For Data Vendors, DaaS Providers & Institutions

200+ enterprise data customers use starmine AI for unsupervised machine learning, artificial intelligence, advanced pattern recognition, context-controlled clustering, hidden relationship discovery or visualization for the Financial Markets (Stocks, Cryptocurrencies, Options, ETFs), Life Sciences/Drug Discovery, Entertainment, Automotive, Travel, Energy, Education, Advertising etc.


Leverage on-demand datasets for equities or cryptocurrencies correlated to features trending in
Global Search, Social, News & Sentiment.
EquityType:
NYSE/Nasdaq
Feature:
Playstation
Feature:
Helium
Feature:
Graphene
GOOG 0.000 0.000 0.000
AAPL 0.000 0.000 0.000
BITCF 0.000 0.000 0.000
... ... ... ...

"...the best way we've found to increase predictablity with additional features... " - Data Vendor

The Tate Collection on GitHub

On-Demand Price Tiers

Free
 

Tier 1: Free (limited)

1 Free on-demand update

Equity Types: NYSE stocks

Data Streams: Featured only

Data Vendor
 

$0.99 per on-demand updates

100 Free on-demand updates

Equity Types: Bitcoin & CryptoCurrencies, NYSE, Nasdaq, OTC

Data Streams: Any

Custom Features

Support

Institutional
 

$1,950.00/mo + $0.99 per on-demand updates

10,000 Free on-demand updates

Equity Types: Any

Data Streams: Any

Custom Features

Support

About

Starmine is a robust and highly scalable platform for constructing, trading and exchanging advanced algorithmically generated on-demand datasets for Machine Learning (ML) and Artificial Intelligence (AI) efforts. Datasets remain at the core of most advances in ML and Al. A dataset is typically made up of rows and columns, similar to an organized matrix or spreadsheet. Specifically, columns contain features along with continuously valued attributes or scores. These features and their scored attributes are stored as 'supercolumns' and can be automatically engineered, traded or exchanged by machines without human intervention. This can also be done with full anonymity using Starmine tokens to execute and transact smart contracts where each dataset or each individual feature column in any dataset can be treated as a tradable asset which is transacted and executed via smart contracts on the blockchain.



A commercial example of a Starmine built dataset with supercolumns can be found at Google's data science community, Kaggle. Starmine supercolumns and datasets are currently being built and transacted by a variety of institutions in the Financial and Cryptocurrency markets including hedge funds, data vendors and Data-as-a-Service (DaaS) providers supported by Starmine’s engineering community where Starmine tokens will be used to transact on-demand updated real-time datasets and supercolumns. The Starmine platform is currently being extended to service ML and AI efforts in additional industries such as Life Sciences/Pharmaceutical, Food, Energy, Travel, Automotive, Entertainment, Advertising etc.

Advanced on-demand datasets powering Machine Learning and Artificial Intelligence efforts are ready to change the world of commerce and society in a large way. This is exemplified in what Stanford professor and Google Cloud chief scientist observed in a recent article titled "The data that transformed AI research—and possibly the world" :

"In 2006, Fei-Fei Li [Stanford professor and Google Cloud chief scientist] started ruminating on an idea. Li, a newly-minted computer science professor at University of Illinois Urbana-Champaign, saw her colleagues across academia and the AI industry hammering away at the same concept: a better algorithm would make better decisions, regardless of the data. But she realized a limitation to this approach—the best algorithm wouldn’t work well if the data it learned from didn’t reflect the real world.

Her solution: build a better dataset.

“We decided we wanted to do something that was completely historically unprecedented,” Li said, referring to a small team who would initially work with her. “We’re going to map out the entire world of objects.” The resulting dataset was called ImageNet."


From our vantage we see a gap that's continuing to grow between data being generated and the data that can actually be used by today's ML and AI systems, otherwise known as, the dataset. If the pillars of ML and AI are the algorithms, then we should be able to say the foundations are made up of datasets. The better your datasets, the better your predictive ability, signal to noise ratio, bottom line returns on a dynamic basket of equities/cryptocurrencies or your ability to uncover a hidden relationship between a disease and a plant compound or a new kind of epigenetically-based cure. This is why we remain interested in advancing approaches in algorithmically generated on-demand datasets.

