# Welcome to bitgrit A Blockchain Project for Democratizing Artificial Intelligence The growing value and significance of artificial intelligence (AI) is undeniable. Yet, as its value skyrockets, it's increasingly monopolised by a handful of large enterprises, leaving smaller organisations at a disadvantage. This is especially prevalent in generative AI, with the big corporations monopolising AI use cases. The result is often that smaller organisations cannot afford data consulting services to drive business decisions and talented data scientists frequently go unrecognised and under-compensated for their pivotal contributions. It is essential to ensure such a revolutionary tool such as generative AI, should be accessible by everyone, as the internet was in the 90s and early 2000s. Therefore at bitgrit, we believe that AI should be for all. Whether it's small business teams with untapped data or global conglomerates, everyone should reap the rewards of the automation and efficiency AI offers. Furthermore, data scientists and engineers deserve to share in the value they create with their innovative AI algorithms. To bring this vision to life, we are launching the BGR Network, designed to cultivate a thriving AI ecosystem. This innovative platform will serve as the backbone for a wide range of AI applications, with external infrastructure providers offering features such as AI competitions, a marketplace for AI models and services, and a Job Board to connect top talent with leading organisations. Our primary focus is on fostering a collaborative environment where AI professionals can connect, innovate, and grow. By integrating these diverse elements, we aim to quantify the value of AI, driving the industry forward and unlocking new opportunities for all stakeholders. To truly democratise AI, bitgrit integrates blockchain technology on the platform, ensuring a fair distribution of value between competition sponsors and data scientists. This is best represented by our BGR tokens (refer to tokenomics sections for more details). In this rapidly growing industry, there is a pressing need for an interactive platform that serves corporate clients, small business owners, AI enthusiasts and data scientists. In response, we’re leveraging blockchain technology to enhance transparency and accountability to this industry. Our BGR token stands as a testament to this commitment, anchored by the value of datasets and models within our platform. As bitgrit expands, we promise an enriched spectrum of high-quality data, innovative algorithms, and sought-after talent. We’re strategically poised to amplify these services, nurturing the perpetual growth of our expansive network. Within our ecosystem, all stakeholders thrive. From companies submitting problem statements and datasets to data scientists honing their skills and reaping financial rewards; from customers acquiring models on an AI Marketplace to our invaluable partners who make it all possible. Collaboration is our core currency, and we invite you to join us and embrace it as your own. # About bitgrit Overview bitgrit stands as a premier online AI network tailored for the global community of data scientists. By harnessing the transformative potential of blockchain technology, bitgrit facilitates the seamless integration of data science and artificial intelligence within societal and business frameworks. Our Foundation is dedicated to managing and launching the BGR token and our cutting-edge AI-driven blockchain. This robust foundation will empower the creation of a dynamic AI ecosystem. While we focus on the core blockchain infrastructure and token management, separate external infrastructure providers will be responsible for building and offering additional features such as AI competitions, an AI marketplace, and an AI Job Board. This collaborative approach ensures that our platform remains versatile and scalable, enabling a wide array of services that cater to the diverse needs of the AI community. Our platform revolutionises the AI development process, championing a democratic, crowd-sourced approach within an inclusive ecosystem. Our mission is to cultivate a nexus where data scientists can convene, establish professional connections, demonstrate their expertise through competitions, access coveted job opportunities, and monetise their proficiency and dedication, while being crowd-funded by community and business sponsors who may also achieve decent returns for their support. ### Key Features of bitgrit 1. bitgrit pioneers a novel paradigm in AI engagement by synergising businesses, data scientists, and data providers within a transparent ecosystem. 2. bitgrit streamlines the pathway between AI challenges and their innovative solutions. 3. bitgrit’s ecosystem boasts a comprehensive data scientist network, designed to foster collaboration between data scientists, data providers, and enterprises, all within a unified, user-friendly interface. 4. Recognising the contemporary challenges businesses encounter in sourcing AI expertise, bitgrit fundamentally reimagines the AI development landscape. 5. bitgrit’s dynamic ecosystem continually adapts to client needs, ensuring the provision of essential computational and storage infrastructure for both our clientele and the data scientist community. 6. bitgrit aims to solve the challenge of quantifying the value of AI using a decentralised and collaborative approach, facilitating better insights, investments, and advancements in the field. ### bitgrit's Strategic Approach bitgrit has meticulously crafted an online ecosystem for data scientists, where participants can engage in competitive challenges to address specific problem statements and explore designated datasets. Our developmental roadmap emphasises fostering transparent and uninhibited discourse on AI models, training methodologies, and other pertinent expertise. This is aimed at nurturing a robust intellectual community anchored in value-driven innovation. Through the implementation of this strategic approach, we at bitgrit are committed to realising a future where AI creation is democratised and disseminated across the expansive data science community. # The State of Artificial Intelligence A Growing Monopoly While AI has long been incorporated into many industries, 2022 marked a significant uptick in its adoption among mainstream consumers. The technology is advancing at an exponential rate, frequently surpassing its own benchmarks and automating