A Deep Dive on the Data-focused Crypto, Vectorspace AI (VXV)

7 min readDec 28, 2019

While ‘data’ might be the new oil, the ‘dataset’ is the refined gasoline that powers every Machine Learning (ML) and AI operation.

Before you begin your deep dive on VXV, you might want to do a shallow dive with these introductory links:

Vectorspace AI: ABT Crypto AMA (Ask Me Anything)

Vectorspace AI & CERN Create Natural Language Processing (NLP) Datasets in Particle Physics with Applications in Artificial Intelligence (AI) for Every Industry

Dexamethasone Announcement Could Have Made Hedge Funds A Fortune, published by Alpha Week

Generating and visualizing alpha with Vectorspace AI datasets and Canvas, published by Elastic (NYSE: ESTC)

3 Genuine Artificial Intelligence + Crypto Companies

Our main community channel Telegram

How to Acquire & Trade VXV — A Step-by-Step Guide

Now lets begin the deep dive:

Name: Vectorspace AI

Symbol: VXV

Site: https://vectorspace.ai

Team background, experience and track record:

We’re a team with a deep background in science, technology and the financial markets.

We’re veteran software engineers, scientific and technical founders. We work in the area of specialized algorithms in ML/AI for Life Sciences and the financial markets.

Here are a few references that might help during any due diligence process on Vectorspace AI:

- We’ve done an AMA on AI in reddit.com/r/AskScience with 16M subscribers: https://www.reddit.com/r/askscience/comments/9k5i8u/askscience_ama_series_were_team_vectorspace_ai/

- We’ve been in the AI industry since 1994 at Genentech executing on pattern recognition algorithms. Before that, we built search engines which have their roots in AI. Ref: “Overlooked No More: Karen Sparck Jones, Who Established the Basis for Search Engines” https://www.nytimes.com/2019/01/02/obituaries/karen-sparck-jones-overlooked.html “Ideas she wrote about are now being put into practice as artificial intelligence research becomes more prevalent.”

- In the bioinformatics industry, we invented new systems and patented commercial products that assisted in finding hidden connections between human genes right after the human genome was sequenced. This involved pattern recognition and prediction (a pillar of AI/ML). This was when the term ‘Data Science’ did not exist when everyone called it ‘Data Mining’ and ‘Knowledge Discovery’ aka AI.

- In the bioinformatics industry, our group worked with Rob Tibshirani and Trevor Hastie, both top level figures in statistics, data mining (AI):

https://en.wikipedia.org/wiki/Robert_Tibshirani

https://en.wikipedia.org/wiki/Trevor_Hastie

- Based on our work, we were invited to Lawrence Berkeley National Laboratory/DOE/DOD to work on ‘special’ projects 10–20 years ahead of their time related to developing systems to find hidden connections between genes that extended human lifespan and chromosomal damage related to LET Radiation (space radiation) for the purpose of space bioscience and extended space travel related future population of habitable planets. We continue work in the area with Mina Bissell, Life Sciences division director at Lawrence Berkeley Lab for 14 years and distinguished scientist: https://youtu.be/xukDIWFMU9Y

- While at the Lab, we had the privilege of collaborating with Michael I. Jordan (teacher of Andrew Ng who built Google’s AI foundation which all others sit on top of today) and David Blei, ‘grandfather’ of LDA (topic modeling, AI related to predicting topics in human language) Ref: Statistical modeling of biomedical corpora: mining the Caenorhabditis Genetic Center Bibliography for genes related to life span — Blei DM, Franks K, Jordan MI, Mian IS. — http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1533868 — Min: 1:01:51 https://www.youtube.com/watch?v=28TefyYoAm4&t=3711s

In the past, we’ve raised capital for more than one startup including $7.4M for our largest startup with 50M MAUs and 250M searches per month along with successfully disrupting the music market related to Apple, Warner and EMI based on our work above and described here:
https://medium.com/startup-frontier/steve-jobs-made-warner-music-sue-my-startup-9a81c5a21d68

More on our work:

‘Word2Vec is based on an approach from Lawrence Berkeley National Lab’https://www.kaggle.com/c/word2vec-nlp-tutorial/discussion/12349

Products:

The easiest way to understand what Vectorspace AI does would be jump right into it by observing a real life event that resulted in an opportunity to profit based on our NLP/NLU real-time datasets and connected to what’s called ‘information arbitrage’.

