Cindicator could pass as a yet another prediction market, however, on closer look it becomes clear it’s not one.
ForkLog contacted the project’s co-founders, Yuri Lobyntsev and Mikhail Brusov so that they could clarify what’s behind their ambitious ideas and plans.
Forklog: Possibly, everyone who studied Cindicator’s basic ideas was intrigued by the formula or “collective wisdom + AI = hybrid intelligence.” What’s the functions that the AI will perform at the prediction market, and how will it interact with user predictions?
Yuri Lobyntsev: First of all, our AI has nothing to do with prediction markets. There are numerous differences between our approach and a classic prediction market, and it’s important. Analysts risk their money by making predictions at a prediction market, and those markets are created by customers who pay for that. In our case, there are no bets, and there are no risks.
Our analysts make their predictions without any risks, and score points for accuracy of those predictions. The AI studies divergence and accuracy of every analyst’s prediction, and dynamically changes the trust weight factor for them. Thus it solves the problem of forming a single accurate prediction on the basis of many predictions by all analysts, and it uses the accumulated stats on all those analysts.
FL: Are the people needed only to teach the AI, or their interaction will run deeper?
Y. L.: People will be needed, of course. People and their constantly changing expectations are a part of the market that is compulsively related to its behavior. That’s the basis of our technology and research.
Mikhail Brusov: We value both accurate long-term predictions and mistakes as well. Ninety per cent of mispredictions are systematic, and we disclose and research that system to have a set of valuable data used for some analytic products. Ninety per cent of traders work in the red. Just like in any other vertical structure, most estimations are wrong, but it doesn’t make the entire dataset less valuable for our machine learning models.
FL: Who is developing the hybrid intelligence?
Y. L.: It’s the work of our big team of seasoned mathematicians, coders, data scientists, traders and strategists.
FL: Has this approach ever been tested before?
Y. L.: Frankly, we haven’t seen anything like that in two years that we’ve spent working on the project. We know that there are a few teams in the world who independently use partially similar approaches. However, we know of no one who used the decentralized mind of financial analysts and machine learning together.
FL: Aside from financial markets, where could this approach also apply?
M. B.: Hybrid intelligence and its sub-products could be used in any industry requiring one to make difficult decisions under uncertain conditions. Value of this approach may vary for different verticals. I see science, business analysis, social studies, politics and sports as the most promising areas for us.
Y. L.: Hybrid intelligence could replace venture investment funds in the future, and Cindicator’s ecosystem could become a new venture investment industry. Some of our analysts could become oracles or delegates for venture areas on Cindicator and assume such functions as project sourcing, due diligance, assessment of cost and attraction of some projects. It also includes factor analysis, risk assessment, analysis of demand, value propositions testing, search for competitors, market research, business development and so on.