tasks previously believed to be beyond its reach. Such rapid progression means that it’s challenging to keep on top of the latest developments in the industry, with new breakthroughs happening seemingly on a daily basis. Companies like Nvidia and OpenAI have become household names while legacy companies like Microsoft and Adobe are investing billions of dollars to weave AI into their core systems, ensuring they remain at the forefront of the competitive landscape. AI is transforming and disrupting lives. While the fundamental principles of AI have remained consistent over the past 30 years, the exponential increase in computational power has propelled us into an era of complex and multi-layered networks. This transformation has been augmented by cutting-edge algorithm design allowing for automatic feature selection as well as back-propagation and pooling across these multiple levels. The combination of increased computing power and novel neural network design has given way to technology with state-of-the-art performance and breakthroughs in multiple fields of computing science. This includes image recognition and categorisation, speech recognition and synthesis, and dozens of other use cases. Today’s data-driven industry leverages robust data science tools to extract meaningful insights from vast datasets, ranging from PDFs and spreadsheets to multimedia content such as images, audio, and video. This data is then harnessed by data scientists, highly proficient in machine learning, pattern recognition, language processing, computer vision, deep learning, and much more. Their goal? To unlock new frontiers of value for companies, driving revenue growth and optimising operational efficiencies in their businesses. While these technological advances amplify AI’s potential to revolutionise various sectors, significant challenges remain. The primary beneficiaries of machine learning have been large, centralised entities that have the means to create or purchase large training data sets. These firms can also hire from the limited pool of talent capable of producing machine learning models that can benefit from training on said data sets. This exacerbates the problem of the global concentration of wealth by a few corporate entities. In the near future, as the IoT ecosystem broadens its reach into the heterogeneous personal computing space, it will accelerate the generation of data suitable to train models. Unfortunately, the current trajectory of the ecosystem suggests a future where a greater concentration of data falls into the hands of a few large companies. Resulting from the growth of AI and data generation, an increasing number of data scientists are entering the space to process new problem statements. Most experts in the field quickly get pulled out of academia and into large corporations, with only a handful of key industry players vacuuming up the majority of available talent. Fortunately, there are many data scientists yet to be discovered, compared to those who are already associated with major players. This presents an opportunity for us to foster a global data scientist community, one that collaboratively and competitively crafts machine learning models that deliver immense value. bitgrit envisions an open and free environment for AI research, rather than an exclusionary and centralised one. More importantly, by recording attribution on an immutable public ledger, we can ensure that people who create the models that deliver value are compensated with the majority of that value. ### Technical Infrastructure Demand Currently, one of the major problems facing data scientists, especially those who work in smaller corporations, is a lack of infrastructure needed to train and deploy their AI Models. While a data scientist may be a subject matter expert in their field and able to write algorithms that turn the data they are working with into a functional AI model, developing their algorithms into a fully-trained AI model requires a depth of technical knowledge outside their field of study. This means that data scientists must become not only an expert in the efficient use of computing power, but more concerning, also have access to computing power which is severely detrimental to data scientists due to limitations in budget or time. Another challenge in the contemporary landscape of AI is the issue that general AI models have become largely ineffective in solving real world problems. In their place, data specific models have taken precedence. With this in mind, the key to successfully bringing AI to the masses is to either adapt a pre-existing algorithm to train on a specific set of data, or create specific algorithms for specific problems. Currently, companies such as Algorithmia and Amazon’s AI Marketplace focus on allowing a user to utilise an AI model for a specific purpose, such as gender recognition AI or a model that colorises a monochrome image. However, in real-world business cases, this level of generalisation is ineffective given the specific AI needs of corporations. Considering this, bitgrit’s focus is not on providing access to general AI models, but rather providing the infrastructure for data scientists to train and deploy AI models for a specific business need. # The Global AI Market Rapid Rise of an Industry As the demand for AI solutions rises, the industry has become one of the world’s fastest-growing sectors. In 2023, the industry has seen massive growth–a testament to AI’s intrinsic value across varied sectors. AI has consistently demonstrated its remarkable ability to reduce inefficiencies, save time, increase productivity, and channel data to make informed business decisions. Precedence Research estimates that the global AI market will reach a value of USD 2.57 trillion by 2032, while it was valued at USD 454.12 billion in 2022, thus projecting it to expand at a (CAGR) of 19% from 2023 to 2032. AI is predicted to disrupt all industries in some way. From finance to the automotive industries, changes are rapidly taking place (citation). The current primary markets for AI are the U.S., Japan, and India (all three countries have shown exponential growth in AI adoption over the past few years). North America holds the largest market share on AI globally, with it valued at USD 167.3 billion in 2022 (36.84% market share), while Japan was valued at USD 20.2 billion in 2022. India, a rising region, is ranked sixth globally in terms of AI funding, spending USD 3.24 billion in 