Our platform, which includes datasets and products that can built on top of them, enable known and hidden relationship detection in data. Our datasets can be used to cluster entities that have known and hidden relationships to outside events, global trends or news. These clusters can represent networks of suppliers, companies working on similar drugs or any entity that might have symbiotic, parasitic or sympathetic latent entanglement with another entity, event or global trend.

Examples and case studies are located here:

https://vectorspace.ai/examples

https://www.alpha-week.com/dexamethasone-announcement-could-have-made-hedge-funds-fortune

https://www.elastic.co/blog/generating-and-visualizing-alpha-with-vectorspace-ai-datasets-and-canvas

Create a visualization from one of our datasets:

http://vectorspace.ai/vis/relationship-network

Real-time heatmap clustering:
https://vectorspace.ai/vis/heatmap/stocks-by-drugs.html

Hidden relationship network clustering:
https://vectorspace.ai/vis/clusters/v2/

More details on our products:
https://vectorspace.ai/vis/heatmap/

An example of how our customers make money with our product, an equity-based case study of the Celgene (CELG) acquisition:

https://vectorspace.ai/assets/Vectorspace-NLP-Dataset-Use-Cases-v03.1.pdf

Revenue model:

VXV Revenue model (with mathematical proof), slide 14:

https://vectorspace.ai/assets/VXV_Deck_External.pdf

(Revenue calculation breakdown: https://vectorspace.ai/assets/the-math.txt)

Tokenomics:

The VXV utility token network diagram is described in detail here, slide 11:

https://vectorspace.ai/assets/VXV_Deck_External.pdf

The value of the Vectorspace VXV utility token:

What is the network worth?

- Billions. This is because we are a data company with a focus on revenue generating customers which are trillion dollar asset management companies, funds and other financial institutions. Our job is to help them make money by providing them with an edge. This happens based on our product, NLP/NLU on-demand datasets that are updated every minute and based on any data they choose. Our revenue model, described, includes related details.

- You’ll also find our token transaction network diagram in that deck which illustrates how our VXV token is utilized. This includes transacting dataset updates along with our Data Provenance Pipeline (DPP) hash which controls data lineage (aka provenance). Knowing where your data comes from and knowing how reliable it is, is extremely important to financial institutions that rely on it to make billion-dollar decisions everyday. We give financial institutions an edge that to them, is worth billions.

Why is the token valuable?

- Value created by our community which includes the core team, the outside team and contributing members of our global community, translates directly into the value of the VXV utility token and as a global public trading vehicle.

- VXV utility tokens do not function like a security or currency and share only minor similarities with e.g. Google Cloud credits, AWS credits or WeWork utility credits due to VXV doubling as a public trading vehicle in a global public marketplace.

- Our top-tier proprietary datasets and algorithms deployed in the financial markets that enable asset management groups, hedge funds and institutions to generate and capture alpha, can only be used by a limited number of customers. In this business, it’s a common requirement from our top-tier customers to prevent saturating the market. It’s like giving everybody the exact same weapons. This means the value of VXV is controlled by our customers, who will also be taking long term positions in VXV. It only makes sense and it’s not in our control.

- In order to serve our customers properly, we’ve carved out a public marketplace which allows them to acquire blocks of VXV and out-bid other customers if they’d like to ‘corner the market’ on particular proprietary datasets. This happens when they sign-up for top-tier services and pay for them by acquiring and bidding for VXV on the open market. Once acquired, their wallet address can be used inside the VXV network to access top-tier services based on the VXV wallet-enabled API key. These services also include tracking of data lineage via the Data Provenance Pipeline (DPP) hash.

