Hybrid Intelligence for financial markets.
ICO dates
Start date: 2017-09-12
End date: 2017-10-12
Registrated in: Russia
Platform: Ethereum
Type: ERC20
PREMIUM ICO
KYC passing required No |
Whitelist No |
Restriction for countries
No
Hard cap | 15,000,000 USD |
Token distribution in ICO |
Price | 0.01 USD |
Acceppting | ETH |
Cindicator search trends in Google
Contents
1 Introduction to Hybrid Intelligence
3
IFI ‡h—t is ry˜rid sntelligen™ec F F F F F F F F F F F F F F F F F F F Q
IFP ere—s of —ppli™—tion F F F F F F F F F F F F F F F F F F F F F F F F Q
IFPFI †enture investments F F F F F F F F F F F F F F F F F F F F Q
IFPFP ƒ™ien™e F F F F F F F F F F F F F F F F F F F F F F F F F F F R
IFPFQ gorpor—tions —nd ˜usinesses F F F F F F F F F F F F F F F F R
IFPFR €oliti™s F F F F F F F F F F F F F F F F F F F F F F F F F F F S
IFQ ry˜rid sntelligen™e for investments —nd —sset
m—n—gement F F F F F F F F F F F F F F F F F F F F F F F F F F F F F S
2 Ecosystem of Hybrid Intelligence
5
PFI golle™tive intelligen™e F F F F F F F F F F F F F F F F F F F F F F F T
PFP erti(™i—l intelligen™e F F F F F F F F F F F F F F F F F F F F F F F F U
3 Token sale
7
QFI ixpedien™y of issuing gxh tokens F F F F F F F F F F F F F F F F V
QFIFI i'e™tive e™onomi™ motiv—tion of —ll e™osystem p—rti™ip—n...
7 Team and stages of development
31
UFI „e—m F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F QI
UFP gurrent progress of the ™omp—ny F F F F F F F F F F F F F F F F F QP
8 Legal considerations
33
VFI veg—l F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F QQ
VFP veg—l st—tus of gxh tokens F F F F F F F F F F F F F F F F F F F F QQ
VFQ veg—l st—tus of ™rowdsour™ed fore™—sting pl—tforms F F F F F F F QQ
9 Conclusion
34
10 Risk factors and disclaimers
34
References
36
P
...
1 Introduction to Hybrid Intelligence
1.1 What is Hybrid Intelligence?
ry˜rid sntelligen™e is the ™om˜in—tion of hum—n intelligen™e —nd m—™hine intelE
ligen™eD —nd their inter—™tion in resolving v—rious t—sksF yne sort of intelligen™e
supplements —nd strengthens the otherF
gle—rlyD one m—y f—™e m—ny ™h—llenges during the de™isionEm—king pro™essF
ry˜rid sntelligen™e —nd other rel—ted systems under development —re —pproE
pri—te for resolving these kinds of di0™ultiesF „his is not only due to the
™riterion of speed in de™isionEm—king E n—melyD the question of why one should
w—ste time on simple t—sks th—t ™—n ˜e resolved ˜y ˜oth individu—ls —nd simple
m—them—ti™—l methods —nd —lgorithmsc st is —lso rel—ted to the ™omplexity of
the t—sks —nd the level of un™ert—inty in the systems used to resolve themF
sn one of his l—test interviews E ilon wusk spe™ul—tes th—t hum—ns should
soon merge with —rti(™i—l intelligen™e —nd ™reÂ...
1.2.2 Science
„he sym˜iosis of these two types of intelligen™e ™ouldD in this ™—seD e0™iently
downpl—y the dis—dv—nt—ges of hum—n 9emotion—l9 —ppro—™hes ˜y strengthenE
ing the de™isionEm—king sign—l with — num˜er of de™entr—lised d—t— —n—lysis
pointsF …sing su™h — method is re—son—˜le in systems with higher un™ert—inty
—nd highly ™omplex of t—sksD for ex—mple in ˜iote™hnologiesF sn — renowned s™iE
enti(™ p—perD rese—r™hers ™re—ted — g—me in whi™h e—™h pl—yer with — di'erent
degree of knowledge ™ould t—ke p—rt in mole™ul—r do™king @— pro™ess th—t helps
predi™t the stru™ture of — future ™hemi™—l element with ™ert—in desired properE
tiesAF i—™h proje™t p—rti™ip—nt ™ould ˜ind — mole™ule of the protein together
in —ny w—yF …sing this ™rowdsour™ed d—t— from — v—riety of experts ™om˜ined
with virtu—l s™reening @™omputer modeling —nd m—™hine le—rningA en—˜les s™iE
entists to ™re—te new medi™ines ˜y ...
1.2.4 Politics
gert—inlyD — simil—r te™hnology ™—n ˜e used for politi™—l purposesF e noteworE
thy ™—se is — wellEknown student proje™t l—un™hed in IWVVD the sow— ile™troni™
w—rketF st turned out to ˜e one of the most pre™ise tools for predi™ting the
results of politi™—l events —nd ele™tions for most ™ountries —round the worldF
€—rti™ip—nts in this 9m—rket9 ™—n ˜uy or sell ™ontr—™ts for the v—rious results
of future politi™—l events @simil—r to short —nd long positions on the sto™k exE
™h—ngeAD thus forming expe™t—tions —nd determining the ex—™t pro˜—˜ility of
vi™tory for one or —nother presidenti—l ™—ndid—teF por two de™—desD this te™hE
nology h—s ˜een predi™ting the results of …ƒ presidenti—l ele™tions with gre—t
pre™isionD when ™omp—red with —ny —n—lyst or ™omp—ny @until the most re™ent
ele™tion in™ident—llyAF
1.3 Hybrid Intelligence for investments and asset
management
…ndou˜tedlyD sto™k ex™h—n...
