker: SNG | Total supply: 1 Billion
The SNG token is an ERC-20 token that acts as a utility token. Its main purpose
is to interact with Synergy’s distributed network.
The SNG token serves the following three different segments of interactors with
Synergy network:
1) Parties who want to sponsor data science, machine learning or artificial
intelligence challenges.
2) Organizations who want to utilize the service of Synergy Advanced
Analytics and Modeling platform.
3) Data-scientist, developers, and teams who want to submit their solution to
the platform.
○ Submitting, in general, doesn’t consume tokens, but in order to
protect the competition from massive vain submission attacks,
sponsors may require that only accounts with tokens in their wallets
be able to participate in the competition. In some cases, to protect
from multiple submissions from the same user, sponsors may also
require participants to deposit a certain amount of tokens that are
sent to participants after the competition due date.
The SNG token is designed to achieve two main objectives.
(1) To guarantee a non-discriminatory access to Synergy services. For
instance, the tokens enable unrestricted-access to consume, evaluate and
integrate state-of-the-art AI solutions.
(2) To enable building and growing a community of talented data scientists
and data engineers to develop innovative solutions.
The economic logic of SNG token is designed to support the continuous demand
of the token from the three segments of interactors with Synergy network. After
the initial token generation event, Synergy will not make official position to the
value of SNG token nor will be formally involved in secondary trading or valuation
of SNG tokens. Future value of the token is set to the market in full
independence from us.
5.1. Token Distribution
The SNG token is minted through a smart contract on Ethereum blockchain. The
contract is set to mint a total of 1,000,000,000 tokens without an option to
increase its maximum supply.
SNG tokens will be distributed as follow:
● 50% will be distributed on token generation event (token sale).
● 20% plus unsold tokens will be reserved for the company. The company
plans to use part of these tokens as a reward to Synergy’s community, to
support development of initial advanced analytics models offering.
● 15% will be distributed on bounties and for partners. The remaining tokens
after bounties are distributed will be reserved for partners.
● 10% will be distributed to Synergy team and advisers.
● 5% will be distributed to founders of Synergy.
--- The End ---

More Related Content

PDF
Synergy on the Blockchain! whitepaper
PDF
Cognizant Community Europe 2017: Mastering Digital: Navigating the Shift to t...
PDF
Harbor Research: 3D Printing Growth Opportunity
PDF
Cognizant Community 2016: Mastering Digital: How to Navigate the Shift to the...
PDF
Introduction to Knowledge Graphs and Semantic AI
PDF
The boom in Xaas and the knowledge graph
PDF
2022
PDF
Israel Cloud Computing
Synergy on the Blockchain! whitepaper
Cognizant Community Europe 2017: Mastering Digital: Navigating the Shift to t...
Harbor Research: 3D Printing Growth Opportunity
Cognizant Community 2016: Mastering Digital: How to Navigate the Shift to the...
Introduction to Knowledge Graphs and Semantic AI
The boom in Xaas and the knowledge graph
2022
Israel Cloud Computing

What's hot (20)

PDF
Artificial intelligence Trends in Marketing
DOCX
Evaluation of both companies
PDF
The Work Ahead: Transportation and Logistics Delivering on the Digital-Physic...
PDF
Strategy Report for NextGen BI
PDF
Artificial Intelligence Industry: An Overview by Segment by Emerj AI Research
PPTX
Open Insurance - Unlocking Ecosystem Opportunities For Tomorrow’s Insurance I...
PDF
Accenture Tech Vision 2020 - Trend 2
PDF
Whitepaper: “Data-as-a-Service - New Business Paradigm Across Organizations”
PDF
Data-Centric Business Transformation Using Knowledge Graphs
PPTX
Data centric business and knowledge graph trends
PDF
Ever–ready for every opportunity
PDF
Cool Vendors Collaboration Social Software
PDF
Location Intelligence
PDF
Driving Digital Experience through the Cloud
PDF
Worldwide Cognitive/AI Software Platforms Market Shares, 2017: Significant G...
PDF
Enterprise Application Services: Moving Business into the Digital Age
PDF
The global innovation 1000 strategy-business winter-2013
PDF
AI Product Manager
PDF
Artificial Intelligence: what value for intelligent machines?
PDF
Driving Digital Experience through the Cloud
Artificial intelligence Trends in Marketing
Evaluation of both companies
The Work Ahead: Transportation and Logistics Delivering on the Digital-Physic...
