ple of this pattern is work on You Tube
recommendations, which started in
various engineering groups, but then
moved to a research team, where the
work continued using a different, and
perhaps deeper, algorithmic basis.
In the same way that it is difficult to
define what exactly constitutes “
research,” it can be difficult to measure
its “success.” In our opinion, a research project is successful if it has
academic or commercial impact, or
ideally, both. Commercial impact at
Google is perhaps easier to measure,
and the company has benefitted from
numerous advances in systems, speech
recognition, language translation, machine learning, market algorithms,
computer vision, and more.
By academic impact we refer to
impact on the academic community,
other companies or industries, and the
field of computer science in general.
Of course, this type of impact has most
traditionally come from publications,
and Google continues to publish research results at increasing rates (from
13 papers published in 2003, to 130 in
2006, to 279 in 2011). Some of our papers are highly regarded and have been
extensively cited. 3, 4, 7 But we feel that
publications are by no means the only
mechanism for knowledge dissemination: Googlers have led the creation
of over 1,000 open source projects,
contributed to various standards (for
example, as editor of HTML5), and produced hundreds of public APIs for accessing our services. In some cases, we
have used these different channels in
symbiotic ways, following up an initial
publication describing the high-level
ideas (MapReduce, GFS, BigTable)
with open source implementations of
particular aspects (Protocol Buffers).
In other cases, projects have started as
open source initiatives from day one:
Android and Chromium are probably
the two most well-known examples of
open source projects and demonstrate
the effectiveness of this approach.
Technology companies invest in re-
search for a number of reasons, in-
cluding: importance to the company’s
products and services, prestige and
contributions to the public good, and
reducing the risk of getting blindsided
by new technology developments.
Many of the world’s computer science
research questions are of great rel-
evance to Google’s business, our tech-
nical leaders, and our user community.
We have chosen to organize computer
science research differently at Google
by maximally connecting research
and development. This yields not only
innovative research results and new
technologies, but also valuable new ca-
pabilities for the company. Our hybrid
approach to research enables us to
conduct experiments at a scale that is
generally unprecedented for research
projects, generating stronger research
results that can have a wider academic
and commercial impact. We also pro-
vide flexible opportunities across the
R&D spectrum for our team members.
While our hybrid research model ex-
ploits a number of things particular to
Google, we hypothesize that it may also
serve as an interesting model for other
1. baluja, s. and Covell, M. Waveprint: efficient wavelet-based audio fingerprinting. in Pattern Recognition, 2008.
2. buderi, r. Engines of Tomorrow: How The World’s
Best Companies Are Using Their Research Labs To Win
The Future. simon & schuster, 2000.
3. Chang, F. et al. bigtable: a distributed storage system
for structured data. in Proceedings of OSDI 2006.
4. dean, J. and ghemawat, s. Mapreduce: simplified
data processing on large clusters. in Proceedings of
5. dodgson, M., gann, d. and salter, a. The Management
of Technological Innovation: Strategy and Practice.
oxford university Press, 2008.
6. enkel, e., gassmann, o. and Chesbrough, h. open r&d
and open innovation: exploring the phenomenon. in
R&D Management, 2009.
7. ghemawat, s., gobioff, h., and leung, s.t. google file
system. in Proceedings of ACM SIGOPS 2003.
8. leifer, r., o’Connor, g. and rice, M. implementing
radical innovation in mature firms: the role of hubs.
in The Human Side of Managing Technological
Innovation. r. Katz, ed., oxford university Press, 2004.
9. reis, C., barth, a., and Pizano, C. browser security:
lessons from google Chrome. ACM Queue 7, 5
10. schalkwyk, J. google search by voice: a case
study. in Advances in Speech Recognition: Mobile
Environments, Call Centers, and Clinics. a. neustein
ed., springer, 2010.
11. stokes, d.e. Pasteur’s Quadrant—Basic Science
and Technological Innovation. brookings institution
12. uszkoreit, J., Ponte, J., Popat, a., and dubiner, M.
large scale parallel document mining for machine
translation. in Proceedings of COLING 2010.
additional references can be found at http://research.
Alfred Spector ( firstname.lastname@example.org) is vice President
of research and special initiatives at google, inc.
Peter norvig ( email@example.com) is director of
research at google, inc.
Slav Petrov ( firstname.lastname@example.org) is senior research
scientist at google, inc.
We acknowledge many discussions on this topic with dan
huttenlocher, who spent a summer at google in 2008,
and contributions and reviews from bill Coughran, Úlfar
erlingsson, Fernando Pereira, Matt Welsh, and John
Wilkes. We also thank the anonymous reviewers for their