Introduction Stellar is developing a high-tech decision-making solution based on the artificial intelligence system in the conditions of the new digital crypto economy. Combining a group of leading financial analysts, statistical data from attracted traders and a modern solution based on artificial intelligence based on neural networks, we create a solution in the form of AI for making decisions in managing the company's investments in the new promising market of crypto-currencies. The relevance and necessity of a decision making system based on AI for investors is: An

● analytical system for efficient management of capital with minimal risks for private investors or licensed companies;

● Monetization of the intellectual labor of analysts, as well as traders, regardless of their trading results;

● Signals for making financial decisions in conditions of market ambiguity; ● The current analysis of the world of digital assets, forecasts based on statistical

information, probable entry points to a growing market; ● Own actual indexes and ratings of digital assets, as well as a public interface of

signals. This document was updated on May 12, 2018.

Table of Contents 1. Introduction to Artificial Intelligence 1.1. What is artificial intelligence? 1.2. Areas of application of artificial intelligence 1.2.1. Finance 1.2.2. Heavy industry 1.2.3. Medicine 1.2.4. Human Resource Management and Recruitment 1.2.5. Music 1.2.6. News, Publishing and Writing 1.2.7. Online and telephone customer support services 1.2.8. Technical maintenance of telecommunications 1.2.9. Entertainment and games 1.2.10. Transport 1.2.11. Other areas of application 2. Issues of creating and using AI 2.1. Artificial Intelligence and Investment 2.2. Ecosystem of Artificial Intelligence 2.3. Analytics and Statistics 2.4. Artificial Intelligence 3. Token Sale 3.1. Feasibility of releasing tokens 3.1.1. Economic motivation of all ecosystem participants 3.1.2. Economic necessity 3.2. Token Sale options 3.2.1. Parameters of release of tokens 3.2.2. Distribution of tokens 3.2.3. Distribution of attracted funds after Token Sale 4. Economic model of the ecosystem 4.1. Products for tokens holders 4.2. Limited access to products 4.3. Trade portfolio of Artificial Intelligence 4.4. Pool for dynamic compensation of analysts and traders 4.6. Monetization of the contribution of analysts and traders 4.7. Infrastructure for funds and private investors 5. Technologies and products used 5.1. Technological infrastructure 5.2. Machine Learning 5.3. Data that we use for optimization 5.3.1. Preparation and verification of data. 5.3.2. Extraction of features. 5.3.3. Construction of hypotheses and mathematical models. 5.3.4. Optimization and confirmation of work models. 5.4. Description of confirmed hypotheses and approaches 5.4.1. Confirmation of correlation between analysts' forecasts and real market behavior

5.4.2. Approach to the development of mathematical models 5.5. Used technologies (libraries, algorithms) 5.6. Technological Roadmap 6. Used and developed analytical products 6.1. Binary questions 6.1.1. Macroeconomic developments 6.1.3. Political events 6.2. Price issues 6.3. Planned new analytical products 7. Team 7.1. Team and Competencies 7.2. Current achievements of the company 8. Legal details 8.1. Legal information 8.2. The legal status of tokens 8.3. The legal status of crowdsourcing forecasting platforms 9. Conclusion 10. Risks List of references

1. Introduction to artificial intelligence 1.1. What is artificial intelligence? The definition of artificial intelligence quoted in the preamble, given by John McCarthy in 1956 at a conference at Dartmouth University, is not directly related to the understanding of intelligence in humans. According to McCarthy, AI researchers are free to use methods that are not observed in humans, if this is necessary to solve specific problems. Explaining his definition, John McCarthy points out: "The problem is that until we can not in general determine which computing procedures we want to call intellectual. We understand some mechanisms of intelligence and do not understand the rest. Therefore, the intellect within this science is understood only as the computational component of the ability to achieve goals in the world. " At the same time, there is a point of view according to which intellect can only be a biological phenomenon. As the chairman of the Petersburg branch of the Russian Association of Artificial Intelligence TA Gavrilova points out, in English the phrase artificial intelligence does not have that slightly fantastic anthropomorphic coloring that it acquired in a rather unsuccessful Russian translation. The word intelligence means "the ability to reason intelligently" and not "intellect", for which there is an English analog intellect. Participants of the Russian Association of Artificial Intelligence give the following definitions of artificial intelligence: The scientific direction, within which problems of hardware or software modeling of those kinds of human activity that are traditionally considered intellectual are put and are being solved. The property of intellectual systems to perform functions (creative), which are traditionally considered the prerogative of man. At the same time, an intellectual system is a technical or software system capable of solving problems traditionally considered creative, belonging to a particular subject area, knowledge of which is stored in the memory of such a system. The structure of the intellectual system includes three main blocks - knowledge base, solver and intelligent interface, allowing to communicate with the computer without special programs for data entry. Science called "Artificial Intelligence" is included in the complex of computer science, and the technologies created on its basis to information technologies. The task of this science is the reconstruction with the help of computer systems and other artificial devices of reasonable reasoning and actions. One of the particular definitions of intelligence common to man and the "machine" can be formulated as follows: "Intelligence is the ability of the system to create programs (primarily heuristic) in the course of self-learning to solve problems of a certain complexity class and solve these problems." Let's look at the current solutions.

1.2. Areas of application of artificial intelligence 1.2.1. Finance Algorithmic trading Algorithmic Trade involves the use of complex artificial intelligence systems to make trade decisions at a speed exceeding the speed at which the human body is capable of. This allows you to make millions of transactions a day without any human intervention. Automated trading systems are usually used by large institutional investors. Market research and data mining Several large financial institutions have invested in the development of AI to use it in their investment practices. Developments of BlackRock 'AI, Aladdin, are used both inside the company and for the company's clients, assisting in making investment decisions. A wide range of functionality of this system includes the processing of a natural language for reading text, such as news, broker reports and social networking channels. Then the system evaluates the sentiments in these companies and assigns them an estimate. Banks such as UBS and Deutsche Bank use an AI system called Sqreem (Sequential Quantum Reduction and Extraction Model) that can process data for the development of customer profiles and compare them with products that they are most likely to , will want. Goldman Sachs uses Kensho, a market analytics platform that combines statistical computations with large data and natural language processing. His machine learning systems use data on the Internet and assess the correlation between world events and their impact on the prices of financial assets. The information retrieved by the AI system from live news broadcasts is used in making investment decisions. Personal finance management There are products that use AI to help people manage their personal finances. For example, Digit is an application based on artificial intelligence that automatically helps consumers optimize their expenses and savings based on their personal habits and goals. The application can analyze factors such as monthly income, current balance and spending habits, then make their own decisions and transfer money to a separate savings account. Wallet.AI, a San Francisco-based start-up, creates agents analyzing the data that the consumer generates, in cooperation with smartphones and social networks, to inform the consumer about their expenses. Financial Portfolio Management Automated assistant advisers are becoming more widely used in the investment management industry. Automated systems provide financial advice and advice in managing a financial portfolio with minimal human intervention. This class of financial advisers works on the basis of algorithms designed to automatically develop a financial portfolio in accordance with investment objectives and risk appetite for clients. It can correct changes in real time in the market and calibrate the portfolio in accordance with the wishes of the client.