Strategy Report for NextGen BI
Artificial Intelligence Industry: An Overview by Segment by Emerj AI Research
Open Insurance - Unlocking Ecosystem Opportunities For Tomorrow’s Insurance I...
Accenture Tech Vision 2020 - Trend 2
Whitepaper: “Data-as-a-Service - New Business Paradigm Across Organizations”
Data-Centric Business Transformation Using Knowledge Graphs
Data centric business and knowledge graph trends
Ever–ready for every opportunity
Cool Vendors Collaboration Social Software
Location Intelligence
Driving Digital Experience through the Cloud
Worldwide Cognitive/AI Software Platforms Market Shares, 2017: Significant G...
Enterprise Application Services: Moving Business into the Digital Age
The global innovation 1000 strategy-business winter-2013
AI Product Manager
Artificial Intelligence: what value for intelligent machines?
Driving Digital Experience through the Cloud
Ad

Similar to Synergy Platform Whitepaper alpha (20)

PDF
AI Trends.pdf
PPTX
HEC Digital Business. Sharing Economy and other trends
PDF
solulab.com-Integrating AI into Product Development A Beginners Guide.pdf
PDF
Top Tech Skills in Demand: How Staff Augmentation
PDF
Data Science v/s Cloud Computing: An Overview
PDF
Smart Data Engineering_ Bridging the Gap Between Information and Actionable I...
PDF
Digital Enterprise Architecture: Four Elements Critical to Solution Envisioning
PDF
3.BITOOLS - DIGITAL TRANSFORMATION AND STRATEGY
PDF
leewayhertz.com-How to build a generative AI solution From prototyping to pro...
PDF
How to build a generative AI solution A step-by-step guide (2).pdf
PDF
eBook-DataSciencePlatform
PDF
How to build a generative AI solution A step-by-step guide.pdf
PPTX
What are the Top Technology Trends For Your Business?
PDF
How to build a generative AI solution A step-by-step guide.pdf
PDF
Deep Learning Market.pdf
PPTX
Why many data science projects fail
PDF
Bringing AI into the Enterprise: A Machine Learning Primer
PDF
Technovision
PDF
The Future of Data Analytics_ Trends to Watch in 2025.pdf
PDF
AI Trends.pdf
HEC Digital Business. Sharing Economy and other trends
solulab.com-Integrating AI into Product Development A Beginners Guide.pdf
Top Tech Skills in Demand: How Staff Augmentation
Data Science v/s Cloud Computing: An Overview
Smart Data Engineering_ Bridging the Gap Between Information and Actionable I...
Digital Enterprise Architecture: Four Elements Critical to Solution Envisioning
3.BITOOLS - DIGITAL TRANSFORMATION AND STRATEGY
leewayhertz.com-How to build a generative AI solution From prototyping to pro...
How to build a generative AI solution A step-by-step guide (2).pdf
eBook-DataSciencePlatform
How to build a generative AI solution A step-by-step guide.pdf
What are the Top Technology Trends For Your Business?
How to build a generative AI solution A step-by-step guide.pdf
Deep Learning Market.pdf
Why many data science projects fail
Bringing AI into the Enterprise: A Machine Learning Primer
Technovision
The Future of Data Analytics_ Trends to Watch in 2025.pdf
Ad

More from Yousef Fadila (12)

PPTX
Trackster Pruning at the CMS High-Granularity Calorimeter
PDF
Recommandation systems -
PPTX
Analysis on steam platform
PPTX
interactive voting based map matching algorithm
PPTX
co-Hadoop: Data co-location on Hadoop.
PPTX
Spot deceptive TripAdvisor Reviews
PPTX
Textual & Sentiment Analysis of Movie Reviews
PPTX
Anomaly Detection - Catch me if you can
PPTX
Tweeting for Hillary - DS 501 case study 1
PPTX
CS 548 KNOWLEDGE DISCOVERY AND DATA MINING Project 1
PPT
Innovative thinking التفكير الابداعي
PDF
Am i overpaying - business proposal
Trackster Pruning at the CMS High-Granularity Calorimeter
Recommandation systems -
Analysis on steam platform
interactive voting based map matching algorithm
co-Hadoop: Data co-location on Hadoop.