- The VXV utility token credit also doubles as a global public trading vehicle available to be transacted, acquired, bought and sold between anyone, including speculators, in the global public crypto markets via the ProBit exchange and soon to be, one or two other larger exchanges. This means a farmer in Kenya or a villager in Borneo can acquire VXV to access a dataset one minute and resell VXV to a trillion dollar asset management company the next minute in exchange for “JPM coin” for example. This is completely out of our control.

- We have plans on enabling machines (data engineering pipelines onsite at customer locations) to transact VXV with one another for the purpose of ‘minimizing loss’ which is at the core of effective ML/AI.

- In the case of an M&A event or strategic investment by one of our customers, or outside companies or investors, the VXV utility token will be the most valuable asset they acquire related to this company based on the above. This is if we allow any M&A in the first place of course. This is how we’ve financially engineered, structured and positioned Vectorspace AI.

Reddit: https://www.reddit.com/r/VectorspaceAI/

Telegram: https://t.me/joinchat/GrCYjA8rPgD8coAiEhRuBA

ERC20 etherscan and contract address: https://etherscan.io/token/0x7D29A64504629172a429e64183D6673b9dAcbFCe

How to acquire & trade VXV on exchanges:
https://medium.com/@492727ZED/vectorspace-ai-vxv-customer-on-boarding-instructions-61aff13b66a9

Chinese: https://vectorspace.ai/assets/VXV-IDEX-on-boarding-CH.pdf

Partners & Collaborators:

S&P Global https://www.spglobal.com/en/

Amazon https://aws.amazon.com/

Microsoft https://www.microsoft.com/

CloudQuant https://info.cloudquant.com/

Neudata https://www.neudata.co/

Elastic (ESTC) https://finance.yahoo.com/quote/ESTC?p=ESTC

WorldQuant https://www.worldquant.com/home/

Lawrence Berkeley National Laboratory https://www.lbl.gov

National Library of Medicine (NLM) https://www.nlm.nih.gov/pubmed

The European Molecular Biology Laboratory (EMBL) https://www.embl.de

CERN — European Organization for Nuclear Research https://home.cern/

LCX https://www.lcx.com/

Trustology https://trustology.io/

Selected Patents, Papers & Awards:

Articles authored by the founders: https://medium.com/@492727ZED

Winner R&D100 Award — Lawrence Berkeley National Laboratory http://newscenter.lbl.gov/news-releases/2008/07/09/berkeley-lab-wins-four-2008-rd-100-awards

System and method for generating a relationship network — K Franks, CA Myers, RM Podowski — US Patent 7,987,191, 2011
http://www.google.com/patents/US798719

Inter-term relevance analysis for large libraries
https://patents.google.com/patent/US20030204496A1/en

Matching and recommending relevant videos and media to individual search engine results https://patents.google.com/patent/US8037051B2/en

Media discovery and playlist generation https://patents.google.com/patent/US8117185B2/

System and method for summarizing search results
https://patents.google.com/patent/US20080154886A1/en

Discovering and scoring relationships extracted from human generated lists
https://patents.google.com/patent/US8108417B2/en

Statistical modeling of biomedical corpora: mining the Caenorhabditis Genetic Center Bibliography for genes related to life span — Blei DM, Franks K, Jordan MI, Mian IS. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1533868

A Search Engine that Thinks, Almost
https://newscenter.lbl.gov/2005/03/31/a-search-engine-that-thinks-almost

Social Media

https://www.reddit.com/r/VectorspaceAI/

https://twitter.com/Vectorspace_AI

https://www.linkedin.com/company/vectorspace-ai

https://vectorspace.ai/press.html

Upcoming news and announcements: Coming soon!