Most recently, we've decided to advance our use of the blockchain to store datasets and their columns as assets after they are constructed by anonymous agents in or outside of our platform. We'd like to engage the community of engineers and scientists to help us determine how best we can leverage smart contracts such that an incentive, in the form of starmine tokens, is provided to anyone constructing, engineering or calculating for high quality features, columns and ultimately, datasets. A more complete description of the platform and its capabilities can be found in one of our whitepapers here. If you have any ideas or directions on ways we can improve this process, drop a note in the contact form below, we'd love to hear from you.

Team

Kasian Franks

Kasian Franks (HN | LinkedIn | Medium | kasian.franks@gmail.com)

As a 25-year Silicon Valley veteran and pioneer in digital content streaming before Netflix and Amazon entered the space. Franks started as a software engineer working for companies such as Genentech, Sun, Oracle, Cisco, Motorola and Morningstar. In 2005, as a genomic research scientist at Lawrence Berkeley National Laboratory, he was the lead inventor of new vector space representations of hidden relationship networks in data along with pattern recognition systems aiming to mimic portions of human cognition. While at the Lab, he co-authored a paper with Michael I. Jordan (machine learning maestro and doctoral advisor to Andrew Ng) titled “Statistical modeling of biomedical corpora: mining the Caenorhabditis Genetic Center Bibliography for genes related to life span - Blei DM1, Franks K, Jordan MI, Mian IS.”. Following this, he co-founded SeeqPod in partnership with Berkeley Lab and the U.S. Department of Energy that was then headed by Steven Chu, Energy Secretary in President Obama’s first term and winner of Nobel Prize in Physics (1997). SeeqPod was a consumer-facing streaming data search/discovery/recommendation platform originally powering Spotify and others while attracting 50 million monthly active users and 250 million monthly search and recommendation queries. In 2008, his team won the R&D100 award. The company was acquired in 2009. He continues to spend his time mentoring startup founders and advising hedge funds on Machine Learning, Natural Language Processing (NLP), Artificial Intelligence and data science strategies.


Mike Muldoon

Mike Muldoon (LinkedIn | mike.muldoon@gmail.com)

Mike's first program was an ad-lib game, which he wrote in 5th grade on a TRS-80 owned by the school's computer club. He has since established a track record of leading large projects from concept to delivery, and brings over 20 years of experience to Starmine.ai. As employee #1 at SeeqPod, he took the product from whiteboard to 50M monthly active users, delivering an architecture that deployed hundreds of servers across seven different data centers pushing 1.6Gb/s of traffic.


Caleb Patee

Caleb Pate (LinkedIn | earthuser@gmail.com)

Caleb is currently working in Data Science, AI & Machine Learning with a focus on feature engineering and cryptocurrencies while continuing to define, explore and solve problems related to recommendation systems. As a member of the founding team at SeeqPod, he built the core Music Recommendation & Curation strategy. He played in a band with an international following and ran an independent music label and continues to create new musical worlds as a Producer, Musician and DJ.

ICO Details & Participation

ICO

Token name: starmine (SME)

Token price: 1 ETH = 2000 starmine Tokens (SME)

Tokens created: 2 billion coins pre-mined

Tokens held by management team: 310 million

Tokens held by pre-ICO participants: 1.090 billion

Tokens available during the ICO: 600 million coins or 30%

Currencies

Accepted currencies: ETH

Transaction of currencies: ETH can be sent to the starmine crowdsale address which will be located only at http://starmine.ai

IMPORTANT: See step 20 for instructions on finalizing your SME transaction

Terms of Contribution
  • You have to send ETH funds from your personal Ethereum wallets like MyEtherWallet, Metamask, Parity, Mist or Ledger (hardware wallet)
  • DO NOT send funds from exchanges like Coinbase, Poloniex etc.
  • DO NOT send your funds before the fundraiser has begun.
  • Set Gas Limit to 90,000 (If it does not work during high-load network, then try increasing the Gas Limit).
  • starmine (SME) tokens will be sent immediately to the wallet from which the ETH arrived.



  • Documentation

    Contact

    Custom Data Streams, Features, Real-time, Context-control