artículo 2c - ai sustainable future

8/12/2019 Artculo 2C - AI Sustainable Future

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14 1541-1672/11/$26.00 2011 IEEE IEEE INTELLIGENT SYSTEMSPublished by the IEEE Computer Society

A I A N D S U S T A I N A B I L I T YEditor: Doug Fisher,Vanderbilt University, [email protected]

Computing and AI

for a Sustainable Future

Douglas H. Fisher, Vanderbilt University

and sustainability. My search was not exhaustive,

largely based on keywords, but it wasnt trivial ei-

ther. Still, little turned up in the intersection of AI

and sustainability in early 2007, and most of what

did, as I recall, was in environmental science pub-

lications and appeared to be dominated by Euro-

pean researchers using evolutionary computation

for the purposes of optimization.1

AI and sustainability has grown substantially in

the last few years. To some extent, this tracks with

increasing interest in sustainability and comput-

ing more generally. However, AI is helping to drivethis larger movement, rather than simply riding

along. Indeed, its hard to imagine that AI would

not be central to understanding and managing the

great complexity of maintaining a healthy planet

in the face of pervasive and transformative human

activity.

A visible and scientifically significant landmark

in this growth of AI and sustainability is the es-

tablishment of the Computational Sustainability

Institute,2with its focus on AI and many sustain-

ability areas, such as biodiversity and alternative

energy. The institute grew from a 2008 Expedi-tion in Computing Award from the NSF to Cor-

nell University, Oregon State University, Bowdoin

College, Howard University, and other partners,

quickly attracting other researchers, educators,

government, and industry. The first conference on

computational sustainability took place in 2009,

followed by a second in 2010 and leading in 2011

to a special track on Computational Sustainability

at the Association for the Advancement of Artifi-

cial Intelligence (AAAI) conference.

Coinciding with the institutes founding was a

groundswell of activity to include sustainability

tracks at other AI-related conferences. Machine

learning and data mining have been strong among

these, and in 2010, a second sustainability-focused

Expedition in Computing award was given to the

University of Minnesota and its partners for data-driven understanding of climate change and re-

lated phenomena.

Forthcoming articles in this new IEEE Intelli-

gent Systems AI and Sustainability Department

will elaborate on AIs deployment in many areas

of sustainability as well as the challenges and op-

portunities that sustainability issues bring to AI

research, education, and practice. This opening

article will touch upon the main themes at the in-

tersection of AI and sustainability, but it will pri-

marily concentrate on the larger contexts of sus-

tainability, and on computing and sustainability,thereby setting the stage for articles to come.

SustainabilityThe United Nations Bruntland report contains a

popular and succinct definition of sustainability:

Sustainable development is development that

meets the needs of the present without compromis-

ing the ability of future generations to meet their

own needs.3

To many, the phrase sustainable development

is an oxymoron, but the needs spoken of in the

Bruntland report are not about the luxuries of thematerially wealthy, but rather about the survival

needs of the poor and starving. As contextualized

in the report, development is about bringing all

those on the planet up to a reasonable standard of

living, rather than on those who are already us-

ing plenty. Indeed, there is a nascent AI for De-

velopment Group4 actively exploring AIs role in

advancing social equity, together with a larger

computing for development group.

More generally, the reference to needs begs

the question as to exactly what these are, both now

and in the future. Achieving and then maintaining

When preparing for a March 2007 talkat the US National Science Foundation(NSF), I searched the Web for scholarly work on

AI and climate change, the natural environment,


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safe and adequate water, food, air,

and other health-related criteria for

all people are high-level goals. When

we work backward from these ulti-

mate human-centric goals, we arrive

at a large number of sustainability

desiderata relating to biodiversity, en-

ergy, toxins, climate change, disease,

community planning, agriculture,

emergency response, transportation,

garbage, materials, economics, pol-

icy, and human behavior, among oth-

ers. An expansion of what is a many-

node and densely connected graph

will define the purview of the IEEE

Intelligent Systems AI and Sustain-

ability area. Readers are encouraged

to trace out what they imagine this

graph looks like from their own per-

spective and to ask students at all lev-

els to do so as well. What we desire to

be sustained shouldnt be simply enu-

merated for people; it should be a fo-

cus of deep, ongoing conversation.

Sustainability has received mixed

attention from academics, govern-ments, and industries over the past

few decades. As the Brundtland re-

port indicates, many have sounded

the alarm for a good long time.