2022. # Latent Potential of AI Navigating Challenges in the Journey Towards Generative AI Integration AI is the fastest moving technology that we are witnessing in terms of it’s impact, and the AI era has just started. This is evidenced with the recent mass adoption of generative-AI across many industries. Yet its adoption (even among companies with valuable, actionable data) remains a challenge due to a multitude of obstacles as listed below: * Trouble determining possible use cases and value that can be extracted from existing data; * Difficulties in translating business challenges into data science problems; * Inability to develop, experiment, and rank a variety of models rapidly; * Hassles identifying the right talent to produce customised models; * Shortage of appropriate data science talent in the market; * Risks of providing people access to confidential data; and * Structuring of data and identification of relevant parameters. # Democratising AI Empowering All Democratic AI represents true democracy in harmony with equitable capitalism. Instead of the power of AI and the means of modern business production being controlled by the few, democratic AI places distributed power in the hands of the masses. Decentralisation provides the benefits of fault-tolerance: no single point of failure, no central authority that could censor information, and distributed trust systems. With decentralised applications (DApps), it is now possible to create open source and profitable applications, such as democratic AI DApps around community controlled AI and data science. Democratic AI allows for competition in the AI space as opposed to the centralisation of money, talent, data, and computing power in a few corporate entities. The current state of centralised AI has led to a winner-take-all economy. Profits from AI benefit a few corporations rather than the data owners and, most deserving, the data scientists themselves. Democratic AI allows for greater accountability in data ownership and AI oversight. With the transparency allowed by blockchain smart contracts, there is a clear structure for accountability in the case of data bias or AI abuse, as opposed to the traditional opaque systems of centralised AI and data. Within the new DApp structure, token ecosystems can be designed to incentivise users to collaborate, with rewards flowing to users based on the merit of their actions and automatically distribute value based on the utility delivered by the system. ### The bitgrit Ecosystem We are excited to announce the launch of the BGR Network, designed to cultivate a thriving AI ecosystem. This innovative platform will serve as the backbone for a wide range of AI applications, with external infrastructure providers offering features such as AI competitions, a marketplace for AI models and services, and a Job Board to connect top talent with leading organisations. Our primary focus is on fostering a collaborative environment where AI professionals can connect, innovate, and grow. By integrating these diverse elements, we aim to quantify the value of AI, driving the industry forward and unlocking new opportunities for all stakeholders. The bitgrit ecosystem is an intricate tapestry of global professionals, encompassing AI engineers, data scientists, data analysts, and other aficionados in the realm of data. This collective boasts expertise spanning the vast spectrum of data science disciplines. ### The Cornerstone of the bitgrit Community Local events serve as the foundational pillar of the bitgrit community. Our commitment to fostering intimate and direct interactions with local data scientists distinguishes us from our contemporaries. This strategic engagement ensures the cultivation of a community deeply rooted in the ethos of value-sharing. Not only does such a community possess a vested interest in bitgrit's vision, but it also plays an instrumental role in the development of invaluable AI algorithms. By seamlessly integrating both online and offline initiatives, the bitgrit community is poised for sustained growth and expansion. ### The bitgrit Advantage #### A Paradigm Shift in AI Solutions bitgrit is pioneering a global online network of data scientists that deliver solutions to data-centric business challenges, and its inclusive ecosystem empowers users worldwide to collaborate on real data science challenges, regardless of identity, location, or background. With integrated blockchain protocols and smart contracts, bitgrit ensures transparent project contributions and fair rewards for participants. At its core, bitgrit democratises AI, making its technology, expertise, and insights universally accessible. Through events like the global WDSF, we champion the open dissemination of knowledge, unlocking AI's transformative potential across industries. #### Trust and Ethical Integrity Our use of blockchain and smart contracts with the Bitgrit DLT Foundation ensures a foundation of trust and transparency, allowing clear oversight of project contributions and fair rewards. #### Addressing the Talent Gap The global shortage in data science and AI expertise is pronounced, with data scientist employment projected to grow 35% from 2022 to 2032. Bitgrit’s ecosystem will address this talent crunch by leveraging our community of data scientists to solve corporate challenges globally. SMEs can access AI expertise without hefty investments in in-house teams, while data scientists gain global opportunities, fostering a dynamic gig economy with project-based engagements that enhance their skills on diverse projects. # Roadmap Project Roadmap ## Project Roadmap ### Q3 2024 * Launch of the BGR Token * Founding DLT Foundation in ADGM * CoinMarketCap and Coin Gecko Listing * Listing on DEX * Listing on Tier 2 Exchanges * Staking ### Q4 2024 * Listing on Tier 1 Exchanges * Introduction of Teams in Competitions to facilitate a collaboration ecosystem * Migration of AI Competitions to a Web3 platform * Host first Web3-style AI competition * Launch API Marketplace ### Q1 2025 * Open the bug bounty and open-source contribution program. * Introduction of token incentive for partners * Launch Alpha AI Marketplace * Implement a Web3 wallet via sibling company DataGateway ### Q2 2025 * Integrate the Job Board with the Web3 platform. * Introduction of Blockchain-backed certification, recognition and rewards # Tokenomics Overview Dynamic tokens designed to align value accrual with bitgrit's success ### **BGR Token** The BGR