wfs: special report

World Future Society Special Reports

The Automation of Invention page 1

The AI Chasers page 7

Timeline for the Future: Potential Developments and Likely Impacts page 14

Emerging Technologies and the Global Crisis of Maturity page 19

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esterday’s inventors toiled away in workshops, painstakingly designing, building, testing, and refining their creations. In contrast, tomorrow’s in-ventors will spend their days writing descriptions of the problems they want to solve, and then hand those descriptions over to computers to work out the solutions.

We don’t have to gaze into a crys-tal ball to find real-world examples of computer-generated inventions created via “artificial invention.” Re-cently, Stanford University professor John R. Koza used a technique he calls “genetic programming” to au-tomatically design a new kind of general-purpose controller that can be used in everything from thermo-stats to cruise control for cars. Koza, who had a 1,000-Pentium cluster computer system constructed for his work, has obtained patents not only on the new and improved controllers but also on the process he used to automatically design them.

Similarly, Gregory Hornby and his team at NASA Ames Research Cen-

By Robert Plotkin

The Automation of Invention

Cybernetic genies are designing

and engineering new products

and creating technological

breakthroughs.

Y

WFS PHOTO COLLAGE / RUSLAN KOKAREV / VASILIY YAKOBCHUK / ISTOCKPHOTO.COM

ter used an “evolutionary algorithm” to automatically generate the design for an antenna that is now orbiting the Earth on a NASA space mission. The space antenna, which looks like an unwound paper clip, violated conventional wisdom about antenna design and confounded the human antenna engineers who first saw it.

And Stephen Thaler, president and CEO of Imagination Engines Inc., used his Creativity Machine to pro-duce the cross-bristle design for the original Oral-B CrossAction Tooth-brush. Thaler has also used the Cre-ativity Machine to write music and to create software for controlling ro-bots.

When Wishing Makes It SoWhat all of these technologies have

in common is that they require initial guidance from humans to set them

in the right direction. The team of NASA engineers told their evolu-tionary algorithm that they needed it to produce an antenna that satisfied a particular set of criteria, such as the ability to transmit and receive sig-nals within a particular range of fre-quencies. John Koza told his genetic programming software that he needed a controller with low “over-shoot”: Think of a thermostat that raises your living room’s tempera-ture to the desired 70° F without get-ting much hotter first. Stephen Thaler provided his Creativity Ma-

chine with the least amount of guid-ance — a sampling of existing tooth-brushes and data about how well each one performed at brushing teeth — before instructing it to “make a better toothbrush based on what the data I have given you teaches you about what makes one tooth-brush better than another.”

In each case, the humans did not tell the artificial-invention program which materials or components to use in the product it had to invent or what it should look like. Instead, people merely provided the com-

1. Human wisher

2. Abstract wish

3. “Genie” invention software

4. Concrete product design

WFS PHOTO ILLUSTRATIONS / PHOTOS.COM / RUSLAN KOKAREV / ISTOCKPHOTO.COM

World Future Society Special Reports 2

because artificial wishes typically de-fine product requirements in mathe-matical language describing the physical properties that a desirable product should have.

Another kind of wish consists of information on existing products and their performance ability. Think of the design and performance data that was used to create the Cross-Action Toothbrush. When making such wishes, inventors need to deter-mine which type of data is relevant and how to represent that data in a format that can be easily processed by a computer.

Computer Programming Meets Evolution

Most inventors who rely on soft-

puter with a detailed description of the problem they wished to solve, written in a language that the com-puter could understand. The com-puter then produced the final design based on this description.

I refer to this process as “inventing by wishing.” The problem descrip-tion provided by the human is like a wish (for a better controller, antenna, or toothbrush). The computer is like a genie that grants the wish by pro-ducing a design for a concrete prod-uct, and the resulting controller, an-tenna, or toothbrush is the wish- come-true.

As the examples above illustrate, the role of tomorrow’s inventor will be to identify a problem that needs solving and then to describe that problem to a computer equipped

Closeup of NASA’s space antenna in front of the racks of computers that designed it at the Ames Research Center. As an “automated-invention” program, Evolutionary Antenna Synthesis has the potential to design better antennas faster and more cost-effectively through a sort of natural selection process.

NASA

with invention automation software. The computer will then produce a design for a product that solves the problem.

Tomorrow’s inventors, therefore, will spend considerably less time en-gaged in actual product design — but they must take pains with what they wish for. One kind of “wish” defines the problem to be solved in terms of criteria (requirements) that a solution must satisfy, such as the minimum fuel efficiency of a new automobile engine or the maximum length of an airplane wing. Writing this kind of wish will require tomorrow’s inven-tors to develop skill at identifying and describing the requirements so that a computer can understand them. Doing so often requires profi-ciency in physics and mathematics,

3 World Future Society Special Reports

of lenses into his software and then telling the software what kinds of properties he wanted a new lens to have. The software then produced designs for lenses having these prop-erties.

How It WorksTechnologies that automate the

process of inventing do so in two ba-sic ways: (1) by generating, evaluat-ing, and modifying potential inven-tions repeatedly until a satisfactory solution is found; and (2) by follow-ing a set of rules to design a product or process that achieves a goal.

The former technique, which I call “inventing by searching,” is often performed manually by human in-ventors. Thomas Edison invented an improved light bulb by hiring a team of experts to search the globe for bet-ter filament material. After testing more than 6,000 alternatives, he set-tled on carbonized bamboo.

Some artificial-invention technolo-gies work their magic by automating the search process. For example, both Koza’s controller and the NASA an-tenna were created using “evolution-ary computation” programs, so named because they solve problems in a way that mimics biological evo-lution, natural selection, and “the survival of the fittest.”

Evolutionary computation designs a product “fit” to solve a particular dilemma. However, the programmer must first provide the software with a set of “fitness criteria” that define the requirements that a successful solution to the problem must satisfy.

For example, the fitness criteria for an antenna might specify that the an-tenna must be capable of transmit-ting and receiving signals within cer-tain ranges of frequencies, and that the antenna must be no larger than a certain size. Note that such criteria do not tell the algorithm which ma-terials to use or how to arrange exist-ing components into a final product.

The evolutionary algorithm then produces an initial random “popula-tion” of possible antenna designs in

into instructions that can be given to computers.

Why Technology Will Augment, But Not Replace Us

When people first learn that com-puters can design products automat-ically, they often assume, or at least worry, that computers will make hu-man inventors obsolete. The history of artificial-invention technology so far should put these concerns to rest. Although such technology most likely will make certain aspects of the inventive process unnecessary or inefficient for human inventors to perform, savvy inventors will use ar-tificial genies as tools to boost their own human inventive capabilities.

In this sense, artificial-invention technology is like every other tool that inventors have used to assist them in the inventive process, from the hammer to the drill to the slide rule. Far from making human inven-tors obsolete, such tools eliminated some of the drudgery from invent-ing, thereby freeing human inventors to engage in more abstract and cre-ative aspects of the inventive pro-cess. Before the advent of electronic calculators, civil engineers spent countless hours manually calculating stresses on the bridges they designed to predict and verify their perfor-mance. Engaging in such tedious work hardly made them better in-ventors. Today’s computers don’t eliminate the need for civil engi-neers; they enable civil engineers to spend more time designing bridges and less time performing arithmetic.

Furthermore, as mentioned earlier, artificial-invention technology can enable inventors to branch out into new fields where they lack technical expertise. For example, John Koza has also used genetic programming software to create new designs for lenses, despite the fact that he admit-tedly knows little more about lens design than what can be learned in a standard textbook on the subject. He accomplished this by feeding stan-dard information about the physics

ware that transforms problem de-scriptions into new product designs make custom modifications to their “artificial genies” to make them work more efficiently at solving a particular problem at hand. Doing so requires skill at computer program-ming. Because such artificial genies often rely on simulators to test and evaluate the virtual designs they cre-ate, inventors with the ability to pro-duce faster and more accurate simu-lators of the real world will be highly valued.

Future inventors will also benefit from a healthy dose of training in bi-ology, particularly evolutionary biol-ogy, and neuroscience, because many of the leading technologies for auto-mating invention operate in ways that mimic biological processes, such as natural selection and the thought processes of the human brain. Early forms of such software were only crude replicas of their biological counterparts. As biologists have learned more about how evolution and the brain work, however, com-puter scientists have incorporated such new insights into the software they develop to make it simulate bio-logical processes more accurately. Continued advances will increas-ingly rely on cross-fertilization be-tween the fields of biology and com-puter science. As a result, we will develop not only software that can produce better inventions, but also inventions that are able to adapt to their environments, learn from expe-rience, heal themselves, and perhaps even create their own inventions.

Invention automation technology will also allow tomorrow’s inventors to move from one project to another, perhaps jumping from the auto in-dustry to the cosmetics industry to the semiconductor industry.

As computer technology increas-ingly levels the playing field with re-gard to skill at product design, the competitive advantage will go to those individual inventors and tech-nology firms that can quickly and ac-curately ascertain the needs of their customers and translate those needs


Then they provide the HDL code to a computer program called a “synthe-sizer,” which automatically creates a processor design based on the HDL description. Although human exper-tise is required to write the HDL code and to tweak the output of the synthesizer, computers automate the bulk of the detailed design work, making it possible to churn out ever-more complex processors every few months.

An HDL synthesizer converts an HDL description of a processor into a specific processor design primarily by following pre-programmed rules about how to transform each instruc-tion in the HDL code into a set of cir-cuit components. For example, the synthesizer is programmed to know that, whenever it encounters an “Add” instruction in the HDL code, it should insert a circuit for adding numbers into the processor design.

Such a process cannot uncover new components in the way that a search-based process can, but even this kind of rule-based automation significantly reduces the complexity of the design process for human de-signers. They are free to focus on adding new features to the processor instead of managing millions of tran-sistors individually. This is the same process that computer programmers have used to create software ever since the first automatic software compilers (which convert human-readable software source code into machine-readable object code) were developed in the 1950s.

