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How to use data scientists and machine learning in the enterprise

Tom Macaulay | March 27, 2017
Working with data scientists requires an alternative approach to business in which logic overrules creativity.

Yandex used historical data to make an accurate model of how best to balance the quality and cost of the mixture, returning with a recipe provided by a machine learning algorithm.

"This recipe often doesn't make sense to them," says Zavalishina. "They look and say 'no it won't work, I cannot do that, I'm not accepting this, I'm doing something different'.

"The funny thing is it will bring better optimisation, but on the other hand you have the experts' [preferences], so how do you deal with that? They are basically not using 80 percent of your recommendations.

"We came up with a solution, which would be another algorithm which looks at the recipe we provided, and on top of that builds the prediction of how probable it is to be accepted by that trader. So optimise the recipe so they became a bit a less optimal from a strictly mathematical point of view, but much more probable to be accepted by humans."

Fears have long been expressed that artificial intelligence could destroy humankind, but the marriage between man and machine learning remains at the foundation of data science.

 

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