KOIOS AI Democratizing Artificial Intelligence for the Global Consumer.

In today’s technology landscape Artificial Intelligence (AI) and Machine Learning (ML) technologies have long been the
realm of only the largest companies around the world. Due to the inherent complexity and significant cost of AI & ML, these
paradigm-shifting technologies have yet to be commoditized for general consumption.

Business leaders are asking: What impact will AI have on my organization, and is our business model threatened by AI disruption?

And as these leaders look to capitalize on AI opportunities, they’re asking: Where should we target investment, and what
kind of capabilities would enable us to perform better?

Cutting across all these considerations is how to build AI in the responsible and transparent way needed to maintain the
confidence of customers and wider stakeholders.

From the personal assistants in our mobile phones, to the profiling, customization, and cyber protection that lie behind more and more of our commercial interactions, AI touches almost every aspect of our lives.

And it’s only just getting started. According to an independent analysis by PWC, the global GDP will be up to 14% higher in 2030 as a result of the accelerating development and take-up of AI – the equivalent of an additional $15.7 trillion USD.
It is clearly evident that the next major area of investment and global spending will be in Artificial Intelligence technologies, which is why Koios seeks to create an ecosystem of products to connect the AI Developer community to a global audience.

Why koios build an AI platform on Blockchain?

To understand why Koios is building an AI ecosystem using blockchain it is important to understand the nature of the AI
development industry in today’s market.

The talent pool for Artificial Intelligence, whilst increasing exponentially, is still a relatively small group of highly skilled data scientists, developers and mathematicians. These individuals are coveted in the tech industry and due to the lack of mainstream adoption and distribution channels for their work they have inevitably been monopolized by large technology firms.

The fundamental challenge, as viewed by our founder, is the lack of a medium for AI developers to learn, create, validate, monetize and distribute their creations to a global audience without an intermediary. The development cycle for Artificial Intelligence requires specific toolsets, large datasets and significant amounts of computing power which is oftentimes
difficult for both the budding and advanced developers to access outside the confines of large enterprise organizations,
research bodies and large tech firms.
This, in turn, led our Founder, Marcus Bowles, to reflect on the industry after speaking to numerous AI developers about the common challenges they experience when trying to create their own content.

The key takeaways were:
● Development cycles are often too expensive for an individual to build viable AI models & constructs.
● The ability to monetize AI content to a global audience is difficult, or non-existent.
● Data sets required to build viable models are difficult to find and costly to obtain at scale.
● Repeatable environments in which to build, iterate and test are difficult to manage.
● The types of systems required for AI development are typically only found in research environments and large tech
firms like Amazon, Google, Alibaba & Facebook.

These key takeaways led to the basic concept behind Koios - provide a globally accessible development environment for the AI community that allows them to innovate and monetize their skills whilst abstracting the common problems they face.

Subsequently, the challenges expressed by the AI community were distilled into technology requirements then the requirements were mapped to product concepts and the ideation phase for Koios was complete.

It was during this ideation phase and feasibility study for building Koios that three key components were identified that
needed to be catered to: a global, collaborative development platform, a distributed computing grid and a marketplace for AI content.

It soon became apparent that the ability to securely trade & store content whilst leveraging a global network of devices in an immutable fashion could be a suitable use case for blockchain.

Further research led to Koios choosing blockchain as a foundational technology for the entire platform.

• BLOCKCHAIN
• USER-DRIVEN
• COST EFFICIENT
• DISTRIBUTED
• SECURE
• SERVERLESS

What is the opportunity?
All industries across the globe are seeking ways to streamline processes, enhance capability and automate repeatable tasks using artificial intelligence. To get an understanding of the magnitude of the opportunity, a subset of samples are listed:

Problems with AI Adoption - Why isn’t AI commoditized yet?

It is important to note that Artificial Intelligence is a broad term used to cover a number of research fields and technology initiatives driving toward a singular goal; the ability for a machine to perform tasks that are characteristic of human intelligence such as planning, understanding language, recognizing objects and sounds, learning, and problem solving without human or programmatic intervention. The single biggest challenge with AI today is the total cost of ownership to build, test and production any viable AI models.

A simple chatbot can cost in excess of $300,000.00 USD to fully implement into a customers environment let alone more complex algorithms used in health, manufacturing, and aeronautics. Whilst there immediate monetary benefits to large organizations who can afford significant capital expenditure, it is the smaller companies, education institutes and startups who have yet to be able to leverage sophisticated AI in an easily consumable, cost-effective manner.

The cost of the technology is forcing business leaders across all industries and everyday consumers alike to an seek an alternative means to leverage these paradigm-shifting technologies.
The second challenge with AI today is human capital - quality AI developers are just hard to find. With the exponential growth of the market combined with increasing interest in the sector, it has lead to a shortage of qualified developers. Koios is well placed to address each of these challenges.

Private Sale

The ICO exchange rate for the Public Sale has been set at an exchange rate of 1:7500 for ETH:KOI.