Democratizing the Invention Process

Today’s artificial-invention tech-nology is used almost exclusively by people who already have training and skill as scientists, engineers, and computer programmers. Continued advances in the technology may, however, enable people completely lacking in technical expertise to be-come inventors. We can already see movements in this direction with the advent of a variety of tools that en-

Search-based automation is partic-ularly well suited for tackling the most difficult problems because com-puters do not form preconceived no-tions about which pathways to ex-plore. As a result, search-based invention automation often produces results that surprise even the ex-perts.

Search-based technologies require fast and powerful computers because they need to be able to generate and evaluate large numbers of possible designs. They also need to be able to accurately simulate how such de-signs will perform in the real world. If computing power continues to ex-pand according to Moore’s law, search-based invention technologies will likely become increasingly com-mon.

The second kind of invention auto-mation is design-based automation. Consider the microprocessor inside your computer, which contains hun-dreds of millions of transistors. No team of human engineers designs a processor manually, transistor-by-transistor. Rather, engineers design a modern processor by writing a de-scription of the functions they want the processor to perform in a “hard-ware description language” (HDL).

the form of computer models. The algorithm simulates the perfor-mances of the antennas and evalu-ates their performance according to the fitness criteria provided by the human engineers. Just as early life-forms on Earth may not have been well suited to survive, most of the initial antenna designs will likely not work well. Those antennas that out-perform the others will form the ba-sis for the next generation.

The software “mates” some anten-nas with each other, producing hy-brid designs that include features from their parents. It also “mutates” some antennas by introducing ran-dom variations into their designs. The software then evaluates the new generation of antenna designs, al-lows those that are most “unfit” to die off, and repeats the process, pos-sibly for hundreds or thousands of generations over a relatively short period of time. Designs are tested and refined quickly and inexpen-sively, potentially without ever hav-ing to build a single physical model until the final version is created. If all goes well, the result is one or more antennas that satisfy the human-pro-vided fitness criteria to a high de-gree.

More Information and Resources

Online:•AutomatingInvention(theauthor’sblogontheimpactsandimpli-

cations of computer-automated inventing): www.automatinginvention .com

•GeneticProgrammingInc.:www.genetic-programming.com•ImaginationEnginesInc.:www.imagination-engines.com•InventNow(aWebsitethatpromotesinnovationtoanewgenera-

tion of young inventors): www.inventnow.org•MITInventorsHandbook:web.mit.edu/invent/h-main.html•U.S.PatentandTrademarkOffice,IndependentInventorsRe-

sourcespage:www.uspto.gov/web/offices/com/iip/index.htm

Print:•A Field Guide to Genetic Programming by Riccardo Poli, William B.

Langdon, and Nicholas F. McPhee, 2008 (a free downloadable introduc-tory text on genetic programming): www.gp-field-guide.org.uk

•Genetic Programming: On the Programming of Computers by Means of Natural Selection by John R. Koza, The MIT Press, 1992. (Koza has also published three successive books on the topic: Genetic Programming II, III, and IV.)

•Introduction to Evolutionary Computing (Natural Computing Series) by A.E. Eiben and J.E. Smith, Springer, 2008.


is premised on the assumption that inventing is difficult, time-consum-ing, risky, and costly, and that we therefore need to provide special in-centives to inventors if we want them to continue spending their time inventing — and if we want them to share their inventions with the pub-lic. If, however, artificial-invention technology makes it possible for any-one — even someone lacking techni-cal skills — to produce products that satisfy their every need relatively quickly, easily, reliably, and at low cost, then there would be good rea-son to eliminate patent law.

Although we may reach that point someday, I don’t think we are close to it yet. For the foreseeable future, inventing will require significant skill, time, risk, and investment. As we have already seen, tomorrow’s inventors will need to be adept at identifying and defining problems that need to be solved and at creat-ing and instructing computers to solve those problems. However, as artificial-invention technology effec-tively increases the inventive skill of the average inventor, the threshold requirement for obtaining patent protection should be increased com-mensurately, to ensure that only in-ventions which required extraordi-nary skill to create can be patented.

The Artificial Invention Age bodes well for both real and virtual inven-tors. And both consumers and busi-nesses will benefit from better, cheaper products being brought to market more quickly than ever be-fore. ❑

About the AuthorRobert Plotkin is the author of The Genie in the Machine: How Computer-Automated Inventing is Revolutionizing Law and Business (Stanford University Press, 2009). He is a patent lawyer special-

izing in patent protection for computer tech-nology and a lecturer at the Boston Univer-sity School of Law. His Web site on invention automation is www.geniemachine .com. E-mail rplotkin@automatinginvention .com.

other search-based invention auto-mation technologies over the Inter-net and at low cost. This would spare customers the need to invest in the necessary hardware and to engage in maintenance tasks such as trouble-shooting hardware and upgrading software.

Of course, after a computer model of a product is created, it must still be manufactured. Traditionally, high manufacturing costs have prevented many garage inventors from success-fully commercializing their ideas. As Thomas A. Easton reported in THE FUTURIST (“The Design Economy,” January-February 2009), continuing improvements in low-cost auto-mated manufacturing technologies, often called “3-D printers” or “fabri-cators,” promise to enable even indi-vidual inventors to produce their products inexpensively.

Businesses are already springing up to leverage such developments. For example, companies such as Po-noko allow individual inventors to upload their 3-D product designs to the Ponoko Web site, where anyone can buy such products. When a pur-chase of a particular product is made, Ponoko “prints” the product on-demand, ships it to the customer, and splits the profit with the designer.

Legal Implications of Artificial Invention

Artificial-invention technology raises challenging legal questions:

•Shouldcomputer-generatedin-ventions be patentable?

•Shouldartificial-inventiontech-nology be patentable?

•Shouldtheinstructionsthatin-ventors give to artificial-invention technology — i.e., “artificial wishes” — be patentable?

•Iftheanswertothesequestionsis“yes,” then what legal standards should we apply if we want to ensure that patent law continues to promote, rather than stifle, innovation?

Taken to its logical extreme, artifi-cial-invention technology could ren-der patent law obsolete. Patent law

able people with no background in computer programming to create software.

For example, TenFold Corporation sells software that includes all of the basic components that a business would need in an enterprise software application, such as accounting, database, e-mail, backup, and secu-rity functions. A manager without any computer programming ability can use TenFold’s software to auto-matically create an application that performs exactly the functions needed. TenFold’s software leads the manager through a detailed inter-view process that solicits information about the features the manager needs (such as whether users should be re-quired to provide a username and password at login and, if so, what kind of password should be re-quired). The software then uses the answers provided by the manager to stitch together existing software components to create software that has all of the features specified by the manager, without requiring the manager or anyone else to write a single new line of code.

Today’s consumers are increas-ingly demonstrating their desire and willingness to modify the products they buy, reflecting the continued movement away from being passive consumers and toward becoming ac-tive “prosumers” (producer-consum-ers). Forward-looking companies have begun to embrace prosumers as sources of innovation that can be in-corporated within the company’s own products. Today’s prosumers, however, are largely limited to mak-ing product designs manually. As ar-tificial-invention technology becomes easier to use, more widely available, and less expensive, we can expect prosumers to be at the leading edge of adopting artificial-invention tech-nology.

It’s possible that cloud computing services will arise that provide in-ventors, prosumers, and small start-ups with access to powerful com-p u t e r s y s t e m s f o r r u n n i n g evolutionary design software and


CHASERSBy Patrick Tucker

The “holy grail” of computer science may be

within reach. A futurist looks toward tomorrow’s

Artificial Intelligence Revolution.AITHE

KIYOSHI TAKAHASE SEGUNDO / ISTOCKPHOTO.COM


CHASERSfallen away. The raw computing power may finally exist—and be cheap enough—to run an AGI pro-gram. But the core semantic and philosophical problems that science has faced for decades are as palpable as ever today. How exactly do you write a computer program that can think like a human?

The War between the

“Neats” and the “Scruffies”

There are two paths to achieving an AGI, says Peter Voss, a software developer and founder of the firm Adaptive A.I. Inc. One way, he says, is to “continue developing narrow AI, and the systems will become generally competent. It will become obvious how to do that. When that will happen or how it will come about, whether through simbots or some DARPA challenge or some-thing, I don’t know. It would be a combination of those kinds of things. The other approach is to specifically engineer a system that can learn and think. That’s the approach that [my firm] is taking. Absolutely I think that’s possible, and I think it’s closer than most people think—five to 10 years, tops.”

The two approaches outlined by Voss—either tinkering with mun-dane programs to make them more capable and effective or designing a single comprehensive AGI system—speak to the long-standing philo-sophical feud that lies at the heart of AI research: the war between the “neats” and the “scruffies.” J. Storrs Hall, author of Beyond AI: Creating the Conscience of the Machine (Pro-metheus Books, 2007), reduces this dichotomy to a scientific approach vs. an engineering mind-set.

“The neats are after a single, ele-gant solution to the answer of human intelligence,” Hall says. “They’re try-ing to explain the human mind by turning it into a math problem. The scruffies just want to build some-thing, write narrow AI codes, make little machines, little advancements, use whatever is available, and ham-mer away until something happens.”

The neat approach descends from computer science in its purest form, particularly the war game studies

be solved,” by which he meant a hu-manistic AI. Public interest dried up when the robot army failed to ma-terialize by the early 1980s, a period that researchers refer to as the “AI winter.” But research, though seem-ingly dormant, continued.