Underwriting Online lender Upstart analyzes a huge amount of consumer data and uses machine learning algorithms to build credit risk models that predict the probability of default. Their technology will be licensed for banks so that they can use it to evaluate their processes. ZestFinance has developed its Zest Automated Machine Learning (ZAML) platform specifically for credit underwriting. This platform uses computer training to analyze tens of thousands of traditional and non-traditional variables (from purchase transactions to how the customer fills in the form) used in the credit industry to assess borrowers. The platform is especially useful for assigning credit scores to customers with a small credit history, such as millenniums. 1.2.2. Heavy industry Robots have become common in many industries and are often engaged in work that is considered dangerous for people. Robots proved effective at workplaces associated with repetitive routine tasks, which can lead to errors or accidents due to a decrease in concentration over time. Also, robots have been widely used in work that people can find humiliating. In 2014, China, Japan, the United States, the Republic of Korea and Germany together accounted for 70% of the world sales of robots. In the automotive industry, a sector with a particularly high degree of automation, Japan had the highest density of industrial robots in the world: 1,414 robots per 10,000 employees. 1.2.3. Medicine Artificial neural networks such as Concept Processing technology in software EMR, used as a clinical decision-making systems for medical diagnosis. Other tasks in medicine that can potentially be performed by artificial intelligence and begin to be developed include:

● Computer interpretation of medical images. Such systems help scan digital images, for example, from computed tomography, for typical manifestations and to highlight appreciable abnormalities, such as possible diseases. A typical application is the detection of a tumor;

● Heart rate analysis; ● The Watson project is another use of AI in this area. The program of questions /

answers, which was created to help doctors-oncologists; ● Robots-assistants for the care of the elderly; ● Processing medical records to provide more useful information; ● Create treatment plans; ● Assistance in repetitive tasks, including management of medication; ● Providing advice; ● Creating medicines; ● Using humanoid dummies instead of patients for clinical training.

Currently, more than 90 start-ups based on the use of AI operate in the healthcare industry.

1.2.4. Human resource management and recruiting Another application of AI is in the management of human resources and recruiting. There are three ways to use AI for human resource management and hiring professionals. AI is used to view the resumes and ranking of candidates in accordance with their skill level. AI is also used to predict the success of a candidate in given roles through job comparison platforms. Finally, AI is used to create chat-bots that can automate repetitive communication tasks. Typically, the process of viewing a resume includes the analysis and retrieval of information in the resume database. Startups, such as Pomato, create machine learning algorithms to automate the resume checking processes. The Pomato AI system is aimed at automating the verification of technical applicants for positions in technical firms. AI Pomato performs over 200,000 calculations per summary in seconds, and then develops its own technical interview based on useful skills. From 2016 to 2017, the consumer goods company Unilever used artificial intelligence to display all the entry level employees. AI Unilever used games based on neurobiology, recorded interviews and analysis of facial / speech signals to predict the success of a candidate in the company. Unilever collaborated with Pymetrics and HireVue to create a new AI-based analysis system and increase the number of candidates from 15,000 to 30,000 in one year. Unilever also reduced the processing time of applications from 4 months to 4 weeks and saved more than 50,000 hours of recruitment time. From screening resumes to neurology, speech recognition and facial analysis, it is clear that AIs have a huge impact on the scope of human resource management. The latest development in AI is to develop chats for recruiting. TextRecruit, released Ari (automated recruiting interface). Ari is a set of chat rooms for recruiting, which is designed for conducting two-way text conversations with candidates. Ari automates publication of vacancies, advertisements, screening of candidates, interview planning and development of relations of candidates with the company as they progress through the recruiting process. Ari is currently offered as part of the participation platform in the TextRecruit project. 1.2.5. Music Although the evolution of music has always been affected by technology, artificial intelligence has allowed to imitate, to some extent, human-like composition with the help of scientific achievements. Among the known early efforts, David Cope created an AI called Emily Howell, who managed to become famous in the field of Algorithmic computer music. The algorithm underlying Emily Howell is registered as a US patent. Other developments, such as AIVA (Artificial Intelligence Virtual Artist), focus on composing symphonies, mostly classical music for films. This development achieved fame, becoming the first virtual composer, which was recognized by the professional music association. Artificial intelligence can even create music suitable for medical use, Melomics uses computer music to relieve stress and pain.

Moreover, initiatives such as Google Magenta, conducted by the Google Brain team, want to know whether artificial intelligence can create an irresistible art. In the Sony CSL research laboratory, their Flow Machines software creates pop songs, learning the styles of music from a huge song database. Analyzing unique combinations of styles and optimization methods, AI can compose music in any existing style. 1.2.6. News, Publishing and Writing Narrative Science makes computer news and reports commercially available, including generalization of sports events based on statistics from the game in English. It also creates financial reports and analysis of real estate. Likewise, Automated Insights generates personalized summaries and previews for Yahoo Sports Fantasy Football. It is expected that by 2014 the company will create a billion stories per year, compared with 350 million in 2013. Echobox is a software development company that helps publishers increase traffic by "sensibly" posting articles on social networking platforms such as Facebook and Twitter. Analyzing large volumes of data, AI learns how specific audiences react to different articles at different times of the day. Then he chooses the best stories for publication and the best time to publish them. It uses both historical data and real-time data to understand what worked well in the past, as well as what is currently tending on the Internet. Another company, called Yseop, uses artificial intelligence to turn structured data into intelligent comments and recommendations in a natural language. Yseop can write financial reports, executive summaries, personalized sales or marketing documents and much more at a speed of thousands of pages per second and in several languages, including English, Spanish, French and German. Boomtrain is another example of AI that is designed to learn how to best attract every single reader to the exact articles sent on the right channel at the right time. It's as if you hired a personal editor for each individual reader to find the best articles for him. There is also the possibility that AI will write literary works in the future. In 2016, the Japanese AI wrote a small story and almost won a literary prize. 1.2.7. Online and telephone customer support services Artificial intelligence is implemented in automated online helpers, which can be viewed as chat bots on web pages. This can help enterprises reduce the cost of hiring and training employees. The main technology for such systems is natural language processing. Pypestream uses automated customer service for its mobile application designed to simplify communication with customers. Currently, large companies are investing in AI to handle problematic customers in the future. In the most recent version, Google analyzes human speech and converts them into text. The platform can identify angry customers through the features of their speech and react accordingly. Companies are engaged in various aspects of customer service to improve this aspect of the company.