The Private Sale will be capped at $10M USD with a tiered discount structure with a maximum of up to 30% dependent on contribution size.
The discount tiers are as follows:

Any remaining Crowdsale tokens from the private and public token generation event will be burned at the conclusion of the ICO.

Token and Fund Allocation

Crowdsale Caps
Soft Cap: 5M USD
Hard Cap: 40M USD

Funds Allocation
Category: Soft / Hard
Development: $2.5M / $20M
Marketing: $1.25M / $10M
Hiring: $750K / $6M
Admin/Legal: $500K / $4M

Allocation of Crowdsale Funds
Token Allocation
Symbol: KOI
Total Supply: 1,000,000,000
Team: 50,000,000
Community: 150,000,000
Crowdsale: 400,000,000
Koios Platform: 400,000,000

Koios offers you a project with mission and vision that is very promising for your future. You are presented with the latest breakthrough with Koios blockchain platform. Do not miss out on a chance to get a chance with koios.

some koios advantages over other blockchain platforms.

THE AI LAB
The "AI Lab" is an AI development platform providing developers with access to a library of complimentary tools, algorithms and data sets created and maintained by the Koios Team.

AI Lab will provide building blocks to not only accelerate AI Development but exposed via API for public or private consumption.

THE TITAN PROTOCOL
The "Titan Protocol" enables Koios desktop and mobile apps to participate in AI development by "renting" their CPU power to the Koios Platform. Known as "Fog Computing" everyday users can earn tokens on Koios by:

• Performing ML Model Training & Data Tagging Tasks
• Performing AI Validation Tasks
• Submitting Data to the AI Lab

THE NEURAL NET

The "Neural Net" component of the Koios Platform is a simple, intuitive marketplace containing a library of AI and ML content. All content submitted by developers will undergo technical review by the Koios Team to ensure it adheres to industry standards and is functional prior to being advertised on the marketplace. Developers can submit the following:

• AI constructs
• Machine Learning Algorithms
• Pre-Trained Machine Learning Models
• Structured and Unstructured Data

Some of the above points explain breakthroughs and differences from koios with other blockchain platforms. The problems and solutions offered by koios are very suitable to the needs of IoT users at this time. And this is a strong foundation for the success of koios in the future.

Do not waste your time, visit https://www.koios.ai/ to get bonus on private sale.

Website : https://koios.ai/
Bounty thread : https://bitcointalk.org/index.php?topic=3053547.0
Quick Pitch : https://storage.googleapis.com/koiosai/Koios_Quick_Pitch.pdf
Whitepaper : http://storage.googleapis.com/koiosai/Koios_White_Paper.pdf
Twitter : https://twitter.com/KoiosAI
Facebook : https://www.facebook.com/KoiosAI
Medium : https://medium.com/@koiosai

Author : 0x6b9e41aF4f0fABF7c736eB18473D803cE2C9e82e

https://bitcointalk.org/index.php?action=profile;u=1016988

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DataBroker DAO - Marketplace for IoT Sensor Data

DataBroker DAO unleashes the potential of currently inaccessible, low value
data into the creation of new transversal Value Added Services. It prevents
people with powerful ideas to rely on people with powerful operational resources,
in a phased, pay-as-you-grow, MVP model. As with the financial markets, where
the importance and value of company data have been apparent for decades,
giving rise to Bloomberg Market Data, Thomson Reuters, FactSet and a lot of
vendors, the same opportunity will emerge for IoT sensor data.
Doing so with a distributed foundational layer for the buying and selling of IoT
sensor data we expect that unimagined uses of the data will emerge to create
incredible value adding services that enrich the quality of life in our cities and our
societies as a whole.

DataBroker DAO is the first marketplace for IoT Sensor data that will connect
sensor owners with purchasers of the data directly, utilising existing
infrastructure from telecommunication providers operating sensor connectivity
networks based on GSM, LoRa, SigFox or via a proprietary gateway of the sensor
owner.
In a sense, the DataBroker DAO can be likened to a “secondary market” for IoT
sensor data and has been referred to as an “eBay” or “Amazon” for IoT sensor
data.

Stakeholders
There are a number of stakeholders in the DataBroker DAO including sensor
owners, gateway operators, data processors and data buyers. Below is a
definition of each of these stakeholders.
Sensor Owners
Sensor owners are the stakeholders who have purchased IoT sensors and make
the data emitted from their sensors available for sale via the DataBroker DAO
platform. This is a diverse group who have generally purchased sensors in order
to improve the efficiency of their operations.
The key role of Sensor Owners in DataBroker DAO is to sell the data from their
sensors on the platform.


Data Buyers
Data Buyers are those stakeholders who will purchase data on the platform. This
purchase may be to use the data in its raw form for their own purposes or to
purchase the data with the intention of transforming/enriching the raw data to
be resold with added value via DataBroker DAO (see Data Processor below).
The use of the data purchased by Data Buyers can be quite straightforward, for
instance, purchasing temperature and rainfall data provisioned by a neighboring
office building to have accurate local readings to the more complex, like
purchasing data to train one's AI.