The field has experienced a re-vival of late. Primitive-level AI is no longer just a Hollywood staple. It’s directing traffic in Seattle through a program called SmartPhlow, guiding the actions of hedge-fund manag-ers in New York, executing Internet searches in Stockholm, and routing factory orders in Beijing over inte-grated networks like Cisco’s. More and more, the world’s banks, gov-ernments, militaries, and businesses rely on a variety of extremely sophis-ticated computer programs—what are sometimes called “narrow AIs” —to run our ever-mechanized civi-lization. We look to AI to perform tasks we can easily do ourselves but haven’t the patience for any longer. There are 1.5 million robot vacuum cleaners already in use across the globe. Engineers from Stanford Uni-versity have developed a fully au-tonomous self-driving car named Stanley, which they first showcased in 2005 at the Defense Advanced Research Projects Agency’s (DARPA) Grand Challenge motor cross. Stan-ley represents an extraordinary im-provement over the self-driving ma-chines that the Stanford team was showing off in 1979. The original self-driving robot needed six hours to travel one meter. Stanley drove 200 meters in the same time.

“The next big leap will be an au-tonomous vehicle that can navigate and operate in traffic, a far more complex challenge for a ‘robotic’ driver,” according to DARPA direc-tor Tony Tether.

In other words, robot taxis are coming to a city near you.

The decreasing price and increas-ing power of computer processing suggest that, in the decades ahead, narrow AIs like these will become more effective, numerous, and cheap. But these trends don’t necessarily herald the sort of radical intellectual breakthrough necessary to construct an artificial general intelligence.

Many of the technical (hardware) obstacles to creating an AGI have

The advent of a human-level artificial intelligence—a ma-chine capable of the richness of expression and nuance of

thought that we associate with hu-manity—promises to generate tre-mendous wealth for the inventors and companies that develop it.

According to the Business Com-munications Company, the market for AI software and products reached $21 billion in 2007, an impressive figure that doesn’t touch on the wealth that a human-level artificial intelligence could generate across industries. At present, the world’s programmers have succeeded in auto mating the delivery of electricity to our homes, the trading of stocks on exchanges, and much of the flow of goods and services to stores and offices across the globe, but, after more than half a century of research, they have yet to reach the holy grail of computer science—an artificial general intelligence (AGI).

Is the tide turning? At the second annual Singularity Summit in San Francisco last September, I dis-covered that the thinkers and re-searchers at the forefront of the field are pitched in an intellectual battle over how soon AGI might arrive and what it might mean for the rest of us.

The Not-So-Rapid Progress

Of AI Research

The scientific study of artificial intelligence has many roots, from IBM’s development of the first number-crunching computers of the 1940s to the U.S. military’s work in war-game theory in the 1950s. The proud papas of computer science—Marvin Minsky, Charles Babbage, Alan Turing, and John Von Neumann —were also the founding fathers of the study of artificial intelligence.

During the late 1960s and early 1970s, money for AI work was as easy as expectations were unreal-istic, fueled by Hollywood images of cocktail -serving robots and a Hal 9000 (a non- homicidal one, presum-ably) for every home. In an ebullient moment in 1967, Marvin Minsky, proclaimed. “Within a generation . . . the problem of creating ‘artifi-cial intelligence’ will substantially


how to do a lot of little things very well: We can spot patterns in na-ture, track multiple moving objects and figure out what they are, devise strategies for catching prey based on rapidly changing conditions, and evade the occasional predator us-ing only our wits. A fancy computer term for this is parallel processing, or working through many millions of seemingly unrelated little prob-lems at once. Computers can paral-lel process, too, but they don’t do so with the fluidity or dexterity of humans. The challenge that today’s AI researchers face is how to even identify, much less emulate, all the little processes that a human brain performs both simultaneously and unconsciously.

Enter the scruffies.The advent of the semiconductor

in the 1950s, which led in turn to the transistor and then to the integrated circuit, opened up a completely dif-ferent area of research in computer science, wherein hardware and code could be combined almost spontane-ously to achieve surprising results. This is the basis for scruffy research.

As a group, scruffies take a more experimental approach to AI and put a heavy emphasis on robotics. Rodney Brooks, former director of the MIT AI lab and founder of the iRobot Corporation (makers of the Roomba robot vacuum cleaner), is perhaps the most famous scruffy. He takes issue with Voss’s five-year time horizon for writing an AGI.

“It’s nice to think of AI as being a single technical hurdle,” Brooks says. “But I don’t believe that’s the case. There’s a whole raft of things we don’t understand yet. We don’t understand how to organize it; we don’t understand what its purpose is; we don’t understand how to connect it to perception; we don’t understand how to connect it to ac-tion. We’ve made lots of progress in AI. We’ve got lots of AI systems out there that affect our everyday lives all the time. But general AI? It’s early days—early, early days.”

Can Machines Learn?

Many researchers have discovered that creating a machine that can learn is an essential first step in develop-

But the success of Deep Blue was limited. While the machine demon-strated technical expertise at chess, it didn’t show any real comprehension of the game it was playing, or of it-self. As Paris Review editor George Plimpton observed after the match, “The machine isn’t going to walk out of the hotel there and start doing extraordinary things. It can’t man-age a baseball team, can’t tell you what to do with a bad marriage.”

The validity of this observation isn’t lost on today’s AI community.

“What we thought was easy turned out to be hard, and what we thought was hard turned out to be easy,” says Stephen Omohundro, founder of the firm Self-Aware Systems. “Back in the early Sixties, people thought that something like machine vision would be a sum-mer project for a master’s student. Today’s machine vision systems are certainly better than they were, but no vision system today can reliably tell the difference between a dog and a cat, something that small children have no problem do-ing. Meanwhile, beating a world chess champion turned out to be a snap.”

Human Hardware

So why are computers bet-ter at chess and people better at distinguishing dogs from cats? The answer lies in the unique nature of the human brain. That three-pound lump of grey mat-ter we’ve got in our skulls sim-ply isn’t well-suited for solving complex, theoretical problems. Few of us can comprehend the dense algorithms that allow Google Maps, the New York Stock Exchange, or the local utility company to operate continuously.

Unlike a machine, which an engineer can design to address specific abstract problems, the human brain evolved in response to natu-ral environments where we were called upon to forage, hunt, avoid physical dan-ger, and cooperate with other members of our spe-cies. As a result, we know

of Von Neumann and his colleagues in the 1930s and 1940s. The 1997 de-feat of world chess champion Garry Kasparov by IBM’s Deep Blue com-puter is considered by many the seminal neat success. Up until that moment, the mainstream scientific community generally accepted the premise that AIs could be written to perform specific tasks reasonably well, but largely resisted the notion of superhuman computing ability. Deep Blue proved that an AI entity could outperform a human at a sup-posedly “human” task, perceiving a chess board (Deep Blue could see 200 million board positions per second) and plotting a strategy (74 moves ahead as opposed to 10, the human record).

“Scruffy” AI expert Rodney Brooks, founder of Roomba maker iRobot Corporation.

iROBOT


Google, Yahoo, Ask.com, or any other search engine to look up some fact or figure, you might be doing more than getting information—you may be teaching a type of burgeoning mind how to think.

Barney Pell, CEO of Powerset, predicts that interest in AI will in-crease as search engine technology advances. Pell’s firm is working on a natural language-based search engine that he hopes will compete with Google.

“Search engines today are built on a concept of keywords,” he says. “They don’t really understand the documents that you search or the user’s query. Instead, they take your query as a bag of words, and they try to match keywords to keywords. The result is that the user, the hu-man, has to try to figure out what words would appear in the docu-ments that [he or she] wants. Some people are very good at that game. They use very advanced syntax and features and they get a better search experience. Others feel like they’re missing something. The time is com-ing when people will be able to use their own natural built-in power to say what they want just in English, for example, and have computers rise to work with the meaning and the expression of the question and match that against the meaning of the documents, giving you a differ-ent search experience. We at Power-set expect to come out with a fairly large search index—where a system has read every single sentence on millions of Web pages and is letting users do a search with natural lan-guage—over the course of the next year.”

Pell forecasts that within the next five years, we’ll be interacting with search engines as fluidly as we do with carbon-based customer-service representatives. But our interaction won’t be limited to what questions a human might be able to answer off the top of his or her head. Instead, we’ll be able to ask any question at all. Want to know why the bluebird sings? Forget the keyword hunt; simply go to your search engine, ask your question, and get a straight reply.

“There are already people tracking the length of the average query, and it’s been steadily increasing from

ing a system that can think.“Bayesian networks [see sidebar]

are a good example of systems that have this ability to learn,” says Omo-hundro, “but the approach is ratio-nal and conceptually oriented. That’s the direction I’m going in. It’s a merger of the two schools. The kinds of systems I build are very carefully thought out and have a powerful rational basis to them, but much of the knowledge and structure comes from learning, their experience of the world, and their experience of their own operation.”

What do you teach a learning sys-tem to compel humanistic thought? According to Peter Norvig, director of research at Google, understand-ing human intelligence means first understanding what the brain does with words.

“I certainly believe language is critical to the way we think—the way we can form abstractions and think more carefully,” says Norvig. “The brain was meant for doing vi-sual processing primarily: A large portion of the cortex is for that. It wasn’t meant for doing abstract reasoning. The fact that we can do abstract reasoning is an amazing trick. We’re able to do it because of language. We invent concepts and give them names, and that lets us do more with a concept because we can move it around on paper. Language derives all our thinking.”

Google is currently working on instantaneous language translation based on probabilistic modeling—translating articles in Chinese into English faster and with greater ac-curacy, says Norvig. “We tell the program that the one is a translation to the other. Then we refine the pro-cess through more data, more words, more articles.”

