Learning from Data through Human-Machine Collaboration

Machine learning (ML) is considered an effective and efficient tool for extracting useful information from vast amounts of data. Indeed, it is increasingly applied for solving real-life problems in industry and academic research. However, the main problem is that applying ML requires an interdisciplinary education that, for example, allows domain experts to tune the parameters and interpret the analysis. As a result, there is an increasing demand for solutions that enable domain experts to apply Machine Learning approaches to their datasets without consulting ML experts. In this scenario, we propose a new paradigm that allows machine and human intelligence to cooperate to join both ML and domain expertise for analyzing user data and producing answers. As proof of concept, we start developing MLAssistant, a library that understands the research question with the help of user interaction, produces a data science pipeline, and automatically executes the pipeline in order to generate analysis. The strength of MLAssistant lies in the design of a rich domain-specific language for modeling data analysis pipelines, the use of a suitable neural network for machine translation of research questions, the availability of a vast dictionary of pipelines for matching the translation output, and the use of natural language technology.