Data Processors
Data Processors are those Data Buyers who purchase data with the explicit
intention of enriching the data and either reselling it or handling it for their clients.
The enrichment may take many forms and Data Processors can be categorized
by the level of insight provided :

● Simple data services are the most common. Data brokers collect data
from multiple sources and offer it in collected and conditioned form —
data which would otherwise be fragmented, conflicted and sometimes
unreliable.
● Smart data services provide conditioned and calculated data, with
analytical rules and calculations applied to derive further insight from the
collected data and aid the decision-making process. (e.g. Artificial
Intelligence)
● Adaptive data services apply analysis to a customer’s request-specific
data combined with data in a context store. This is a more advanced form
of service.

It is estimated that there are more than 5,000 data processing companies
worldwide relying on a vast array of open datasets published by government agencies and non-governmental organizations (Moore, 2016) in combination with their proprietary datasets and algorithms to enrich publicly available data.


These range from specialized boutiques, such as CB Insights, Fico, Intelius, etc.
to large global consultancies such as McKinsey, Deloitte, PWC. It is estimated
that 75 percent of analytics solutions will incorporate at least ten or more data
sources from second-party partners or third-party providers by 2019 .

It is expected that Data Processors will make up the majority of Data Buyers on
the DataBroker DAO platform.


Gateway Operators
The data emitted by the billions of devices deployed globally flow across a
wireless sensor network (WSN) operated generally (but not exclusively) by large
telecommunications companies in each country. This may be a traditional GSM
network, a LORA network or an alternative such as SigFox.
The key role of Gateway Operators in DataBroker DAO is to expose the gateway
they operate to enable sensor owners to sell their data on the platform.

Who “wins" in this story?

Sensor owners (data providers) are able to directly monetise their data to
generate passive income that will turn a sunk cost into a potential money maker
and at least the opportunity to recoup some of their investments in IoT sensors
(purchase, installation, maintenance, software licenses to interpret the sensor
data). A sensor owner will earn 80% of the amounts received and pay a small
recurring fee for putting the sensor on the platform.
Data buyers and data processors get data as a service so do not need to make
the upfront investment in hardware to get the data they require. Another
advantage for both buyer types is that DataBroker DAO provides access to data
that would otherwise be trapped in the data silos of sensor owners.

Gateway providers: gain scale and speed in the adoption of their network/devices as the partner connected to DataBroker DAO can present a win-back to their enterprise accounts, a clear USP. These gateway operators are the gateway through which the data flows to the DAO, and as such, they are also paid out immediately for each sale in the platform and will receive 10% of the fee. The DAO takes the remaining 10% of all funds, depending on market conditions, received on the platform to cover operating costs

Who “loses" in this story?


Sensor manufacturers: While manufacturers will increase sales in the short run
due to the higher profitability of IoT projects. However, on the longer term,
“sharing” sensors may reduce their day-to-day sales. This can be balanced by
higher replacements due to the higher amount of sensors in the field.
However, hardware margins are in a "race to the bottom” and are already razor
thin. Sensor providers already make most of their money via software and
services. From this perspective, the producer can pull resources and capital out
of unprofitable hardware manufacturing and allocate these to successful SaaS
offerings. From our discussions with manufacturers, they are very enthusiastic
about this prospect.

Who will sell data?

There are a number of data sellers identified and the overview of the sectors

already investing in sensors from Gartner highlights the key potential sellers of

data for the years to come. The diagram below identifies the 2 groups (business,

consumer) and the sub- groups that constitute each. It is clear that the business

group is the main driving force in sensor deployment globally.

The business group is led by the following sectors:

Manufacturing and Natural Resources: the so-called industrial IoT consists of

companies that are deploying sensors in order to improve operations. Their

primary purpose for deploying sensors is to improve the efficiency of operations

to reduce their cost base. DataBroker DAO presents the opportunity to sell

selected data that will not reveal to competitors specifics of their manufacturing

process.

TRANSPORTATION

The data for transportation consists of both traffic and vehicle specific data. Traffic data includes for instance congestion and for instance data for shipping of goods like temperature sensors in food shipping containers. This also includes sensors for managing public transportation such as trains and busses. Vehicle specific data includes a wide array of sensors in cars and trucks both personally owned vehicles and fleets measuring everything from CO2 emissions to speed to preventive maintenance.

UTILITIES AND GOVERMENT

Utility providers deploy sensors for “smart” utilities en-masse to deliver more efficient utility services to their clients including smart grids and smart meters primarily for electricity and water. Government sensors are also wide ranging including everything from water level sensors to detect flooding, air quality monitoring to smart street lights.

The DataBroker DAO Alliance

In the future we envision, the world where the DataBroker DAO platform will be

an integral part of the “IoT data”-fabric, there will be, more than ever, a need for

collaboration between the stakeholders in this ecosystem. These parties will

need to find ways to work together to further their collective businesses and

use-cases.