The vast amount of data, news re-ports, and language content that Google accesses is part of the rea-son the 10-year old Internet firm has a bigger stake in AI than just about anybody. There’s plenty of money to be made, but more importantly, any program that receives language in-put from humans on a massive scale could, theoretically, evolve over time into a humanistic AI—or provide a working basis for one. Every time you go to your computer and open

BAyeSIAN NeTWORkS

Bayesian nets use a probabilistic model to assign a mathematical value to certain variables. To pose an extremely simple example, if the janitor comes on Wednesday and Friday, and the janitor is here today, the chances of today being Wednesday are very good. We can award the “it’s Wednesday” option a 50% likelihood. The system is refined when you add more relevant data: If the janitor comes in the morning on Wednesday, and the afternoon on Friday, and the janitor is here, and it’s 10 a.m., then the already good chances of it being Wednesday double (assuming a universe where janitors are always where they’re scheduled to be).

What seems like a silly children’s riddle may also be the key to accomplishing something remarkable—teaching a system of code, transistors, and electricity to meaningfully differentiate between Wednesday and Friday. —PMT

iROBOT

The PackBot, a military robot developed by Rodney Brooks’s iRobot Corporation, is “rugged and yet light enough to be de-ployed by one person. A video-game style controller makes this robot easy to learn and use . . . [and] keeps warfighters and first responders safe.”


version later in 2008. “These simpler virtual animals are an important first step,” says Goertzel, “but I think the more major leap will be taken when linguistic interaction is introduced into the mix—something that, tech-nologically, is not at all far off. Take a simpler virtual animal and add a language engine, integrated in the appropriate way, and you’re on your way.”

Goertzel’s, Pell’s, and Norvig’s research suggests that a real thinking machine is just as likely to emerge in front of our eyes on our home com-puters as it is to come out of DARPA. If it succeeds, we can all take a little morsel of the credit.

Growth in the use and importance of these Internet-based AI systems is virtually guaranteed, Kurzweil writes in The Singularity Is Near. In-formation exchange is based on the trading of data. Robots can com-municate data more efficiently than babbling humans. “As humans, we do not have the means to exchange the vast patterns of inter-neuronal connections and neurotransmitter concentration levels that comprise our learning, knowledge, and skills, other than through slow, language- b a s e d c o m m u n i c a t i o n , ” s a y s Kurzweil.

Unlike people, AI entities can communicate completely and imme-diately via binary code and electric current. More communication means faster command execution, and that

systems in conversation,” he says. “But we’re a long way from that. In the meantime, over the next decade, we’ll expect to use voice rather than type to interface with all our sys-tems—voice in, voice and data out.”

Life in SecondLife

Like Norvig and Pell, Ben Goertzel, a long-haired, jeans-clad AI super-star and author of From Complexity to Creativity ( Plenum, 1997), also sees the birth of AGI as intimately bound up in the Internet. But Goertzel be-lieves that online games offer a more promising avenue of research than search engines alone.

“My prediction is that AI in virtual worlds may well serve as the cata-lyst that refocuses the AI research community on the grand challenge of creating AGI at the human level and beyond,” he writes in a recent essay for KurzweilAI.net. Goertzel’s software firm, Novamente, is experi-menting with artificially intelligent pets for the popular massively mul-tiplayer online role-playing game SecondLife. He says the pets can “carry out spontaneous behaviors while seeking to achieve their own goals, and can also specifically be trained by human beings to carry out novel tricks and other behaviors, which were not programmed into them, but rather must be learned by the AI on the fly.” Goertzel and com-pany hope to launch a commercial

two words to three words, steadily approaching four words,” says Pell. “There’ll be a crossover point where queries expressed in regular En glish will exceed the proportion that use keywords. It’s a concrete metric we can track. I’m going to call that in five years from now. Once that point is reached, companies will start pour-ing more money into natural lan-guage technology, AI, conversational interface, and semantics. The pace will pick up, and it will take people by surprise.”

Pell sees conversational artificial intelligence—a precursor to AGI—becoming part of our daily lives away from the keyboard, as well. In the future, he says, we’ll think of AI as a household utility as common as running water, operating in the back-ground of our daily lives. “We’ll defi-nitely get to the point where you will expect to engage your household

FOLLOWINg THe BRAIN MAPThe only thing that robotics en

gineers seem to like talking about more than computers is neuroscience, particularly functional magnetic brain imaging or fMRI. In the past decade, fMRI, which takes live pictures of the blood flow being diverted throughout the brain during thought processing, has given the world a unique window into the origins of thought.

For researchers considering how to design a physical system that can think, referring to the quintessential thinking machine—

the human brain—is a nobrainer. In his bestselling book The SingulariTy iS near, inventor Ray Kurzweil contends that an artificial general intelligence (AGI) will necessarily be patterned off the biological processing of a brain and that fully 3D scans will allow us to reverseengineer a human brain sometime in the 2020s. Reverse engineering, he contends, is a key strategy for creating an AGI.

Other researchers, like Steve Omohundro, contend that, while AI watchers have a great deal to

learn from neuroscience, following the brain map may lead to a dead end.

“I don’t prefer the brain scan idea as a route to AI,” he says. “I don’t think we want to build machines that are copies of human brains. The direction I’m pursuing, potentially, could actually produce a much more powerful system based on theorem proving. But theorem proving is very hard. No one has been able to do it.” —PMT

The PackBot Explorer from iRobot.

iROBOT


will eventually create it. We’re doing so, incrementally, already. But what does that mean for the future?

Chickening Out of the Brave New World

“If popular culture has taught us anything, it is that someday mankind must face and destroy the grow-ing robot menace. . . . How could so many Hollywood scripts be wrong?” writes robotics engineer Daniel Wil-son in his satirical book, How to Stop a Robot Uprising. In it, Wilson cap-tures our half-serious, half-ironic robot phobia with great aplomb. Hollywood has spent the last quarter century turning AI’s worst-case sce-nario—the robot insurrection—into an absurd cliché. Between the suc-cessful Terminator and Matrix fran-

Kurzweil may have already invented a system to do precisely that.

According to its official patent (#6,647,395), the Kurzweil “poetic” computer program can actually read a poem, analyze what the poem is about, and then use that information to write coherent lyrical prose based on what the program perceives to be human language patterns. As Kurz-weil told reporter Teresa Riordan of the New York Times, “The real power of human thinking is based on rec-ognizing patterns. The better com-puters get at pattern recognition, the more humanlike they will become.” The program is available from Kurz-weil’s Web site for $19.

Whether born of the Internet or the military, in one decade or 10, AGI is coming. If human-level AI exists within the realm of possibility, we

means greater productivity.As we continue to transfer our

knowledge to the Web, posting more blogs, technical reports, news articles, academic writings, etc., and as we continue to develop programs and AI systems to help us categorize, store, retrieve, and analyze data, so those interlinked systems are accumulating more knowledge about human civi-lization. If Kurzweil, Hall, and other AI watchers are correct, these systems will eventually learn to behave and process information in a humanis-tic way. We may be hastening a day when any labor-intensive task can be automated or outsourced to an arti-ficially intelligent entity, a day when such entities might be able to commu-nicate, perform, govern, and even cre-ate art more effectively, persuasively, or beautifully than human beings.

HOW TO SuRvIve A ROBOT uPRISINg By Daniel Wilson

A Carnegie Mellon roboticist makes light of the worst-case scenario.

I wrote How to Survive a Robot Uprising while I was a graduate student at the Robotics Institute of Carnegie Mellon University. After I spent years working around robots (and roboticists), it started to really irritate me that robots were getting such a bad reputation from Holly-wood. There were various models of Terminators busy exterminating the Sarah Connors of the world, but just as bad were the droves of sniveling robots that seemed to strive at all costs to become hu-man beings. Thanks to pop culture, most people seem to think not only that robots are dangerous, but that they’re also inherently inferior to human beings.

Like most problems, I decided that fixing this disconnect was best accomplished via the power of sar-casm. And so I wrote a book that very seriously delivers roboticist-approved advice on how best to survive the most outlandish fic-

tional robot uprising scenarios. I’m told the book is now a robotics primer at the United States Naval Academy, so score one for the ro-bot builders.

In the book, I poke fun at the popular misconception of robots, but as artificial intelligence (AI) applications enter our daily lives these viewpoints take on real im-portance. Do we need to under-stand AI in order to use it? Human beings tend to think of human intelligence as some kind of gold standard that robots are trying to reach. In reality, relatively few sci-entists are trying to create an artifi-cial human-level intelligence—the problem is too unconstrained. In-stead, the focus is on using learning algorithms to solve very specific problems, such as how to brake a car so that it won’t spin out on an icy road. In these limited domains, AI can operate at superhuman lev-els to make thousands of decisions in the blink of an eye and for years on end. Unfortunately, humans who are busy trying to spot hu-manlike intelligence are oblivious

to the very real, very intelligent ar-tificial brains in our cars, our com-puters, and even our toys.

In the future, I foresee more sin-gle-purpose AI algorithms infiltrat-ing our daily lives and taking over tasks that humans can’t or won’t attempt. Innocuous little routines will help vacuum-bots map living rooms, spam filters search e-mail, and even make existing home se-curity systems smart enough to learn the normal patterns of daily life. As the field advances, these little machines will get better at do-ing more humanlike things—rec-ognizing and understanding faces, speech, gestures, and emotions. In the long term, someone is bound to put them all together and, who knows, maybe a humanlike intel-ligence will emerge someday.

About the AuthorDaniel Wilson is the author of How to Survive a Robot Uprising (Bloomsbury USA, 2005). Visit www.robotuprising.com


mason, cobbler, or singer of epic poems becomes a relic. Knowledge, through disuse, is lost.

In his landmark novel The Time Machine, the Victorian writer H.G. Wells portrays a future culture simi-lar to that of the robotically run uto-pia of Kurzweil and Brooks. But in the Wells scenario, the privileged classes—those with unlimited ac-cess to labor-saving devices and ser-vices—have no need to expend effort to care for themselves in any way. As a result, they’ve devolved into a race of mute, effete creatures, the Eloi—physically dependent on mechani-cal processes they can’t comprehend, unaware of any past or future, and doomed, by and large, to a miserable and violent death. AI probably won’t turn us into Eloi, at least not over-night. But, like any technology, it has the power to either liberate or limit depending on the choices, talents, and wisdom of those who use it.

