Artificial intelligence has been around for a while now. Even though it hardly resembles sentient computers seen in sci-fi movies, it has found numerous use cases in businesses and the sciences. Neural networks and deep learning have made it possible for computers to process huge amounts of data and even successfully mimic human behavior.
Using artificial intelligence for financial markets isn’t news either. Computers processing big data and returning some predictions are among the most popular solutions for professional traders and brokers. However, the efficiency of such solutions can be surprisingly low sometimes.
This all may change if artificial intelligence is combined with the collective intelligence of human beings.
The Collective Intelligence Phenomenon
In 1906, British scientist Francis Galton found himself at a fair where visitors were offered to guess the weight of an ox. With nearly 800 people participating in the lottery, their guesses were fairly different, especially considering the fact that there were either professional farmers and townsmen who knew nothing about agriculture. Surprisingly enough, when Mr. Galton calculated the direct average of their guesses, it turned out to be 1,197 lbs, while the actual weight of the bull was 1,198 lbs.
This instance demonstrates the phenomenon of collective intelligence; with a vast selection of people, their average prediction often proves to be correct, or a close approximation. Most notably, the selection has to include both professionals in the field and those who have no idea of what is going on. If only one of such groups is present, the results will be biased and most likely far from correct.
Collective Wisdom at the Service of International Finance
Even though such an approach has proved its efficiency on numerous occasions, there have been only a few projects that opted to use it in combination with artificial intelligence directly.
One of them is Estimize, a service that gathers market estimations from professional finance experts, and then processes it with its AI system to return a consensus prediction.
The method proved to be quite trustworthy, and, according to the service’s website, is usually at least 74 percent more accurate than data sources commonly used on Wall Street.
The service has been around for more than five years and has garnered considerable recognition within the professional community.
Another notable project is Cindicator, which has gone a bit farther, and uses the collective intelligence of a random selection of people from around the world.
What makes it stand out, even more, is that it uses cryptotechnologies to bring decentralization to the entire process, and lay a foundation for a decentralized autonomous organization where humans and machines work together.
Synergy of Humans and Machines
Cindicator claims that centralization poses the biggest problem for today’s financial analytics. The reason why professional analysis often proves to be wrong is down to the mutual influence of different opinions and isolation of the professional community. Only a handful of professional analysts offer their forecasts and predictions, so the selection of opinions is not very large.
On the other hand, Cindicator uses an app to collect unbiased predictions from people around the world who have different professional backgrounds, education, gender, political views and so forth. This ensures that the selection of forecasters will be sufficiently randomized, and therefore the laws of collective intelligence will come into effect.
Users are incentivized not only by money (which, of course, is still an important part of the model), but also by gamification of the entire process, and, most importantly, by the involvement of forecasters in transactions and investment. This ensures that the forecasters feel their responsibility and in the long run promotes the model of futarchy, which is deemed perfect for decentralized autonomous organizations.
When the predictions are made, artificial intelligence comes into play. It uses various mathematical models in order to make a single and rock-solid prediction as to the outcome of a certain event.
The system has been tested by a score of banks and hedge funds and proved to be much more reliable, inexpensive and accurate than traditional financial reports compiled by individuals.
Conclusion
The combination of human and machine wisdom could prove especially valuable for traders and other financial experts as they deal with the most volatile ecosystem where a single mistake could cost millions of dollars.
In sci-fi novels and movies, a motif of a merger between the human mind and artificial intelligence is commonplace. It is suggested that it could create a brand new form of interaction, and bring about processing efficiency never seen before.
While this might be only the first steps in this direction, such a symbiosis has already proved that it can hold a great promise for the world of tomorrow.
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