They formed the DataBroker DAO alliance to help facilitate this collaborative

ecosystem. Apart from gaining essential insights into the requirements of the

platform, we feel that guiding the stakeholders through this foundational change

in doing business together will be the linchpin in the further development of the

platform.

At this time there are 7 companies that have formally joined the alliance. They

include players in each of the stakeholder groups and are a good cross section of

the ecosystem.

OBSTACLE TO SUCCESS

The biggest obstacle to the success of DataBroker DAO and the full valorisation

of IoT sensor data is on the supply side of the equation. That is the adoption of

the marketplace by data sensor owners who are generating data. DataBroker

DAO enables sensor owners to sell their data directly to interested 3rd party data

consumers and are thus provided with the opportunity to recoup their sunk costs

for IoT sensor hardware and software (>600 billion USD today) incentivising

them to provide access to their proprietary data.

To overcome this obstacle, one of the first priorities will be hiring an experienced

team of enterprise sales profiles. Their focus will be to guide the gateway

operators through the sales cycle, onboard them into the DataBroker DAO

Alliance and push for the integration of the dAPI into their systems.

Below is an assessment of the current beta version of DataBroker DAO based on
these 6 pillars:


● Creating a central point of “discoverability”: the DAO pulls together data
that is otherwise locked in organisational silos controlled by the sensor
owners.
● Supporting interoperability: the DAO defines standard metaformats for
data descriptions and will integrate several processes to bring actual data
into standardised formats in the next iteration of the platform.
● Achieving consistent data quality: data streams come directly from the
gateway so there is no point in the process that is open to manipulation of
data. In the next iteration of the platform, a reputation system that allows
data buyers to provide feedback on data quality will be added to further
enhance the controls on data quality.
● Building an ecosystem: the DAO brings the stakeholders in the IoT sensor
data market together. It is the foundational layer of the ecosystem.
● Opening up new monetization opportunities: Sensor owners are
incentivised through direct remuneration from data buyers. In a future
iteration, the platform will introduce additional data enrichment and
display options that service providers can monetise through the platform.
The roadmap includes graphical packages from mapping to charts.
● Enabling crowdsourcing: Sensor data is crowd-sourced directly from
sensor owners.

The past few months the team has been hard at work to build the working platform. Leading up to the token sale we will open-source more and more of the code underlying the platform. Check out the beta version at https://beta.databrokerdao.com

THE DTX TOKEN ​(DaTa eXchange)

The DTX token is a utility token in the Databroker DAO platform . The DTX token is a ERC20 compliant token with 18 decimals. The token will serve as the credits

to buy and sell sensor data within the platform.

The MiniMe token

Apart from the initial use in the platform, the token is based upon the MiniMe standard.

A MiniMe token is easy to clone. This means it allows us to create new tokens

with an initial distribution identical to the original token at a specified block, either

to upgrade the token contract, or to create spin of tokens for e.g. governance.

The token Solidity code is available at https://github.com/DataBrokerDAO

Size of the market

To determine the market potential and future worth of the token we need to look

deeper at the potential market for IoT data.

The market size of the primary market for IoT sensors grew from a 600 billion

euro in 2015, to a staggering 900 billion in 2017. The market is projected to reach

1.3 trillion in 2020 and up to 1.6 trillion in 2024.

Initial value of the DTX token

The goal is to have 1 DTX token to cover the average value of the data from a

sensor for one week. This allows us enough granularity (at 18 decimals) to work

with micropayments, even after significant growth and price increases.

We determine the corresponding price per token by looking at the market

predictions in the previous section for 2024. At that time we project to have 2.5

billion USD flowing through the platform for 225 million sensors.

The average sensor has a value of ~12 USD per year, ~1 USD per month, or 0,25

USD per week and as such the value of 1 DTX token should equate initially to this

number. At an ETH price of 1000 USD / ETH, 1ETH will get you 4000 DTX tokens.

We determine the maximum number of tokens issued to be 225 million, the

amount of sensors on the platform in 2024.

5% is reserved for team incentives over the coming 4 years. The majority of this

team fund will be distributed to team members joining the project and will be

vested in stages over 3 years, and the unvested tokens return to the fund in case

the team member leaves the team. The rest is distributed to current team

members and advisors.

An additional 10% is reserved for the platform fund. The majority of these tokens

will be used to allow enterprise users to buy tokens using fiat currency to ease

adoption of these crucial users. This will happen if no other solution via

exchanges can be found, and gradually over the next 4 years as not to influence

the market.

Our earliest supporters, those who purchased the old DATA token, will get an

equivalent of their original ETH investment at current prices in DTX tokens at a

bonus rate of 60% to reward them for their trust in the project. This amounts to

6,5% in total.

30% or 67.500.000 tokens will be locked up until January 1st, 2021. Effectively

decreasing the available supply significantly for the foreseeable future.

0,5% of the tokens is reserved for the bounty campaign.