token is the utility token that powers bitgrit's ecosystem. Meticulously designed to function as a catalyst, it promotes active engagement from all stakeholders. bitgrit envisions this token will be leveraged to motivate users towards actions that augment the network's value, primarily by fostering a culture of open dissemination of AI insights through models and data. Recognising the profound influence such a system can exert in guiding user behaviour, bitgrit remains committed to the highest responsibility standards. Distribution and circulation of BGR tokens will be for the features on the BGR Network (including but not limited to external providers that provide AI Competitions and a Data Scientist Job Board), and will be managed through a smart contract. Details on the smart contract address will be made available publicly upon reaching certain milestones. #### **Utility** The following entails possible use cases within the BGR Network that bitgrit envisions can be developed by external BGR Network Participants ("Infrastructure Providers"): #### AI Competitions Companies that require AI solutions (Competition Providers) to their problems can submit a competition request to an Infrastructure Provider on the BGR Network. In exchange for custom AI models addressing their company-specific needs, they provide the prize pool in USD or BGR (therefore, the companies are paying through prize pools instead of salaries). In order to enter an AI competition, each data scientist wishing to participate would be required to deposit or stake a set amount of BGR tokens. After the end of each competition, each participating data scientist will receive a portion of their staked BGR back by way of a tiered ranking system. Through this tiered ranking system, most participants who submitted completed models will receive all of their staked BGR back, with Data Scientists who submitted lower-ranking models or uncompleted AI models forfeiting portions of their BGR. The amount of BGR returned will vary per competition. The top data scientists in each competition will receive the USD or stablecoins supplied to the prize pool and (in some instances) newly minted BGR to encourage access to future competitions. #### Job Board As a Data Scientist participates in the community, activity and contributions are logged to their Job Seeker profile. Eternal employers wishing to gain access to the qualified candidates listed through the Job Board, must supply BGR in order to gain access to the Job Board platform. Upon identification of successful candidates, their deposited BGR will be burned. #### Community Participation BGR is minted and provided to data scientists or any other community members in exchange for actions or events that enhance or favourably promote the bitgrit ecosystem. This would include, but is not limited to: * Uploading their own algorithms directly onto AI Marketplaces supplied by Infrastructure Providers; * Evaluating and reviewing other Data Scientist algorithms on AI Marketplaces supplied by Infrastructure Providers; * Assisting bitgrit with scoring AI models in competitions that are held by Infrastructure Providers; * Improving other Data Scientist algorithms within the bitgrit Network to improve the Network's quality; * Creating and publishing data sets that improve the Network; * Reporting any suspected illegal algorithms that are uploaded (e.g., plagiarism, piracy, etc.) within the Network; * Writing and publishing articles that enhance and promote the bitgrit ecosystem and Network (e.g., the solution is scaling democratised AI ecosystems rather than centralized AI systems, how to use an AI Marketplace within the bitgrit Network or real-world examples of training AI marketplace algorithms on LLMs); * Providing suggestions, creating and assisting with Network AI competitions run by Infrastructure Providers; and * Creating Data Science educational materials for bitgrit to distribute to the community. # Supply and Distribution BGR Supply and Distribution BGR will be issued on the Avalanche C-chain. The maximum supply of BGR tokens would be 10 billion. 50% of the maximum supply would be allocated to the founding team, advisors, early investors and -adopters, discretionary reserves and marketing incentives. The remaining BGR will be community incentives (50%), including a 2% initial airdrop allocation where BGR is minted and provided to data scientists or any other community members in exchange for actions or events that enhance or favourably promote the bitgrit ecosystem and Network. ## Supply
Beneficiaries AllocationCliff/VestingDetails
Core team and advisors23%6 months
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32 months		Allocation is for the core team that contributing to the project, as well as strategic advisors. Longer cliffs and vesting periods are to reduce the likelihood more than 1% of total supply is dropped on the market at any given point in time after the cliff.
Early Supporters5%0 months
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24 months		BGR financial and otherwise supporters from inception or very early stages of the project.
Early Partners7%0 months
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32 months		BGR financial and otherwise supporters from early stages of the project.
Community Emissions40%0 months
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32 months		This allocation is used for the Ambassador Partner program, contest rewards, community dataset provider rewards, data scientist incentives rewards, hackathon, airdrop, liquidity provider reward.
Marketing & Events Reserve10% 0 months
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32 months		This allocation is to be used for initiatives that promote Job Boards within the Network.
Early Marketing Reserve2%0 months
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0 months		Early Marketing Reserve that ensure the engagement of the most needed community members early in the project’s lifecycle, early giveaway and early partnership.
Discretionary Reserve 8%0 months
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24 months		A reserve the bitgrit team can use to promote the project and ensure its success, especially against unforseen challenges.
Liquidity Reserve5%0 months
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0 months		A liquidity reserve to ensure liquidity is upheld across markets, especially deep liquidity or concentrated liquidity concepts (i.e., a DEX).
