Chapter 18
Afterword: A Discovery Cloud
“It would appear that we have reached the limits of what it is possible to
achieve with computer technology, although one should be careful with
such statements, as they tend to sound pretty silly in five years.”
—John von Neumann
We hope that the preceding pages have given you some concrete ideas about how
you can use the cloud in your research. Perhaps the cloud, for you, will simply be a
place to store your research data s ecurely and cheaply, or to perform computations
that you could not easily run before. Or perhaps you are inspired to embrace
the power of the cloud to transform how you run your laboratory, conduct your
research, and interact with your community. No matter how yo u approach the
use of these technologies, we are confident that you will find the experience both
rewarding and fun.
We cannot resist th is last opportunity to prognosticate. The pioneering cyber-
netician an d organizational theorist Stafford Beer wrote in 1972 [70]:
The question which asks how to use the computer in the enterprise [is]
the wrong question. A better formulation is to ask how the enterprise
should be run given that computers exist. The best version of all is
the question asking what, given computers, the enterprise now is.
We, in turn, are fascinated by the following variant of Beer’s best question:
Given cloud, and all that its scalab le and cost-effective automation and
outsourcing entail, what the scientific enterprise now is.
We propose the following likely outcomes.
Industrialization of data pro-
duction
via large-scale automated experiments and observations, already occurring
in astronomy [
241
], functional genomics, and materials science [
159
], will expand to
many more domains. The resulting data glut will in turn drive
industrialization
of data analysis
, by which we mean large-scal e computational platforms that
automate quality control, analysis, inference, and other step s. These developments
will greatly reduce the costs of hypothesis generation and testing. They will also
improve reproducibility because experimental configurations and data processing
steps will be captured precisel y.
Meanwhile, the digital encoding of large quantities of scientific knowledge
from such experiments and other sources (e.g., the scientific literature) will enable
the creation of a
universal knowledge base
supporting both rapid access a nd
automated inference. It will become routine to ask questions via a scientific search
engine, to be notified of potential inconsistencies across existing knowledge, and to
vote on the next set of experiments to be performed by industrial-scale facilities.
Other experiments will be performed by quasi-independent
robot scientists
[
171
,
262
] that apply inference and experiment design methods to guide their choice of
the next experiment.
These steps towards economies of scale may sound dehumanizing, but experience
suggests that, if implemented in the right way, they can unleash a flood of creativity.
If the universal knowledge base is treated as a globally accessible public good, then
the scientific playing field becomes more level. A high school student in Angola,
India, or New Zealand will be able to search for new drugs for rare diseases or
new materials sourced from local materials. They will use powerful tools accessible
from this
discovery cloud
[
124
] to collect new data, analyze extant and new data,
test hypotheses, and contribute to knowledge.
This discovery cloud will also empower the bench scientist. Ama zon’ s Echo
and Alexa services can keep our calendar, order a pizza, and summon a car service,
and Alexa can invoke an Amazon Lambda function to start an experimental
analysis in the cloud. All of these actions can be driven by voice commands.
As machine learni ng continues to progress, future s cientists will benefit from a
cloud-based research assistant
that not only monitors experiments but also
performs background research, such as scanning the literature for related work
and checking our mathematical d erivations. Such a system will respond to vocal
instructions while also reading (and writing) our computational notebooks.
Some of these developments may b e some way out, but the techno logy is
evolving fast. As Roy Amara observed, “[w]e tend to overestimate the effect of a
technology in the short run and underestimate the effect in the long run.”
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