Silent Spring,5Rachel Carsons book

on environmental poisoning through

pesticides and the like, was published

in 1962. It is often credited for an en-

vironmental awakening, but one that

has waxed and waned over the years.

The first scientific reports on rising

CO2 levels and the implications forwarming the planet were published

by the early 1960s, reaching levels of

scientific consensus by the 1980s.6

Nevertheless, a significant (but mi-

nority) proportion of Americans, to

take but one nation, arent simply

skeptics, but are dismissive7and eas-

ily shiftedthe materially wealthy

world in general has been slow to re-

act. So we have to wonder whether

the new initiatives will have stay-

ing power, and having staying power

through this and all subsequent gen-

erations is critical, at least if we view

the planet from the perspective of the

human time scales of decades, centu-

ries, and millennia.

In the US, a new NSF initiative

at fully 10 percent of the NSFs pro-

posed budget for the upcoming fiscal

yearwill support science, engineer-

ing, and education for sustainability.

The SEES initiative follows a host of

discipline-focused programs, but now

the emphasis is squarely on the need

for strong interdisciplinary partner-

ships leading to a science of sustain-

ability.8 The Proceedings of the Na-

tional Academy of Scienceslaunched

a sustainability science section and

academic departments and schools of

sustainability are springing up.9

It is striking, however, that com-

puting is typically not a component

in these sustainability curricula, per-

haps in part, because computer sci-

entists themselves do not actively

recognize its relevance to sustain-ability. Yet computing is pervasive

and transformative, potentially af-

fecting human behavior in disruptive

ways, so it seems wise to consider it a

core part of the emerging science of

sustainability.

Computing andSustainabilitySustainability science can be reason-

ably viewed as a new and vitally im-

portant discipline, but sustainabilityconcerns should not be stove-piped.

If we are designing a planet that sus-

tains humanity for millennia (or even

centuries and decades) at anything

like current levels, with wealth ac-

ceptably distributed, sustainability

motivated thought and action must

be at the core of everything we do

and must permeate the societal mi-

lieu. Considering that computing is

already embedded in much of society,

the prescription that sustainability

should be so embedded would result

in a frequent and necessary align-

ment of sustainability and comput-

ing. These realizations have only re-

cently started to take center stage.

In May 2008, the Organization

for Economic Cooperation and De-

velopment (OECD) hosted the Inter-

national Workshop on Information

and Communications Technology

(ICT) and Environmental Challenges

in Copenhagen, following projec-

tions on ICTs growing environmen-

tal footprint.10,11 The workshop was

convened to share strategies on miti-

gating these footprints, to include en-

ergy, greenhouse gasses (GHG), and

waste. These direct or first-order ef-

fects of ICTduring the use, manu-

facture, and disposal phasesare

typically detrimental.

Importantly, the speakers and

national delegations also discussed

ICTs higher-order effects, many of

which lead to decreasing ecologi-

cal footprints in other sectors suchas travel and transportation. Exam-

ples of computings second-order ef-

fects include more accurate and rapid

identification of species populations

through image and audio recording

and processing, static and dynamic

routing of vehicles to eliminate con-

gestion and the idle time associated

with it, the use of video-conferencing

systems instead of travel for meetings,

and proposed smart grid applications

such as electricity load balancing.In turn, third-order effects of ICT

alter the ways that people and other

processes operate, in quality and/or

quantity, and these third-order ef-

fects can have profound effects, both

positive and negative, on ecological

footprints. For example, rebound ef-

fects occur when efficiency improve-

ments in the per unit costs (such as

energy) of a process result in the in-

creased use of that process so that the

collective costs (notably energy used)


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16 www.computer.org/intelligent IEEE INTELLIGENT SYSTEMS

becomes even greater than the collec-

tive costs before the improvements.

These rebound effects are but one ex-

ample of unanticipated (though not

necessarily unforeseeable) effects that

are detrimental to the environment.