Will faster and better AI systems receive any sort of serious govern-mental scrutiny? If they generate the sort of wealth that people like Thiel, Kurzweil, and others predict, the probable answer is No. As a species that has prospered by virtue of our inventiveness, modern humanity is perennially eager to incorporate new technologies into our daily lives and then let government or the free mar-ket address the effects of our short-sightedness after the fact.

This messy, ill-considered process brought us the automobile and, re-ciprocally, the safety belt; Scotch-gard and the mandatory smoke detector; asbestos and the asbestos class-action lawsuit. It’s the story of our stumbling, haphazard method of inventing things and throwing them out into the world, a method that we—blindly and blissfully—call progress. It’s also the likely story of how artificial intelligence will evolve in the future. ❑

About the AuthorPatrick Tucker is the senior editor of THE FUTURIST and director of communica-tions for the World Future Society. E-mail [email protected]. This article draws from several interviews,

viewable at www.wfs.org.

chises and countless Saturday morn-ing cartoon show villains, it’s simply impossible to take the threat of what researchers call “runaway AI” very seriously. Not surprisingly, many AI watchers dismiss the scenario as well.

“I don’t think we’re going to have runaway AI in any sort of intentional form,” says Brooks. “There may well be accidents along the way where systems fail in horrible ways because of a virus or bug. But I don’t believe that the malicious AI scenario makes sense. There may be malicious intent from people using AI systems as ve-hicles. But I don’t think malicious in-tent from the AI itself is something that I’m going to lose sleep over in my lifetime. Five hundred years from now? Maybe.”

Others, like Omohundro, take a more cautious view. “The worst case,” he says, “would be an AI that takes off on its own momentum, on some very narrow task, and, in the process, squeezes out much of what we care most about as humans. Love, compassion, art, peace, the grand vi-sions of humanity all could be lost in that bad scenario. In the best sce-nario, many of the problems that we have today, like hunger, diseases, and the fact that people have to work at jobs that aren’t necessarily fulfill-ing, all of those could be taken care of by machine. This could usher in a new age in which people could do what people do best, and the best of human values could flourish and be embodied in this technology.”

There’s no way to know whether the worst-case scenario is realistic until our new borg overlord IMs us with a list of demands. Dwelling on this scenario is probably unproduc-tive. As venture capitalist and PayPal co-founder Peter Thiel says, “AI is so far out that it’s the only thing that makes sense—from a venture capital perspective—to get involved in. The Singularity will either be very suc-cessful and the greatest thing to hap-pen to markets ever, or it would be a disaster, destroy the world, and there would be nothing left to invest in. If you’re betting that the world is going to end, even if you’re right, you’re not going to make a lot of money.”

A more interesting, complex, and frightening question is, How might

the AI Era change human culture and behavior?

In the techno-utopia of Kurzweil and others, humans interact effort-lessly with machines and no piece of information is ever out of reach for longer than the fraction of a second required to digitally process it. As a result, many of the skills and much of the knowledge we’ve worked hard to build up over centuries are as irrelevant to daily life as the abil-ity to forage for food or hunt with a bow. The only foreseeable way that the assortment of abilities, aptitudes, and talents that we call “expertise” might endure in the context of ever-expanding AI is if society makes a conscious decision to perpetuate them. Millions of people would have to voluntarily choose to do their own data research, write their own reports, read their own books, make their own stock trades, drive their own cars, and the like, even though other, more-immediate methods for accomplishing similar errands are readily available.

This is not an encouraging pros-pect. The notion that people would voluntarily choose an antique tech-nology over the immediacy and convenience of a machine that can do the thinking and acting for them flouts our most basic understanding of human nature.

Rodney Brooks waves the scenario away.

“When I was a boy in elementary school,” he says, “there was a big fuss about using ballpoint pens, even fountain pens. We had to know how to use a nib and ink because, they said, ‘if we lost that skill later in life, we would not be able to get along.’ People keep saying, ‘they’re losing that skill and this.’ But they’re gaining other skills and they’re adapting to modern life. I just don’t buy it. People can become fantastic at using Google and getting information. Maybe a dif-ferent set of people were fantastic at using other skills, but it’s a new set of survival skills, and people that are better at it will prosper.”

I’m less certain.Every new technology forces the

society that created it to make a trade-off. A skill or activity that had been important becomes unimport-ant. The artisan, the welder, stone-


In Future Shock (1970), Alvin Toffler wrote that technology had accelerated the pace of change so much that people were beginning to lose their moorings. The old, familiar world in which they had grown up was van-ishing so quickly that they no longer knew where they stood. The result was a pervasive insecurity that could only get worse as the transformation gained still greater speed. In particu-lar, long-term planning would be-come increasingly difficult.

At that time, the personal com-puter, which would prove to be the greatest single force for change since the Industrial Revolution, had yet to

be invented. Genetic engineering was barely a fantasy, and nanotech-nology was even further in the future. In 1970, clearly, technology still had a lot of accelerating to do, and chances are that it still does.

In order to better understand what’s happening, let’s look at the product cycle. The useful life of a product goes through four stages:

• Idea (a theoretical breakthrough, such as something that would be considered for a Nobel Prize).

• Invention (a patentable proto-type).

• Innovation (the first consumer product).

• Imitation (cheap competitors flooding the discount stores).

Early in the twentieth century, the product cycle was 40 years. By World War II, the cycle had shrunk to 30 years. Today, for most consumer products, it is about six months. In computers and cutting-edge elec-tronics, it is more like six weeks. Bring out a really hot product and it is likely to be reverse-engineered, manufactured in China, and avail-

able on eBay in two weeks or less.With this rapid evolution in mind,

it is worthwhile to ask what technol-ogy has in store for us. The timeline presented here offers some basic in-formation to help with planning for the years ahead. Each of the innova-tions on this list represents a general kind of change. The timeline deals with emerging opportunities and their potential impacts on our lives, rather than with any particular toys.

About the Timeline for the Future

This timeline was first developed by British Telecommunications in 1991. It has been updated every two or three years under the leadership of futurologist Ian Pearson of Futuri-zon GmbH in Ipswich. Forecasting International’s update of the 2005 timeline has been assembled from the work of six contributors. Our panelists were:

•DennisBushnell,chiefscientistatthe NASA Langley Research Center.

Timeline for the Future: Potential Developments and Likely ImpactsBy Marvin J. Cetron

continued on page 17

Designer babies, fiber-optic

plants, synthetic celebrities,

and more. A timeline suggests

when we’ll see the evolving

technologies that will radically

reshape human life.


2010-2014Artificial Intelligence and Artificial Life

Biotechnology, Health, and Medicine

Business and Education

Computing

Environment and Resources

Home and Leisure

Machine / Human Interface

Security, Law, and War

Space

Wearable and Personal Technology

Robotics

Travel and Transportation

Behavior alarms based on human mistake recognition 2010Software is trained, rather than written 2010Artificial nervous systems for autonomous robots 2010

Retinal implants linked to external video cameras 2010Designer babies 2012

80% of U.S. homes have PCs 2010Virtual reality used to teach science, art, history, etc. 20123-D video conferencing 2014

Optical neurocomputer 2012DNA computer 2014Supercomputer as fast as human brain 2014

Commercial magma power stations 2011Clothes collect and store solar power 2012Effective prediction of most natural disasters 2014

Fiber-optic plants used in gardens 2010Smart paint containing computer chips is available 2013

Voice-activated interface for home appliances 2010Computer screens in clothes 2010Tactile sensors, comparable to human sensation 2012Computers linked to biological sensory organs 2012

First Net war fought between cybercommunities 2011People’s courts on Internet for minor disputes 2012Virtual reality routinely used in courtrooms for evidence presentation 2013ID cards replaced by biometric scanning 2014

Near-Earth space tours (suborbital) 2012

Portable translation device for simple conversation available on consumer market 2010

2011: First Internet war fought between cyber-communities. A scene from Second Life, a virtual online world inhabited by millions of residents from around the globe.

LINDEN LAB

2014: DNA computer. University of Wisconsin–Madison scientists have taken DNA computing from the test tube to a solid surface. The gold chip shown here con-tains millions of DNA molecules capable, with the help of en-zymes that act like software, of solving a relatively complex problem.

JEFF MILLER / UNIVERSITY OF WISCONSIN–MADISON

2012: Clothes collect and store solar power. This jacket from Scottevest utilizes thin-film solar technology that allows you to charge your cell phone and iPod while you walk.

SCOTTEVEST INC.

Wild Card:

Zero point energy engi-neered/commercialized; all other energy sources

become obsolete.


2015-2019Machine knowledge exceeds human knowledge 2020Electronic life-form given basic rights 2020Artificial insects and small animals with artificial

brains 2020

Artificial liver 2020Nanobots in toothpaste attack plaque 2020Fully functioning artificial eyes 2020Artificial peripheral nerves 2020

Library of Congress contents available in sugar-cube-sized device 2020

Desktop computer as fast as human brain 2021

Experience-recording tech-nology developed 2023

First Bionic Olympics 2020

Realistic nanotech toy sol-diers are built 2022

Airplanes 75% more fuel-efficient 2020Driverless truck convoys using electronic towbar 2022

Computer-enhanced dreaming 2020

2020-202425% of TV celebrities are syn-

thetic 2015

Artificial heart (lab-cultured or entirely synthetic) 2015

Some implants start to be seen as status symbols 2017

Artificial lungs, kidneys 2017

Quantum computer 2015All technology imitates thinking

processes of human brain 2018

Insectlike robots used for crop pollination 2015Carbon-dioxide fixation technologies for environmental

protection 2015Synthetic, nonpetroleum aviation fuel 2018

Living rooms decorated with virtual-reality scenes 2015Holographic TV 2018

Global sensor grid 2018

Self-diagnostic, self-repairing robots 2015Houses built by robots 2015Self-monitoring infrastructures 2015Robots for almost any job in homes and hospitals 2015

Electromagnetic communications disrupted 2015

Space tugs take satellites into high orbits 2015

Reservations required for some key roads 2018

Spectacles that translate signs, labels 2015

2015: Artificial heart. The AbioCor is a completely self-contained replacement heart designed to sustain the body’s circulatory sys-tem. It is intended for end-stage heart failure patients. Battery-operated and equipped with an internal motor, the AbioCor is able to move blood through the lungs and to the rest of the body, simulating the rhythm of a heartbeat.