Spot deceptive TripAdvisor Reviews
Textual & Sentiment Analysis of Movie Reviews
Anomaly Detection - Catch me if you can
Tweeting for Hillary - DS 501 case study 1
CS 548 KNOWLEDGE DISCOVERY AND DATA MINING Project 1
Innovative thinking التفكير الابداعي
Am i overpaying - business proposal

Recently uploaded (20)

PPTX
Introduction-to-Cloud-ComputingFinal.pptx
PDF
Galatica Smart Energy Infrastructure Startup Pitch Deck
PPT
Reliability_Chapter_ presentation 1221.5784
PDF
.pdf is not working space design for the following data for the following dat...
PPTX
Database Infoormation System (DBIS).pptx
PPTX
Major-Components-ofNKJNNKNKNKNKronment.pptx
PPT
Chapter 2 METAL FORMINGhhhhhhhjjjjmmmmmmmmm
PPTX
The THESIS FINAL-DEFENSE-PRESENTATION.pptx
PDF
Mega Projects Data Mega Projects Data
PPTX
Introduction to Knowledge Engineering Part 1
PPTX
A Quantitative-WPS Office.pptx research study
PPTX
Business Acumen Training GuidePresentation.pptx
PPTX
Business Ppt On Nestle.pptx huunnnhhgfvu
PDF
Taxes Foundatisdcsdcsdon Certificate.pdf
PPTX
05. PRACTICAL GUIDE TO MICROSOFT EXCEL.pptx
PPTX
Global journeys: estimating international migration
PPTX
IB Computer Science - Internal Assessment.pptx
PDF
22.Patil - Early prediction of Alzheimer’s disease using convolutional neural...
PPTX
MODULE 8 - DISASTER risk PREPAREDNESS.pptx
PDF
“Getting Started with Data Analytics Using R – Concepts, Tools & Case Studies”
Introduction-to-Cloud-ComputingFinal.pptx
Galatica Smart Energy Infrastructure Startup Pitch Deck
Reliability_Chapter_ presentation 1221.5784
.pdf is not working space design for the following data for the following dat...
Database Infoormation System (DBIS).pptx
Major-Components-ofNKJNNKNKNKNKronment.pptx
Chapter 2 METAL FORMINGhhhhhhhjjjjmmmmmmmmm
The THESIS FINAL-DEFENSE-PRESENTATION.pptx
Mega Projects Data Mega Projects Data
Introduction to Knowledge Engineering Part 1
A Quantitative-WPS Office.pptx research study
Business Acumen Training GuidePresentation.pptx
Business Ppt On Nestle.pptx huunnnhhgfvu
Taxes Foundatisdcsdcsdon Certificate.pdf
05. PRACTICAL GUIDE TO MICROSOFT EXCEL.pptx
Global journeys: estimating international migration
IB Computer Science - Internal Assessment.pptx
22.Patil - Early prediction of Alzheimer’s disease using convolutional neural...
MODULE 8 - DISASTER risk PREPAREDNESS.pptx
“Getting Started with Data Analytics Using R – Concepts, Tools & Case Studies”

Synergy Platform Whitepaper alpha

  • 1. A blockchain based collaborative platform for developing Data Science​ and ​Artificial Intelligence​ products White Paper November 15, 2017 Written By: Yousef Fadila
  • 2. Executive Summary ● Synergy utilizes blockchain distributed trust technology to build a smart contract powered research and development platform for developing state-of-the-art machine learning and artificial intelligence products. Collaborators and researchers are incentivized to improve other fellow’s solutions in rounds and in exchange receive SNG tokens. Therefore, driving innovation and continuous improvement. ● Synergy provides a fair and decentralized development tournament platform that tracks and rewards all contributors to the final solution. ● Synergy organizes all developed products in a web based analytics and modeling platform. A product can be available in two modes: (1) As a model architecture with optimally tuned parameters to be trained from scratch with user-specific data and (2) As a pre-built model which is ready for deployment. The platform features building AI solutions as a flow of drag and drop components to make them available for non-technical users. As a result, they can quickly build state of the art data science products and AI agents ● Synergy analytics and modeling platform features smooth deployment of data science products and AI agents. Deployment can be done by a simply dragging and dropping a deployment component to the flow. As a result, the model becomes accessible through REST Queries. ● Subscriptions to Synergy analytics and modeling platform are fully managed through smart contracts using SNG tokens. The smart contract fairly distributes tokens to all contributors and therefore provides perpetual rewards to all developers and contributors. This is an extra incentive to our community of developers and data scientist to continually participate in Synergy competitions and improve models.