Digital Genius, an AI startup, more effectively explores the database of information (from past conversations and frequently asked questions) and provides clues to agents to help them more effectively solve queries. IPSoft creates a technology with emotional intelligence to adapt the client's interaction. The answer is related to the tone of the client so that he can show sympathy. Another element that IPSoft develops is the ability to adapt to different tones or languages. Inbenta is focused on the development of natural language. In other words, understanding the meaning of what someone is asking, rather than simply analyzing the words used, using context and processing in natural language. One of the elements of customer service Ibenta has already been achieved is the ability to automatically respond and respond to e-mail requests. 1.2.8. Telecommunications Maintenance Many telecom companies use heuristic search on the board by their employees, for example, BT Group deployed a heuristic search in a scheduling application that provides working schedules for 20,000 engineers. 1.2.9. Entertainment and games In the 1990s, the first attempts were made to mass-produce home-oriented types of basic AI for education or recreation. This has greatly advanced with the digital revolution and helped people, especially children, learn about different types of AI, in particular, in the form of Tamagotchi and pets, iPod Touch, the Internet and the first widespread Furby robot. A year later, the improved type of home robot was released in the form of Aibo, a robotic dog with intelligent functions and autonomy. Companies like Mattel create an assortment of toys with AI support for children aged three. Using patented AI systems and speech recognition tools, they can understand conversations, give intelligent answers and quickly learn. AI is also used in the game industry, for example, video games use bots that are designed to play the role of opponents where people are unavailable or desirable. In 2018, researchers from Cornell University created a couple of generative and adversarial networks and trained them on the example of a DOOM joker game. During the training, neural networks defined the basic principles of building the levels of this game and after that they became able to generate new levels without the help of people. 1.2.10. Transportation For automatic transmissions in cars, fuzzy logic controllers have been developed. For example, in 2006 Audi TT, VW Touareg and VW Caravell use DSP a gearbox that is based on fuzzy logic. A number of models Škoda (Škoda Fabia) also currently includes a controller based on fuzzy logic. Today's cars now have auxiliary functions based on AI, such as self-loading and advanced cruise control. AI is used to optimize traffic management applications, which in turn reduces waiting times, energy consumption and harmful emissions by as much as 25 percent. In the future, fully autonomous cars will be developed. It is expected that the AI on transport will ensure safe, efficient and reliable transportation, minimizing the detrimental impact on the environment and society.

The main problem for the development of this AI is the fact that transport systems are inherently complex systems, including a very large number of components and different sides, each of which has different and often conflicting goals. 1.2.11. Other Areas of Application Various AI tools are also widely used in the areas of security, speech and text recognition, data mining and spam filtering in e-mail. Also, applications are developed for the recognition of gestures (understanding the sign language by machines), individual voice recognition, global voice recognition (from a lot of people in a noisy room), face recognition for the interpretation of emotions and non-verbal signals. Other applications include robotic navigation, obstacle clearance and object recognition. The combination of artificial intelligence with experimental data accelerated the creation of a new variety of metallic glass 200 times. The glass nature of the new material makes it more durable, lightweight and corrosion-resistant than modern steel. The team, led by scientists from the National Laboratory of Accelerators SLAC of the Department of Energy, National Institute of Standards and Technology and Northwestern University of the United States, reported a reduction in costs for the detection and improvement of metallic glass for a fraction of the time and cost. According to a representative of the development group, Apurva Mehta: "We were able to make and select 20,000 options in one year." 2. Issues of the creation and use of AIAI Among theresearchers there is still no dominant point of view on the criteria of intellectuality, the systematization of the goals and tasks to be solved, there is not even a strict definition of science. There are different points of view on the question of what to think of as intelligence. The most heated debate in the philosophy of artificial intelligence is the question of the possibility of thinking the creation of human hands. The question "Can a machine think?", Which prompted researchers to create a science about the modeling of the human mind, was delivered by Alan Turing in 1950. Two main points of view on this issue are called the hypotheses of strong and weak artificial intelligence. The term "strong artificial intelligence" was introduced by John Searle, his approach is characterized by the same words: "Moreover, such a program will not just be a model of reason; in the literal sense of the word

itself will be the mind, in the same sense in which the human mind is the mind. " At the same time, one must understand whether a" pure artificial "mind (" meta-reason ") is possible, understanding and solving real problems and, together with that, devoid of emotions, characteristic for a person and necessary for his individual survival. On the contrary, advocates of weak AI prefer to consider programs only as a tool that allows to solve certain tasks that do not require a full range of human cognitive abilities. The thought experiment "The Chinese Room" by John Searle is an argument in favor of the fact that passing the Turing test is not a criterion for the machine to have a genuine thinking process. A similar position is taken by Roger Penrose, who in his book "The New Mind of the King" argues the

impossibility of obtaining a process of thinking on the basis of formal systems. To date, the financial market uses a system of group pools of financial companies whose transactions are executed together. And this trend every year goes to strengthening due to the success of many companies participants. In addition to such group pools for the execution of warrants, to which the funds and private large investors are participants, there is an annual investment pool Angel List. What is the reason for the desire of many investors and companies for such a group entrance to the market? The main prerequisite for such a model of performance is the collective intelligence of counteracting risks from possible inaccuracies and erroneousness of collective thinking, since a single investor can make the wrong decision based on erroneous or false information presented as an insight or false trend, as well as possible inexperience in the chosen industry or a shortage information on the current state of the financial product with which it will be operated. In the group decision-making, the experience and opportunities of all participants is diverse and gives an opportunity to view the product and area of financial activity from different angles, and if there are any doubts, refuse to execute the transaction. As practice often shows, if there is any doubt, the best solution is the absence of a solution. The best and most progressive solution will be a symbiosis of the collective solution and experience in the form of statistical data superimposed on the matrix of inputs of the neural network, which in real time, based on a variety of data and signals, as well as the results of past decisions of many investors and the network itself, conducts an instant assessment of the economic component operated financial product, signals to the user about making a decision to buy or sell this asset, not relying on emotions or other external irritation And guided only by the experience of many such decisions on the basis of statistical information for other users. It's no secret that at the moment the main prerequisite for making decisions for many investors is also emotions. 2.1. Artificial Intelligence and Investments Financial markets are without doubt the main movable force for the development of artificial intelligence. The specificity of working with finance is that traders have to analyze and make decisions on millions of volumes in a short time based on personal experience and a lot of conflicting information, as well as courses, financial indicators. In essence, working with financial instruments is often a prediction of the probability of changing the chosen solution of the trader in the right direction. When and how best to buy shares, oil, dollar or Bitcoin? In all these areas, analysts and traders predict results every minute. At the current moment, consider that all analytics of financial markets, the forecast of changes, are created by analysts on the basis of the same algorithms and methods and based on the same information in different interpretations of the same data.

