Cindicator (CND) Coin Review : It Is Good Or Bad Review

About Cindicator (CND) Coin

Cindicator (CND) Coin creates the social and technological infrastructure needed to make effective decisions under the volatile conditions of the new economy. By combining a large number of diverse Financial analysts and a set of machine learning models into a single system, we are developing a Hybrid Intelligence infrastructure for the efficient management of investors’ capital in traditional Financial and crypto-markets. a technological and analytical infrastructure for the efficient and safe management of investors’ capital by investors themselves. an opportunity for analysts to monetize their intellectual assets without risking their own funds.

Cindicator (CND) Coin Hybrid Intelligence is the combination of human intelligence and machine intelligence, and their interaction in resolving various tasks. One sort of intelligence supplements and strengthens the other. Clearly, one may face many challenges during the decision-making process. Hybrid Intelligence and other related systems under development are appropriate for resolving these kinds of difficulties. This is not only due to the criterion of speed in decision-making – namely, the question of why one should waste time on simple tasks that can be resolved by both individuals and simple mathematical methods and algorithms? It is also related to the complexity of the tasks and the level of uncertainty in the systems used to resolve them.

Cindicator (CND) Coin In one of his latest interviews – Elon Musk speculates that humans should soon merge with artificial intelligence and create a new kind of interface. This symbiosis could help people settle one of the most complex tasks facing mankind: predicting the future with high accuracy. People have long tried to resolve this issue in all areas of business by using various technologies with varying degrees of success. Investors and traders try to predict future share prices or company success to increase the portability of investment deals. Political analysts try to predict the results of presidential elections, while corporations put a great deal of resources into attempts to foresee future technological trends. Many of them have already used intellectual crowdsourcing to undertake these tasks to a greater or lesser extent. Let’s take a look at the existing solutions.

Cindicator (CND) Coin Storage Key Points

Venture Investments

Most investment venture deals are closed by syndicates at the moment. This means that several investors take part in one round of the deal at the same time. This trend has been increasing year on year. In addition to syndicate deals – which involve partner venture funds – specific associations and collective investment clubs emerge each year (Angel-List is the most famous example)

One of the reasons for such deal structures lies in the use of collective intelligence systems for risk hedging against the potential mistakes of group thinking. This could happen when an investor makes the wrong decision about a deal based on a false insight, trend, or insouciant competence in a given area. In a syndicate, a set of competencies and the investors’ previous experience can be very deferent, which allows them to view the startup as a whole, as well as looking at the team and the potential risks from various angles and to cancel the deal if there are sound reasons to do so. For most venture investments, the best deal is a deal that has never been done before

Politics

Politics Certainly, a similar technology can be used for political purposes. A noteworthy case is a well-known student project launched in 1988, the Iowa Electronic Market. It turned out to be one of the most precise tools for predicting the results of political events and elections for most countries around the world. Participants in this ‘market‘ can buy or sell contracts for the various results of future political events (similar to short and long positions on the stock exchange), thus forming expectations and determining the exact probability of victory for one or another presidential candidate. For two decades, this technology has been predicting the results of US presidential elections with great precision, when compared with any analyst or company (until the most recent election incidentally).

Hybrid Intelligence for Investments and Asset Management

Undoubtedly, stock exchanges are still the leaders in researching and using Hybrid Intelligence in business – this is an area where traders have to make decisions on millions of dollars every second (and trading robots do so every millisecond). Financial markets themselves are the daily prediction of the future in its pure form. At what price and when is the best time to buy Facebook shares, Brent crude oil, the US dollar, or Bitcoin? All these questions are the subject of daily predictions of traders and analysts. Major current market analytics related, for example, to forecasting the future in terms of Inances are created by a limited number of professionals using roughly the same information. Each year the pace of information retrievals accelerates and the value of such reports falls, with fewer and fewer professional traders reading them or taking them seriously.

Cindicator works by using a large dataset that is transferred to a mathematical block consisting of a machine learning model ensemble (cleaning, clustering methods, linear regressions, Bayesian models, xgboost on decision trees, genetic algorithms, and neural networks). Machine learning models dynamically calculate various weightings for each forecaster, identify stable systematics in their errors and calculate corrections for the errors, eliminate noise, and generate nal predictions and trading signals

The complexity of every goal set. For results to have any relevance, they cannot be derived from extremely complicated questions addressed to different users (i.e., the expected Bitcoin price in USD in 376 days). However, the collected signal should present sufficient value. Aside from the complexity of the set task/issue, there is a need to create the most convenient infrastructure for each participant to make such forecasts. To do so, in December 2015 we launched a mobile platform where we focused on the interface used by forecasters. As a result, it takes three to ve minutes for each user to generate one data point.