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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
By Robert Plotkin
The Automation of Invention Cybernetic genies are designing and engineering new products and creating technological breakthroughs.
WFS PHOTO COLLAGE / RUSLAN KOKAREV / VASILIY YAKOBCHUK / ISTOCKPHOTO.COM
esterday’s inventors toiled away in workshops, painstakingly designing, building, testing, and refining their creations. In contrast, tomorrow’s inventors 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 crystal ball to find real-world examples of computer-generated inventions created via “artificial invention.” Recently, Stanford University professor John R. Koza used a technique he calls “genetic programming” to automatically design a new kind of general-purpose controller that can be used in everything from thermostats 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-
2. Abstract wish
3. “Genie” invention software
1. Human wisher
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 produce the cross-bristle design for the original Oral-B CrossAction Toothbrush. Thaler has also used the Creativity Machine to write music and to create software for controlling robots.
When Wishing Makes It So What all of these technologies have in common is that they require initial guidance from humans to set them
4. Concrete product design WFS PHOTO ILLUSTRATIONS / PHOTOS.COM / RUSLAN KOKAREV / ISTOCKPHOTO.COM
in the right direction. The team of NASA engineers told their evolutionary algorithm that they needed it to produce an antenna that satisfied a particular set of criteria, such as the ability to transmit and receive signals within a particular range of frequencies. John Koza told his genetic programming software that he needed a controller with low “overshoot”: Think of a thermostat that raises your living room’s temperature to the desired 70° F without getting much hotter first. Stephen Thaler provided his Creativity Ma-
chine with the least amount of guidance — a sampling of existing toothbrushes 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 toothbrush 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 comWorld Future Society Special Reports
puter with a detailed description of the problem they wished to solve, written in a language that the computer could understand. The computer then produced the final design based on this description. I refer to this process as “inventing by wishing.” The problem description 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 producing a design for a concrete product, and the resulting controller, antenna, or toothbrush is the wishcome-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
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 engaged 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 inventors to develop skill at identifying and describing the requirements so that a computer can understand them. Doing so often requires proficiency in physics and mathematics,
because artificial wishes typically define product requirements in mathematical 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 determine 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-
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.
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ware that transforms problem descriptions 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 programming. Because such artificial genies often rely on simulators to test and evaluate the virtual designs they create, inventors with the ability to produce faster and more accurate simulators of the real world will be highly valued. Future inventors will also benefit from a healthy dose of training in biology, particularly evolutionary biology, and neuroscience, because many of the leading technologies for automating 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, computer scientists have incorporated such new insights into the software they develop to make it simulate biological processes more accurately. Continued advances will increasingly rely on cross-fertilization between the fields of biology and computer 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 experience, 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 industry to the cosmetics industry to the semiconductor industry. As computer technology increasingly levels the playing field with regard to skill at product design, the competitive advantage will go to those individual inventors and technology firms that can quickly and accurately ascertain the needs of their customers and translate those needs
into instructions that can be given to computers.
Why Technology Will Augment, But Not Replace Us When people first learn that computers can design products automatically, they often assume, or at least worry, that computers will make human 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 artificial 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 inventors obsolete, such tools eliminated some of the drudgery from inventing, thereby freeing human inventors to engage in more abstract and creative aspects of the inventive process. Before the advent of electronic calculators, civil engineers spent countless hours manually calculating stresses on the bridges they designed to predict and verify their performance. Engaging in such tedious work hardly made them better inventors. Today’s computers don’t eliminate the need for civil engineers; 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 admittedly knows little more about lens design than what can be learned in a standard textbook on the subject. He accomplished this by feeding standard information about the physics
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 properties.
