In recent years, computing has both expanded as a field and grown in its importance to society. Similarly, the research conducted at Google has broadened dramatically, becoming more important than ever to our mission. As such, our research philosophy has become more expansive than the hybrid approach to research we described in our CACM article six years ago and now incorporates a substantial amount of open-ended, long-term research driven more by scientific curiosity than current product needs.
We believe successful industry research requires managing a portfolio of projects with time horizons, levels of risk and goals appropriate for the organization. Our approach to research has always been flexible, but there are two reasons that our research philosophy today adds more fundamental, or "pure basic," research than it has previously. First, Google's increasingly diverse businesses, longer-term outlook and greater scale let us pursue ambitious projects that involve more technical risk than ever before. Our hybrid research model was explicitly designed to achieve success in this kind of environment, but we have learned we need to extend it further. Second, machine learning (ML) is a transformative technology that touches everything we do as a company. Therefore, fundamental advances in machine learning technology are likely to produce value across the organization, even when developed without a close connection to a specific application or product. ML is not unique in this regard; the same argument applies to other technologies outside of ML that are critical to the company.