Vectorspace AI (VXV) Targets
Vectorspace AI (VXV) may also reverse in Q1 2022
We may see some targets get hit soon - would be logical to say
As you may see the market in general is acting irrational and in my opinion is highly manipulated ( even tho we do have a strong correlation with the traditional financial markets , kryptoS have the change to get more manipulated under this pretexts )
about the project
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.
Our platform powers research groups in space biosciences along with 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.
We may see some targets get hit soon - would be logical to say
As you may see the market in general is acting irrational and in my opinion is highly manipulated ( even tho we do have a strong correlation with the traditional financial markets , kryptoS have the change to get more manipulated under this pretexts )
about the project
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.
Our platform powers research groups in space biosciences along with 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.