Conversely, ICT is responsible for im-

proved data collection and evidence-

based decision making, which itself

might increase these behaviors.8,12

The Oberlin dorm energy monitor-

ing project, for example, used com-puting technology to visualize energy

and water usage in an attempt to alter

their human behavior.13

The OECD 2008 and 2009 meet-

ings resulted in an important con-

ceptual framework for expressing re-

lationships between computing and

the environment.11 Coincident with

this, mathematicians14and computer

scientists were hosting workshops

on sustainability themes. The NSF

awarded the Computer Science and

Telecommunications Board (CSTB)

of the US National Academy of Sci-

ences a grant to explore, organize,

and report on the opportunities for

computer science research contribu-

tions to sustainability. More recently,

in February 2011, the Computing

Community Consortium (CCC) of

the Computer Research Association

(CRA) convened a similarly intended

meeting, issuing a report on the

broad swath of challenges at the in-tersection of computing and sustain-

ability, requiring truly interdisciplin-

ary partnerships between computing

researchers and domain scientists.15

Taken from and nicely abstracting

the CCC report, Figure 1 highlights

the critical role that observational

data and computational models

play in sustainability science. Chal-

lenges in the future of modeling in-

clude downscaling global mod-

els, say of climate, to better inform

regional planning and policy as well

as the integration of various sources

of information, from social and phys-

ical, to plan for the human burden

on the natural environment. Some

of these challenges might be met by

agent-based modeling approaches,16

where agents correspond to small

regions and/or particular information

sources.

The CCC report makes recommen-

dations for computing subdisciplines,

a few of which we can highlight here.

For example, social computing is

changing the way that humans com-

municate, collaborate, compete, and

play.17Yet, we have not substantially

tapped into the possibilities of social

computing for advancing a sustain-

ability agenda. Encouraging new, sus-

tainable behaviors and growing col-

lective intelligence through social

networking is a goal, though find-

ing the incentives that will motivate

people to act is a challenge. Green IT

refers to mitigating the first-order en-ergy and material effects of comput-

ing due to its manufacture, use, re-

cycling, and disposal. Advances in

energy efficiency and energy harvest-

ing through GHG-neutral means are

relevant. Software is also relevant in

areas such as server virtualization

and all forms of intelligent control.

In addition to research and prac-

tice, the report also stressed the im-

portance of education, in particular

the infusing of computing curriculawith sustainability, and inversely the

infusion of computation into sustain-

ability curricula. Finally, we can ex-

trapolate beyond the US context in

which the CCC report was prepared

and emphasize that government fund-

ing provides incentive for the interdis-

ciplinary and international research

collaborations necessary to advance

sustainability desiderata. These col-

laborations would not simply be be-

tween environmental scientists and

Figure 1. Many subdisciplines of computing will contribute to and will be

challenged by sustainability objectives. Achieving sustainability goals will require

that computer scientists enter into interdisciplinary collaborations with other

scientists, and vice versa, and that researchers across fields integrate their efforts

with education, development, and practice. (Based on a figure in From Science,

Engineering, and Education of Sustainability: The Role of Information Sciences and

Engineering.15Used with permission.)

Big data

Areas

ofdiscoveryand

innova

tion

Modeling andsimulation

Human-centered andsocial computing

Cyberphysical systems

Optimization

Intelligent systems

Privacy and security

Systems engineering

Collaborative, use-inspiredfundamental research

Energy Transportation Environment and climate

fundamental research

Core

Green IT

Cradle-to-cradle design

Power-aware computing

Energy complexity analysis of

algorithms Energy harvesting

Sustainability:meeting the needs of present and future generations

Stakeholders

Coordinated federal investment

Researchers

Computer scientists,systems engineers,

social scientists

ResearchersComputer scientists

Educators

Domain experts

IT manufacturersIT operators

Domain experts

Electrical engineers, transportationengineers, environmental scientists,

biologists, climatologists

Stakeholders


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computer scientists, but because hu-

mans are key to sustainability solu-

tions, there is a great need for socio-

technical sciences that anticipate,

evaluate and design cognizant of re-

bound effects8and other influences

of technology on human behavior.

AI and SustainabilityFinally, we come to the area that this

article inaugurates: AI and sustain-

ability. The computing areas that I

highlighted earlier all invite AI meth-

ods to facilitate progress. In green

IT, for example, there are intelligent

controls during use phases, and plan-

ning and scheduling concerns dur-

ing manufacture, such as shortening

supply chains to reduce ecological

footprints. There is also a nascent

movement toward AI for sustainable

design, including cradle-to-cradle

design,18 intended to eliminate waste

through low-energy reclamation

processes.

Although Figure 1 labeled intel-ligent systems as an area distinct

from optimization and cyberphys-

ical systems (CPSs), they are not

mutually exclusive. Optimization has

a rich history both in and outside of

traditional AI boundaries. Exemplar

applications of optimization for sus-

tainability include supply-chain plan-

ning, optimal wind-farm arrange-

ment on small and large scales, and

reserve and corridor design, where

land is purchased for the benefit ofselected species under budget con-

straints.2We can imagine that in each

of these examples, climate change

(whether the reader believes it is hu-

man caused or not) will alter what is

optimal, and thus characterizing so-

lution robustness and adapting solu-

tions in the face of change are impor-

tant challenges.