We note that the current BGR token in circulation, already traded with existing liquidity on secondary markets, represents an earlier version within our ecosystem. The introduction of the new token signals our decision to replace its predecessor, emphasising that trading with the previous token will no longer be supported within our ecosystem. # Token Circulation Circulation through AI Competitions

Circulation of BGR through AI Competitions

Data Scientists can obtain BGR tokens in the following ways: * By participating in community activities (including through airdrops), as discussed as part of the “Utility” section; * Purchasing BGR in the open market; or * Achieving a top placement in some of the AI competitions (hosted by Infrastructure Providers). For AI competitions, bitgrit will coordinate with Infrastructure Providers that host these competitions, to facilitate any required smart contract interactions with BGR for the Network's purpose. For example, in order to enter a competition, each data scientist wishing to participate would be required to stake a fixed amount of BGR tokens. The total BGR required to enter a competition will vary, depending on factors such as the time commitment and complexity, as well as the value of the prize pool on offer. The BGR staked for participation will be stored by way of a bitgrit smart contract for the duration of the competition. After the competition has been concluded and the ranking of each participating data scientist has been finalised (in line with the predetermined rules of the competition set by the Infrastructure Provider): * BGR will be issued from the Infrastructure Provider to the top winners of the AI competition; * the BGR stake per participant will be returned to the Data Scientists; and * A portion of the unreturned BGR will be returned to the community participation reserve pool, and the remaining unreturned BGR will be burned. # Features Our Foundation is dedicated to managing and launching the BGR token and our cutting-edge AI-driven blockchain. This robust foundation will empower the creation of a dynamic AI ecosystem. While we focus on the core blockchain infrastructure and token management, separate external infrastructure providers will be responsible for offering additional value-adds within the Network to drive the democratisation of AI, such as AI competitions, an AI marketplace, and an AI Job Board. This collaborative approach ensures that our platform remains versatile and scalable, enabling a wide array of services that cater to the diverse needs of the AI community. # Competitions A new way to build AI models Although external infrastructure providers will host competitions, bitgrit envisions the following: Exciting features of competitions where the winning models and algorithms are not exclusively used by one party and the rewards don’t stop at the prize pool. An improved value system would be to integrate these winning AI models into AI marketplaces provided by infrastructure providers (the same as the competition hosts or other parties). These marketplaces could act as hubs for algorithm creators to showcase their work, while small businesses, enterprises, and community members can access these powerful solutions. ## Competition Platform Innovative solutions often emerge from competitive environments. bitgrit envisions competitions designed by Infrastructure Providers that harness this principle, enabling real-world data skill assessments and engaging both internal teams and external expert communities with pressing business data challenges. Infrastructure Providers (in collaboration with Competition Providers and/or other partners can offer bespoke, white-labelled or community driven data science competitions. These competitions could empower businesses to: * Identify pivotal AI applications within their operations; * Source AI models from a vast community of over 30,000 data scientists; and * Seamlessly integrate these solutions without establishing an in-house data team. For every challenge presented, a multitude of data scientists converge to design optimal AI models tailored to address specific business issues. A crowd-based AI as a Service (CAIaaS) model ensures innovative small businesses receive the crème de la crème from thousands of submissions, facilitating swift scalability at a fraction of the cost of traditional team recruitment. With top-tier models being listed on AI Marketplaces provided by infrastructure providers, this allows other businesses with analogous challenges to benefit, while simultaneously generating income for the contributing Data Scientists. In summary, the CAIaaS is the epitome of efficiency in custom AI model development. The competitions are envisioned to operate on fiat rails, where Competition Providers offer the prize money (via a smart contract) to the winning data scientists in USD (or the stablecoin equivalent) in exchange for the winning AI models. # AI Competition Life Cycle Data Science Competition Life Cycle Data science competitions are at the heart of our mission to democratise AI. These competitions are designed to bridge the gap between companies seeking innovative solutions to data problems and the talented data scientists eager to tackle these challenges. This section outlines the structured process of how data science competitions are envisioned to work within the bitgrit ecosystem and Network. #### Problem Definition and Preparation There are two ways in which challenges could be provided for data science competitions. One is when a Competition Provider already has a problem they want to solve, and set a competition through a Infrastructure Provider. The other is when an Infrastructure Provider independently sets challenges that are either currently needed or likely to be needed in society to stoke innovation. When a Competition Provider already has a problem they want to solve, the process is straightforward. The challenge is used in the competition as it is, or with modifications (if the company does not want to publicly disclose their own challenges), according to the needs of the sponsor. On the other hand, when an