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How does crowdsourcing lead to optimized algorithms?

Crowdsourcing is probably the most efficient way to get optimized AI models. But before we discuss why that is, we need to determine what we mean by “optimized.”

An AI model is optimized when it has been tuned to the point that it consistently performs at the highest level of precision for a given use case and a given set of custom constraints. It must also perform with a high degree of confidence under production conditions. Because optimization depends on specific use cases and constraints, the same model can be optimized for performance at one company and still perform badly at another. 

There are four key steps towards achieving an optimized model:

  1. Clearly define the business objective and translate it into one or more data analytics challenges. 
  2. Establish trustworthy, structured sources of input data. 
  3. Define the optimal way of combining input data and converting it into features that the model can leverage. 
  4. Identify the models or combination of models that provide the highest accuracy for the specific business challenges being addressed. 

Although these steps are quite straightforward, following them successfully requires rapid experimentation and evaluation of many different approaches to building the model. The various approaches can be compared, thereby identifying the one with the best performance. Now the optimized model has been revealed. 

Most businesses seeking to leverage AI solutions try to accomplish these steps with traditional approaches, like working with a team of data scientists. Unfortunately, this approach is limited by the biases of the specific scientists working on the problem. This means that optimal models are often dismissed by the people involved before even being tested. 

In the CrowdANALYTIX crowdsourcing model, hundreds or thousands of data scientists compete against each other to produce AI solutions. Companies can compare thousands of different approaches in only a few weeks, with the confidence that biases have been avoided. Only crowdsourcing provides an efficient and cost-effective way of getting many data scientists to work independently on the exact same problem. 

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