Machine learning is another im-

portant methodology for sustainabil-

ity. Machine learning methods are

used for such varying applications

as learning to identify and count in-

dividuals, or otherwise estimate dis-

tributions of a particular species;19

learning patterns of use for different

appliances from simple household

sensors;20and learning to predict fail-

ures in aging civil infrastructure.21

Learning is also integral in real-

izing a great promise of computing

for customization, where nuanced

characterizations of individuals

are possible that are much richer than

binary-valued opinion-poll labels such

as liberal, conservative, waste-

ful, or thrifty. With these richer

characterizations, we can fit sustain-

ability-relevant actions to individuals,

resulting in large savings in energy

and waste, for example, in comfort-driven services. Consider hotel air

conditioners that are left running

so that a new guest will not experi-

ence a few minutes of uncomfort-

able warmth. In an integrated cyber-

physical-social network that has

learned my preferences, air condition-

ers in a room reserved for me will be

shut down, at least until my arrival.

Intelligent CPSs, as the last illustra-

tion suggests, are yet another class of

systems that will receive considerable

attention in this new AI and Sustain-

ability department. CPSs are at the

intersection of computing and the

physical world. They include static and

dynamic sensor networks and smart

appliances, buildings, cars, highways,

and cities. Through monitoring and

action in the physical world, CPSs will

have second-order effects relative to

sustainability concerns, and these ef-

fects might be environmentally harm-

ful or beneficial. Robotics is another

highly relevant CPS class, particularly

for monitoring the environment.

Considerable work is underway on au-

tonomous underwater vehicles (AUV)

for monitoring ocean and fresh-

water ecosystems and autonomous

aerial and ground vehicles for moni-

toring in emergencies ranging from

nuclear accidents to wildfires.

Looking AheadThis article has barely touched on

the vast possibilities for intelligent

systems to address sustainabilityconcerns. Work in this area will of-

ten boil down to augmenting human

decision-making capabilities in the

face of uncertainty and other complex-

ities. In some cases, such as emergency

response, intelligent systems will re-

duce the latency of response while in-

creasing its quality. In other settings

requiring and allowing for delibera-

tion, intelligent systems can facilitate

better-informed and better-reasoned

decisions. We can hope that reli-ance on intelligent systems will have

positive second- and third-order ef-

fects on the manner in which humans

reasona machine learning system,

for example, typically requires data

and decisions stemming from their

recommendations will be informed

by evidence, perhaps serving as ex-

emplars of reasoning. Pedagogical

goals and strategies can be designed

into these systems from their incep-

tion, thus not simply offloading work

Work in this area will

often boil down to

augmenting human

decision-making

capabilities in the face

of uncertainty and othercomplexities.


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18 www.computer.org/intelligent IEEE INTELLIGENT SYSTEMS

and/or providing recommendations,

but helping humans to become better

problem solvers at the same time.

Clearly, research, education, and

application in sustainability will chal-

lenge AI along many trajectories, tak-

ing us outside our usual boxes, as ap-

plication inspired and use-driven basic

research often does.22 Its an impor-

tant time for AI as we grapple with the

complexities of designing a sustainable

and equitable society. I am excited to

see what emerges and hope that much

of it will be reported in these pages.

AcknowledgmentsI thank Erwin Gianchandani and Mary

Lou Maher for helpful comments on earlier

drafts of this article. I acknowledge support

from the US National Science Foundation,

under the auspices of the Intergovernmen-

tal Personnel Act, where I served as a pro-

gram director from 2007 to 2010, during

which much of my experience in computing

and sustainability was amassed and ideas

formulated. The opinions expressed hereinare not necessarily those of NSF or the col-

leagues who I have acknowledged.

References 1. S. Dzeroski et al., Equation Discovery

with Ecological Applications, Machine

Learning Methods for Ecological Ap-

plications, A.H. Fielding, ed., Kluwer,

1999.

2. C.P. Gomes, Computational Sustain-

ability: Computational Methods for a

Sustainable Environment, Economy,and Society, The Bridge, vol. 39,

no. 4, 2009, pp. 513; www.nae.edu/

File.aspx?id=17673.