Infrastructure Provider independently sets challenges, they are based on discussions among members with diverse backgrounds and societal stakeholders. The goal is to set challenges that are likely to become popular topics or will be needed in society in the future, and the data is prepared through unique channels. For small business and enterprise competitions, the Infrastructure Provider is expected to work with the entity to establish the problem statement and data set. Thereafter, the Infrastructure Provider takes on the responsibility of preparing the necessary data (gather, clean, and structure the data, preparing it for analysis). This ensures a level playing field for all participants and saves valuable time that would otherwise be spent on data preprocessing. #### Formulating Mathematical and Machine Learning Problems With the problem and data in hand, a mathematical and machine learning problem is formulated. This step is essential to provide participants with a clear understanding of the task at hand. A balance between complexity and feasibility is sought, enabling both beginners and experts to participate. #### Hosting the Competition The competition phase typically spans more or less two months, during which participants, including data scientists and machine learning enthusiasts (collectively, Data Scientists), compete to develop the most effective solutions. We offer continuous support, maintaining an open line of communication with all participants. Effective marketing efforts are deployed to engage our community of experts, ensuring a diverse range of perspectives and ideas. #### Competition Judgment and Reward Upon the conclusion of the competition, the smart contract set up by the Infrastructure Provider will ensure that the top contenders receive their well-deserved recognition and rewards. The Infrastructure Provider must validate that the algorithms submitted comply with all the rules and guidelines, where additional restrictions have been added beyond the Smart Contract. Each competition culminates with a leaderboard, showcasing the performance of all submissions. The top-performing individuals or teams (usually top three) are crowned the winners, with the prize contingent upon: * Delivery of winning solutions accompanied by comprehensive documentation; and * Granting a global, perpetual, non-exclusive license for the client's commercial use of the solution. The top Data Scientists are awarded a share from the net prize pool based on their ranking in the competition, fostering a competitive yet collaborative spirit within the community. The Competition Provider will provide the prize money for the competitions in USD or stablecoins (some instances together with BGR). The Infrastructure Provider may charge a commission on each competition (% on a case-by-case basis) as a fee for hosting and facilitating the competition. *Leaderboard* The "leaderboard" in a data science competition is a ranking system that publicly displays the performance of participants' models based on specified evaluation metrics such as accuracy or F1 score. The leaderboard indicates how well participants' models perform compared to other participants. The benefits of using a leaderboard in a competition are as follows: 1\. **Motivation**: The leaderboard stimulates competitiveness and provides participants with the motivation to strive for better results. Seeing their own rank and its changes on the leaderboard serves as a driving force for participants to invest time and effort into improving their position. 2\. **Visibility**: High ranks on the leaderboard enhance visibility and recognition within the data science community. Participants' names may gain attention and they may be recognized as exceptional data scientists by companies and potential employers, contributing to the activation of both participants and the community as a whole. 3\. **Eligibility for rewards**: Participants who achieve top rankings are eligible for rewards. The leaderboard plays a critical role in determining the winners (as well as detecting fraudulent activities) and deciding the eligibility for rewards. This aspect is important for participants to compete either for recognition or monetary rewards. As a new feature to further enhance data science competitions in the future, it is also being considered to provide a platform for participants to exchange information. In such a platform, participants who have achieved top rankings can engage in discussions with one another, allowing them to learn approaches and techniques they may not have been aware of. Such an exchange platform provides a cooperative environment where participants can discuss strategies, share ideas, and learn from each other. # How to get involved How data scientists and companies can get involved #### Competition Topics When a Competition Provider has a problem they want to solve, the process is straightforward. The challenge is used in the competition as it is, or with modifications (if the company does not want to publicly disclose their own challenges), according to the needs of the Competition Provider. #### The steps to join the competition The steps for a data scientist to participate in a competition are fairly straightforward. First, they register their email address on the respective Infrastructure Provider's platform or web interface. Once the registration is complete, they can access the competition data by accepting the NDA for the ongoing competition and participate. Please note that it is not possible to register multiple accounts using the same email address, and participating in competitions with multiple accounts using multiple email addresses is strictly prohibited. Additional security measures are listed as part of the “Anti-exploit measures” section. In order to enter a competition, each Data Scientist wishing to participate would be required to deposit or stake a set amount of BGR tokens. After the end of each competition, each participating data scientist will receive a portion of their staked BGR back by way of a tiered ranking system. The amount of BGR returned will vary per competition.