The rest, 108.000.000 tokens (48%) will be sold in to this sale event

TOKEN SALE

The token sale will accept purchases in ETH. The tokens will be delivered the

week after the sale completes.

Pre-sale starts March 19th, 2018 4PM CET

The presale phase of this token sale event starts on March 19th, 2018 at 4PM

CET. During this presale a 50% token bonus applies (6000 DTX/ETH) and the

minimum purchase amount during this period is 10 ETH.

Main sale starts March 26th, 2018 4PM CET

The main sale will start March 26th, 2018 at 4PM CET.

The sale will run for 4 week. The rate for this phase is 4000 DTX per ETH. ()

Only on the first day a 10% bonus will be awarded.

Before and during the token sale, a referral system is in effect. Contributions via a

referral link will result in a bonus of 5% of the tokens sold via a referral link. These

tokens are part of the locked reserve and do not increase or affect the total

amount of tokens, nor the maximum amount of tokens offered.

Unsold tokens will be kept by the platform. In case of a significant ETH rate

(1000 USD/ETH) change leading up to the sale will cause a recalculation

according to the same formula used above.

Trading starts April 30th, 2018

The tokens will be issued and be tradable 1 week after the sale ends (April 30th,

2018).

At this point in time we have a commitment to list the DTX token on

Chankura.com at that time and are identifying one or two more exchanges to list

it on, by that time.

Roadmap

MARCH, 19TH 2018 4PM CET

PRESALE

The presale phase of this token sale event starts on March 5th, 2018 at 4PM CET. During this presale a 50% token bonus applies (6000 DTX/ETH) and the minimum purchase amount during this period is 10 ETH.

MARCH, 26TH 2018 4PM CET

TOKEN SALE

The main sale will start March 26th, 2018 at 4PM CET. The sale will run for 4 week. The first day a 10% bonus will be awarded.

APRIL 30TH 2018

TRADING OPENS

The tokens will not be tradeable until 1 week after the sale ends (April 30th, 2018). At this point in time we have a commitment to list the DTX token on Chankura.com at that time and are identifying one or two more exchanges to list it on, by that time

Q2 2018

MAINNET RELEASE

With the availability and the ability to trade the DTX token, the platform can move to the mainnet.

Q2 2018

SETUP AND ONBOARDING OF AN EXTENDED TEAM

One of the larger challenges for DataBroker DAO will be scaling the team fast enough to cope with market demands. Onboarding a new sales team and additional developers is a daunting task. Since September this has been an active focus and this will be the case for the years to come.

Q2 2018

GATEWAY OPERATOR INTEGRATIONS

The main road to mass adoption is integrating with gateway operators that enable the onboarding of millions of sensors in one go. The DataBroker DAO platform will be integrated with the gateways of these gateway operators. We will be working on both common standards and libraries to ease integration, and perform the initial integrations for the first operators in the DataBroker DAO Alliance.

OFFICIAL LINK: https://databrokerdao.com/

ANN THREAD:  https://bitcointalk.org/index.php?topic=2113309.0

WHITEPAPER: https://databrokerdao.com/whitepaper/WHITEPAPER_DataBrokerDAO_ENG.pdf

TWITTER: https://twitter.com/databrokerdao

FACEBOOK: https://www.facebook.com/DataBrokerDAO/

TELEGRAM: https://t.me/databrokerdao

REDDIT: https://www.reddit.com/r/DatabrokerDAO/

BLOG: https://medium.com/databrokerdao

AUTHOR: https://bitcointalk.org/index.php?action=profile;u=1016988

ETH ADDRESS: 0x6b9e41aF4f0fABF7c736eB18473D803cE2C9e82e

 

BitSchool - The World’s First Education Platform integrating AI eLearning, Teaching and Tutoring

homepage= https://bitschool.io/#home



BitSchool is the world’s first integrated eLearning platform that provides seamless connection and synergy between AI eLearning, Teaching and Tutoring and will realize a perfect personalized learning eco system. 
Everything’s in the video so learn BitSchool and enjoy!

Personalized Learning is the ultimate goal for anyone in the education arena and this powered the recent surge of efforts to apply high technology such as AI to education or the emergence of various education platforms such as tutoring or MOOC. However, at BitSchool, we believe that a true and ideal individualized learning is hard to achieve by taking separate approaches and can only be attained through integrating AI eLearning with Teaching and Tutoring. This integration was based on BitSchool’s philosophy that the sum is always greater than its parts and look how it’s applied in the education reality.

1. Al assessment
Teacher/tutors run the Al Assessment quizzes which upon completion will produce the PLC (Personalized Learning Checklist) listing up the learner's weaknesses and improvement areas.

2. Al Adaptive Lesson Plans
Teacher/tutors use the Al lesson plan to design an individualized learning plan matching the learner's learning needs, style and pathway.

3. Al Mini Assessment
Teachers/tutors provide lessons and run mini Al Assessments in-between to check the learner's improvement and revise their lessons accordingly.

