- Working Product
- Paying Customers
- Revenue Positive
- Blockchain company with an emphasis on generating revenue which is atypical in this space
While everything in the market is very bullish, we take a look at VXV as a potentially “lagging” pair behind the market, just waiting for its breakout. Some indicators are giving promising signs, and the price action is supporting these.
Looking at the Stoch RSI, we see it moving out of oversold conditions and that without any significant hick-ups. The Bollinger Bands have touched, and now VXV is heading against the mid-band, which would give impulsive propulsion once broken. The volume currently is rather low and is showing that the move to the downside is instead a retrace and not a new full trend, due to the rule that the direction always has to be confirmed by the volume.
Combining the bullish indicators and the price action we see right now, where VXV is bouncing from uptrend support, there is a reasonable chance of VXV turning over into a bullish state.
Vectorspace AI is a San Francisco based startup and is created by a team with a deep background in science, technology, and financial markets. They are specialized on-demand data and dataset companies with a focus on revenue-generating customers which are trillion-dollar asset management companies, funds, and other financial institutions.
Vectorspace AI enables data engineers and scientists to save time by testing hypotheses or running experiments for additional data interpretations from improving music and movie recommendation systems to enabling research or discovering a hidden relationship in nature. Vectorspace AI finds correlations between data and can help for example hedge funds and asset management companies generating ALPHA (the term ALPHA is a measure of the active return on investment).
Additionally, you can also listen to this great podcast where the Twitter content creator Mr. Backwards interviews Kasian Franks the CEO of Vectorspace AI.
Vectorspace AI creates on-demand datasets using Natural Language Processing and Understanding (NLP/NLU) to find hidden relationships acrossequities, assets, entities, and trading vehicles. Over time, Vectorspace learned to ‘hot rod’ datasets by augmenting or joining feature vectors represented by word embeddings. This resulted in generating new visualizations, interpretations, hypotheses, or discoveries. Augmenting time-series datasets for the financial markets with these kinds of feature vectors may produce unique signals or generate alpha.
The NLP/NLU approach is proprietary and very difficult to replicate. In 2002 at Lawrence Berkeley National Laboratory, Vectorspace created feature vectors based on Natural Language Understanding (NLU), also known today as word embeddings. Feature vectors were used to generate correlation matrix datasets to analyze hidden relationships between genes related to extending lifespan, breast cancer, and DNA damage repair resulting from space radiation.
The data sources included results from lab experiments, scientific literature from the National Library of Medicine, ontologies, controlled vocabularies, encyclopedias, dictionaries, and other genomic research databases.
Back then, they also implemented AutoClass, a Bayesian classifier used to classify stars, and used it to classify groups of genes based on a dataset containing gene expression values. Losses were minimized and results became more useful when augmenting datasets with word embeddings and topic modeling. At the time, the goal was to mimic conceptual connections a biomedical researcher might make right before a discovery, in silico. Some of this work went into a published paper describing hidden relationships between genes related to extending the lifespan of nematodes. In 2005, the US Navy’s SPAWAR division got involved, which allowed more resources to expand research into areas like the financial markets.
The datasets are accessed via a subscription that can be paid in USD, BTC, ETH, or VXV. Half of the revenue is used to purchase VXV on the open market and custody is handled via Trustology. Wallets with VXV double as api access points and the datasets are distributed via the API enabled wallets. Building on a blockchain is a strategic choice that benefits the ecosystem and technological design. Vectorspace utilizes the native hashing function on the Etherem blockchain as its Data Provenance Pipeline, this allows us to verify the integrity of the data, view the calculations on the data, track the data sources, and makes troubleshooting much easier all while leveraging the enhanced security of a public blockchain.
Vectorspace AI is a data company with a focus on revenue-generating customers which are trillion-dollar asset management companies, funds, and other financial institutions. Vectorspace AI helps its customers to make money by providing them with an edge. Outside of financial services, the Vectorspace AI datasets can be utilized to find hidden relationships between different genomes or drug compounds. In all fairness, the platform can be utilized to extract hidden relationships or test hypotheses from any type of data source. The diversity of the platform is highlighted by their recent COVID-19 datasets that aided scientists in repurposing drug compounds to combat the global health pandemic.
Launched in June 2018, the Vectorspace AI platform aims to enable real-life uses beyond its cryptocurrency VXV. This happens based on their NLP/NLU on-demand datasets that are updated every minute and based on any data their customers choose.
The Dataset augmentation provides services in the form of static and real-time, context-controlled, correlation matrix datasets based on NaturalLanguage Processing (NLP), and Natural Language Understanding (NLU). The datasets can be applied in all industries to generate new interpretations, hypotheses, and discoveries.
Customers that use the API of Vectorspace AI have access to near-real-time (NRT) datasets that update as frequently as once per minute (1440 API calls per day), allowing for near real-time correlation scores and insights that can be generated in isolation, or as an augmentation to any external or internal dataset.
For advanced users, optional solutions for data provenance via the Data Provenance Pipeline (DPP) are offered. The DPP rigorously controls the origin of the data and ensures that the customer always knows exactly where his data comes from and how it was processed. This is a must for bio-science and financial institutions that rely on our data sets for their daily billion-dollar decisions.
There are numerous possible fields of application for the NLP-based correlation matrix. For example, you can create unique sectors or clusters and by augmenting traditional time series data with context control you can uncover hidden relationships across equities; real-life events. These thematic baskets outperform the S&P whilst combatting traditional algorithmic signal decay. Recently, Vectorspace AI created a basket of stocks with correlations to the coronavirus. The result of trading these stocks can be seen below.
*= Trade closed on 5/22/2020
Users can access https://vectorspace.ai/datasets to create similar baskets. Premium services such as custom data sources and real-time updates require a subscription service.
While many applications add unnecessary friction by introducing a token, the Vectorspace AI platform is greatly enhanced with the implementation of programmable money. The token can be used to pay for subscription access to Vectorspace services, although customers can also pay for these subscriptions with USD, ETH, or BTC. Beyond payments, the tokens wallet address serves as an API key for the distribution of datasets. From a software engineering standpoint, wallet addresses function precisely the same way API keys do. This allows the delivery of datasets to be seamless and allows our customers to utilize the highest data integrity level. Finally, and arguably most importantly, the token is being used in our Data Provenance Pipeline. Data Provenance is essentially the life cycle of data. It’s used to track the origin of data, the source of data that computations were conducted on that data, and provides the framework for troubleshooting issues amongst datasets. On a blockchain, all transactions are natively hashed. This means that its data provenance pipeline has the highest level of data integrity and transparency. As a bonus, the native hashing on blockchains makes the troubleshooting cycle much more comfortable. It allows developers to identify duplicate data sources quickly or discover issues in the calculation of their datasets. We understand that cryptocurrencies are difficult to utilize. We’ve made it easy for our customers to use the benefits without having to deal with their custody via our partnership with Trustology.
As a data platform, Vectorspace AI immediate competition is other data-focused blockchain platforms.
The team of Vectorspace AI has a deep background in science, technology, and financial markets. They have been in the AI industry since 1994 at Genentech executing pattern recognition algorithms.
You can find the full list of team members and advisors here.
Last Update 23/06/2020
Vectorspace AI’s social presence is relatively healthy but smaller than most of the other projects, we reviewed so far. Especially on Twitter, they have only 832 followers considering that this project already exists since 2018. Most of the activity takes place on Telegram but the member count is also relatively low, however, admins and team members are present, and they answer member's questions most of the time.
Ask Science AMA on Reddit
Vectorspace AI publishes company-wide weekly conference calls. The archive can be found here.