# Impact on business ROI AI competition has the following benefits which may have a direct impact on business ROI **1. Reduction in personnel costs for machine learning model development:** Typically, hiring dedicated machine learning engineers is necessary for developing machine learning models. In recent years, there has been an increase in the application of machine learning within companies to improve services and streamline operations. Additionally, the commercialisation of data science has expanded. Acquiring and retaining machine learning engineers involves significant costs. Furthermore, models built by these engineers may carry the risk of not being the best possible models developed at that time, as they are influenced by the resources available to the engineer and their own experience. Conversely, if data collection and preprocessing for building machine learning models have already been completed (bitgrit provides consulting services from data selection for competitions to data collection and preprocessing), organizing a competition immediately can allow for the development of the best model from a large number of participants compared to development by in-house dedicated engineers. This enables a significant reduction in the costs associated with hiring machine learning engineers and managing resources, while also obtaining the best model. Additionally, conducting interviews with developers of models adopted through competitions as a secondary effect allows for gaining insights into the process of model development and acquiring knowledge for future in-house model development. **2. Recruiting machine learning engineers:** Data science competitions provide a platform to connect with and potentially hire engineers who rank highly on the competition leaderboard. Being ranked among the top performers in a competition with numerous participants indicates a high level of quality and makes it easier to screen for qualities required in potential hires, such as their areas of interest and technical knowledge. Hackathons, which often involve engineers competing against each other to solve specific problems, are commonly held for the purpose of recruiting engineers who excel in certain domains. In this sense, data science competitions can also be seen as a platform for gathering engineers well-versed in specific fields of machine learning. Additionally, many competition participants include competition achievements on their resumes, which can facilitate the hiring process as there is alignment of interests between the sponsoring company and the winners. # AI Marketplace Access to Niche Models AI Marketplaces are hubs where AI-solvable challenges are presented, accompanied by pertinent data. If the community crafts a valuable solution, small businesses and relevant stakeholders can procure access to these models. The essence of a marketplace is the transaction of trained models and datasets via an order-based mechanism, involving two primary stakeholders: Data Scientists and businesses aspiring to develop AI algorithms. Stakeholders seeking an algorithm navigate a customer journey akin to an order-based system, searching metadata of algorithms or datasets to retrieve relevant offerings. Similarly, Data Scientists can pinpoint pertinent proposals based on their expertise. Stakeholders can acquire AI in two formats\*: * Proprietary AI models with full ownership rights (i.e., Data Scientists place specific proprietary completed AI models on the marketplace for general use or for sale); and * Access to an AI API linked to a trained model via API credits (i.e., Community Competition AI models). *\* This is subject to each competition's set of rules for AI models (set at the time of the competition) set by Infrastructure Providers, depending on the businesses' and data scientists' requirements. These rules will dictate how the model is accessed, utilised and monetised.* # Job Board Find the Best Talent Data scientists, through the BGR Network, have the opportunity to distinguish themselves and access premier data science roles globally. We equip our community with essential learning resources and a forum for interdisciplinary discussions, spanning academia, industry, research, and more. Job Boards have the potential to connect small businesses, enterprises and community members with competition victors and AI collaborators for various roles, ensuring a perfect alignment of skills and requirements. This boosts the value proposition of the wider Network. Hiring the right data scientist has become a priority yet a challenge for many organizations. The intricacy of matching the right talent with the specific needs of a project often leads to extensive recruitment timelines and resource investments. This is where JobBoard platforms comes into play as a valuable asset for both employers and job seekers in the data science domain. JobBoard are specialized job posting platforms that provide a gateway for companies to tap into a global pool of data science talent. It showcases career opportunities in top global and domestic companies in Japan, covering a vast spectrum of industries ranging from consulting and finance to e-commerce and healthcare​​. These platforms are particularly tailored to cater to the AI and data science sector, ensuring a focused and relevant pool of candidates for the listed job openings​​. One of the distinguishing features of the BGR Network is its global reach, connecting data scientists, data providers, and corporations involved with AI technology from around the world. This global connectivity is invaluable in a field like data science where the expertise varies across different domains and regions. With a vast user base, employers have a better chance of finding the right match for their project needs within the bitgrit Network. Moreover, the diversity in expertise among data scientists registered on bitgrit Network amplifies the chances of finding the right fit for specific project requirements. The competitions may allow data scientists to demonstrate their skills by solving complex business problems using AI and data analysis, with top performers being rewarded for their prowess​​. This effectively creates synergies between JobBoards and competition platforms across the BGR Network, enabling employers to gauge the capabilities of potential hires in a real-world scenario before making a recruitment decision. Furthermore, the cost-effectiveness of JobBoards, as compared to traditional recruitment methods, is an added advantage for companies. With the ability to host a job post for a minimum of 30 days and receive an unlimited number of applicants, this would provide cost-effective alternatives to the conventional recruitment channels​​. Through the Network effect, companies can not only reach out to a diverse talent pool but also validate the expertise of potential hires through real-world competition, making the recruitment process more efficient and effective. ### Access Job Board As a Data Scientist participates in the community and Network through the various activities available (as mentioned in the utility section, activity is logged to their Job Seeker profile. Employers wishing to gain access to the qualified candidates listed through Job Boards, must supply BGR in order to gain access to these platforms. Upon identification of successful candidates, their deposited BGR will be burned. # Datasets In today's world, a vast amount of data is being generated daily. However, the availability of data that can be obtained and used for data science competitions is limited. The bitgrit community consists of engineers with diverse backgrounds in machine learning, DevOps, web, network, and more. As a result, it is possible to create datasets utilising the skill sets of community participants, which may include synthesising new data from existing sources. These newly created datasets (the challenges themselves can also be sourced from the community) could significantly improve innovation among AI competitions for specific societal needs, and improves the range of problems that can be solved through these data science competitions. Community participants who create and publish datasets for use by the bitgrit community and Network, can receive BGR for their participation in the community (community participation related to datasets include evaluating and improving datasets). This could offer additional future benefits if the users choose to place their created datasets on the marketplace.