  • 3. Abstract The value of data science and the power of machine learning is growing exponentially. It is a matter of time for businesses to lose to its competitors if they not utilizing their data efficiently. Data-mining and machine learning are advanced sciences that can accurately predict customer behavior patterns, logistics and distribution issues, future trends and more. Without data-mining and machine learning technologies, a company is at a significant disadvantage. That is, it won’t know what its competitors know about future trends and the current market. Data scientist has been called the “The Sexiest Job of the 21st Century” ]. There1 is no doubt that almost every business needs to integrate data science in its decision-making process. However, due to the veracity and variety of big data2 and the need of it in different fields makes it very challenging to hire the right data scientists. Furthermore, many companies are moving to the data based decision making models and as a result there aren’t enough data scientists to meet the increasing demand in the current market. The rapid innovation in the field makes it even more challenging for companies to cope with state-of-the-art techniques needed to evaluate their data-science models. Synergy solves that by providing a rich data analytics and model-building platform, featuring human-in-demand machine learning, and reusable off-the-shelf models. Synergy provides a fully transparent and decentralized data science competition management platform powered by public smart contracts on public blockchain. The platform is able to track all contributors to the final solution in all rounds, encourage collaboration to improve other solutions and to build state-of-the-art models that meet the sponsor needs. 1 Harvard business review ​https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-cent​ury 2 4-Vs-of-big-data ​ ​http://www.ibmbigdatahub.com/infographic/four-vs-big-data
  • 4. Table of Contents 1. Introduction 5 1.1. What is Data Science? 5 1.2. Why Data Science Matters? 6 1.3. Why Companies Need Data Science Tools? 6 1.4. How Synergy is Addressing the Market Needs? 7 2. Synergy Analytics and Modeling Platform Overview 9 2.1. Synergy Analytics and Modeling Platform 9 2.2. ZeroDriver artificial intelligence and machine learning modeling 10 2.2.1. Smooth Deployment of Data Science and AI Apps 12 2.3. Human-on-demand Machine Learning 13 2.3.1. Host a Competition in a Platform that Drives Innovation. 13 2.3.1.1. Submission and Reward 14 2.3.2. Perpetual Compensations 15 2.4. Summary of Platform Top Features: 16 3. Why Blockchain? 17 3.1. What Problem does a Blockchain Solve? 17 3.2. What are Smart Contracts? 17 3.3. How Synergy is utilizing Blockchain Technology? 17 4. High-level Roadmap 19 4.1. 2018-2019 Roadmap of Synergy Competition Host Platform 20 4.2. 2018-2019 Roadmap of Synergy Analytics and Modeling Platform 20 5. Token Info 22 5.1. Token Distribution 24
  • 5. 1. Introduction 1.1. What is Data Science? Data science is an interdisciplinary field about scientific methods and processes to extract insights from data. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science, in particular from the subdomains of machine learning, classification, cluster analysis, data mining, databases, and visualization. Data scientists usually have advanced degrees and training in statistics, mathematics, business, computer science and information management. Today, a lot more organizations are opening up their doors to big data and unlocking its power. The advancement in big data technologies that brings low cost storage and cheap computing power makes it feasible for businesses to collect big data for either external entities such as competitors and market trends or internal use such as company’s operations. However, data without the right people to process it or without the right tools to extract insight from it is worth nothing, the real business value lies in processing and analyzing data – and that is where, a data scientist steps into the spotlight.
