FinTech Innovators Partner to Turn NLP into Dollars

Vector Space Biosciences
3 min readDec 21, 2020

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CHICAGO & SAN FRANCISCO — (BUSINESS WIRE) — Vectorspace AI in partnership with CloudQuant announce the availability of novel datasets that reveal relationships between global equity products. Vectorspace AI datasets are designed to boost precision, accuracy, signal or alpha based on Natural Language Processing and Understanding (NLP/NLU) using the VXV utility token wallet-enabled API.

“The ability to use language to generate event signals for specific companies opens a whole new range of investment opportunities”

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Data can be viewed as unrefined crude oil; Vectorspace AI datasets are the refined gasoline powering Artificial Intelligence (AI) and Machine Learning (ML) systems. Latest research suggests that the next big breakthrough in AI will be intuitive use of language (Wilson & Daugherty, 2020).

The algorithmically generated datasets are based on formal NLP/NLU models including OpenAI’s GPT-3, Google’s BERT along with word2vec and experimental models built at Lawrence Berkeley National Laboratory and the US Dept. of Energy (DOE).

“The ability to use language to generate event signals for specific companies opens a whole new range of investment opportunities,” said Morgan Slade, CEO of CloudQuant. “It allows the portfolio manager to accelerate innovation.”

Datasets are updated and designed to augment or append existing proprietary datasets such as gene expression datasets in life sciences or time-series datasets in the financial markets. Example customer and industry use cases include:

  • Particle Physics: Predicting hidden relationships between particles.
  • Life Sciences: Predicting which approved drug compounds might be repurposed to fight an infectious disease. Applications include processing 1500 peer reviewed scientific papers every 24 hours for real-time dataset production.
  • Financial Markets: Generating investment signals based on (unlimited) topics, themes, and global events. These signals can be used to generate thematic portfolios (position baskets) for real-time investment and visualization.

Kasian Franks, CTO commented, “we are excited to have our data available on the CloudQuant platform as it will bridge the gap between raw data and alpha generation for our clients. In an industry where innovation happens in real time, this partnership helps our clients access highly curated NLP datasets in a research ready format using CloudQuant’s Liberator data API.”

About Vectorspace AI:

Vectorspace AI provides high value correlation matrix datasets to enable researchers the ability to accelerate their date-driven innovation and discoveries using patent protected NLP/NLU. Clients save time in the research loop by quickly testing hypotheses and running experiments with higher throughput. Vectorspace AI originated in the Life Sciences dept. of Lawrence Berkeley National Laboratory (LBNL) where the founders developed the patents that drive the company’s innovation.

www.vectorspace.ai

Reddit: r/VectorspaceAI

About CloudQuant

CloudQuant provides last-mile delivery of research-ready alternative data to fundamental and quantitative investors. It offers institutional-grade analytics (SaaS) technology and example investment strategies to accelerate client research. Its data showcasing services to data suppliers include bespoke Machine Learning services to identify and measure alpha content and educational resources for prospective data buyers.

www.cloudquant.com

Twitter: @CloudQuant

References

Wilson, H. J., & Daugherty, P. R. (2020, September 23). The Next Big Breakthrough in AI Will Be Around Language. Retrieved from Harvard Business Review: https://hbr.org/2020/09/the-next-big-breakthrough-in-ai-will-be-around-language?utm_source=cloudquant&utm_medium=press&utm_campaign=vs

Contacts

For Media Inquiries Please Contact:
J. Tayloe Draughon
tdraughon@CloudQuant.com
Or
Christopher Bartlett
chris@thinkgem.com

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Vector Space Biosciences

Accelerating discovery through advanced language modeling for hidden relationship detection in biological data.