# Disclaimer ### Legal Nature and Token Management This whitepaper is provided for informational purposes only and does not constitute an offer to sell, a solicitation of an offer to buy, or a recommendation for any security, cryptocurrency, or any other financial instrument. The tokens described herein are intended to serve as utility tokens within the specified ecosystem and are not intended to constitute securities or other financial instruments in any jurisdiction. ### Compliance with ADGM Regulations Our project is committed to full compliance with the regulations of the Abu Dhabi Global Market (ADGM), including adherence to data protection requirements and all relevant regulatory standards. The foundation overseeing this project, including token issuance and governance, operates under the legal framework provided by ADGM's Distributed Ledger Technology (DLT) Foundations Regulations. Any statements, representations, or advertisements contained within this whitepaper are intended to be truthful and not misleading. We adhere to all content-related requirements as mandated by the United Arab Emirates, the Emirate of Abu Dhabi, and ADGM. ### Due Diligence and Investor Responsibility Potential investors and participants are strongly encouraged to conduct their own thorough due diligence (DYOR) regarding the project's viability, the underlying technology, and the associated risks before making any investment decisions. Consulting with financial, legal, and tax advisors is highly recommended. ### Amendment Conditions The information contained in this whitepaper is subject to change, and the foundation reserves the right to amend the whitepaper and the terms and conditions related to the token at any time. Any amendments will be communicated through official channels and will comply with ADGM regulatory requirements. ### Caveats and Disclaimers 1. No Guarantees: This whitepaper does not guarantee the future performance or value of the token. All forward-looking statements are subject to risks and uncertainties. 2. Liability Waiver: The foundation, its team members, and associated parties are not liable for any direct, indirect, incidental, or consequential losses arising from the use of this whitepaper or the tokens. 3. Regulatory Risks: The regulatory status of digital tokens and blockchain technology is evolving, and regulatory actions could significantly impact the project's operations and token value. ### Foundation Details The foundation managing this project is registered under the ADGM DLT Foundations framework. The registration details and number \[Foundation Registrar Number Placeholder] will be provided upon completion of the registration process. ### Privacy policy In alignment with this disclaimer, we recommend all users to consult our website's privacy policy for comprehensive insights into the type of personal data required to interact with the ecosystem and how it might be utilised within our ecosystem. ### Whitelist and Blacklist Procedures To ensure regulatory compliance and enhance the security of our token ecosystem, we will implement a comprehensive whitelist procedure. This will include conducting thorough KYC and AML checks to verify the identity and legitimacy of all participants prior to their engagement in any token transactions. In addition, the DLT Foundation reserves the right to blacklist specific token holders under certain conditions. These conditions include, but are not limited to: * Participation in fraudulent or illegal activities * Breach of terms of service or user agreements * Failure to comply with applicable regulatory requirements * Conduct that negatively impacts the integrity or reputation of the project Tokens associated with blacklisted individuals or entities will be excluded from open circulation and restricted from participating in the token economy. ### Profit Distribution Policy Based on bitgrit’s fiscal policy, the following provisions are established regarding the distribution of profits and Foundation Assets: #### **Provisions on Profit Distribution:** bitgrit’s fiscal policy mandates that no profits generated by the Foundation will be distributed to token holders, members, or any third parties. All profits will be retained within the Foundation to further its objectives and sustain its operations. #### **Non-Distribution of Foundation Assets:** In alignment with the fiscal policy, Foundation Assets, including but not limited to financial reserves, intellectual property, and other resources, will not be distributed to token holders, members, or any third parties. These assets are to be preserved for the long-term growth and stability of the Foundation. #### **Circumstances Triggering Mandatory Distribution:** Despite the general prohibition on profit and asset distribution, the following circumstances may trigger a mandatory distribution: * Legal or Regulatory Requirements: If a distribution of profits or Foundation Assets is mandated by applicable laws or regulatory authorities, the Foundation will comply accordingly; * Dissolution or Liquidation: In the event of the Foundation’s dissolution or liquidation, remaining assets, after satisfying all liabilities, will be distributed in accordance with the applicable legal framework and the Foundation’s governing documents; or Exceptional Situations: Other exceptional circumstances as determined by a supermajority vote of the Foundation’s governing body, provided such distribution aligns with legal obligations and the overarching mission of the Foundation. ### Service and Organisational Clarifications * The platforms on bitgrit - the competition platform, the AI marketplace and the job board - are owned and operated by an external provider, bitgrit LTD. bitgrit DLT foundation only manages the BGR network. * The bitgrit DLT Foundation does not engage in profit-generating activities. Profit-oriented operations of bitgrit are conducted by external commercial entities (Infrastructure Providers such as bitgrit LTD). * Bitgrit LTD is not operated as a financial entity and does not require authorization to provide financial services.
# Anti-exploit measures The following measures will ensure that bitgrit prevents exploits related to discretionary distribution of rewards: * Clear and Transparent Rules: Each competition’s guidelines will be published on bitgrit.net, detailing the judging criteria such as innovation, impact, and technical feasibility. * Multi-layered Review Process: Entries are first screened by a technical team to ensure they meet basic requirements, then evaluated by an independent panel of industry experts. * Anonymised Submissions: Participants submit their entries through a portal that assigns unique IDs and removes personal information before judging. * Smart Contracts: A smart contract is set up to distribute prizes automatically once winners are verified. * Anti-Fraud Measures: Implement email verification and use behaviour analysis tools to detect multiple submissions from the same participant. * Whitelisting Judges: Competition judges are selected based on their expertise and are required to sign conflict-of-interest statements.