“It’s not enough to tell someone, ‘This is done by boosted decision trees, and that’s the best classification algorithm, so just trust me, it works.’ As a builder of these applications, you need to understand what the algorithm is doing in order to make it better. As a user who ultimately consumes the results, it can be really frustrating to not understand how they were produced. When we worked with analysts in Windows or in Bing, we were analyzing computer system logs. That’s very difficult for a human being to understand. We definitely had to work with the experts who understood the semantics of the logs in order to make progress. They had to understand what the machine learning algorithms were doing in order to provide useful feedback. … It really comes back to this big divide, this bottleneck, between the domain expert and the machine learning expert. I saw that as the most challenging problem facing us when we try to really make machine learning widely applied in the world. I saw both machine learning experts and domain experts as being difficult to scale up. There’s only a few of each kind of expert produced every year. I thought, how can I scale up machine learning expertise? I thought the best thing that I could do is to build software that doesn’t take a machine learning expert to use, so that the domain experts can use them to build their own applications. That’s what prompted me to do research in automating machine learning while at MSR [Microsoft Research].” Alice Zheng ( 2015 )

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