Vectorspace AI platform enables dynamically generated intelligent “token baskets” based on user-selected trends that exist in search, social media and news. The reason they’ve included the word ‘intelligent’ is based the ability for these baskets to determine for themselves whether or not to include additional cryptocurrencies or components from related baskets that may increase overall returns. Baskets that interact this way with one another will conduct these kinds transactions between one another using the Vectorspace utility token, VEC which is also required to dynamically generate baskets.
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A single trend represented by a concept, keyword, hashtag, URL or news story can
represent a network of cryptocurrencies based on their relationship to one another and the
context or concepts that surround that trend. This relationship network of cryptocurrencies can represent a tradable token basket or closely related group of cryptocurrencies that have known and hidden symbiotic, parasitic and sympathetic relationships. They may trade in a group and move up and down together. They might all be impacted negatively or positively by news that contains a particular set of keywords, context, concepts or trends.
Traders, investors and hedge funds can engage is quick information arbitrage using the
platform. For example, if a cryptocurrency spikes up 50% in minute, you can insert its
symbol, a trend, concept, keyword, hashtag or news story related to the cryptocurrency that ran up and then, in seconds, have an automatically generated token basket of related cryptocurrencies, a tradable targeted token basket of cryptocurrencies that all have sympathetic, symbiotic and parasitic relationships with one another. This can be done faster than any manual research effort can uncover these connections. A rising tide lifts all boats or a lowering tide lowers them.
A large body of publicly available data is required as input. Meaningful relationships
between cryptocurrencies are found when statistically scored associations are generated
over thousands or up to millions of attributes and objects. Smaller collections do not
contain enough associations to produce meaningful results.The system partitions the input into frames of context (Pinker, S., 1997). An autoassociation strategy (Xijin Ge, Shuichi Iwata, 2002) is applied to the objects that comprise each frame. Using the resulting data, the system scores how strongly related each two unique cryptocurrencies are. These relationship scores are stored in a collection of vectors, which the system saves as an associative memory module.
Vectorspace focus on context-controlled NLP/NLU (Natural Language Processing/ Understanding) and feature engineering for hidden relationship detection in data for the purpose of powering advanced approaches in Artificial Intelligence (AI) and Machine Learning (ML). Vectorspace platform powers research groups, data vendors, funds and institutions by generating on-demand NLP/NLU correlation matrix datasets. Vectorspace are particularly interested in how Vectorspace can get machines to trade information with one another or exchange and transact data in way that minimizes a selected loss function.
In short, cryptocurrency research analysts and curators get awarded Vectorspace (VEC)
tokens for mining relationships between worldwide events, global trends, local trends,
news, URLs, keywords, hashtags and cryptocurrencies. In turn, Vectorspace (VEC) can
then be used to purchase subscriptions to the platform, purchase API calls, queries or
transact token baskets in the future. Other utility oriented actions Vectorspace intend to implement include ongoing competitons, rewards and bounties for assembling optimized baskets which provide returns that outperform all other baskets.
The statistical scoring of cryptocurrency attributes can be compared to how humans
discover relationshpis via reinforced learning (Wenhuan, X., Nandi, A. K., Zhang, J.,
Evans, K. G., 2005) and the process of auto-association. Important relationships are
strengthened when reinforced. System uses these principles to create AMMs, which can be used to provide the following data:
• How cryptocurrencies are related.
• In what contexts they are related.
• How strong those relationships are.
In silico emulation of biomimetic object-association strategies has proven to be very
effective in relationship discovery and data science, leading to new findings in numerous
industries including life sciences, finance, search, social and contextual advertising.
Proprietary algorithms are based on variants proven within advanced Natural Language Processing/Understanding (NLP/NLU) and Machine Learning (ML) applications. These variants are based on patents Vectorspace team has invented including a System and Method for Generating a Relationship Network – K Franks, CA Myers, RM Podowski – US Patent 7,987,191, 2011 in collaboration with Lawrence Berkeley National Laboratory. Others such as Google have derived variants of this in 2010.
Mike Muldoon CO-FOUNDER, ENGINEER & CTO
Raymond Walintukan DISTINGUISHED ENGINEER
Caleb Pate CO-FOUNDER
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