The wisdom of the crowd could be the ideal model for teaching artificial intelligence

We all know some of the biggest Internet crowdsourcing experiments, such as Wikipedia and Amazon Mechanical Turk. But today crowdsourcing is applied to new and high technologies, such as artificial intelligence. And it is the crowd that could humanize our use of the AI ​​and help to instill some common sense into the machines of the future.

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Increasing resources in machine learning and artificial intelligence ensure that these platforms will become increasingly intelligent. But these systems are not yet self-learning. However, optimism exists at the top for crowdsourcing systems.

For example, Eric Schmidt, longtime president and CEO of Google, said in 2016 that the The next Google will be an AI company based on crowdsourcing. The crowd can work to label and define data, for example to label an image like a sunrise or a sunset. In the future, the crowd will provide different sets of data, such as information on the individual's daily health.

This would help companies to develop new drugs, for example, and the next generation of crowdsourcing will see the crowd taking data payments or people offering data for free to help move a good cause forward.

Blockchain for the first time makes it economically feasible to send micropayments regularly. This has the potential to encourage key world researchers and institutions to collaborate on a distributed or decentralized version of Watson that is not owned by a centralized company, but rather by token holders. This is the model pursued by Seoul Mind AI.

It is this monetary incentive that could be the key to pushing IA technology forward as the current artificial intelligence technology is not at all close to human intelligence, at least in terms of general cognitive abilities.

Modern artificial intelligence does not learn too quickly and does not have the ability to adapt to abstractions, reasoning, language comprehension and rapid change in circumstances. Deep learning is rudimentary at best in certain linguistic tasks, planning and explaining why actions are undertaken.

There are also questions about the nature of the crowd. What happens if, in the future, the same crowds include machines that collaborate with the group? Crowdsourcing will therefore take an interesting turn and will involve many people and machines and platforms working together in real time to generate data and insights.

Could it be that the best researchers and research institutes will earn tokens along with robots?

While developers continue to work to build models of artificial intelligence with in-depth learning capabilities, it will be the case with more data, the better. Knowledge is the lifeblood of artificial intelligence and will be instrumental not only to help build the model it will work on, but also to teach us more about how to build the perfect AI.

The knowledge of crowdsourcing seems to be an effective method; for the multiplicity of data results, it offers the best chance to educate an AI as widely as possible.

Encrypted micropayment incentives can play a key role in this. By helping to obtain previously unimaginable volumes of knowledge in an artificial intelligence model, a crowd of unique individuals may prove to be the best solution to generate volumes of data needed for truly intelligent artificial intelligence.

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