Crowdsourcing is one of the most efficient ways to obtain optimized AI models. But before we discuss why that is the case, we should clarify what we mean by "optimized".

When an AI model is optimized, it is tuned to work with a consistently high level of accuracy for a specific use case and customized constraints. Of course, even under real production conditions, it has to work with high accuracy. Optimization depends on specific use cases and constraints, so the same model can work well for one company and not for another.

There are four key steps to arrive at an optimized model.

1. .Clearly define business objectives and transform one or more data into analytical challenges.
2. Ensure a reliable and structured source of input data.
3. .Define the best way to combine input data and transform it into a form that the model can use.
4. Identify the most accurate model, or combination of models, for a specific business problem of interest.

While these steps are fairly straightforward, their execution requires rapid testing and evaluation of multiple approaches to model building. By comparing different approaches, the best performing model is found. This is how the optimization model is revealed.

Many companies looking to leverage AI solutions attempt to accomplish these steps with traditional approaches, such as working with teams of data scientists. Unfortunately, traditional approaches are limited by the bias of the team of scientists trying to solve the problem. This means that people involved often filter out the best models without even testing them.

CrowdANALYTIX In our crowdsourcing model, hundreds or thousands of data scientists compete with each other to create AI solutions. Firms can reliably eliminate the effects of bias and compare thousands of approaches in just a few weeks. Only crowdsourcing offers an efficient, cost-effective way to get many data scientists to work independently on the exact same problem.

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Source:https://www.crowdanalytix.com/how-does-crowdsourcing-lead-to-optimized-algorithms/
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