ABIOMED

2018: All technology imitates thinking processes of human brain. MIT’s Nexi MDS Robot has been designed to effec-tively convey a wide range of human emotions.

DONNA COVENEY / MIT

2018: Holographic TV. Three-dimensional television may soon hit the consumer market, and interactive 3-D applications such as online games are being developed as well. Recent developments in 3-D screens mean that special viewing glasses are no longer required.

PHILIPS

Wild Card:

Significant IT attack brings down major country economy.


Artificial Intelligence and Artificial Life

Biotechnology, Health, and Medicine

Business and Education

Environment and Resources

Machine / Human Interface

Robotics

Security, Law, and War

Travel and Transportation

Space

2025-2029Geneticallyengineeredelectronictoy/petdeveloped2025

Only 15% of deaths worldwide due to infectious diseases 2025Life extension at one year per year 2025

Molecular manufacturing 2025Individualized education programs for all students 2025

Full direct brain link 2025

Robot population surpasses human population in the developed world 2025

Emotion-control chips used to control criminals 2025

Space hotel accommodates 350 guests 2025

Teleportation at the particle level 2025FAA approves autonomous drone airliners 2026Hydrogen-fueled executive jets (cryoplanes) 2028

•IanPearson,theforecastermostfamiliar with this timeline.

•WilliamHalalofGeorgeWash-ington University, whose company, TechCast LLC, periodically devises a similar timeline (see page 39).

•MurraySmith,Professional Pilot’s publisher and resident expert on the future of aviation.

•AseniorR&DexpertattheDe-partment of Defense who chose to remain anonymous.

•ThestaffofForecastingInterna-tional.

The six wild cards were provided by John L. Petersen, president and

founder of The Arlington Institute, a research institute that specializes in thinking about global futures. The wild cards do not necessarily repre-sent the opinions of the author or the World Future Society.

What the Timeline Reveals

Since the previous iteration of this timeline was published in THE FUTURIST (March-April 2006), the panel has adjusted its expectations for some events. For instance, the an-ticipations of fully functional artifi-cial eyes and peripheral nerves have been pushed ahead from the 2030s and beyond to the 2020s.

In choosing target dates for the timeline, we assume that the item will be readily available, but not yet a commodity item. Consumer prod-ucts will be found at specialty stores and perhaps high-end department stores, but not yet at Wal-Mart.

Our panel members agreed fairly well about when most of the new technologies could be expected. In many cases, all six participants chose the same date. Where due dates were spread, we generally took the me-dian date. If we at Forecasting Inter-national felt especially strongly about the issue, we may have had our thumbs on the scale when mak-ing the final decision, but it did not

continued from page 14

Wild Card:

Discovery of artifacts that force reconsidering

significant aspects of common understanding

of human history.

Wild Card:

Bio / nano experiment gets out of control; regional or global

impact.


Robots are physically and mentally superior to humans 2032

Artificial brain implants 2030

Renewable energy replaces fossil carbon 2030

Robots replace humans in workforce completely 2035

Space factories for commercial production 2035

Asteroid diversion technology used as weapon 2040

Moon base the size of a small village is built 2040First manned mission to Mars 2040Start of construction of manned Mars laboratory 2048Use of human hibernation in space travel 2052

Teleportation of a human being 2040

2030-2039 2040 and Beyond

happen frequently.Often, the date when a technology

reaches practical use depends less on any technical obstacles than it does on external factors. To be adopted, an innovation must be technically feasible, economically feasible, and both socially and politically accept-able.

The space program is one obvious example. The space-related events on our timeline all assume that put-ting human beings into space will re-main a priority, but that is not guar-anteed. In the United States, for instance, future administrations might downgrade human spaceflight in favor of automated probes. In that

case, the events on our timeline will be replaced by safer, if less stirring, activities, and the dates will need significant adjustment.

In some cases, the fate of a techni-cal innovation can be decided by a very small group of managers. In other cases, the decision must be reached by a much broader consen-sus. Sometimes it is a matter of polit-ical will.

However, business and life both require management that is becom-ing ever more difficult in a time of accelerating change. We hope that this timeline will help to make the future just a bit less shocking and bring it a bit more under control.

“What must be remembered by anyone preparing for the future is that technology change isn’t very im-portant in itself,” says Pearson. “What matters is what this change enables or destroys.” ❑

About the AuthorMarvin J. Cetron is president of Forecasting International Ltd. and a member of the World Future Society board of directors. E-mail [email protected].

This article draws from an earlier version published by Professional Pi-lot magazine (October 2008) and is used with permission.

2040: First manned mission to Mars. A perspective view of Hebes Chasma on Mars. A chasma is a deep valley with steep sides.

ESA / DLR / FU BERLIN (G. NEUKUM)


It’s clear now that a technology revolution is under way as ever more sophisticated

information systems create unprecedented gains in knowledge, leading to breakthroughs every-where. The latest forecasts from the TechCast Project are presented here to show that modern societies can realistically envision renewable en-ergy replacing oil, medical control over the ge-netic process of life, computer power becoming cheap and infinite, mobile communications at lightning speeds, robots serving as helpers and caregivers, and much more to come. Forecasters and futurists are especially excited over the accel-erating pace of this progress; the unique power of the infotech, biotech, and nanotech fields; and ar-tificial intelligence becoming good enough to

By William E. Halal

Emerging Technologies and the Global Crisis

of Maturity

As technological development surges,

the ability of institutions to handle

change is stifled by outmoded social

systems. To survive the technological

revolution in the midst of global crisis,

a social revolution is also needed that

will bring institutions and civilization

to a higher stage of maturity.


vances are driving a creative transformation of business, society, the global order, and even what it means to be human. First I briefly outl ine the TechCast research method, which pools the knowledge of 100 experts online. Then I inte-grate the forecasts into longitudinal scenarios that “macro-forecast” the most likely path civilization will fol-low over the next 20 years — a vir-tual trip through time.

The major conclusion from this analysis is that the world is facing a global crisis of maturity, the most sa-lient example being the near-collapse of the global banking system in Oc-tober 2008. Warnings of massive transformations have been antici-pated for decades by the Club of Rome and many others. Today, how-ever, the acceleration of change seems to be producing a mounting series of severe global disrup-tions — energy shortages as oil sup-plies peak, impending climate change and environmental decline in general, spreading of weapons of mass destruction, continuing terror-ism, and other yet unforeseen threats as globalization inexorably strains old systems to the breaking point.

Threats of this magnitude are hard to grasp within existing worldviews, so I draw on previous studies to sug-gest that the crisis of maturity can be best understood as part of a “life cycle of evolution.” The path of global development has been driven by successive waves of increasingly powerful technology frontiers — ag-riculture, mass production, services, information, and now knowledge. This broader analysis reveals a life cycle of the entire planet, similar to but vastly larger than the life cycle of all organisms, culminating in a phase of maturity that transcends early stages.

From this perspective, the world seems poised at the cusp of a great discontinuity, much like the life of a teenager when thrust into the passage to adulthood. As with a teen, com-mon sense is not very useful because the world is likely to change abruptly and dramatically. As I hope to show, the tantalizing prospect of global ma-turity offers bold ideas and thought-provoking policies for making a his-toric passage to a world that works.

stance, expect to achieve immortality through nanotech medicine, to up-load and download the mind, and to see humans eclipsed by intelligent machines. Pioneering computer sci-entist Vernor Vinge has said that in-telligent machines “would use [people] the way we’ve used oxen and donkeys.” Is it possible to sort out exaggerations from realistic fore-casts? Previous claims of the “paper-less office,” “nuclear energy too cheap to meter,” and “excessive lei-sure with nothing to do” come to mind.

This article presents an authorita-tive forecast of technology break-throughs, showing that relentless ad-

spread smart machines throughout the nooks and crannies of life.

The buzz over this wave of break-throughs is growing at such a fe-vered pace, however, that it also presents the normal extravagant claims and the inevitable unforeseen consequences. Corn-based ethanol looked so promising that the U.S. Congress supported the industry with tax breaks — only to create a global food crisis while harming the environment and raising energy costs.

Some claims are so grandiose that they seem reminiscent of the dot-com boom. The Singularity and transhumanist movements, for in-

The TechCast Project’s Research Method

The TechCast Project’s scien-tific approach is empirical in nature, gathering the best back-ground data available and or-ganizing it into a careful analy-sis of each technology. Experts are taken through these analy-ses online and instructed to es-timate the most likely year when each technology will en-ter mainstream use, the poten-tial size of the economic market when it matures, and their con-fidence in the forecast. To keep the analysis honest, TechCast includes opposing trends that hinder technology, such as po-litical obstacles, social resis-tance, or other barriers.

More than snapshots in time, the technology forecasts are a continuous tracking process that improves as technologies arrive. Comments from the ex-perts and new data are also used to update the analyses pe-riodically. The project has used this method for 15 years, and the average variance of all fore-casts is plus or minus three years. Some technologies vary widely because they are contro-versial, while others show little variance because they are well understood. We have also re-corded arrivals of several tech-

nologies roughly within this er-ror band of three years. The results are more compelling when considering the fact that the expert panel changed over this time, as did the prospects for various technologies and other conditions. “Prediction markets” have demonstrated remarkable accuracy recently using the same method, accord-ing to the Journal of Economic Perspectives.

