8/14/2019 Automating Science

1/2

stand to suffer further losses; in Mauritius, the

largest nonthreatened native frugivore is the

gray white-eye, a bird weighing a mere 0.009

kg (see the supporting online material).

An extended megafauna concept has the

potential to promote synergy between other-

wise disparate research and conservation foci,

and to facilitate broader syntheses of eco-

system-level effects of extinctions of the

largest vertebrates and the resulting ecological

shrinkage. It is high time to more fully under-

stand and ameliorate the recent and ongoing

losses of all the hugest, and fiercest, and

strangest forms (13).

References and Notes1. R. Dirzo, A. Miranda, inPlant-Animal Interactions:

Evolutionary Ecology in Tropical and Temperate Regions,P. W. Price, T. M. Lewinshon, G. W. Fernandes, W. W.Benson, Eds. (Wiley, New York, 1991), pp. 273287.

2. P. L. Koch, A. D. Barnosky,Ann. Rev. Ecol. Evol. Syst. 37,215 (2006).

3. A. D. Barnosky et al.,Science 306, 70 (2004).4. D. H. Janzen, P. S. Martin,Science 215, 19 (1982).5. P. R. Guimaraes Jr., et al., PLoS ONE3, e1745 (2008).6. P. S. Martin, R. G. Klein, Quaternary Extinctions: A

Prehistoric Revolution (Univ. of Arizona Press, Tucson,1995).

7. R. N. Owen-Smith,Megaherbivores: The Influence of VeryLarge Body Size on Ecology(Cambridge Univ. Press, NewYork, 1988).

8. H. J. Meehanet al.,J. Biogeogr. 29, 695 (2002).9. D. M. Hansen et al., PLoS ONE3, e2111 (2008).

10. R. G. Colevatti et al.,Mol. Ecol. 12, 105 (2003).11. S. J. Wright et al., Biotropica 39, 289 (2007).12. W. Hallwachs, inFrugivores and Seed Dispersal, A. Estrad

T. H. Fleming, Eds. (Kluwer, Boston, 1986), pp. 28530413. A. R. Wallace, The Geographical Distribution of Animal

(Harper, New York, 1876).14. Supported by the Velux Foundation (D.M.H.), Fundao

de Amparo Pesquisa do Estado de So Paulo (M.G.),and Conselho Nacional de Desenvolvimento Cientfico Tecnolgico (M.G.).

Supporting Online Materialwww.sciencemag.org/cgi/content/full/324/5923/42/DC1Fig. S1Table S1References

10.1126/science.11723

www.sciencemag.org SCIENCE VOL 324 3 APRIL 2009

CREDIT:ROBERTK.

LINDSAY

PERSPECT

The idea of automating as-

pects of scientific activity

dates back to the roots of

computer science, if not to Francis

Bacon. Some of the earliest pro-

grams automated the processes of

creating ballistic tables, cracking

cyphers, collecting laboratory

data, etc., by carrying out a set of

instructions from start to finish.

Starting with DENDRAL in the

1960s (1), artif icial intelligence

programs such as Prospector (2

),Bacon (3), and Fahrenheit (4)

automated some of the planning,

analysis, and discovery portions

of the scientific enterprise. How-

ever, most of these programs were

still designed to run a calculation

to completion, produce an answer,

and then stop. They did not fully

close the loop in the sense of examining the

results of their actions, deciding what to try

next, potentially cycling forever.

Two reports on pages 85 and 81 of this

issue push the boundaries of automatic scien-

tific experimentation and discovery. Kinget al. (5) describe a robotic system for running

biological experiments, evaluating their

results, and deciding what experiments to try

next. Schmidt and Lipson (6) describe their

work on discovering compact equations that

characterize complex nonlinear dynamical

systems, derived from visual observation of

such systems. As these reports show, it is pos-

sible for one computer program to step

through the activities needed to conduct a con-

tinuously looping procedure that starts with a

question, carries out experiments to answerthe question, evaluates the results, and refor-

mulates new questions.

The main goals of automation in science

have been to increase productivity by increas-

ing efficiency (e.g., with rapid throughput), to

improve quality (e.g., by reducing error), and

to cope with scale, allowing scientific treat-

ment of topics that were previously impossi-

ble to address. Tycho Brahe spent a lifetime

recording observations that allowed Johannes

Kepler to formulate Keplers laws of planetary

motion; today, computer-controll

data collection is commonplac

and necessary for both exper

mental and observational scienc

Automating many activities b

yond data collection offers eve

more benefits.