How It Works Technologies that automate the process of inventing do so in two basic ways: (1) by generating, evaluating, and modifying potential inventions repeatedly until a satisfactory solution is found; and (2) by following 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 inventors. Thomas Edison invented an improved light bulb by hiring a team of experts to search the globe for better filament material. After testing more than 6,000 alternatives, he settled on carbonized bamboo. Some artificial-invention technologies work their magic by automating the search process. For example, both Koza’s controller and the NASA antenna were created using “evolutionary computation” programs, so named because they solve problems in a way that mimics biological evolution, 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 antenna must be capable of transmitting and receiving signals within certain 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 materials to use or how to arrange existing components into a final product. The evolutionary algorithm then produces an initial random “population” of possible antenna designs in World Future Society Special Reports
More Information and Resources Online: • Automating Invention (the author’s blog on the impacts and implications of computer-automated inventing): www.automatinginvention .com • Genetic Programming Inc.: www.genetic-programming.com • Imagination Engines Inc.: www.imagination-engines.com • Invent Now (a Web site that promotes innovation to a new generation of young inventors): www.inventnow.org • MIT Inventors Handbook: web.mit.edu/invent/h-main.html • U.S. Patent and Trademark Office, Independent Inventors Resources page: 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 introductory 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. the form of computer models. The algorithm simulates the performances of the antennas and evaluates their performance according to the fitness criteria provided by the human engineers. Just as early lifeforms on Earth may not have been well suited to survive, most of the initial antenna designs will likely not work well. Those antennas that outperform the others will form the basis for the next generation. The software “mates” some antennas with each other, producing hybrid designs that include features from their parents. It also “mutates” some antennas by introducing random variations into their designs. The software then evaluates the new generation of antenna designs, allows those that are most “unfit” to die off, and repeats the process, possibly for hundreds or thousands of generations over a relatively short period of time. Designs are tested and refined quickly and inexpensively, potentially without ever having 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-provided fitness criteria to a high degree. 5
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Search-based automation is particularly well suited for tackling the most difficult problems because computers do not form preconceived notions about which pathways to explore. As a result, search-based invention automation often produces results that surprise even the experts. 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 designs will perform in the real world. If computing power continues to expand according to Moore’s law, search-based invention technologies will likely become increasingly common. The second kind of invention automation is design-based automation. Consider the microprocessor inside your computer, which contains hundreds of millions of transistors. No team of human engineers designs a processor manually, transistor-bytransistor. Rather, engineers design a modern processor by writing a description of the functions they want the processor to perform in a “hardware description language” (HDL).
Then they provide the HDL code to a computer program called a “synthesizer,” which automatically creates a processor design based on the HDL description. Although human expertise 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 evermore 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 instruction in the HDL code into a set of circuit 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 designers. They are free to focus on adding new features to the processor instead of managing millions of transistors individually. This is the same process that computer programmers have used to create software ever since the first automatic software compilers (which convert humanreadable software source code into machine-readable object code) were developed in the 1950s.
Democratizing the Invention Process Today’s artificial-invention technology 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 become inventors. We can already see movements in this direction with the advent of a variety of tools that en-
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 security functions. A manager without any computer programming ability can use TenFold’s software to automatically create an application that performs exactly the functions needed. TenFold’s software leads the manager through a detailed interview process that solicits information about the features the manager needs (such as whether users should be required to provide a username and password at login and, if so, what kind of password should be required). 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 increasingly demonstrating their desire and willingness to modify the products they buy, reflecting the continued movement away from being passive consumers and toward becoming active “prosumers” (producer-consumers). Forward-looking companies have begun to embrace prosumers as sources of innovation that can be incorporated within the company’s own products. Today’s prosumers, however, are largely limited to making product designs manually. As artificial-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 technology. It’s possible that cloud computing services will arise that provide inventors, prosumers, and small startups with access to powerful computer systems for running evolutionary design software and
other search-based invention automation technologies over the Internet and at low cost. This would spare customers the need to invest in the necessary hardware and to engage in maintenance tasks such as troubleshooting 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 successfully commercializing their ideas. As Thomas A. Easton reported in THE FUTURIST (“The Design Economy,” January-February 2009), continuing improvements in low-cost automated manufacturing technologies, often called “3-D printers” or “fabricators,” promise to enable even individual inventors to produce their products inexpensively. Businesses are already springing up to leverage such developments. For example, companies such as Ponoko allow individual inventors to upload their 3-D product designs to the Ponoko Web site, where anyone can buy such products. When a purchase 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: • Should computer-generated inventions be patentable? • Should artificial-invention technology be patentable? • Should the instructions that inventors give to artificial-invention technology — i.e., “artificial wishes” — be patentable? • If the answer to these questions is “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, artificial-invention technology could render patent law obsolete. Patent law
is premised on the assumption that inventing is difficult, time-consuming, risky, and costly, and that we therefore need to provide special incentives to inventors if we want them to continue spending their time inventing — and if we want them to share their inventions with the public. If, however, artificial-invention technology makes it possible for anyone — even someone lacking technical skills — to produce products that satisfy their every need relatively quickly, easily, reliably, and at low cost, then there would be good reason 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 creating and instructing computers to solve those problems. However, as artificial-invention technology effectively increases the inventive skill of the average inventor, the threshold requirement for obtaining patent protection should be increased commensurately, to ensure that only inventions which required extraordinary skill to create can be patented. The Artificial Invention Age bodes well for both real and virtual inventors. And both consumers and businesses will benefit from better, cheaper products being brought to market more quickly than ever before. ❑ About the Author Robert 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 specializing in patent protection for computer technology and a lecturer at the Boston University School of Law. His Web site on invention automation is www.geniemachine .com. E-mail rplotkin@automatinginvention .com. World Future Society Special Reports
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CHASERS The “holy grail” of computer science may be
within reach. A futurist looks toward tomorrow’s Artificial Intelligence Revolution.
