Visualizing alpha with Vectorspace AI datasets and Elasticsearch

Vectorspace AI utilizes Natural Language Understanding (NLU) and the Elastic Stack to create and visualize datasets that uncover hidden relationships between genes, stocks, and other entities in life sciences and financial markets. Initially developed at Lawrence Berkeley National Laboratory, these datasets leverage feature vectors and word embeddings to analyze correlations in data sources such as scientific literature and genomic databases. By augmenting financial datasets with these vectors, Vectorspace can generate alpha by predicting price movements in stocks, demonstrated through events involving companies like Merck and Celgene. The approach draws parallels between the behavior of stocks and genes, using NLU-based correlations to identify latent interconnections that can be visualized with tools like Kibana's Canvas. This methodology not only aids in financial markets by optimizing signal-to-noise ratios and generating actionable insights but also supports research in areas like human spaceflight safety. Through collaborations with institutions such as NASA, DARPA, and Genentech, Vectorspace continues to expand its applications in both scientific discovery and market analysis.

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