3. G.H. Brundtland, ed., Report of the

World Commission on Environment

and Development: Our Common

Future, United Nations, 1987.

4. N. Eagle and E. Horvitz, eds., Papers

from the AAAI Spring Symposium on

Artificial Intelligence and Develop-

ment, tech. report SS-10-01, AAAI ,

2010; www.aaai.org/Press/Reports/

Symposia/Spring/ss-10-01.php.

5. R. Carson, Silent Spring, Houghton

Mifflin, 1962.

6. S. Weart, The Discovery of Global

Warming: Revised and Expanded

Edition, Harvard Univ. Press, 2008.

7. A. Leiserowitz et al., Global Warmings

Six Americas, Yale Project on Climate

Change Communication, Yale Univ.

and George Mason Univ., 2011; http://

environment.yale.edu/climate/files/

SixAmericasMay2011.pdf.

8. D.H. Fisher, Sustainability, Leader-

ship in Science and Technology: A

Reference Handbook, W.S. Bainbridge,

ed., Sage Publications, 2011.

9. E. Redden, Schools of Sustainabil-

ity, Colleges of the Environment,

Inside Higher Education, 2009; www.

insidehighered.com/news/2009/07/23/

sustainability.

10. A. Kohler and L. Erdmann, Expected

Environmental Impacts of Pervasive

Computing, Human and Ecological

Risk Assessment, vol. 10, no. 5, 2004,pp. 831852.

11. S. Roberts, Measuring the Relationship

between ICT and the Environment, Or-

ganization for Economic Co-operation

and Development (OECD), 2009; www.

oecd.org/dataoecd/32/50/43539507.pdf.

12. B. Tomlinson, Greening through IT,

MIT Press, 2010.

13. J.E . Petersen et al., Dormitory Resi-

dents Reduce Electricity Consumption

when Exposed to Real-Time Visual

Feedback and Incentives, Intl J. Sus-tainability in Higher Education, vol. 8,

no. 1, 2007, pp. 1633.

14. D. Mackenzie, Mathematics of Climate

Change: A New Discipline for an Un-

certain Century, Mathematical Sciences

Research Inst., 2007; www.msri.org/

attachments/workshops/462/

MathClimate.pdf.

15. R. Bryant et al., Science, Engineer-

ing, and Education of Sustainability:

The Role of Information Sciences and

Engineering, version 18, Computing

Community Consortium, 2011; http://

cra.org/ccc/docs/RISES_Workshop_

Final_Report-5-10-2011.pdf.

16. E. Bonabeau, Agent-Based Modeling:

Methods and Techniques for Simulating

Human Systems Proc. Natl Academy

of Sciences, vol. 99, suppl. 3, 2002,

pp. 72807287; www.pnas.org/

content/99/suppl.3/7280.full.

17. D. Zeng, K. Carley, and F.-Y. Wang,

Social Computing, IEEE Intelli-

gent Systems, vol. 22, no. 5, 2007,

pp. 2022.

18. D.H. Fisher and M.L. Maher, eds.,

Papers from the AAAI Spring Sym-

posium on Artificial Intelligence and

Sustainable Design, tech. report SS-11-

02, AAAI, 2011; www.aaai.org/Press/

Reports/Symposia/Spring/ss-11-02.php.

19. T. Dietterich, Machine Learning in

Ecosystem Informatics and Sustainabil-

ity, Proc. 21st Intl Joint Conf. Arti-

ficial Intelligence, Morgan Kaufmann,

2009, pp. 813.

20. S. Gupta, M.S. Reynolds, and S.N. Patel,ElectriSense: Single-Point Sensing Using

EMI for Electrical Event Detection and

Classification in the Home, Proc. Conf.

Ubiquitous Computing(UbiComp),

ACM Press, 2010, pp. 139148.

21. P. Gross et al., Predicting Electricity

Distribution Feeder Failures Using Ma-

chine Learning Susceptibility Analysis,

Proc. 18th Conf. Innovative Applica-

tions of Artificial Intelligence(IAAI-

06), AAAI Press, 2006, pp. 17051711.

22. D. Stokes, Pasteurs Quadrant: BasicScience and Technological Innovation,

Brookings Inst. Press, 1997.

Douglas H. Fisher is an associate profes-

sor of Computer Science at Vanderbilt Uni-

versity. Contact him at douglas.h.fisher@

vanderbilt.edu.

Selected CS articles and columns

are also available for free at

http://ComputingNow.computer.org.

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