Annually new tools and products are being created for obtaining analytics and signals, but unfortunately the relevance and effectiveness of such reports is decreasing, the number of traders who use them every year is less and less, as most create their own strategies and use their expertise for decision-making . But the paradox is that many companies allocate huge budgets for such reports. For example, only in 2015, private traders and companies spent more than $ 50 billion on these reports. As many companies point out, the demand for such reports and their costs are growing every year due to the emergence of new financial instruments, information and experience in the operation of which it is very difficult to obtain at short intervals. One of the famous examples - in the USA, 54% of residents bought at least once in their life shares, and also from the 80s more than 85% of housewives had shares of various companies, in the same China, forex trades more than 30% of residents, while the amount of operation in the market is from 10 000 USD to 500 000 USD. 2.2. Ecosystem of Artificial Intelligence About a hundred regular and attracted analysts provide the Stellar system with a variety of economic forecasts, answering a number of topical questions regarding the financial levels of various assets, economic indicators and events affecting them. Examples:

● Forecasting the bitcoin rate for the next 7 days and other most popular currencies; ● Change in the value of shares of leading companies; ● Changes in US employment indicators every first Friday of a new month; ● Will the company raise funds in the first week of the ICO? ● What world news and how did it affect the market?

All this statistical information and a set of analytical data is transferred for processing into a mathematical data processing module, implemented on the basis of possible AI training algorithms:

● Data cleaning; ● Clustering; ● Linear regression; ● Bayesian models; ● Hosting; ● Random forest; ● Decisive trees; ● Genetic algorithms; ● Neural networks.

This module takes care of data processing, finding the weight of each entry point, determining the quality and success of the analyst's forecast and the frequency of its successful forecasts, finding the pattern of errors, clearing of false signals and signals having influence. After the completion of all these activities, the block conducts the creation of a single trade forecast

based on all data and reflects the likelihood of successful actions for the selected financial direction. The fundamental strategy for the implementation of our system is the symbiosis of Artificial Intelligence and groups of analysts with traders who provide independent of each other, providing statistical data and analytical forecasts for processing by the Artificial Intelligence module. Let's consider all these elements in more detail. 2.3. Analytics and statistics We work with the received statistical data and analytics from our and third-party analysts and traders to follow the principles of working with the information received. The tasks that we set for our employees should be simple and easy to test, as it is impossible to obtain correct results by asking unimaginable questions, the solution of which can go beyond human capabilities. As an example, we do not ask you to forecast a change in the exchange rate of the instrument for a year or more, even six months in the financial industry can not be planned because many factors can influence the situation, it is most optimal to track changes from day to week, catching factors and signals that affected the market fluctuations . Each prediction or result has sufficient value regardless of its result, since incorrect derivation and the decision made on its basis can be used to train the system for its non-repetition. Employees of our company and third-party attracted analysts working in our team, united in a single system, have a variety of experience, knowledge, a view of things. But despite the final merger of all the results into one for further processing, all our employees who provide statistics and analytics do not overlap together, because the influence of one of the employees can overlap the view of the other and thereby give erroneous data. This will not allow us to obtain a variety of independent points of view for the same event, thereby obtaining the greatest number of factors and probabilities. One of the main moments of work of our company with involved analysts and traders is motivation and involvement in the system, to provide the most accurate data and the desire to more rigorously and responsibly conduct analysis. We use the following levers of influence:

● Financial reward in proportion to the success and reliability of forecasts. The higher the percentage of correct forecasts, the greater the employee's monetary reward.

● In the future, we implement the internal rating system and additional rewards for the first 10 and 100 analysts, as well as other incentives.

● Involvement in the creation of a product. Each of our employees in the future will receive a part of the company's profits according to his participation and the invested forces and time.

● Personal development. Since giving the opportunity to create forecasts and get paid for it and encourage for the most successful and accurate, we force many to more scrupulously study the subject and expand their view on things and factors having an impact on the market.

2.4. Artificial intelligence All the data we receive from our employees for training the system are the first most monotonous and to some extent difficult stage of the system operation since all data in the current form can not be used and it is necessary to spend time for their correct conversion to the machine view for further study of them by AI Stellar modules. To do this, we use the following elements: 1. Analyzing and determining the degree of accuracy of the obtained analytics and statistical information that is dynamic and passes also additional verification and internal change for each source according to the following parameters: The

● percentage of accuracy of each of the signals according to its type and subject; ● Evaluation of the result of the analytical forecast or statistical transaction according to

the forecasted results in the real model; ● The presence of a similar pattern obtained earlier for analytics and statistics for its

subsequent systematization. 2. Imposition of existing trading strategies and reaction models on the market in order to identify regularities and the most successful scenarios, taking into account the minimization of risks and forecasting the system's actions on such factors. For this we: We

● constantly search for and analyze existing strategies on several sites simultaneously; ● We study new methods of creating strategies, as well as hypotheses of working with the

market. 3. Token Sale By launching the ICO process and releasing our tokens, we engage all interested users, investors and funds, together with the Stellar team, to create new unique projects that respond to the trends and needs of the new digital economic component, which will provide all participants with effective tools for operating capital and information. Stellar products that will also be launched in the future based on the analysis of AI are:

● Insurance service; ● Independent decentralized bank; ● Crypto Exchange; ● Service of general pools master.

Each of the ICO participants will also receive a certain percentage of participation in subsequent projects of the company and its products according to the number of tokens that it owns. Stellar tokens will be placed on stock exchanges and sites, providing the opportunity to purchase them to all users where possible and does not contradict the norms and laws. Each of the coin holders receives the right of access to the company's products and equity participation in its subsequent development, and also has the opportunity to transfer their coins to other interested persons.