  • 6. 1.2. Why Data Science Matters? Data science can add significant value to businesses by the addition of statistics and insights across all business processes. It helps in making better decisions, through measuring, tracking and modeling performance metrics. Furthermore, Data science supports forecasting future trends, identifying opportunities, discovering current flaws and outliers, predicting future behavioral patterns, providing personalized experience through recommendation systems and identifying target audiences. 1.3. Why Companies Need Data Science Tools? The number of devices sending data is growing rapidly while the cost of storing data continues to decline. Companies today collect tremendous amounts of data. However, they still struggle in the process to extract business values of their data. There is no doubt that traditional tools are becoming obsolete because they can’t handle the current scale of data. In the current rapid growth market, there is a need to continually improve current tools and develop new ones to add automation and to maximize the benefits that data can bring to companies. Luckily the rapid innovation in the field brought up many tools to fit the new scale
  • 7. and allow quick modeling and prototyping. But that also means that new talent and skills are required to efficiently run these tools and build better models. Today, the role of a data scientist is one of the most in-demand positions. This means that data scientists can be difficult to find and also expensive to hire and retain. At Synergy we try to solve this by providing two things: (1) a platform to outsource data science and artificial intelligence research and development - human-on-demand machine learning (2) a Zero-code, model-building and model-tuning(selecting optimal parameters) platform. This innovation replaces the skills that data scientists bring in to develop machine learning models, such as feature selection, model selection and model tuning for specific datasets. 1.4. How Synergy is Addressing the Market Needs? In current market, there is a growing demand for data scientists and AI products that is likely to continue in the foreseeable future. Almost no company could survive without integrating data science products and AI. For some companies, off-the-shelf products could be sufficient while for many others it may not. In addition, many companies may not be able to hire enough engineers to build complete AI products internally. Synergy addresses all of these needs by offering (1) off-the-shelf products through Synergy analytical and modeling platform and (2) human-on-demand machine learning for companies who don’t want to use off-the-shelf products. Synergy platform deploys human-on-demand developed products to the analytical and modeling platform, therefore, allowing non-technical users and companies to access state-of-the-art models that are developed from talented data scientist. In addition to addressing the market need of organizations, Synergy also meets the market need of developers, freelance data scientists and researchers to
  • 8. monetize their work with assurance to track back the credit to them. Synergy is able to provide a fair reward to all contributors in the final solution by tracking all forks and improvements to the solution in all rounds. Not only that but Synergy also guarantees perpetual reward for contributors after their models are deployed in Synergy analytical and modeling platform. By using blockchain distributed trust network and the power of smart contracts, Synergy is able to both (1) incentivize researchers and developers without having trust concerns and (2) guarantees the competition’s sponsor that its minimum acceptance quality is met before the promised reward is distributed. This happens in fully transparent mode, thanks to the blockchain technology.
  • 9. 2. Synergy Analytics and Modeling Platform Overview Value extraction from business data is a crucial mission for every company: data mining and machine learning can accurately predict customer behavior patterns, product-matching, logistics and inventory needs. However, the shortage of data scientists in the market and the high demand that makes hiring an experienced data scientist very expensive cause many businesses to lag behind. Forcing them to use outdated analytics tools or simple machine learning models that simply cannot maximize the value from business data. We, at Synergy, believe that for many companies, especially small and medium sized companies, better technology can be affordable. As companies prefer to invest less for more, we are proposing a rich data analytics platform combined with a ZeroDriver machine learning modeling platform which makes machine learning accessible to non-expert users. We do this by auto-tuning the process from data cleaning and features selection to model evaluation. The platform is backed by a community of data scientists who are incentivized to build reusable off-the-shelf models in a collaborative manner, improving each others’ solution. To summarize Synergy Platform consists of: 1) Component-Rich data analytics platform combined with ZeroDriver artificial intelligence and machine learning modeling 2) Fully transparent and decentralize human-on-demand machine learning development platform with rewards, powered by smart contracts on a public blockchain. 2.1. Synergy Analytics and Modeling Platform Synergy aims to build a component-Rich data analytics web platform backed with community of highly talented data scientists and data engineers who are incentivized to continuously improve these components. Synergy aims to bring
  • 10. predictive intelligence to judgments made by decision makers by offering a wide range of techniques and algorithms. All without writing a single line of code. Synergy relies on several open-source platforms to act as infrastructure for the Analytics and Modeling Platform. The modeling platform PoC (proof of concept) was built on Apache Zeppelin but we still consider other candidate for the final solution. Apache Zeppelin is an open-source award winning web-based notebook that enables data-driven and interactive data analytics in the browser. The following is a screenshot of advanced analytics page of Synergy’s PoC for illustration purpose only. A screenshots of Synergy’s proof of concept - advanced analytics page. 2.2. ZeroDriver artificial intelligence and machine learning modeling In the ZeroDriver artificial intelligence approach, the user defines a flow by building components that start with data connection and ends with the model evaluation component. In addition, the user defines a measurement factor
  • 11. (evaluation metric) such as Accuracy, Precision, F1 measure or any other custom measure that could be calculated in the model evaluation component. Synergy platform will tune the model parameters to maximize the measurement value. (optimally tuning) A screenshot of Synergy’s proof of concept- build a flow page. The right side shows samples of configurable components that can be used to build a flow.