E will f—n™or ™olle™t more th—n IHH million during the (rst week of sgycY
E wh—t is the pro˜—˜ility of „rump9s impe—™hment during the next three
monthsc
gindi™—tor works ˜y using — l—rge d—t—set th—t is tr—nsferred to — m—them—tE
i™—l ˜lo™k ™onsisting of — m—™hine le—rning model ensem˜le @™le—ningD ™lustering
methodsD line—r regressionsD f—yesi—n modelsD xg˜oost on de™ision treesD geneti™
—lgorithmsD —nd neur—l networksAF w—™hine le—rning models dyn—mi™—lly ™—l™uE
l—te v—rious weightings for e—™h fore™—sterD identify st—˜le system—ti™s in their
errors —nd ™—l™ul—te ™orre™tions for the errorsD elimin—te noiseD —nd gener—te
(n—l predi™tions —nd tr—ding sign—lsF
et the ™ore of our ry˜rid sntelligen™e system is the synergy of the ™olle™tive
intelligen™e of — l—rge group of dissimil—r de™entr—lised —n—lysts ™om˜ined with
—rti(™i—l intelligen™e @m—™hine le—rningD —nd — selfEle—rning model ˜—sed on Â...
r—nkingsF e™™ordinglyD e—™h user9s ™ompens—tion depends on their person—l —™E
tivities —nd the —™™ur—™y of the fore™—stsF
Competitive motivation.
‡e h—ve developed intern—l user r—nkingsD spe™i—l nomin—tionsD —nd other g—mE
i(™—tion elements to enh—n™e the ™ompetition f—™torF
Involvement with trades and investment.
yn our pl—tformD users do not merely fore™—st in order to m—ximise their pointsY
e—™h fore™—st is — mi™roEinvolvement of every user in — re—l or simul—ted tr—dE
ing tr—ns—™tion or investmentF yur tr—ding ro˜ots ™omplete — re—l or model
tr—de linked to every question posedF „his is — signi(™—ntly promotion of the
involvement of every p—rti™ip—ntD ˜oth individu—ls —nd the group —s — wholeD
—nd in™re—ses responsi˜ilityF
Training.
qetting d—ily feed˜—™k on the —™™ur—™y of their fore™—sts —s well —s in™re—sing
their level of knowledge ˜efore prep—ring e—™h predi™tion helps fore™—sters ...
tokens does not viol—te lo™—l l—wsAD g—in —™™ess to new produ™tsD or sell them
to interested tr—dersD —n—lystsD or investment fundsF „okens ™—nnot ˜e sold
to residents of the …ƒeD ƒing—poreD €‚gD or other ™ountries where the s—le of
tokens m—y require registr—tion —s — se™urityF
vegisl—tion on the ™ir™ul—tion of se™urities in ™ert—in ™ountriesD su™h —s the
…ƒeD ƒing—pore —nd €‚g prohi˜its the s—le of gxh tokens to the residents
of those ™ountriesF ‡hen you ˜uy gxh tokensD you should ˜e —w—re of the
restri™tions on their su˜sequent s—le —nd promise to follow our instru™tions
—ndGor those of the ex™h—nge when reselling them to other usersF
3.1 Expediency of issuing CND tokens
„he issu—n™e of our own infr—stru™ture tokens is ™onditioned ˜y the need to
™re—te —n intern—l e™onomy in the e™osystem th—t will est—˜lish tr—nsp—rent
—nd f—ir rel—tions —mong —ll p—rti™ip—nts ™omprising the systemX fore™—sters
—n...
Cindicator Roadmap
November 2014
Idea of the hybrid intelligence
December 2015
Global public release of 1.0 version of the collective intelligence platform on iOS
June 2016
Successful acceleration program in New York, raised $300,000 in pre-seed venture round
August 2016
Start of forward-testing and trading
November 2016
Membership at Microsoft BizSpark ($120,000 grant)
December 2016
Release of 1.0 version of trading signals API, test integrations with 14 hedge funds
March 2017
Top-1 startup at Moscow Stock Exchange accelerator, successful public pilot with MOEX
September 2017
Token Sale
November 2017
Trading indicators, indexes, analytical products, ICO ratings, Web-version release of the collective intelligence platform
Q1 2018
1st payout for forecasters from the dynamic motivational pools (ETH/BTC/CND); trading robots
2019
Tech infrastructure for investment funds, crypto ETF, secure infrastructure
Founder, CEO at Cindicator
Product Advisor
CEO & Founder and project leader
Bitcoin Pioneer
Blockchain Expert, Co-Founder @ Bitcoin Foundation
Founder of the Bitcoin Foundation and founder and CEO of BitInstant LLC
Founder at CryptoIQ | Founder at Bitcoin Foundation
Blockchain Advisor
Early Bitcoin Evangelist & Investor
Bitcoin Pioneer
Advisor
Technical Advisor
Investor & Business Advisor
DLT Regulations and Legal Adviser
Corporate Attorney, Partner at Velton-Zegelman PC
Corporate lawyer, Partner at Velton Zegelman PC (San Francisco)
Advisor, legal services
CHIEF LEGAL OFFICER
Co-Founder and Partner of Velton & Zegelman
Advisor
Investment Advisor