Tokens can not be sold to residents of the United States, Singapore, China and other countries where the sale of a token may require registration as a security, unless permitting changes were made at the time of the posting of the tokens. 3.1. Expediency in the release of tokens The launch of third-party investment by conducting an open ICO is due to the need to create a more stable economic system for the internal economy of the project and to develop it more quickly and enter a new level of operation that will allow the Stellar team to create a more flexible and efficient work model, the implementation of other relevant products in conjunction with the company's investors. 3.1.1. Economic motivation of all participants of the ecosystem. Transparent model of financial management based on decentralized solutions. Blocking fits all the parameters of the system and complements the product, thereby allowing us to perform quality and open work not only with our investors, but also with attracted analysts and traders, allows us to create a model motivation of long-term cooperation and partnership of not only third parties, but also employees of the company, which allows to deduce the productivity and efficiency of the company AANII to a higher level, in which each of the interested parties. For fair and qualitative motivation of the project participants, both current and future, we use a system of qualitative assessment of both the team and the individual to contribute to the development of the product and the results achieved in general. This can be: a high-quality statistics of trading results, a sound and detailed analytics with a high percentage of achievement of predicted results, intellectual work on the introduction of new algorithms and methods for processing these strategies, programming and optimization of the entire system as a whole, and its provision and popularization of the development of AI technology and products on the base. Based on the above, a part of the funds received by raising capital will be placed in a separate new trading pool under the management of the system, the profit from which will go to replenish the dynamic motivational portfolio, which will be distributed among all participants in the system in proportion to their internal motivational rating. In this way we implement an additional motivational strategy for the success of the current product of the company and all its subsequent developments. This motivation is applicable only for company employees, attracted analysts, traders and does not apply to holders of Stellar tokens. 3.1.2. Economic need Starting in 2015, after implementing the test model of the system, we negotiated with many foundations and private investors on cooperation in developing an AI model with work in classical markets. From the beginning of 2016, having evaluated the prospects of a new digital assets market, we began to introduce the model into existing trading platforms to monetize our technology by using the API of existing services. The main problem at the beginning of our work with digital assets was the questions of scalability, as well as the availability of data for training

the system, since not all the classical approaches used for stock trading were applicable to the digital market and not all worked correctly, and for many, this market and its prospects raised questions and doubts. Due to doubt, many investment funds were skeptical about our design and the idea of working with digital markets, or offered us the purchase of the company for inappropriate means and engaging the development team in more urgent and urgent problems, in their opinion. But a sharp increase in the exchange rate created interest among new investors, which allowed us to obtain the necessary capital for an active start of the fund's work with current ready-made strategies and behavior models. The results obtained gave us the opportunity to continue testing and debugging the model and become independent developers. Creating tokens is a new step in the technological development of our infrastructure and the model of the system as a whole. Companies and private investors who will be ready to buy our service subscription will receive a unique product:

● API; ● Analysis and forecasting module; ● Module for searching and identifying prospects; ● The module of trade in which the APIs of leading exchanges are integrated.

All these products will be combined into one user interface, processing and operating a multitude of data streams in Real-Time mode. For the use of our product, the most optimal model that we see will be the service charge and the percentage of turnover carried out by our system. The percentage for the maintenance of the system will fully go to the pool of motivation of the participants in our system. The complete readiness of our entire system is planned for 2019. To maintain the effectiveness of our technology and its further development, we propose to obtain a joint income - owning coins Stellar. 3.2. Token Sale options 3.2.1. Token release parameters of tokens Stellar will be released on the Ethereum blockbuster platform using the ERC20 protocol. The primary location of the tokens will begin on March 1 and continue until May 1. Before the open sale, we plan to pre-sell the tokens in several iterations. Perhaps all tokens will be sold through these stages before the start of open sales. In this period, 100% of tokens will be released. Available ways to buy: ETH, Bitcoin, as well as available EPS. Price 1 token = $ 0.01 Maximum target amount = $ 10,000,000. 3.2.2. Token distribution Tokens will be distributed as follows:

● 75% - To buyers of tokens; ● 15% - Stellar; ● 3.8% - for Advisers and partners; ● 1% - Bonus program; ● 5% - To analysts and traders of the system according to their total rating.

3.2.3. Distribution of funds received after Token Sale The funds raised will be distributed as follows: 45% - Budget for the continuation of scientific work, infrastructure development, release and promotion of new products, development of the AI platform: 1) Development and development of such things as:

● Data Science; ● Machine Learning; ● Modules AI; ● Mobile applications; ● Web-based versions; ● Products; ● API; ● Hosting services; ● Server power.

2) Trading:

● Trading services and terminals; ● Development of trading algorithms and infrastructure.

3) Operating expenses:

● Salaries; ● Rent of offices; ● Operating expenses.

30% - The financial pool of AI Stellar for the continuation of the technology, the accumulation of statistical trade data and a motivational pool for employees and analysts of the company, as well as paying dividends to investors. The results of trading this portfolio will also serve to accumulate the history of transactions, which will contribute to the growth of interest and demand for Stellar products in the professional market of investors and traders. 10% - Marketing: promotion of the AI platform and similar solutions along with Blockchain technology. 5% - The services of lawyers, the creation of a structure for the protection of investors' rights, as well as the patenting of technology. 5% - Monthly fund for analysts and traders. 5% - Funds for possible purchases of new products or modules.

4. Economic model of the ecosystem 4.1. Products for Tokens Holders When selling coins, we offer future token owners access to a portion of the infrastructure being developed, their future profits and other projects being developed in the future. Holders of coins will also be able to obtain a different level of access to Stellar's analytical products and the ability to make decisions on system development by voting. The owners of coins will have access to the following products:

● Indicators of traditional and crypto-markets with a more understandable and friendly interface;

● Auxiliary products for trading, notification and monitoring systems; ● Analytical products; ● Market indices generated by AI Stellar; ● The profit according to the purchased coins from the operated pool in the amount of

30% of the collected funds during the ICO. The above analytical tools will have the format of news and information tools for additional market analysis in the process of making an investment decision. The bulk of the end product, intended for capital management, will remain in the management and access of the Stellar team and attracted investment funds. 4.2. Limited access to products The level of access and the set of tools will be limited and implemented for Stellar coins, as well as on a paid subscription basis and percentage of the turnover of operations performed by the Stellar AI module. A certain part of the products will be available to the holders of the tokens in accordance with their level of balance. The exact formation of access levels will depend on the results of sales and determined by the appropriateness for the domestic economy of Stellar. We will publish access levels to our products after the completion of the ICO. All products will be implemented in various ways as the work on each of them is completed, combining into one universal module:

● Daily / weekly / monthly dispatch of indicators; ● Personal cabinet with a functional view of indicators, data and AI analytics on various

events; ● Mobile app; ● Access by API.

4.3. Trade portfolio of Artificial Intelligence To develop the technology of AI and to improve the quality of analytical products Stellar will create a trading pool of AI. The pool will be divided into several parts to cover the most interesting trade areas, as well as for risk diversification, all parts will be managed by the following strategic modules:

● Active trading based on Stellar technologies and received data from the AI module. The share of this part of the portfolio will depend on the liquidity and volatility of the market.

● Passive portfolio of instruments using the most tranquil and promising directions in the future, or protected from sudden fluctuations and jumps.