It is often thought that meth-ods like this are subjective, whereas quantitative methods are precise. However, quantita-tive methods also involve un-certainty because they require underlying assumptions that often are doubtful. This ap-proach subsumes quantitative forecasts into the background data and allows the judgment of experts to resolve the uncer-tainty that remains. Experts may have their own bias, natu-rally, but it is usually distrib-uted normally, washing out in the aggregate results. If the present level of uncertainty is defined as 100%, we have found that this process reduces uncer-tainty to about 20%–30%. Good enough to get you in the right ballpark. — William E. Halal


Paths define an entire string of out-comes as evolution unfolds.

Unless one thinks civilization is far more likely to collapse, this analysis suggests there’s a good chance of making passage to the other side, possibly soon and in good shape. This is also supported by TechCast data and current trends.

Scenarios for the Technology Revolution

Let’s now do a little macro-fore-casting to outline how the world is likely to evolve decade by decade over the foreseeable future. Three longitudinal scenarios are presented below to explain how this natural cycle of the planet is likely to pass through the crisis of maturity. We don’t hope to get the details right, of course, and there is a margin of un-

MAD (mutually assured destruction) that successfully restrained the United States and Soviet Union from unleashing their nuclear arsenals is unlikely to hold up with a dozen or more nations going nuclear. And a way has yet to be found to block the destructive power of terrorism. This mega crisis seems insurmountable because the present world order is not sustainable. Some new form of global order is needed to avert disas-ter.

There’s no assurance we will make such a transition, of course, but it is reasonable to hope for some sort of successful passage in a decade or so. There are three possible paths through the crisis of maturity: “Pes-simistic,” “Optimistic,” and “Most Likely.” Although “paths” are simi-lar to scenarios, scenarios differ in representing one possible outcome.

Hardly a perfect world, of course, but a functioning global order.

A Virtual Trip through Time

The TechCast Project at George Washington University has devel-oped a sophist icated Web site (www.TechCast.org) that surveys 100 high-tech executives, scientists and engineers, academics, consultants, futurists, and other experts around the world to forecast breakthroughs in all fields of science and technol-ogy. Think of it as an online research system, a scientific version of Wiki-pedia, social networks, and endless other participative Web 2.0 sites that are raising global awareness dramat-ically. Our studies show that techno-logical advances, their adoption pat-terns, and social impacts follow well-defined cycles that can be fore-cast rather accurately. The TechCast Project strives to be the most com-plete forecasting system available, covering the entire span of techno-logical innovation and updated con-stantly.

Figure 1 (pages 42-43) summarizes the results, showing forecasts for roughly 70 technologies organized into seven fields identified by the site’s color code. The broader social and policy implications will be dis-cussed in a moment, but first let’s define the longitudinal scenarios noted in Figure 1 to highlight how these dramatic advances are likely to transform our lives. Although sce-narios are most commonly used to pose alternative situations, here I use a sequence of scenarios to define the most likely path ahead.

The crucial point is that the world is heading toward what we define as a global crisis of maturity. Technology is creating an electronically unified world that is largely industrialized but that also faces unprecedented challenges in energy, climate change, the environment, weapons of mass destruction, terrorism, and other threats that require sophisticated re-sponses unimaginable by present standards.

World GDP should double by 2020 and almost quadruple by 2030, pro-ducing commensurate increases in all of the threats noted above. In global power politics, the system of

Alternative Paths through the Crisis of Maturity

•Pessimistic. If the world reacts slowly or half-heartedly, the result will likely prove di-sastrous. Climate change could destroy life as we know it, en-ergy shortages would render societies impotent, ecological systems might collapse, and de-clining law and order could en-courage war, crime, and other conflict. While this is a serious possibility, trends presented a bit later will show that change is occurring and could easily accelerate. Ultimately, pessi-mism is not a viable option but a failure of civilization, and muddling through is not likely. The TechCast Project rates the probability of this path at 20%–30%.

•Optimistic. Conversely, if the world were to react quickly and strongly, this transition could be made smoothly in a decade or two. In this happy state of affairs, serious energy shortages, climate change, eruptions of global conflict through WMD, etc., are largely avoided, and the world enters

global maturity unscathed about 2020–2030. This is compa-rable to Al Gore’s proposals for energy and climate change. Given the enormity of the chal-lenges and the natural inclina-tion to procrastinate, TechCast rates this alternative as quite unlikely, about 10%–20% prob-ability or less.

•Most Likely. With a 20%–30% probability of global disas-ter and a 10%–20% probability of a smooth transition, the re-maining 50%–80% describes the “Most Likely Path” forward. Action may start slowly in this case, but the threats are so mas-sive that they spur continued efforts, and far more powerful technical capabilities are avail-able. The sense of urgency builds as threats increase, push-ing humanity to find solutions, as we are struggling to do even now. There may be minor disas-ters along the way but little that is catastrophic, making the transition in the nick of time at about 2030. — William E. Halal


Likewise, averting an ecological calamity will require agreement among nations to curb climate change, to collaborate on developing advanced energy technologies, and to become responsible stewards of nature. These are heroic challenges requiring existential courage and en-lightened self-interest beyond what is normally possible. North Korea, Iraq, and Iran show that containing nuclear proliferation and terrorism cannot be achieved with military force alone, but will require collabo-ration to bring radical states into the modern world where conflict is tran-scended. The development of new approaches for such conflict resolu-tion may be viewed as advanced so-cial technologies, as Futuring author Edward Cornish has termed them.

Things look especially bleak be-cause that’s the normal situation facing any system struggling through maturity — a teenager, a nation, or an entire civilization. It’s obvious that global consciousness seems fool-hardy in a world that celebrates to-day’s culture of ruthless capitalism, power politics, money, glamour, con-sumerism, and “me.” The 2008 finan-cial crisis, however, is widely under-stood to mark an end to that era, and the outpouring of support around the world for the Obama presidency signals the possibility of global unity.

Beneath the surface, deep rivers of fresh thought are bubbling up. In his latest book, The Way We’ll Be, profes-sional pollster John Zogby has ana-lyzed his data over the past 20 years to conclude that “we are in the midst of a fundamental reorientation of the American character … away from wanton consumption and toward a new global citizenry in an age of lim-ited resources.” It is especially note-worthy that young people lead in embracing this global view, despite the common image of disheveled youngsters oblivious to all but their cell phones and iPods. Zogby finds that young adults 18 to 29 years old constitute the “First Globals.” This “digital generation” accepts all races, sexual orientations, national cultures, and other differences equally, and they are intent on living sustainable lives in a unified world.

by a factor of threefold to fourfold, possibly even fivefold.

About this very time when the planet teeters between calamity and salvation, the TechCast Project fore-casts also suggest that routine hu-man thought should increasingly be automated by far more sophisticated IT networks, a second generation of more powerful computers, smart ro-bots that think and talk, and other forms of artificial intelligence that approach human skills. For example, the advent of GPS navigation sys-tems means that the problem of get-ting from point A to point B has been solved.

The Information Age should ma-ture by about 2020, leading to an era focusing our attention beyond knowledge. As even better machine intelligence takes over common men-tal tasks, we will move up another level on the evolutionary hierarchy to address the global challenges that seem overwhelming. In the years ahead, artificial intelligence is likely to automate routine knowledge work, relieving us of the details, so global attention will shift to seriously address the global crisis of maturity.

•Scenario 2030: Global Con-sciousness. Advances in informa-tion technology pave the way for an emerging global consciousness, which rises mainly out of the neces-sity to tackle this global crisis of ma-turity. It’s impossible to really grasp the reality of a different era, but something like a global conscious-ness is likely to emerge, focusing on higher-level understanding, produc-tive compromise, and on working out together the tough existential choices needed to survive. It might be called a “Global Era,” “Unified World,” “Global Community,” etc. Whatever the terms, the fact is that strategic planning, dialogue, collab-orative problem solving, diplomacy, conflict resolution, ceremonies, me-diation, prayer, and other yet un-known “technologies of conscious-ness” may offer the next logical step in this evolutionary process. As Gen-eral David Petraeus explained to the Washington Post about gaining the support of 70,000 Sunni leaders in Iraq: “We cannot kill our way to vic-tory. Tribal engagement and local reconciliation work.”

certainty surrounding each forecast. But I think these scenarios identify the dominant themes of each period and thereby lay a pretty solid foun-dation for understanding the emerg-ing global order.

•Scenario 2010: The World On-line. The waning first decade of the twenty-first century should continue to see powerful advances in informa-tion systems and e-commerce. The cluster of white and yellow bubbles surrounding 2010 in Figure 1 show that the world is almost certain to be smarter, faster, and fully wired, set-ting the stage for the breakthroughs to come. About 2014, for example, it should be common for most people around the world to interact via in-telligent PCs, the Internet, TV, smart phones, and global media, translated automatically. Even with the turmoil that is sure to follow, this will mark the serious beginning of a unified global intelligence, what some have forecast as the emergence of a “global brain” — a fine web of con-scious thought directing life on the planet.

•Scenario 2020: High Tech Ar-rives. This decisive period should see major technological break-throughs. The forecasts in Figure 1 show that green business, alternative energy, and other ecological practices are likely to foster sustainability. Good artificial intelligence should begin to permeate life, and the next generation of quantum, optical, and biological computing will permit huge advances in telemedicine, vir-tual education, and e-government. Biotech should provide personalized medicine, genetic therapy, cancer cures, and other advanced health-care.

