Vectorspace AI, a subsidiary of Vector Space Biosciences, Inc., now enables cryptocurrency exchanges with a thematic crypto baskets REST API available here. This API enables an exchange to offer tradable baskets of cryptos related to a theme, event or topic of any kind in real-time. The API is designed to spawn an ecosystem of new products for retail traders and investors providing them with abilities similar to hedge funds or asset management companies operating advanced data engineering pipelines. "It's like having your own dedicated NLP, AI, or Machine Learning pipeline," remarked Kasian Franks, Founder and CEO of Vector Space Biosciences, Inc. "This opens up a new world of thematic investing where baskets of cryptos or stocks can be generated based on a theme or global event in real-time using similar'language modeling' techniques used to predict the way proteins fold by DeepMind's AlphaFold2." VectorScreen is an additional filtering package powered by the core crypto baskets API, enabling advanced screening and filtering resulting in additional alpha.

"The Next Big Breakthrough in AI Will Be Around Language" - Harvard Business Review While data might be the new oil, the dataset is the refined gasoline that powers every Machine Learning (ML) and AI operation. We focus on context-controlled NLP/NLU (Natural Language Processing/Understanding) and feature engineering for hidden relationship detection in data related to space biosciences. Our platform powers advanced approaches in Artificial Intelligence (AI) and Machine Learning (ML) using experimental and formal language models including well-known models such as OpenAI's GPT-3 (2020), Google's BERT (2018), word2vec (2013) combined with experimental methods developed at Lawrence Berkeley National Laboratory (2008) Biosciences division. 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.