  • 12. 2.2.1. Smooth Deployment of Data Science and AI Apps Synergy’s artificial intelligence and machine learning modeling platform enables a smooth deployment of pre-built or user developed models as web apps. Users can easily initiate the deployment process by adding an API access component to the flow, and then connect it to a responding model. Synergy allows full customization of the flow by allowing users to add input filtering components or output customizing components at any stage in the flow. The platform enables many advanced features, among them, are the bagging and aggregation features. The aggregation feature allows users to build AI apps that are composed of multiple models rather than one model. This is achieved by enabling the user to build a flow that forwards the API queries to multiple models. For example, a user wants to deploy an image classification model to distinguish cats from dogs. Let’s assume that there is a pre-built model in Synergy's catalog for such task and the user has created an additional two models using different architectures for the same purpose. In many cases, there is no one model that is always more accurate than others in all inputs. In such case, a user can consider a multiple models approach to build a more accurate app. Therefore, instead of building an app that consists of one model, the user can decide to deploy all three models to respond to API queries with a policy, such as a majority vote, to agree on the final answer. Synergy enables building such scenario by using drag-and-drop components without writing a single line of code.
  • 13. 2.3. Human-on-demand Machine Learning 2.3.1. Host a Competition in a Platform that Drives Innovation. Building state-of-the-art machine learning models and data science products can’t be done without collaboration between mathematicians, scientists, and domain-specific researchers. Synergy incentivizes collaboration on a decentralized iteration process to build better data science and artificial intelligence models. Synergy is composed of a research and development environment with a reward system and evaluation platform where developed models could be evaluated on new datasets. Synergy features multi-stages, multi-round, fully transparent, smart contract powered competitions that encourage collaborators to build and expand upon each other’s work. Synergy platform tracks all contributions in all stages to the final solution. A screenshot of Synergy’s PoC - host a competition page. The proof of concept features a user friendly interface to build and deploy contracts on the blockchain
  • 14. 2.3.1.1. Submission and Reward Let’s look at this example to illustrate how it works: ⇒ A new competition is published to develop artificial intelligence based trading algorithm for cryptocurrencies. Rewards Rules ​- (all rules are set in a smart contract that escrows all tokens): 1) 1000 SNG tokens to the final solution. 2) 1000 SNG tokens for top two solutions in each round. 3) 5000 SNG tokens for all contributors in the chain lead to the final solution. 4) Number of rounds: three 5) Evaluation metric: Return on investment in a cryptocurrency data. 6) Minimum accepted quality: the minimum value of the assigned metric required by the final solution in order to accept it and distribute rewards. In round 1​, submitter of A and B will share 1000 tokens reward which will be In round 2, all solutions should be forked from round 1 solutions with enhancement. Submitter of ​E and F solutions will share the 2nd round reward of 1000 tokens. In round 3 (final)​, Submitter of solution K​ will be rewarded with 1000 tokens. 5000 tokens ​for all contributors in the chain lead to the final solution.B, F and K will share these 5000. ⇒ Tokens are distributed from the smart contract of the competition to the submitters’ SNG wallets.
  • 15. 2.3.2. Perpetual Compensations At the end of each competition, if the solution can be reused for different problems, Synergy or a contributor will wrap the solution with Synergy catalog API and add it for later usage by Synergy analytics and modeling platform. In addition, a smart contract is created to define all contributors to the final solution for future rewards. The smart contract will allow receipt of tokens and automatically distribute them to all contributors according to the predefined rules. The contributors will gain perpetual tokens based on their model’s usage rate. To illustrate that, let’s continue with previous example, assume the winner, solution K, has the potential to be applied for different commodity trading. Let’s assume that the rules of perpetual compensations are: 1. 60% equally to the chain lead to the final solution(B and F in the example). 2. 30% for the submitter of the final solution. 3. 10% for the contributor who worked on the API wrapping.
