With the recent explosive growth of the data industry in the recent years, we’re learning new ways we can utilize this rapidly growing source of information.
Summary
VectorSpace AI is a blockchain oriented machine learning software development team founded in 2018. There are countless correlations, arbitrage and algorithms within the market. Similarly such correlations are visible within the health and biotech industry. Vectorspace specializes in dataset technologies with the belief that. “If data is the new oil, then datasets are the refined gasoline that powers every machine learning and AI.” Vectorspace is working within refining datasets and artificial intelligence, specifically within natural language processing and understanding, which defines the process of machine learning and performing. With engineering features of detecting hidden relations within data.
By offering datasets to firms and individuals, those purchasing these datasets will be provided valuable information and the ability to analyze the hidden relation within the industries their profession is in. Contrarily, unlike many other crypto projects which aim to solve problems within either decentralized finance or building networks. Vectorspace is aiming to target a specific section of the data industry, in datasets and relations. While quite a niche sector of focus, the total addressable market for a project like this can range from medical and biosciences, to Wall Street and financial institutions, to any data and analytic research firm. Vectorspace AI is able to build valuable datasets that can educate and assist our future development in all sectors.
Utility
The utility of Vectorspace AI begins at their ability to connect hidden relations within seconds, that would previously have required significant labour and capital. Some examples of the utility of these NLP include; creating a cluster based on hidden relationships and concepts compared to SP500 equities performance, identifying symbiotic, parasitic and sympathetic relationships in various equities or creating a list of equities based on concepts or keywords. Initially, the idea of VectorSpace AI was a project built within the sector of life sciences and biosciences, since then they have begun diversifying their target market. Vectorspace AI has built datasets detecting hidden relations within drug compounds associated with viruses, genes and proteins. As they are currently studying and monitoring the behaviour and up to date information about COVID-19. While this is just the surface of the potential Vectorspace AI could disrupt the bioscience industry, with the future potential to detect hidden relations that lead to lethal illnesses.
Vectorspace AI has also built baskets of related equities that change accordingly to news and trending keywords to optimize their rate returns. These datasets enable researches from any industry an innovative advantage over their competition with datasets that can be trusted as the technology they use are patent protected NLP/NLU. The addition of these high value correlation datasets exponentially increases the efficiency of these firms, as it saves them the time used to input and test hypotheses and theories. The data they input into their systems are peer reviewed scientific research and literature, market analysis, financial statements and press releases.
With the ability of machine learning, Vectorspace AI is able to provide data augmentation in real time that is able to learn and adapt optimally with the provided input data allowing for up to date and accurate data to be displayed for new hypotheses and discoveries. The API Vectorspace AI has built allows their clients to have access to near real time datasets that update as frequently as once per minute, including correlations score and analysis insights, which can be generated individually or within a basket of other data sources. They have also built a data provenance pipeline, enabling clients to track where their data originates from and how it was processed. Their data provenance pipeline is built concentrated towards large firms that rely on the accuracy of datasets for substantial decisions, such as picking out the right combination of drug compounds, or building an ideal basket of securities with a certain circumstance. Vectorspace AI uses their machine learning technology and patents by utilizing their correlation datasets to build thematic baskets of equities or cryptocurrencies that share known hidden relationships together and react similarly to news events. Allowing traders not only build an optimal basket to maximize their returns but with the frequency of aggregating input news into their system, traders are given an “informational” arbitrage advantage with this technology.
The team understands that the world is growing exponentially faster than ever before with the power of data allowing the transfer of information almost instantly. The speed our information is growing at, a search about “GME short squeeze today” and “GME short squeeze tomorrow” draw dramatically different results, it becomes difficult to manage and process all the new growing information to be utilized, they believe the use of their datasets can assist in utilizing new data with machine learning to optimize efficiency. They also understand that single sources of data can only portray so much information. Their aim to build dynamic datasets that change according to new data inputted into their machine learning, will bring near real time datasets that automates changes when there is any new input.
Team
The Vectorspace AI team’s core consists of a Chief Executive Officer, Chief Visionary Officer, Chief Technology Officer and a Chief Data Scientist and CVO of Vectorspace AI is Kasian Franks, an engineering entrepreneur who completed his fields of study at Stanford University and UC Berkeley for biology and computer sciences respectively. Kasian began gaining work experience within engineering in 1992 at Franklin Templeton Investments, eventually working in the engineering and software industry for companies such as Motorola, Sun Microsystems, Cisco, Oracle, TiVo and more. Kasian has also had previous experience within biosciences with a history of working within AstraZeneca as a consultant for genomics and Lawrence Berkeley Research Centre as a scientist. As an entrepreneur, Kaisan has also built start-ups prior to Vectorspace AI with projects such as SeeqPod and Mimvi. Mike Muldoon, the CTO similar enough to Kasian, has a deep history with computer sciences, graduating from Ohio State with a Bachelors in Computer Sciences. Mike eventually went on to work in the software and engineering industry for a plethora of firms such as Luminant Worldwide, BugScan, Akira and SeeqPod where Kasian and Mike first began their partnership. The CDS of VectorSpace AI is Raf Podowski, Raf has a solid education and experience with biosciences and software engineering. From a PhD in Bioinformatics along with a Bachelors and Masters in mechanical engineering and engineering physics respectively. With work experience in this sector with roles such as, a text mining consultant for AstraZeneca, senior product manager at Oracle, Chief scientist at SeeqPod and worked as an independent engineering consultant from 2013–2017.