The task of AI under the control of analysts is to determine the optimal ratio of the operated products and tools to minimize and at the same time maintain financial results at a sufficient level. All portfolio results will be reflected in Bitcoin, as well as in relation to the currencies according to the current exchange rate. Management of this pool will begin within 3 months after the end of sales. By this time, the entire infrastructure, accounts / accounts and legal structure will be completed. Management of this pool will be carried out by AI under the control of traders and analysts of Stellar, who will use the data signaling the entrance to the market received from the module AI. Active portfolio implies more aggressive short-term transactions, passive will conduct long-term investment activity. Criterion for applying this or that strategy and allocation of funds in pools, in this or that asset, will be the signal of AI, as well as successful confirmation of the group of analysts and traders. Our team will compile detailed monthly reports on the conducted transactions and their results and publish them for the community. 4.4. A Pool for Dynamic Remuneration of Analysts and Traders A motivational pool will be created for Stellar's stable domestic economy. Tokens of this pool will be used to encourage Stellar's analysts and traders, as well as other employees of the company, who will bring meaningful results to the development of AI. Tokens pool to encourage analysts and traders will flow into the pool from the sale of Stellar products. Each quarter, we will publish reports on the portfolio managed by AI to reward analysts and traders for their intellectual contribution to the development of the system, as well as investors who bought tokens. In the case of positive results in relation to the initial state of the portfolio for each period, we will distribute the profit as follows:

● AI% - This percentage stays in the portfolio of AI Stellar, ensuring its growth for the next period;

● TS% - Fee for maintenance and maintenance of Stellar;

● Z% - Tools for replenishment of dynamic motivational pool; ● C% Dividends to holders of tokens.

4.6. Monetizing the contribution of analysts and traders Analysts and traders who create the information base for Stellar training are an important part of the system, as their data is an important part of development. To effectively develop the system, it is necessary to support the personal motivation of analysts and traders, as well as create an overall development strategy. The Stellar system provides an opportunity for analysts and traders to monetize their intellectual work in analyzing markets and predicting their behavior and reactions to irritants. This component is an integral part of the product of AI Stellar, in which analysts and traders can use their intellectual experience to receive both financial reward and moral from the contribution to an interesting product and doing what they love. Each analyst and trader in our system, creating analytics and generating statistics, creates his own personal rating, which depends on the accuracy and correctness of the data provided. The rating of all users is dynamic and can change both in the positive and negative aspects. The rating of analysts and traders is currently closed, in order to obtain more accurate data and to exclude human factors, in the future it will be public after the necessary database is accumulated. Fixing the rating takes place on the basis of the results of each month and the most accurate receive additional motivation in the form of financial reward from the company's internal fund. Monthly compensation is formed from the reserve fund of remuneration of analysts and traders and depends on their quantity and available capital of the company. At the end of each month, this rating is reset, but there is also a global rating for additional analysis, which is from the moment the analyst and trader begin work. The goal of the group of analysts and traders is directly related to the result of the financial activity of the AI Stellar, they are an integral part of his work. 4.7. Infrastructure for funds and private investors The main objective of our project is to create a complete infrastructure for funds or private investors that will give them access to a unique set of tools that will enable them not only to receive analytics but also to perform financial transactions within the same interface with access to the leading trading floors. Users who purchase this technology (not considering the purchase of tokens) will pay monthly a maintenance fee and a percentage of turnover made through the interface of the AI module, to which the dynamic pool of motivation of all active participants of the system will replenish. The number of funds and private investors who will be able to access the full infrastructure will be limited in the first stage for the purpose of preliminary additional testing of the infrastructure at each site. 5. Technologies and products used

5.1. Technological infrastructure The Stellar system already implemented at the time of release of the tokens consists of several main modules. Business logic module:

● A system with the main business logic of working with events; ● Administration system; ● Investment system; ● Mobile apps (iOS + Android in development); ● Web application.

Forecast module:

● Data sources; ● Processing and cleaning of incoming data; ● Identification of irritants; ● Identification of patterns and the imposition of mathematical models; ● Optimization of information processing; ● Generation of forecasts.

Trade module:

● Signaling from AI about probable opportunities and precedents; ● API connection to exchanges; ● Practical testing of several models of AI work;

5.2. Machine Learning The purpose of Machine Learning is to use data from analysts and traders to compare them with market behavior and predict similar situations in the future, identifying patterns. There are such basic directions:

● Training with the teacher - for each precedent the pair "a situation - the required decision" is set: 1. Artificial neural network;

1. Deep training; 2. Method of error correction; 3. Method of back propagation error; 4. Support vector method.

● Training without a teacher - for each use case, only the "situation" is specified. It is

required to group the objects into clusters using data on pairwise similarity of objects, and / or to reduce the dimensionality of the data: 1. Alpha-reinforcement system; 2. Gamma-reinforcement system; 3. The nearest-neighbor method.

● Training with reinforcement - for each precedent there is a couple of "situation-the accepted decision": The 1. genetic algorithm.

● Active learning is distinguished by the fact that the learning algorithm has the ability to

independently designate the next situation under investigation, on which the correct answer becomes known.

● Training with a partial involvement of the teacher.

● Transductive education - training with partial involvement of the teacher, when the

forecast is supposed to be done only for precedents from the test sample.

● Multitasking is the simultaneous training of a group of interrelated tasks, each of which is given its own pairs "situation is the required solution".

● Multivariate learning is learning when precedents can be grouped together, in each of

which there is a "situation" for all precedents, but only for one of them (and, no one knows), there is a pair of "situation is the required solution".

● Boolean is a procedure for sequentially constructing a composition of machine learning

algorithms, when each subsequent algorithm seeks to compensate for the shortcomings of the composition of all previous algorithms.

● Bayesian network.

How information is processed. Stage 1. Study of analysts and traders:

● Identification of the behavior model; ● Identification of regularities; ● Distribution of analysts and traders by groups: bears or bulls, work by price levels,

analyze the market, operate with a trend, use technical, fundamental analysis; ● Frequency of errors; ● On which market the main reaction is volatile or calm.

Stage 2. Data processing for each of the groups. Stage 3. Testing the models and initializing the procedure for sequential construction of the composition of machine learning algorithms. Stage 4. Analysis of time intervals of the market and the imposition of the obtained analytics and statistics on them. Stage 5. Optimization of machine learning models and their parameters. 5.3. Data that we use to optimize

Data that we have:

● Data analytics (gender, age, country, work strategy, education, professional experience); ● Analytics and trade statistics on digital currencies; ● Historical market data.