4. Al Dynamic Marking
Teachers (or even tutors) hold exams and use the Al Dynamic Marking System to mark questions and provide individualized feedbacks.

OPTIONAL

- Location (based tutoring)
Student/customers locate tutors in the nearest locations using the Location-Based Tutoring Service and schedule instaneous in-person tutoring sessions

- On-Demand Tutoring
Students use the On-Demands Tutoring Service (ODT) in or out of class to get quick, spontaneous help from tutors regarding specific topics, questions or assigments.

Teacheruse the ODT to outsource part of all of their masking task to stand-by paid tutors.

features:
Adaptive AI Assessment
Adaptively adjusts the difficulty of each question to ensure students are both challenged and motivated!


Adaptive Lesson Plans
Suggested lesson activities and customised timings depending on the needs of the class!


Adaptive Video Technology
Delivers customised videos tailored to the student’s learning needs, style and pathway


Automatic PLC Generation
Displays the precise knowledge level of each student down to the micro level of each sub-topic studied!


Dynamic Marking System
Enables class teacher, peer, paid tutor and automatic assessment of assignments seamlessly!


Gamified Learning
Levels, points, rewards and more are all used to boost engagement and motivation of students!


On-Demand Tutor Service
Anyone can request spontaneous micro-lessons for specific topics or problems where time is charged on a per-minute basis.


BitSchool Bidding System
The two-way Bidding System guarantees the best prices both for the students and the tutors!


GoGreenFund

Providing equal learning opportunities for low income students across the globe!

Blockchain is such an integral part to the BitSchool business model as it powers the launch, expansion and doing good of BitSchool! Blockchain provides the BitSchool startup cost through the token sale, allows BitSchool to expand via innovative Token Reward Programs and secured transparent transactions, and empowers BitSchool’s GoGreenFund to contribute to realizing equal learning opportunities in our world!

AI eLearning apps comprising over 6 innovative products

BUY

- Global tutoring service and products

- Spontaneous On-Demand Tutoring Service

- Integrated Web Solution for effective online lectures

SAVE

- 50% of AI eLearning apps purchase price

- 50% of Integrated Web Solution purchase price

- By Deep Purchase Discounts for the AI eLearning Wholesale Plan (School Clients only)

EARN

- By feeding learning questions, answers, and feedbacks to the AI Database

- By referring tutors, teachers, and customers to BitSchool

- By volunteering your tutoring talents

- By applying for Scholarships (required to be qualified as Low-Income Students)

- By leaving service or customer-experience reviews

- By contributing invaluable opinions or ideas on improving the BitSchool Platform

- By promoting BitSchool worldwide

FINANCIAL ROADMAP

PROFIT

Token Sale Plan

• Total Token supply: 400,000,000 Tokens

• Max Cap (Max Public Funding Target): 300,000,000 Tokens

• Min Cap (Min Public Funding Target): 4,000,000 Tokens

• Token Name: BitSchool Token (BSCH)

• Token exchange rate: 1ETH=6000 Token

• Funding coins: ETH

• Minimum purchase: 0.01ETH

• Official Smart Contract Address: Details of BitSchool’s smart contract address (wallet) to

receive Ether will be announced at least 48 hours before the start of the presale and

crowdsale respectively.

The Max Cap simply states a crowdsale goal of BitSchool and is not a requirement to start the

BitSchool Platform development and launch. In the event that we successfully hit the Max Cap,

we will have secured sufficient funds to develop advanced AI products that include machinelearning

capabilities, all the requirements of our tutoring-service platform, form our global

operations, and execute aggressive global marketing strategies. If the BitSchool crowdsale only

reaches the Min Cap, we will still be able to develop our tutoring-service platform and basic AI

prototypes, but may also have to rely on talent contributions and reaching out to other funding

sources.

Also note, when sending Ether to participate in the BitSchool Token Sale, participants shall

ensure that the spelling of the BitSchool smart contract address is correct or else the

participation will be invalid and participants will not receive any Token.

• GoGreenFund: 5% is allocated to ensure sustainable scholarship endowments by the

BitSchool GoGreenFund.

• Reserve: The 2% Reserve was set aside for contingencies and Token Rewards for the

planned BitSchool Pilot Quiz and referrals. Following the BitSchool Platform launch, the

Tokening rewards will come from the Reserve.

Unsold Tokens

All unsold Tokens will be donated to the BitSchool GoGreenFund and be used to provide

Scholarships to Low-Income Students and issue Tutor Volunteer Coupons for Tutor Volunteers.

Token Sale Timetable

Following is the Token supply amount and schedule. Additional Token will be given as bonus to

all presale participants and crowdsale participants who participate in week 1-3, and the amounts

are as below.