In the near term, a useful met

phor is to consider computers

intelligent assistants. Some assi

tants gather data and attend

such tasks as noise filtering, da

smoothing, outlier rejection, andata storage. Other assistants a

specialists at statistical analys

still others at bench work. Th

metaphor has driven many r

search projects over the past se

eral decades and has led to man

of the most successful applic

tions of computers.

An early articulation of this metaph

is Joshua Lederbergs effort at Stanfor

University School of Medicine to develop a

automated biomedical laboratory to examin

the soil of Mars for traces of life, as part

the 1975 Viking mission deployed by thU.S. National Aeronautics and Space A

ministration. The robot assistant Lederbe

designed, with engineer Elliott Levintha

consisted of a conveyor belt that scooped u

samples of Martian soil and deposited the

within a computer-controlled mass spectrom

eter. Each soil sample was bombarded wi

electrons, producing a fragmentation patte

that sorted the charged particles (ions) accor

ing to their mass. This pattern was transmitte

to Earth, where scientists could analyze it f

Computers with intelligence can design and

run experiments, but learning from the result

to generate subsequent experiments requires

even more intelligence.Automating ScienceDavid Waltz1 and Bruce G. Buchanan2

COMPUTER SCIENCE

1Center for Computational Learning Systems, ColumbiaUniversity, New York, NY 10115, USA. 2Computer ScienceDepartment, University of Pittsburgh, Pittsburgh, PA15260, USA. E-mail: buchanan@cs.pitt.edu; waltz@ccls.columbia.edu

Semiautomated. Scientists at Stanfords Instrumentation Research Laboratory(circa 1970) linked a gas chromatograph and high-resolution mass spectrome-ter to computers to automate studies of biological fluids, meteorites, and othermaterials. Stanfords DENDRAL Project experimented with automated interpreta-tion of the data and experiment planning to specify nuclear magnetic resonanceor infrared data that would resolve ambiguities in the mass spectral data.

Published by AAAS

8/14/2019 Automating Science

2/23 APRIL 2009 VOL 324 SCIENCE www.sciencemag.org4

PERSPECTIVES

evidence of organic compounds and microbial

life. In addition, part of Lederbergs vision for

this instrumentation was also to close the loop

by performing the analysis onboard the space-

craft to inform a next round of experiments

without waiting for Earth-based instructions.

This was, in part, the motivation for the DEN-

DRAL project at Stanford in which an intelli-

gent assistant hypothesized the molecular

structure of organic molecules on the basis of

mass spectrometry data (see the figure).

Intelligent assistants are currently numer-

ous and well integrated into the activities of

science and industry. In the longer term, how-

ever, new kinds of computer programs are

needed to cope with the sheer volume of data

that can be collected automatically (79) and

with the volume of relevant information avail-

able in the literature.

Closing the loop from experiment design

and data collection to hypothesis formation

and revision, and from there to new experi-

ments, will be one important way to cope withthe volume of data. A new wave of programs

will test the efficacy of using computers in

closed-loop fashion and will explore the ques-

tions of which activities can be automated, and

which ones we would even want to automate.

Even for the relatively straightforward task of

data collection, there are myriad questions to

answer before streaming data from a labora-

tory instrument into a computer, including

why particular data are being collected, which

variables should be measured, and which

instrument will measure them. If no such

instrument exists, can it be designed and built?

Beyond coping with the volume of data,

however, computers need to be called into serv-

ice to cope with the volume of information and

background knowledge relevant to any scien-

tific question. Search engines and automated

libraries will return more articles in response to

a query than anyone has time to read. (For

example, Google returns about 200,000 hits for

the phrase laboratory automation and 10 mil-

lion hits for the pair of terms science +

automation.) Programs that have the intelli-

gence to read and interpret the online informa-

tion for us will contribute to the next level of

closing the loop. This is already an active area

of computer science research (10).

For any such program to select the most

cost-effective and informative hypotheses,

prune hypotheses that cannot be realized

experimentally, avoid repeating unsuccessful

experiments that have already been tried byothers, etc., it must include a rich model of

the entire process of the loop, as well as

knowledge of the specific scientific area

being automated. This will increasingly

involve a substantial modeling effort, as is

already required for planning and interpret-

ing experiments in systems biology or

weather and climate.

