Vectorspace AI Announces Datasets Available to Power Machine Learning Systems

(SAN FRANCISCO)– Vectorspace AI (VXV) announces datasets that power data engineering, machine learning (ML) and artificial intelligence (AI) systems. Vectorspace AI alternative datasets are designed for predicting unique hidden relationships between objects including current and future price correlations between equities.

Vectorspace AI enables data, ML and Natural Language Processing/Understanding (NLP/NLU) engineers and scientists to save time by testing a hypothesis or running experiments faster to achieve an improvement in bottom line revenue and information discovery. Vectorspace AI datasets underpin most of ML and AI by improving returns from R&D divisions of any company in discovering hidden relationships in drug development.

“We are happy to be working with Vectorspace AI based on their most recent collaboration with us based on the article we published titled ‘Generating and visualizing alpha with Vectorspace AI datasets and Canvas’. They represent the tip of the spear when it comes to advances in machine learning and artificial intelligence. Our customers and partners will certainly benefit from our continued joint development efforts in ML and AI.” Shaun McGough, Product Engineering, Elastic (NYSE: ESTC).

Increasing the speed of discovery in every industry remains the aim of Vectorspace AI along with a particular goal which relates to engineering machines to trade information with one another connected to exchanging and transacting data in a way that minimizes a selected loss function. Data vendors such as, asset management companies and hedge funds including WorldQuant, use Vectorspace AI datasets to improve and protect ‘alpha’.

AI VentureTech, Inc. also announced plans to collaborate with Vectorspace AI (VXV) for the development of applications within the finance and biomedical research sectors.

Thomas Bustamante, the Founder & CEO of AI VentureTech, Inc. commented, “We are very excited to be collaborating with VectorSpace AI in utilizing their platform to develop new machine learning applications. VectorSpace Natural Language Processing/Understanding (NLP/NLU) can enable pathways for generating new insights, hypotheses or discoveries, which could prove very beneficial for such industries like finance, and biomedical research.”

Limited releases of Vectorspace AI datasets will be available in partnership with Amazon (Nasdaq: AMZN) and Microsoft (Nasdaq: MSFT).

How to Purchase VXV Token

The Company put together a detailed outline on Medium how investors, or potential data researchers, could purchase VXV token through. To find out more please visit 

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About Vectorspace AI

Vectorspace AI focuses 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). Our platform powers research groups, data vendors, funds and institutions by generating on-demand NLP/NLU correlation matrix datasets. We are particularly interested in how we can get machines to trade information with one another or exchange and transact data in a way that minimizes a selected loss function. Our objective is to enable any group analyzing data to save time by testing a hypothesis or running experiments with higher throughput. This can increase the speed of innovation, novel scientific breakthroughs and discoveries. For a little more on who we are, see our latest reddit AMA on r/AskScience or join our 24 hour communication channel here. Vectorspace AI offers NLP/NLU services and alternative datasets consisting of correlation matrices, context-controlled sentiment scoring and other automatically engineered feature attributes. These services are available utilizing the VXV token and VXV wallet-enabled API. Vectorspace AI is a spin-off from Lawrence Berkeley National Laboratory (LBNL) and the U.S. Dept. of Energy (DOE). The team holds patents in the area of hidden relationship discovery. 

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