Although technological powers will be vast and progress will likely be made, the normal level of social resistance and political stalemate is likely to oppose change. Thus, it may take an occasional environmental collapse, global wars and terrorism, or yet unknown calamities to force the move to global consciousness. In-dustrialization will reach most de-veloping nations at this point, with as many as 5 billion people living at modern levels of consumption to-ward the end of the decade, escalat-ing all the crises we have focused on continued on page 25


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Source: © TechCast LLC, graph designed by Evan M. H. Faber

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EUROPEAN COMMISSION AUDIOVISUAL LIBRARY

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Modern economies are adapting to new realities with a wave of innova-tive energy sources, many tucked into the interstices of society: hybrid cars, solar panels on roofs, windmills on a farm, ethanol plants in Iowa, and nuclear power plants where they are wanted. Sustainable practices promise to become one of the most crucial sectors of the economy. In Earth in the Balance, Al Gore noted that pollution control was a $500 bil-lion market in 2000 and is expected to reach $10 trillion in 2020, larger than auto, health care, and defense.

The U.S. government could invite major corporations and other gov-ernments to work together on im-proving environmental management, alternative energy, and other sustain-able technologies. These same groups should agree on a system of carbon taxes or caps to internalize the costs of producing greenhouse gases and allow the market to solve environ-mental problems more efficiently. We also need to encourage innovative solutions, like sequestering carbon dioxide, planting trees, and using in-dustrial ecology.

With hard work and good leader-ship, the world could realize the ben-efits of ecologically safe living dur-ing the next 10 to 20 years. A rising interest in protecting the environ-ment is starting to integrate indus-tries, energy systems, farming, homes, and offices into a living tap-estry that sustains life. Authors Paul Hawken , Amory Lovins , and L. Hunter Lovins call it a “natural capitalism,” in which the environ-ment is recognized as a valuable as-set that produces $33 trillion of eco-nomic benef i ts annual ly. The challenges are enormous but being resolved, and the path ahead is fairly clear. Now we need to improve the technology, implement it widely, and find the political will.

Shifting the Structures of Society

One of the great dilemmas posed by the crisis of maturity is to reform institutions for this different world. Trends noted in my book, Technolo-gy’s Promise, suggest possibilities for transforming social institutions us-ing a combination of enterprise and community. For example, that’s how

make a shift in consciousness them-selves. The previous discussion fo-cused on a science-based, objective view in order to forecast how the cri-sis is likely to be resolved. While this may be accurate in the abstract, countless people must take very dif-ficult actions based on commitment, values, and tough choices at the per-sonal level to make forecasts a real-ity. From this personal or strategic view, we now address what can be done to avert calamity and encour-age successful passage through the crisis of maturity. Here’s my best thinking about the policy implica-tions for energy and the environ-ment, business, government, and health care.

Solving the Energy and Environment Crisis

Despite the present mess in energy and environment policy, there is great opportunity for sustainable, unifying growth. The financial crisis of 2008 is likely to leave a long and painful legacy, but this downturn could draw entrepreneurs and gov-ernments to direct unused labor, cap-ital, and knowledge toward the cru-cial challenge of sustainability and even pull the global economy out of recession.

Not only is the energy and envi-ronment issue an opportunity in dis-guise, but also the intertwined prob-lems facing corporat ions and governments encourage the type of collaboration badly needed today. There is a unifying purpose to serv-ing this higher calling of protecting the earth, and the prospects are so great that they justify a Green Man-hattan Project.

Figure 1 shows that we expect business to create an economic boom as green practices move into the mainstream over the next five years or so. The decade of the 2010s should prove critical to address global warming, which would also help in the transition to alternative energy by about 2020. These forecasts sug-gest the move to sustainability is be-ginning, and we have a rough time-table of how and when it will occur, although with the normal level of doubt that accompanies historic change.

Other prescient voices are advocat-ing global unity. Strobe Talbott — for-mer U.S. ambassador to the United Nations, deputy secretary of state in the Clinton administration, and now president of the Brookings Institu-tion — thinks global governance is coming. In his recent book, The Great Experiment, he writes, “Individual states will increasingly see it in their interest to form an international sys-tem.” And the Millennium Project’s 2008 State of the Future notes: “Ours is the first generation with the means for many to know the world as a whole … and seek to improve global systems.… This does not mean world government; it means world gover-nance.”

Today’s emerging global order seems to possess a life cycle all its own that is unfolding rapidly, pro-voking a series of mental shifts to ad-dress this crisis. The obstacles are enormous, but it is precisely because so many people are so deeply con-cerned that a change in conscious-ness is under way. We have accepted women in power, transformed planned economies into free mar-kets, and begun to protect the envi-ronment. The tough challenge of shaping global consciousness lies ahead.

Implications for Business And Government

Obviously, things are not likely to work out so neatly, but that’s beside the point. This mental exercise of vir-tual time travel through progressive longitudinal scenarios is not in-tended to get the details right but to grasp the trajectory of technology in advancing civilization through higher levels of development. The specific facts can’t be known, but the broad arc of this path through a cri-sis of maturity and its resolution is rather clearly marked. I realize this runs counter to much prevailing pes-simism; however, Arthur C. Clarke noted that a failure of imagination can easily obscure our vision, and a lack of courage can prevent accept-ing new realities that are quite ap-parent.

At this point, readers are asked to

continued from page 22


Whether a teenager shedding the baggage of youth to become a re-sponsible adult or a civilization facing the crisis of maturity, the im-perative is much the same: Grow up or perish.

One case that bears scrutiny is General Motors. After losing its dominance of auto markets steadily over the past 30 years to Toyota, GM engineers rallied around the goal of introducing the world’s first plug-in hybrid car with advanced lithium-ion batteries. GM could still fail, ob-viously, but Maryann Keller, a long-time analyst of the company, thinks it’s “a generational change.”

Historic transitions on the scale of the technology revolution are hard to grasp because they lead to a more sophisticated way of life that has never existed before. Understanding the evolutionary forces at work — both in hard technologies and in so-cial systems — helps us see that the world is undergoing a natural pro-cess of maturity, with global intelli-gence and awareness increasing dra-matically. Our great challenge now is to assure that social institutions evolve and mature along with the material technologies. It will be nec-essary to replace today’s cumber-some social systems, religious dog-mas, heated emotions, partisan ideologies, and other commonly out-moded forms of thought and con-sciousness that now form the major obstacles to progress. ❑

About the AuthorWilliam E. Halal is professor emeritus of science, technol-ogy, and innovation at George Washington Univer-sity, Washington, D.C., co-founder of the Institute for Knowledge & Innovation,

and President of TechCast LLC. He may be contacted at [email protected].

Portions of this article are adapted from his book Technology’s Promise: Expert Knowledge on the Transformation of Busi-ness and Society (Palgrave Macmillan, 2008). A longer version of this essay appears on the World Future Society’s Global Strategies Forum (www.wfs.org/gsforum.htm), where the author invites feedback.

The author gratefully acknowledges the constructive critiques of Evan Faber, a graduate student in the Elliott School of In-ternational Affairs at George Washington University.

and thereby allow market forces to improve the system.

•Minimal added cost or bureauc-racy. This solution would simply shift costs from employers to indi-viduals, resulting in little added cost or federal programs. The costs of vouchers for the poor could be offset by higher tax revenue as corpora-tions are better able to drive robust growth and as market forces im-prove efficiency of the entire system.

Time to Grow Up or Perish

Technological, economic, and po-litical projections make it clear that the world must mature if it is to sur-vive. The crisis of maturity may not prove catastrophic if acted on in time, but a major turning point seems inevitable as the multiple threats of worldwide industrializa-tion, energy shortages, climate change, environment collapse, nu-clear holocaust, spreading terrorism, global conflict, and other unknown crises reach critical levels about 2020. The transition could happen any-time, but it is hard to conceive of a future in which today’s systems could survive much beyond 2020, let alone 2030.

This may seem too heroic, but re-call our discussion of how techno-logical evolution drives a life cycle of the planet, much like the life cycle of any organism but infinitely larger.

the United States might improve health care and relieve its mounting costs, which are approaching 20% of GDP. While the political right argues for letting the free market solve the complex dilemma and the political left wants a government-paid sys-tem, a solution seems to be emerging that synthesizes government support and market forces. Here is a quick outline of the new consensus on U.S. health care:

•Universal insurance coverage. The federal government would re-quire all to have basic health-care in-surance, and it might organize “ex-changes” through which people can select among competing plans. The poor would be offered free vouchers good for basic health coverage, while the rich may be able to opt out by be-ing self-insured.

•Employers relieved of responsi-bility. Corporations and other em-ployers would be freed of the re-sponsibility for health care. Business could then become more competitive by avoiding the $500 billion they now spent annually on health insur-ance.

•Providers evaluated on results. One of the great flaws in the present system is that there is little or no in-formation to help make sound deci-sions. But plans are under way to re-quire hospitals and physicians to be evaluated for providing results. Pa-tients could then make wiser choices

For Further Reading

•JeromeC.Glenn,TheodoreJ.Gordon,andElizabethFlorescu,2008 State of the Future(MillenniumProject/WorldFederationofUN Associations, 2008).

•WilliamE.Halal,“TheLifeCycleofEvolution:AMacro-Tech-nological Analysis of Civilization’s Progress,” Journal of Future Studies (August 2004) Vol. 9, No. 1, pp. 59-74.

•WilliamE.Halal,Technology’s Promise: Expert Knowledge on the Transformation of Business and Society (Palgrave Macmillan, 2008).

•PaulHawken,AmoryLovins,andL.HunterLovins,Natural Capitalism: Creating the Next Industrial Revolution (Little, Brown and Company, 1999).

•StrobeTalbott,The Great Experiment: The Story of Ancient Em-pires, Modern States, and the Quest for a Global Nation (Simon&Schuster, 2008).

•JohnZogby,The Way We’ll Be: A Zogby Report on the Transfor-mation of the American Dream (Random House, 2008).


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wfs: special report

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