For the foreseeable future, the prospect of

using automated systems as assistants holds

vast promise as these assistants are becomin

not only faster but much broader in their cap

bilitiesmore knowledgeable, more creativ

and more self-reflective. Human-machin

partnering systems that match the tasks

what each partner does best can potential

increase the rate of scientific progress drama

ically, in the process revolutionizing the pra

tice of science and changing what scientis

need to know.

References and Notes

1. R. K. Lindsay, B. G. Buchanan, E. A. Feigenbaum, J.

Lederberg,Applications of Artificial Intelligence for

Organic Chemistry: The DENDRAL Project(McGraw-Hill

New York, 1980).

2. R. O. Duda, J. Gaschnig, P. E. Hart, in Expert Systems in

the Microelectronic Age, D. Michie, Ed. (Edinburgh Uni

Press, Edinburgh, 1979), pp. 153167.

3. P. Langley, Cognit. Sci. 5, 31 (1981).

4. J. M. Zytkow, J. Zhu, A. Hussam, in Proceedings of the 8

National Conference on Artificial Intelligence (AAAI Pre

Menlo Park, CA, 1990), pp. 889894 (www.aaai.org/

Papers/AAAI/1990/AAAI90-133.pdf).

5. R. D. King et al.,Science 324, 85 (2009).

6. M. Schmidt, H. Lipson,Science324

, 81 (2009).7. C. Anderson, Wired16.07, June 2008 (www.wired.com/

science/discoveries/magazine/16-07/pb_theory).

8. A. Srinivasan, S. H. Muggleton, M. J. E. Sternberg, R. D

King,Artif. Intell. 85, 277 (1996).

9. S. Muggleton, Nature 440, 409 (2006).

10. Machine Reading: Papers from the 2007 AAAI Spring

Symposium (www.aaai.org/Library/Symposia/Spring/

ss07-06.php).

11. Supported in part by NSF grant 0738341 (B.G.B.) and

NIH grant 5 U54 CA 121852-03 C11, and by

Consolidated Edison Corp. grant CU08-8987 (D.W.).

10.1126/science.11727

Projections of global energy needs for

sustainable development suggest a

nearly 50% increase by 2030 (a mere

21 years hence) (1). This increase can be met

satisfactorily by only one kind of alternative

energythe Sun. One approach to convert

solar energy into a fuel is to use it to split

water into H2 and O2. A number of strategiesfor the visible lightdriven splitting of water

are being pursued with varying levels of suc-

cess. On page 74 of this issue, Kohl et al. (2)

describe a very different reaction system for

water splitting that uses light but also has

thermally driven steps. The basis of this

approach, in which key steps involve ligand

modification, shows that reactions that har-

vest solar energy can be found among the

unlikeliest of compounds.

Research in solar energy conversion fol-

lows three principal strategies. The first is the

direct conversion of light into electrical

energy, as in the photovoltaic (PV) devices

that are currently being produced and installedaround the world. Challenges here include

increasing the efficiency and durability of

such devices while reducing their cost to make

them competitive with cheaper but environ-

mentally problematic coal-fired power plants.

The Energy Information Administration of

the U.S. Department of Energy projects that

global use of coal for electricity will grow rel-

ative to other sources in the next 21 years (1).

Considerable research on dye-sensitized solar

cells (3, 4) has made them an interesting alter-

native to traditional silicon-based PVs, wi

demonstration units being deployed. Effor

also continue for new PV devices based o

thin-film designs that use either amorphou

silicon, cadmium telluride, copper indiu

gallium selenide, or organic charge-transf

compounds on flexible supports that can b

manufactured on a massive scale (5, 6).The second strategy is to use natures ph

tosynthetic apparatus to produce biofue

from plants or waste agricultural by-product

Some of these approaches, such as corn-t

ethanol, are marginal in terms of econom

and climate-change benefits (7). Other bi

mass sources, such as switchgrasses and agr

cultural by-products, may be economical

more viable and have a sufficiently high pos

tive net energy balance. To be feasible, meth

ods must be developed for the facile catalyt

A metal complex splits water into hydrogen an

oxygen through an unusual series of steps.Rethinking Water SplittingRichard Eisenberg

CHEMISTRY

Department of Chemistry, University of Rochester,Rochester, NY 14627, USA. E-mail: eisenberg@chem.rochester.edu

Published by AAAS