How Cindicator works

Q: For those who are unfamiliar with Cindicator, can you give an overview of what the project is?

A: Sure. We are developing the platform for effective decision-making, mostly for investment decisions and for asset management. We combine the collective intelligence of financial analysts and artificial intelligence to make this decisions more and more effective.

Q: Awesome. So when you say you combine their intelligence. How does that actually work, how do you take, say, artificial intelligence, financial analyst, data scientist? You have a whole pool of different people, different data. How do you actually combine that into a decision?

A: We have a platform where financial analyst register, there are about 85,000 and so financial analysts (Edit: almost 100,000 already!). They are answering different questions about the traditional and crypto market. Those are different events that are expected to happen. There are different types of questions about different factors and of different types. Analysts answer these questions. We’re collecting the data and the statistics of their answers for two years already. We have quite a big data set which is analysed by our machine learning algorithms. Our pipeline of machine learning models enhances the accuracy of indicators that are taken from these crowdsourced predictions. This is the main technological workflow that we have in production at this moment.

Q: And the people who are participating in the system are they getting compensated for it or what’s the incentive for them to go?

A: We are working on a multidimensional system of motivation for them. At this moment, first of all they get points based on the accuracy of their answers. The more points they get, the more rewards they get at the end of each month. Now we do these rewards with Ether, quite soon we’re going to switch this and make rewards with CND tokens.

There are other motivations. Our research shown that some analysts are motivated by the challenge, by the game. So we’re increasing the game mechanics in the user experience. Some of them are motivated by the education, by increasing their knowledge and skills in the area.

Another motivation for the core of these analysts is the feeling of identifying themselves with being a part of the team. Some of them are also token holders and their contribution makes an impact on their wealth. So like Nassim Taleb is writing in his “Skin in the Game”, they have more and more skin in the game.

Q: Is Cindicator primarily focused on financial markets or is the idea that people are going to use these data skills in other arenas as well?

A: Yes the idea is that we’ll evolve. There is a history of how we came to the stage where we’re at this moment. So we started Cindicator three years ago and it was started from the research on the wisdom of the crowds. We tried that on different areas, including politics, sports, fashion, arts, technology and finance. And after testing some business models, we decided to focus on financial markets because this is the most fruitful ground for the growth of this technology, of this organism.

Catching Black Swans

Q: So we’re having this conversation in March 2018. The markets have just crashed 75–80% in the last couple months. Do you think that using a mix of crowd intelligence and AI we can predict these kinds of really extreme events or do you think it’s more about the fluctuations of 20%? Do you think we’ll be able to use crowd intelligence to predict extreme events or even potentially Black Swans? Or is that by definition, because that’s what everyone’s thinking is going to happen, it’s hard to predict?

A: This is the goal of our research. The goal is to create an organism that is more sophisticated and more intelligent than human, than just an individual. This will move the border of what it is possible to predict and will redefine what the Black Swan is for this organism.

What types of events are Black Swans for humans now? Those are events that have the level of complexity that could be infinite. But this could change. And the more intelligence there is, there more black swans are not black for you anymore. So we’re moving the capacity and how it can predict. Now I can say that this hybrid organism is more accurate than any human. The accuracy is growing, still there is a space where the black swans are coming. I think that uncertainty and Black Swans essential part of life, and they will always exist, even after we will evolve into a new species, when we will live in new bodies, when we invade the space — there will be some kind of new uncertainties that we will have to deal with.

Building the neural network

Q: Can you talk a bit about some of the results you’ve seen so far over the last few months in terms of how accurate the hybrid intelligence is, how it’s becoming more accurate over time, how traders may be using that information? Can you just walk us through some of the results you’ve seen so far?

A: Yes sure. I can say that, first of all, we gonna publish it (Edit: recently published indicators sent since Q4 2016). The accuracy in general is growing steadily. This is caused by three factors: the number of forecasters is growing, their level of engagement is growing and the models of machine learning that we’re testing and putting into production is evolving too. So these are three main factors.

I can say that during January and February, during the crash, the accuracy in this period of time was lower than in other two months’ period because of this kind of Black Swan. But at the same time, I can say that in March we have developed our first live neural network model, which is very different from the other models that were used before. Previous models were all linear and we did experiments with neural networks before and they were not as effective as classical, linear model or statistical ones. Now we have come to that moment, where neural networks work and that is only because of the amount of data that we have collected. So now our ecosystem has enough experience to be analysed by a neural networks and to be more sophisticated and more accurate.

We see this in backtests. That’s how we decided that it is good — that’s the best test of the model. And the results of this new pipeline of machine learning are very promising, much more promising that what we’ve had before. But still none of the data scientists can promise anything. It’s not the magic ball, and to have some level of certainty and confidence, there always must be forward test. And that’s what we are doing now. So the experiments continues.

An evolving token economy

Q: In order for people to get access to Cindicator predictions they have to stake a certain amount of tokens, is that right?

A: Yes, currently this is the only way to get access to the product, but it will evolve.

Different factors influenced this decision. One of them was that this is reasonable for this stage of the internal economy that we had. It’s much easier to start using the product and not pay for that, not loose the capital and just to hold it. It’s psychologically and emotionally much easier.