The core team of Vectorspace AI is heavily built around the knowledge and experience of engineering and biosciences, both an essential part of Vectorspace AI’s roadmap and target audience. The team also consists of roles such as a scientific core team, marketing team, engineering team along with legal and financial advisors, all playing an essential role to ensuring Vectorspace AI to be built out the way they envision.
Tokenomics
Vectorspace AI’s tokenomics can be briefly described as a medium of exchange on the Vectorspace AI platform as a subscription payment to access the datasets provided. While payments can also be made in fiat, $BTC and $ETH. Each individual $VXV wallet address serves as an API key for the distribution of datasets. The most essential part of the utility of the $VXV token is for the Data Provenance Pipeline. With data provenance being arguably the most essential part of data, knowing the origin and source of where information was generated. Blockchain transactions are natively hashed, meaning data integrity and transparency are optimized, contrary to other data provenances using alternatives to a blockchain. This function allows users to conveniently troubleshoot and correct any errors detected within the datasets.
There are currently no staking or reward mechanisms for $VXV, there is a potential plan in the longer term for a system in which users can “lend” out their $VXV for access to datasets. While there are no reward incentives for the project yet, Vectorspace AI will be using 50% of their revenue for buybacks. The reasoning for buybacks is that VectorSpace AI believes by managing the supply in this way, market manipulation will be made much harder to execute for those who try.
$VXV has a total supply of 50 million, with a circulating supply of about 38 million. There was no ICO for $VXV, but rather an angel investing round along with friends and family, which raised about $700,000. The tokens held by the team and certain partners have a four year lockup period to prevent volatile price action from the selling of early investors.
Partners
Vectorspace AI’s partners utilize the datasets provided to them for research and development of their respective firms. Some of these firms are CERN, an organization in Europe for nuclear research. As an academic collaboration, with CERN accessing datasets built for tracking relations to particle physics. CloudQuant is an alternative data company targeted towards investors and traders. The partnership with Vectorspace AI will allow over 400 thousand users on CloudQuant to access datasets and hidden relationships in global equities. Some larger companies working with Vectorspace AI consist of SP500 Global and Bloomberg, which will be using this partnership to have Vectorspace AI to transform the copious amounts of data they store into datasets they can monitor.
Ironically, the only cryptocurrency partner Vectorspace has at the moment is with LCX, the Liechtenstein Crypto-asset Exchange. The goal of this partnership will be to bring smart baskets built from the use of Vectorspace AI’s datasets to allow for potential arbitrage opportunities within other exchanges. Vectorspace AI believes in the future of cryptocurrency and what it has to offer, as the idea of partnering with a DeFi organization to build datasets and smart baskets within this space is in their plans for the future.
Potential
Vectorspace AI is vastly different from most cryptocurrency projects in the sense that it doesn’t deal directly with finance, while also having utility in other sectors such as biosciences. While the project may seem much more centralized and controlled by a party, a project like this is geared more towards the traditional world. Yet Kasian made the decision to build Vectorspace AI as a crypto project, due to his beliefs of attaching trading vehicles to innovative products, along with the transparency that is provided. The team has previously had issues with transparency and legality while working start-ups in the traditional finance world and believes in the freedom and community driven aspects of cryptocurrencies.
With the understanding they are not an orthodox crypto start-up, the potential for Vectorspace AI to be a household name project in the coming years is definitely possible. The total addressable market for Vectorspace AI will gradually continue to grow as more data is produced, firms in all industries will need to make better use of the data they have to continue innovation. Currently, there is no competition with what Vectorspace AI is working on. The team understands the possibility for competitors is inevitable, but also understand that the dataset industry is so large that there will be other dataset companies offering other types of sets, but they believe what will set them apart from their future potential competition will be their first mover advantage and experience in this field along with the focus on NLU, a valuable niche.
With the possibility of firms such as financial corporations along with medical research labs and any other industry with large amounts of data utilizing the datasets provided by Vectorspace AI, the amount of capital inflow into this project would be substantial. Large firms are willing to pay billions in order to get an edge over their competitors with data refined into datasets or improve their research and efficiency with clusters of data and detected hidden relations. The disruption of data from datasets, machine learning, and NPL/NPU will change how we gather and analyze information, and Vectorspace AI aims to be the leader in providing these products. Additionally, with the buyback and possible future lending feature, investors will be incentivized to lend out or sell their $VXV tokens back to the team for passive and additional income. As the amount of data continues to grow in our world, the valuation of Vectorspace AI’s services of building datasets and clusters will become more valuable.
*None of the information listed is financial or investment advice and should only be taken as entertainment or educational as I’m not a financial advisor*
About Me
Hey, thanks for taking the time to read my work. I’m your average 20 year old, currently in school for Economics and Finance. Some of my hobbies consist of sports, working out and staring at price charts.
I initially began interested in the crypto space after frustrations with legacy markets. From the second I read about the Ethereum ecosystem, I fell in love. An entire ecosystem built on one platform that anybody can access? Unheard of, until now.
With how fast this space is developing, I try and find projects within this industry that show promise and potential to disrupt our modern world. All this fundamental analysis not only helps me better understand these projects better, but hopefully gives you guys some newfound information!
If there are any projects you’d wish to suggest me take a look into, I’m always available on Twitter