We use the classical model of machine learning. 5.3.1. Preparation and verification of data The most important part of working with the data obtained is the processing and preparation of data - since the source of errors is a person, then do not forget about the probability of errors due to human factors, this may be an incorrect number order or the specified tool. The presence of such errors can create a false shift in the logic of the system or blur its perception. At the moment the most relevant methods are: IQR, Grubbs Test. 5.3.2. Extraction of traits Each trader, analyst and financial instrument is characterized by its signs and patterns that can be identified and systematized. Our algorithm developed on the basis of Training with reinforcement, which is one of the methods of machine learning, conducts the identification of signs and regularities and also establishes the estimate and weight of each of the found. 5.3.3. Building hypotheses and mathematical models The models that we use in our work:

● Wisdom of crowds; ● Bagging; ● Busting (Schapire, 89); ● AdaBoost (Freund & Schapire, 1996).

5.3.4. Optimization and confirmation of work models All the resulting algorithms and models of behavior are obtained by linking to one of the tools that were processed. At the moment we are also working on the creation of a universal algorithm. Each model and algorithm, depending on the operating tool, has its own parameters, as well as a library of regularities and weights of annoying factors. How do we determine that the model obtained is a good model? We should be able to assess its predictive performance in some way. Valuation indicators are measures of a predictive model with the ability or accuracy. Some of them are direct measures how well the model predicts the objective of this model is variable, for example, the root-mean-square error, while others are concerned about how well the model performs in prediction things that can’t be directly optimized in the model, but often closer to to what we see in the real world. Valuation indicators provide a standardized way of comparing the

performance of the same model with different parameter settings and comparing characteristics of different models. With the help of these indicators, we can perform a model selection to choose the most effective variant of the model from the set of models that we want to evaluate. We use the following methods:

● Root Mean Squared Error (RMSE); ● Mean Squared Error; ● Mean Average Precision at K.

5.4. Description of confirmed hypotheses and approaches 5.4.1. Confirming the correlation between analysts 'forecasts and real market behavior To identify and determine the correlation between analysts' forecasts, the actions of traders and real market behavior, we use the basic mathematical algorithms and functions that, as practice has shown, reflect the correlation between forecasts and the real state of the market. This allows us to improve our mathematical model, the task of which will be to extract data from forecasts and statistics. 5.4.2. Approach to the development of mathematical models All our analysts and traders have a unique and individual approach to forecasting and analyzing financial markets, each of the individuals has both strengths and weaknesses, ranging from the vastness of the operated instruments and understanding of the correlation between them, and ending with the possibility of evaluating and perception in different time intervals. Each of the analysts and traders we systematize according to the parameters in which it operates best:

● Time range; ● State of the market; ● Work with the trend; ● Finding the correlation; ● Operating with news.

The systematization and construction of additional algorithms on these parameters also allows improving and improving algorithms and learning models. Approaches of machine and in-depth training that we use:

● Bayesian approach; ● Bayesian networks of trust; ● HMM; ● Models for building a booster; ● Build regression models;

● Creation of algorithms for clustering and separation of incoming data; ● Imposing results on historical data.

5.5. Used technologies (libraries, algorithms)

● Languages: Python, Scala, R, C #, GO, Java; ● Libraries: numpy, scipy, pandas, sklearn, matplotlib, seaborn, keras, theano, xgboost; ● Algorithms: regressions, clusterizations, ARIMA, Boosting, Decision Tries, Random

Forest, Deep Learning. 5.6. Technological Roadmap In the future, as technology advances, we plan to:

● Improve the methods of Neural Networks and Deep Learning; ● Implement on the basis of Reinforcement Learning a single trading interface with an API

connection to leading sites; ● Improve mathematical models for constructing market predictions; ● Collaboration with leading specialists in finance, data science, ML / DL; ● We plan to implement a platform for managing trading strategies; ● Develop an algorithm for visualizing the presentation of forecasts; ● Creation of a single group of companies and products

6. Used and developed analytical products Since the global launch of the platform in December 2016, more than 1,000 forecasts were created by more than 250 analysts and traders. Since July 2016, trading tests and testing of various trading strategies have been launched. The main analyst was focused on the following issues:

● Binary tasks; ● Price issues; ● The impact of macroeconomic events; ● Impact of Corporate Events; ● Political events.

6.1. Binary questions Binary questions are a posed question, which has only two answers - yes or no. Analysts should give their answer as a percentage of the probability of the execution of this event from 0% to 100%. The probability from 0% to 49% is "No" with a different confidence, and the probability of 51% to 100% is "Yes" with a different degree of certainty. Typically, this type of questions is used to predict political, macroeconomic, corporate and other types of events, as well as to predict the movement of prices to certain levels. At the same time, each of the analysts uses different strategies in answering these questions. As a result, artificial intelligence models weigh the responses received and assess analysts on the history of their forecasts.

At the output, we get a valuable aggregated signal that can be used in various trading systems and strategies. 6.1.1. Macroeconomic developments Macroeconomic developments are the most relevant and significant economic events. 6.1.3. Political events Political events in our application include elections of the first persons and parties in different countries, resignations, significant meetings, the adoption of certain political decisions (for example, the introduction or extension of sanctions, the entry of countries into various blocks), and others. 6.2. Price issues In addition to questions about events, binary issues are also used when there is a need to determine the movement of prices. For example, we want to know if the price of an asset will grow to a certain level. Asking such a question, at the output we get the probability of reaching the price of this level. Like other Stellar signals, the minimum and maximum signals are universal and can be applied to any assets: crypto-currencies and crypto-active assets, shares, futures, currency pairs, etc. In addition, the generation of these signals is possible for different timeframes, for example, day, week, monthly or quarterly. The specificity of questions about cryptographics, as well as issues with long timeframes, is greater volatility. An example of how our technology copes with great volatility is our regular weekly crypto signals. Twice a week we receive Hybrid Intelligence signals on the top 3 crypto assets in terms of their capitalization: Bitcoin, Ethereum and Ripple. 6.3. Planned new analytical products Our experiments and the experiences of our colleagues, studying various aspects of the work of artificial intelligence, will allow us to create the following set of analytical products for their integration into the overall capital management infrastructure:

● Evaluation of the strength and influence of news in the markets; ● Integration of analysts into forecasting accuracy groups; ● Analysis of the existing investment portfolio of a particular AI trader; ● Visual map of the market, the probability of growth / fall of a certain asset.