  • 16. The deployed smart contract has an attached ERC20 payment address that automatically distributes 60% of the recipient SNG tokens to the submitter of B and F, 30% to the submitter of K, and 10% to the submitter of the solution to the catalog (or to Synergy company in case the wrapping and submitting was done by an internal employee). To learn more how it works, see section 3.3.How Synergy is utilizing Blockchain Technology? 2.4. Summary of Platform Top Features: Synergy Analytics and Modeling Platform ● Automated multiple modelling ● Support multiple data sources ● Multiple visualization method ● Multiple analytics methods ● Rich data pre-processing library ● Build a flow throw drag and drop components. ● Code-less, optimally tuning mode ● Pre-build statistical models ● Pre-build classical machine learning models. ● Acquire state-of-the-art models from Synergy's competitions submitted models. Synergy Competition Host Platform ● Human-on-demand: Host a competition or challenge in a simple click ● Competition host can set a minimum acceptance quality criteria that the proposed solutions have to meet in order to be rewarded. ● Multi-round, multi-stage competitions to maximize proposed model performance. ● Bases on distributed trust blockchain technology. Foster data scientists to participate without trust concerns. ● The winning models can generate perpetual compensations for contributors.
  • 17. 3. Why Blockchain? 3.1. What Problem does a Blockchain Solve? Blockchain technology allows exchange of digital assets without an intermediary trust entity. This technology enables a distributed trust network with no single trusted arbiter to verify trust and the transfer of value. It transfers power and control from one entity to the many entities, enabling safe, fast and cheaper transactions despite the fact that we may not know the entities we are dealing with. A blockchain lets us agree on the state of the system, even if we all don’t trust each other. 3.2. What are Smart Contracts? Smart contracts are self-executing pieces of logic that run on the blockchain. The smart contract contains logic that two parties agree on that can’t be altered by any of the parties. Smart contracts can also act as payment recipient with a defined logic about when and how to distribute received assets. 3.3. How Synergy is utilizing Blockchain Technology? Synergy platform consists of two major services: (1) Human-on-Demand machine learning development platform with rewards. (2) Component-Rich data analytics and modeling platform. First, Synergy leverages blockchain to provide a fully transparent incentivized research and development platform. This enables trustworthy transactions in a trustless world. Collaborators and developers can expand on each others solutions without fear of losing credit or manipulation. Collaborators do not need to trust the sponsor of the challenge as the SNG tokens are escrowed into the smart contract. The sponsor of the challenge do not need to trust the developers as the contract will not execute the payment if the final solution does not meet
  • 18. the minimum accepted quality which is the minimum value of the assigned metric required by the final solution in order to accept it and distribute rewards. Secondly, Synergy utilizes blockchain technology to provide a fully automatic and transparent way to give perpetual rewards for the developers of the off-the-shelf models and analytical components. Subscriptions to the data analytics and modeling platform are paid to a global smart contract address that splits all received SNG tokens between the company and model’s developers. This is done by having a hierarchy of smart contracts with ERC20 addresses attached to them that distributes all SNG tokens sent to its address to all contributors to off-the-shelf models based on model usage ratios. The following figure shows how Synergy is managing to provide perpetual rewards to all developers using smart contract on public blockchain. The figure illustrates Synergy’s smart contracts hierarchy that manage the subscription to Synergy Analytics and Modeling Platform. All contracts are deployed on public blockchain and can be audited by the public.
  • 19. 4. High-level Roadmap Synergy has started in Q2 2017 by a group of Fulbright scholars and data engineers at WPI and Brown university. The team built the first proof of concept during Q3 2017. Afterwards, the idea has attracted a lot of interest.ater, during Q4 2017, Synergy’s core and advisory teams were expanded with highly talented technologists and business leaders who have accumulated 100+ years of experience in engineering, technology management, entrepreneurship and marketing. During Q4 , 2017 we surveyed a group of R&D managers in our network. The survey shows an increasing demand for a decentralized competition host platform rather than the Synergy Analytics and Modeling Platform in businesses with mature AI integration. One of the respondents explained that he will be willing to outsource parts of his development or model improvement efforts to Synergy community even if it is in beta release as it does not involve internal bureaucracy due to its risk free nature, thanks to “pay only for a quality solution” feature. Conversely, switching to a different cloud based analytics platform is a complex process inside the organization that involves training efforts, risk assessment and top management approval. The result of the survey made us adopt an agile go-to-market strategy which allows us to provide a quick answer to the growing market needs. We decided to deploy the platform in two phases. In the first phase, we plan to launch Synergy competition host platform only. That means Synergy Analytics and Modeling Platform would have only one function; Human-on-demand machine learning which allows to host a competition or a development challenge. This helps Synergy in having quick access to the market and to start building a community based on data scientists and data engineers.