When we opened the access in December, it was the first time we opened it to a wider audience. It was an essential element of growing the collective intelligence part of the ecosystem. Our approach is based on the changed paradigm — unlike in the traditional markets, there is no border between the service provider and client. We are blurring this line and we want to collaborate and we are aiming to engage them into this collective intelligence. So we’re building this community and building the relationships with this type of contribution to the ecosystem.

It’s also a good reason for holding tokens. So it’s a very positive for the internal economy. I can say that what we’re proud that 40% of tokens in circulation are secured by people who are using our products. As far as I know, this is the best result for all the utility tokens that exist at this moment. That 40% of all the tokens are secured for usage, not for speculations. And that makes a difference.

And another part that we had to do is gather feedback. We had to make a survey, and make decisions about pricing based on some experiments. So this was the starting point for experiments, just launching our first customer development and service with these people. We’re researching who they are, how they are satisfied, what they want, how much they would rather pay and recommend to others. This knowledge is essential part for the future decisions.

And how it will evolve? There are different hypotheses, we’re testing them all. The most obvious is that there will be a way to pay with our tokens — not to hold a lot, but to pay a bit. For some people it will be easier. There always will be a moment exclusivity. That’s still in place. Access will be more and more exclusive. So the value of the indicators has its capacity and it could not be utilized by an infinite number of people. At some point, that could be the reason for us to launch a competitive level of access. There could be different types of competition, it could be monetary competition, so that will be an auction. But that might not be the best way to compete. There will be competition in the level of value contributed to the ecosystem.

Actually, I forgot to mention that we have a system of motivation for analysts. We already have this as a reward — top analysts get access to the products. Their impact, the value that they contribute to the system is very big and we highly appreciate it. They must be properly rewarded. So they will always get access, in some way, to different layers of decision making.

The future: developing the ecosystem, helping humanity

Q: So you’ve shared a couple things that you have planned for the future. Improving the artificial intelligence, changing or experimenting with new pricing models. What are some other things you have planned for the future for Cindicator?

A: There are future plans for internal involvement of the ecosystem and there are plans of the external integration to the ecosystem of digital assets revolution and integrations with others companies. I can talk a bit about the inside dimension and then we can move to the outside dimension.

So the inside are about growing the number of analysts. We believe that we can grow this number significantly. The current number 85000, I think a bit more, has grown organically without any marketing campaign (Edit: there are almost 100,000 analysts now). We are preparing the marketing for that and this number will grow. So that will be quiet big activity that we’re planning. There will also be educational systems. So those will be partnerships with different institutions that will help us to make different events and activities that will engage more and more analysts in the system. Of course we’re planning to increase the number of experiments in data science to improve the quality of the models. Also we’re planning to structurize the work with the community.

First of all there are two types of community. The first one is community of traders, those who are taking the biggest risk and those who are currently using our indicators and utilising the value of them. So some of them are generating profits, they take the biggest risk with their capital and generate profit, so we will work with these people mostly. We’re currently working with both of the types, those who are generating profits and those who are losing, both of them are very important, but we will increase the experience of how to effectively utilise this. So this will be big part of collective intelligence, this hybrid organism.

Another type of community is data scientists. Currently all of our models that we’re testing are tested internally by our employees. We will run a number of challenges. I think, probably even this year. There are some other tracks in the community that we will move from manual mode to more automated. That’s the inside part of this.

From the outside dimensions, we see that we hold a very strong position in the market and we feel the responsibility for the process that is happening in the world. And we see emerging organisations, we call them network types of organisations, where we can see not only the hierarchical structure of the company, of the business, but also a decentralised network of people who are giving their best in decision making. There are more and more organisations of this type. And this is a new type that just appears right here, and it’s all about collaboration. And it’s a new type of people and new type of thinking, that we believe is responsible for the future of human civilisation and the main challenges of humanity. So in this dimension we’re about to create alliances and join with other organisations of this type of thinking, manifesting the values of this sustainable evolutionary development, blurring the borders, and bringing transparency, trust and security to the people. And this is about the open conversation in which we will take a big part.

Q: How do you think blockchain technology can help humanity solve some of its more important problems?

A: I can talk about that for us — we will manage our shared resources with the maximum effect and the maximum benefit for the ecosystem. So we will give the opportunity for collective intelligence to decide. What is good and what is not good? What is important right now, what is not? How to solve the main challenges? What are the main challenges?

All of us have to realise the moment of history we’re living in, that human civilization is coming to turning point. We can evolve into a new kind of life being or we can destroy the civilization. And this will happen just in thirty years or even less. It’s happening right now. And we’re the generation that is responsible for the life. And all these types of organizations, first of all, are about changing the way of thinking, changing the type of relationships: how we make decision, how we make transactions, how we exchange the values, how we help each other, how we collaborate. Each of the projects that we see is coming from different perspective and optimizing different, very important, essential parts of our life.

So those are both directions solving and optimizing some local problems of the society. And at the same time there is a global process, where we unite all together, and we all know what we’re doing and why we’re doing that.