• Presale

Token supply: 4,000,000 Token (1% of total supply)

Presale bonus: 35% of Token purchase amount

Presale schedule: 26 Feb. 2018 09:00 (GMT) ~ 19 Mar. 2018 09:00 (GMT)

31

• Crowdsale

Token supply: 296,000,000 Token (74% of total supply)

Crowdsale bonus plan:

 1

st week (4/16 09:00 GMT ~ 4/23 09:00 GMT) 15% of Token purchase amount

 2

nd week (4/23 09:00 GMT ~ 4/30 09:00 GMT) 10% of Token purchase amount

 3

rd week (4/30 09:00 GMT ~ 5/7 09:00 GMT) 5% of Token purchase amount

 4

th week (5/7 09:00 GMT ~ 5/14 09:00 GMT) no bonus

Crowdsale schedule: 16 Apr. 2018 09:00 (GMT) ~ 14 May. 2018 09:00 (GMT)

Token Distribution

Token will be distributed to the investors’ wallets 15 days after the Token Sale is finished.

Public Fund Distribution

The Public Fund raised through the Token Sale will be stored in an offline cold multi-sig wallet

which will require three private keys out of four to authorize the release of funds. The four private

keys will be separately and independently held and managed by four core members of BitSchool,

and stored in four separate safe deposit boxes at a bank. Once the fund releases Ethers, they

will be exchanged into fiat currency which will be then deposited at a reputable bank and

executed according to the Public Fund distribution plan. To guarantee a transparent and accurate

distribution and execution of the Public Fund, BitSchool will outsource the audit of the Public

Fund management and execution process annually to an external auditor.

The following chart describes the Public Fund distribution plan:

• AI Products: The highest portion of 50% is allocated for the design and development of the

AI product line – Adaptive AI Assessment, Automated PLC Generation, Dynamic Marking

System, Adaptive Lesson Plan, Adaptive Video Technology, and Gamified Learning.

• Tutoring Platform: 10% of the fund is expected to be spent for the development of

BitSchool’s tutoring platform including but not limited to On-Demand Tutoring Service,

Location-Based Tutoring Service, IWS (Integrated Web Solution), Blockchain-Based

Transaction System and Token Reward Programs, and the Bidding System.

• Marketing: A significant fund of 23% is assigned to marketing expenses to run global

marketing researches, build and manage the BitSchool brand identity, conduct on/offline

advertisement and PR, and manage sales of AI products to schools, teachers and tutors.

• Operations: 10% is allocated to cover the business expenses to establish and manage

BitSchool’s global back-office functions including but not limited to Global Operations,

Customer Service, Office Spaces, and various overhead expenditures.

• Legal/consulting: 3% will cover the advisory expenses expected to be incurred in the setup

and management of BitSchool’s global operations.

• Security: The 2% security fund is set up to ensure consistent, thorough security of the

BitSchool Platform.

• Reserve: 2% is set aside for contingency purposes.

Blockchain Powered Platform

Employing the blockchain technology is a major differentiation factor for BitSchool as it willprovide a high level of transaction transparency and security that are realized through the decentralization of booking transactions and storing transaction data. Furthermore, blockchain enables Token transactions that will significantly improve the weaknesses and inconveniences of traditional payment systems like PayPal and provides a strong advantage against competitors offering similar online tutoring or eLearning platforms with traditional payment systems only. The blockchain-based Token allows the introduction of diverse reward and scholarship programs that will help not only expand BitSchool’s teacher, tutor and customer base, but also realize BitSchool’s Green Education Spirit.

Payment Method

- BitSchool combines a centralized client server architecture and blockchain-based decentralized

client server architecture. The centralized server environment accommodates a traditional

payment system like PayPal while the decentralized server allows transactions through Tokens.

It is expected for the utilization of Tokens to increase significantly once customers get familiar

with and interested in this new way of payment.

- Token

The Token is built upon the ERC20 Protocol on the Ethereum blockchain. Token is issued to

secure the required startup cost to develop and grow BitSchool and will be used in online

transactions on the BitSchool Platform. The Token BitSchool will issue through the crowdsale

will be named BitSchool Token with an acronym of BSCH. Customers can directly purchase

Token at Cryptocurrency Exchanges that trades Token, or earn Tokens via the Tokening

process.

- Token Utility

Token can be used to purchase tutoring service or related products at BitSchool. Additionally

Token can be used to purchase or renew the license of the AI products and the IWS.

Customers will be incentivized to pay in Token for these apps as the Token price will be fixed at

50% of the cash price for the first year following the launch of the BitSchool Platform (this

discount is subject to annual review and renewal). Token holders can also exchange Token to

cash or other cryptocurrencies at cryptocurrency exchanges that trade Token.

Founders & Team

The BitSchool team comprises teachers, tutors, students, software developers, global business

experts, blockchain experts, neuroscientists, Artificial Intelligence experts and parents,

passionate about developing excellence in education. The team has a combined over 50 years of

experience providing high quality education to a wide range of demographics.

Founders

Kiung (Tom) Ku

CEO / Co-Founder

Tom has extensive years of experience in the financial sector as a risk-management

expert before he received his MBA from Columbia University in 2007. After his MBA,

Tom worked for Ernst & Young, New York as a risk-management and –system

consultant and from 2010, founded his Top MBA Admission Consulting Firm and also

developed the Online Admission Consulting Platform (selfeditor.co.kr) in 2016. Tom is

an expert in Global Business, Education, and IT Design and Development.