  • 20. We believe that being backed by strong diverse technical community will also boost the development of Synergy Analytics and Modeling Platform. We can outsource part of the development to our community who will be willing to participate in the development in exchange with perpetual rewards once their models are deployed. Furthermore, the risk-free nature of Synergy competition host platform is likely to motivate many R&D managers to outsource part of their model’s to our community. This will allow us to increase our initial offer of off-the-shelf models when launching Synergy Analytics and Modeling Platform in the second phase. 4.1. 2018-2019 Roadmap of Synergy Competition Host Platform The Synergy team has scheduled the beta release of essential smart contracts that powers the Synergy Competition Host Platform to the end of Q3 2018, the smart contracts will be developed using Solidity[ ] over Ethereum testnet.3 During Q1 2019, Synergy will release a Graphical User Interface (GUI) to provide seamless interaction with Ethereum public blockchain. During Q2 2019, after completing testing and fixing on testnet, Synergy will deploy the contracts to Ethereum mainnet and the GUI to a separate domain. 4.2. 2018-2019 Roadmap of Synergy Analytics and Modeling Platform With the focus on Synergy Competition Host Platform during phase one, there will be no releases of Synergy Analytics and Modeling Platform during 2018. Synergy team is currently working with industrial partners to integrate their requirements in the project roadmap. The alpha release of Synergy Analytics and Modeling Platform is scheduled to Q3 2019, the release will feature data connectors components that support various proprietary and open-source databases, pre-processing components and classical machine learning models. The following release will feature big data analytics and resource allocation for 3 ​http://solidity.readthedocs.io/en/develop/index.html
  • 21. the algorithms. Storage and computational power will be performed either by cloud computing, such as AWS, or by exchanging SNG token to other ERC20 tokens to buy resources (For example, filecoin.io that provides decentralized storage network). Synergy adopts the agile methodology for all internal development. Feedback from our industrial partners will be integrated in all scrums, which means that the roadmap could be shifted to meet the future market needs and future trends.
  • 22. 5. Token Info Token type: ERC-20 | Ticker: SNG | Total supply: 1 Billion The SNG token is an ERC-20 token that acts as a utility token. Its main purpose is to interact with Synergy’s distributed network. The SNG token serves the following three different segments of interactors with Synergy network: 1) Parties who want to sponsor data science, machine learning or artificial intelligence challenges. 2) Organizations who want to utilize the service of Synergy Advanced Analytics and Modeling platform. 3) Data-scientist, developers, and teams who want to submit their solution to the platform. ○ Submitting, in general, doesn’t consume tokens, but in order to protect the competition from massive vain submission attacks, sponsors may require that only accounts with tokens in their wallets be able to participate in the competition. In some cases, to protect from multiple submissions from the same user, sponsors may also require participants to deposit a certain amount of tokens that are sent to participants after the competition due date. The SNG token is designed to achieve two main objectives. (1) To guarantee a non-discriminatory access to Synergy services. For instance, the tokens enable unrestricted-access to consume, evaluate and integrate state-of-the-art AI solutions. (2) To enable building and growing a community of talented data scientists and data engineers to develop innovative solutions.
  • 23. The economic logic of SNG token is designed to support the continuous demand of the token from the three segments of interactors with Synergy network. After the initial token generation event, Synergy will not make official position to the value of SNG token nor will be formally involved in secondary trading or valuation of SNG tokens. Future value of the token is set to the market in full independence from us.
  • 24. 5.1. Token Distribution The SNG token is minted through a smart contract on Ethereum blockchain. The contract is set to mint a total of 1,000,000,000 tokens without an option to increase its maximum supply. SNG tokens will be distributed as follow: ● 50% will be distributed on token generation event (token sale). ● 20% plus unsold tokens will be reserved for the company. The company plans to use part of these tokens as a reward to Synergy’s community, to support development of initial advanced analytics models offering. ● 15% will be distributed on bounties and for partners. The remaining tokens after bounties are distributed will be reserved for partners. ● 10% will be distributed to Synergy team and advisers. ● 5% will be distributed to founders of Synergy.
  • 25. --- The End ---