7. The team 7.1. Team and competencies

Stellar Fund is a trademark of the company, founded in 2016, which is a self-learning investment project built on the basis of artificial intelligence, which is constantly learning to work with finances, investments, preserving and increasing capital, and choosing the right tools for the investment portfolio. Using the symbiosis of human experience and unlimited possibilities of self-learning Artificial Intelligence. Creating the company Stellar Fund, our task was to unite human experience and high-tech Artificial Intelligence, creating a unique system of work and analysis of the prospective market of digital assets, providing developers and customers with a reliable tool for managing financial assets. By providing our service, we are committed to long-term and transparent cooperation. Using the best knowledge in the field of investment and investment, as well as modern solutions of AI, we are guided by the following principles:

● Goal setting; ● Develop an action to achieve that goal; ● Be focused on results; ● Take into account their own experience and the experience of others; ● Be patient and persistent; ● Preservation and augmentation of the capital of the company and its clients.

7.2. Current achievements of the company In March 2016, a global release of the first version of the platform for investment activities was implemented. The creation of the necessary artificial intelligence system was launched.

● July 2017. Running a beta version of the working model of the system and testing on real tools.

● September 2017. Successful conclusion of a contract with third-party investors to increase the operating capital of the system and the ability to work with multiple destinations.

● October 2017. Getting satisfactory indicators using third-party capital, interest from large investors.

● November 2017. The opening of a free investment system for all customers using the Internet.

● December 2017. Correction of the system operation taking into account open access for all comers and the introduction of a pool system.

● February 2018. Start of work on documenting the system for subsequent patenting. ● August 2018. Load testing of the system to consolidate the obtained, the development

of the module of the analysis of moods in media spaces.

8. Legal Details 8.1. Legal information Preparation for the sale of Stellar tokens was carried out in accordance with all standards and with an understanding of all the responsibilities incumbent on our company. Given the uncertain status of crypto-currencies and digital tokens in different jurisdictions, our company has spent considerable time and resources on the analysis of the legal status of the Stellar business model and tokens in those jurisdictions where we plan to operate. Due to the uncertainty in the regulatory system at the global level, the legality of Stellar's hybrid intelligence platform, or the ability to structure and license a future investment fund based on our platform can not be guaranteed by our company in any jurisdiction. However, our company will strive for openness and flexibility in the event of any regulatory request in our direction. 8.2. The legal status of tokens Stellar Tokens are tokens specially designed for functional use on the Stellar hybrid intelligence platform. Stellar's tokens are not securities. Tokens can not be returned after purchase. We do not recommend buying Stellar tokens for investment speculation. To access the Stellar hybrid intelligence platform, Stellar tokens are used. Tokens are sold as a digital asset, like downloadable software, digital music, and the like. We do not recommend buying Stellar tokens if you do not have a prior experience with cryptographic tokens and software based on a blockbuster. 8.3. Legal status of crowdsourcing platforms There is no single regulatory framework applicable to crowdsourcing forecast platforms. In some jurisdictions, these products and services are regulated on the basis of existing systems for regulating games and / or financial services, while in others they remain unregulated. Before choosing a specific jurisdiction, we will conduct a legal analysis of the current regulatory rules in this jurisdiction. Depending on the burden of regulatory compliance and its stages, our company will either take the necessary steps to obtain any necessary licenses and / or permits in such jurisdiction, or refuse to work in such jurisdiction. For the convenience of our users, Stellar White Paper, the website and other documents related to our company are available in several languages. In the event of a discrepancy between the English version and the version in another foreign language, the English version is considered to be the standard. 9. Conclusion The ultimate goal of Stellar is the creation of a decentralized intellectual technology that effectively realizes the potential of Artificial Intelligence for the benefit of all participants in the ecosystem. All the work of technology in the future will be fully automated: the only necessary resource for the operation is the mental investments of analysts. In the future, Hybrid Intelligence will be used not only in financial and economic markets, business and technology.

Stellar Token Sale is an excellent opportunity to join the development of the symbiosis of the mind of people and machines. 10. Risks THIS DOCUMENT DOES NOT CONSTITUTE AN OFFER TO SELL, AN INVITATION TO INDUCE AN OFFER, OR A SOLICITATION OF AN OFFER TO ACQUIRE SECURITIES. THIS DOCUMENT IS PROVIDED FOR INFORMATIONAL PURPOSES ONLY AND DOES NOT CONSTITUTE INVESTMENT ADVICE. THE SALE OF CND TOKENS CONSTITUTES THE SALE OF A LEGAL SOFTWARE PRODUCT UNDER GIBRALTAR LAW. THIS PRODUCT SALE IS CONDUCTED BY Stellar LTD (SLOVAKIA), A SLOVAKIA PRIVATE LIMITED COMPANY, OPERATING UNDER SLOVAKIA LAW. IT IS THE RESPONSIBILITY OF EACH POTENTIAL PURCHASER OF CND TOKENS TO DETERMINE IF THE PURCHASER CAN LEGALLY PURCHASE CND TOKENS IN THE PURCHASER'S JURISDICTION AND WHETHER THE PURCHASER CAN THEN RESELL THE CND TOKENS TO ANOTHER PURCHASER IN ANY GIVEN JURISDICTION. ALL POTENTIAL RISKS YOU CAN CHECK HERE. OUR WHITE PAPER MAY CONTAIN ѕORWARD LOOKING STATEMENTSї - THAT IS, STATEMENTS RELATED TO FUTURE, NOT PAST, EVENTS. IN THIS CONTEXT, FORWARD-LOOKING STATEMENTS OFTEN ADDRESS OUR EXPECTED FUTURE BUSINESS AND FINANCIAL PERFORMANCE, THE PERFORMANCE, AND ACCURACY OF Stellar INTELLIGENCE PLATFORM, AND OFTEN CONTAIN WORDS SUCH AS 'EXPECT', 'ANTICIPATE', 'INTEND', 'PLAN', 'BELIEVE', 'SEEK', 'SEE', 'WILL', 'WOULD', 'ESTIMATE', 'FORECAST' OR 'TARGET'. SUCH FORWARD LOOKING STATEMENTS BY THEIR NATURE ADDRESS MATTERS THAT ARE, TO DIFFERENT DEGREES, UNCERTAIN. WE CANNOT GUARANTEE THAT ANY FORWARD LOOKING STATEMENTS, BACKTESTS OR EXPERIMENTS MADE BY US OR EXPECTED RESULTS OF OPERATION OF Stellar INTELLIGENCE PLATFORM WILL CORRELATE WITH THE ACTUAL FUTURE FACTS OR RESULTS. FOR THE CONVENIENCE OF OUR USERS, Stellar WHITE PAPER, WEBSITE AND OTHER RELATED DOCUMENTS ARE AVAILABLE IN A NUMBER OF LANGUAGES. IN THE EVENT THERE IS ANY CONFLICT BETWEEN THE ENGLISH LANGUAGE VERSION AND A FOREIGN LANGUAGE VERSION, THE ENGLISH LANGUAGE VERSION SHALL GOVERN. List of references

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