Google’s Hybrid Approach to Research
Alfred Spector Google Inc. email@example.com
Peter Norvig Google Inc. firstname.lastname@example.org
Slav Petrov Google Inc. email@example.com
In this paper, we describe how we organize Computer Science (CS) research at Google. We focus on how we integrate research and development (R&D) and discuss the beneﬁts and risks of our approach. The challenge in organizing R&D is great because CS is an increasingly broad and diverse ﬁeld. It combines aspects of mathematical reasoning, engineering methodology, and the empirical approaches of the scientiﬁc method. The empirical components are clearly on the upswing, in part because the computer systems we construct have become so large that analytic techniques cannot properly describe their properties, because the systems now dynamically adjust to the hard-to-predict needs of a diverse user community, and because the systems can learn from vast data sets and large numbers of interactive sessions that provide continuous feedback. We have also noted that CS is an expanding sphere, where the core of the ﬁeld (Theory, Operating Systems, etc.) continues to grow in depth, while the ﬁeld keeps expanding into neighboring application areas. Research results come not only from universities, but also from companies, large and small. The way that research results are disseminated is also evolving and the peer-reviewed paper is under threat as the dominant dissemination method. Open source releases, standards speciﬁcations, data releases, and novel commercial systems that set new standards upon which others then build, are increasingly important. To compare our approach to research with that of other companies is beyond the scope of this paper. But, for reference, we note that in the terminology of Pasteur’s Quadrant , we do “use-inspired basic” and “pure applied” (CS) research.  and  discuss information technology research generally, pointing out the movement in industrial labs towards research that strongly considers product needs. Recent articles, such as  and , illustrate related issues on how ﬁrms do research and catalyze innovation.
Research in Computer Science at Google
The goal of research at Google is to bring signiﬁcant, practical beneﬁts to our users, and to do so rapidly, within a few years at most. Research happens throughout Google, exploring technical innovations whose implementation is risky, and may well fail. Sometimes, research at Google operates in entirely new spaces, but most frequently, the goals are major advances in areas where the bar is already high, but there is still potential for new methods. In these cases, simply establishing the feasibility of a research idea may be a substantial task, but even greater effort is required to create a true success or useful negative result. Because of the time-frame and effort involved, Google’s approach to research is iterative and usually involves writing production, or near-production, code from day one. Elaborate research prototypes are rarely created, since their development delays the launch of improved end-user services. Typically, a single team iteratively explores fundamental research ideas, develops and maintains the software, and helps operate the resulting Google services – all driven by real-world experience and concrete data. This long-term engagement serves to eliminate most risk to technology transfer from research to engineering. This approach also helps ensure that research efforts produce results that beneﬁt Google’s users, by allowing research ideas and implementations to be honed on empirical data and real-world constraints, and by utilizing even failed efforts to gather valuable data and statistics for further attempts. 1
Implications of Google’s Mission and Capabilities
Google’s mission “To organize the world’s information and make it universally accessible and useful,” both supports and requires innovation in almost...
References:  Donald E. Stokes. Pasteur’s Quadrant - Basic Science and Technological Innovation. Brookings Institution Press, 1997.  Robert Buderi. Engines of Tomorrow: How The Worlds Best Companies Are Using Their Research Labs To Win The Future. Simon & Schuster, 2000.  Mark Dodgson, David Gann, and Ammon Salter. The Management of Technological Innovation: Strategy and Practice. Oxford University Press, 2008.  Richard Leifer, Gina OConnor, and Mark Rice. Implementing radical innovation in mature ﬁrms: The role of hubs. The Academy of Management Executive, 15, 2001.  Ellen Enkel, Oliver Gassmann, and Henry Chesbrough. Open r&d and open innovation: Exploring the phenomenon. R&D Management, 39, 2009.  Jakob Uszkoreit, Jay Ponte, Ashok Popat, and Moshe Dubiner. Large scale parallel document mining for machine translation. In Proc. of COLING, 2010.  Charles Reis, Adam Barth, and Carlos Pizano. Browser security: Lessons from google chrome. ACM Queue, 7, 2009.  Jeffrey Dean and Sanjay Ghemawat. Mapreduce: Simpliﬁed data processing on large clusters. In Proc. of OSDI, 2004.  Sanjay Ghemawat, Howard Gobioff, and Shun-Tak Leung. Google ﬁle system. In Proc. of ACM SIGOPS, 2003.  Fay Chang, Jeffrey Dean, Sanjay Ghemawat, Wilson C. Hsieh, Deborah A. Wallach, Mike Burrows, Tushar Chandra, Andrew Fikes, and Robert E. Gruber. Bigtable: A distributed storage system for structured data. In Proc. of OSDI, 2006.  Johan Schalkwyk, Doug Beeferman, Francoise Beaufays, Bill Byrne, Ciprian Chelba, Mike Cohen, Maryam Garrett, and Brian Strope. ”your word is my command”: Google search by voice: A case study. In Amy Neustein, editor, Advances in Speech Recognition. Springer, 2010.  Shumeet Baluja and Michele Covell. Waveprint: Efﬁcient wavelet-based audio ﬁngerprinting. Pattern Recognition, 11, 2008. Additional references can be found at http://research.google.com/pubs/papers.html
Please join StudyMode to read the full document