Philip Leipper

CTO / Co-Founder

Phil is a Computer Science teacher in the UK with over 6 years of teaching experience.

He also has over 12 years of programming and development experience including full

stack web development. As a computer science major, Phil is a very talented IT

system designer and developer, and has worked for various IT projects. Most recently,

based on his teaching experience and IT expertise, Phil has designed and developed

the Adaptive Learning App built upon an Artificial Intelligence Algorithm (AI) and is also heading the design and implementation of the blockchain technology at BitSchool.

Team Members

Bad Edwards

CMO

Bad has worked in marketing, brand management, database marketing and market

research for telecoms, IT, satellite TV and fast consumer goods industries in UK and

Asia. She holds a Diploma in Computer Science, BSc (Honors) in Operational

Research with Computing from University of Leeds, UK and Postgraduate in ICT

teaching. Currently, the Head of Computer Studies at a UK high school and a teacher

in IT and Computer Science. Bad has been in the education industry for 15 years.

Evan Stanfield

Marketing Manager

Evan is a Digital Marketing Specialist, Web Analyst, and Graphic Designer with over a

decade of experience with expertise in print design, digital campaign creation, and

identifying correlations between numbers. With a background in brand marketing,

analytics and graphic design at one of the top education production firms in the

country and at one of the leading behavioral health providers in Los Angeles, Evan

has created skills that allow him to tap into mind of any target audience.

Deok Gun Park

Chief AI Architect

Duck received a BS in electrical engineering and an MS in biomedical engineering at

Seoul National University, Korea. Before coming to the US to obtain his PhD, Duck

mainly worked in IT Architect positions at IT startups. He will receive a PhD in

computer science at the University of Maryland in May 2018 and has interned at

Google, IBM Watson, and MS. His areas of research and expertise span Visual

Analytics, Text Mining, and Artificial General Intelligence. Duck will head and be the

core designer of BitSchool’s AI R&D Center.

Lina Kim

Executive Assistant / Market Researcher

Lina holds significant experience as a seasoned executive assistant at major

multinational corporations and also expertise in marketing research, brand

management and strategy, and brand communication. Her experience crosses diverse

industries including banking, consumer products and education.

Andrew Spero

Project Manager

Andrew is a computer science teacher with over 4 years experience at one of UK’s

leading secondary schools. He is passionate about making a difference to lives of

young people and has attained both a Master’s degree in education and is Prince2

qualified. Andrew has over 10 years of IT industry experience and oversees more than

400 employees as the Operations Manager within the company.

Matthew Bowen

Backend Developer

Matthew is a UK software engineer and computer scientist with over 10 years

experience. He has worked as a computer scientist at CERN contributing to the CMS

experiment, and then as a software engineer for GE Oil & Gas, developing robust

software deployed to high-risk natural environments. He has also spent 10 years

teaching Japanese martial arts to school children, as well as having volunteered for 5

years as the leader of the Debate & Current Affairs section of The Student Room.

Michael Leipper

Frontend Developer

Mike is a frontend developer with expertise in React, JavaScript and angular 2.0. He

has recently undertaken a 4-year computer science course at the open university

while working as a manager at a top UK betting shop demonstrating both his hardwork

attitude and willingness to succeed. Mike intends to specialize in big data

analytics which will form a perfect synergy for his future here at BitSchool. 

Michael Demetriou

Blockchain Developer

Michael is an engineer and entrepreneur with 10 years experience. He majored in

civil and environmental engineering at the University of Cyprus with a minor in

Management. His experience includes photography, car journalism, project

management, engineering, product design, and web/mobile/blockchain applications

development. His leadership roles include the co-founder of outofbounds.gr, design

head in nemomobile OS project and Maemo County council member.

Mohammed Hoque

Community Manager

Mohammed has 9+ years of experience working at top UK educational organizations

where he has gained extensive experience across various positions and functions,

perfecting his overall understanding and expertise of the educational systems and

processes. For the last 3 years Mohammed has been a customer service advisor

delivering outstanding customer support both externally and internally.

OFFICIAL WEBSITE: https://bitschool.io

WHITEPAPER: https://bitschool.io/files/whitepaper_en.pdf

BITCOINTALK THREAD: https://bitcointalk.org/index.php?topic=2886666.0

TWITTER: https://twitter.com/@BitschoolAI

FACEBOOK: https://www.facebook.com/BitschoolAI/

REDDIT: https://www.reddit.com/r/Bitschool/

MEDIUM: https://medium.com/bitschool

TELEGRAM: https://t.me/BitschoolAI

AUTHOR: https://bitcointalk.org/index.php?action=profile;u=1016988

ETH ADDRESS: 0x6b9e41aF4f0fABF7c736eB18473D803cE2C9e82e