Smart Contracts for On-Demand Datasets in Machine Learning & Artificial Intelligence
"picture machines transacting tokens with one another in exchange for a reduced error rate..." - Data Vendor
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.
On-Demand Price Tiers
Tier 1: Free (limited)
1 Free on-demand update
Equity Types: NYSE stocks
Data Streams: Featured only
$0.99 per on-demand updates
100 Free on-demand updates
Equity Types: Bitcoin & CryptoCurrencies, NYSE, Nasdaq, OTC
Data Streams: Any
$1,950.00/mo + $0.99 per on-demand updates
10,000 Free on-demand updates
Equity Types: Any
Data Streams: Any
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'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 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
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%
Accepted currencies: ETH
Transaction of currencies: ETH can be sent to the starmine crowdsale address which will be located only at http://starmine.ai
Terms of Contribution
Custom Data Streams, Features, Real-time, Context-control