By Patrick Tucker KIYOSHI TAKAHASE SEGUNDO / ISTOCKPHOTO.COM
he advent of a human-level artificial intelligence—a machine capable of the richness of expression and nuance of thought that we associate with humanity—promises to generate tremendous wealth for the inventors and companies that develop it. According to the Business Communications 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 automating 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 researchers 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 M insky, 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, presumably) for every home. In an ebullient moment in 1967, Marvin Minsky, proclaimed. “Within a generation . . . the problem of creating ‘artificial intelligence’ will substantially
be solved,” by which he meant a humanistic AI. Public interest dried up when the robot army failed to materialize by the early 1980s, a period that researchers refer to as the “AI winter.” But research, though seemingly dormant, c ontinued. The field has experienced a revival 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 managers in New York, executing Internet searches in Stockholm, and routing factory orders in Beijing over integrated networks like Cisco’s. More and more, the world’s banks, governments, militaries, and businesses rely on a variety of extremely sophisticated computer programs—what are sometimes called “narrow AIs” —to run our ever-mechanized civilization. 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 University have developed a fully autonomous self-driving car named Stanley, which they first showcased in 2005 at the Defense Advanced Research Projects Agency’s (DARPA) Grand Challenge motor cross. Stanley represents an extraordinary improvement over the self-driving machines 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 autonomous vehicle that can navigate and operate in traffic, a far more complex challenge for a ‘robotic’ driver,” according to DARPA director Tony Tether. In other words, robot taxis are coming to a city near you. The decreasing price and increasing 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
fallen away. The raw computing power may finally exist—and be cheap enough—to run an AGI program. 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 something, 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 mundane programs to make them more capable and effective or designing a single comprehensive AGI system— speak to the long-standing philosophical 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 (Prometheus Books, 2007), reduces this dichotomy to a scientific approach vs. an engineering mind-set. “The neats are after a single, elegant solution to the answer of human intelligence,” Hall says. “They’re trying to explain the human mind by turning it into a math problem. The scruffies just want to build something, write narrow AI codes, make little machines, little advancements, use whatever is available, and hammer away until something happens.” The neat approach descends from computer science in its purest form, particularly the war game studies World Future Society Special Reports
of Von Neumann and his colleagues in the 1930s and 1940s. The 1997 defeat of world chess champion Garry Kasparov by IBM’s Deep Blue computer 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 supposedly “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.
But the success of Deep Blue was limited. While the machine demonstrated technical expertise at chess, it didn’t show any real comprehension of the game it was playing, or of itself. 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 manage 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 summer 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 doing. Meanwhile, beating a world chess champion turned out to be a snap.”
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So why are computers better 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 matter we’ve got in our skulls simply 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 natural environments where we were called upon to forage, hunt, avoid physical danger, and cooperate with other members of our species. As a result, we know
how to do a lot of little things very well: We can spot patterns in nature, 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 using only our wits. A fancy computer term for this is parallel processing, or working through many millions of seemingly unrelated little problems at once. Computers can parallel 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 different area of research in computer science, wherein hardware and code could be combined almost spontaneously 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 action. 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-
ing a system that can think. “Bayesian networks [see sidebar] are a good example of systems that have this ability to learn,” says Omohundro, “but the approach is rational 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 system to compel humanistic thought? According to Peter Norvig, director of research at Google, understanding 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 visual 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 accuracy, says Norvig. “We tell the program that the one is a translation to the other. Then we refine the process through more data, more words, more articles.” The vast amount of data, news reports, and language content that Google accesses is part of the reason 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 input 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
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 increase 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 human, has to try to figure out what words would appear in the documents 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 coming 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 different search experience. We at Powerset 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 language—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
The PackBot, a military robot developed by Rodney Brooks’s iRobot Corporation, is “rugged and yet light enough to be deployed by one person. A video-game style controller makes this robot easy to learn and use . . . [and] keeps warfighters and first responders safe.”
Bayesian Networks Bayesian nets use a probabil istic model to assign a math ematical value to certain vari ables. To pose an extremely simple example, if the janitor comes on Wednesday and Fri day, and the janitor is here to day, the chances of today be ing Wednesday are very good. We can award the “it’s Wednes day” option a 50% likelihood. The system is refined when you add more relevant data: If the janitor comes in the morn ing on Wednesday, and the af ternoon on Friday, and the jani tor is here, and it’s 10 a.m., then the already good chances of it being Wednesday double (as suming a universe where jani tors are always where they’re scheduled to be). What seems like a silly chil dren’s riddle may also be the key to accomplishing some thing remarkable—teaching a system of code, transistors, and electricity to meaningfully differentiate between Wednes day and Friday. —PMT
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Following the Brain Map The only thing that robotics en gineers seem to like talking about more than computers is neuro science, particularly functional magnetic brain imaging or fMRI. In the past decade, fMRI, which takes live pictures of the blood flow be ing 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—
two words to three words, steadily approaching four words,” says Pell. “There’ll be a crossover point where queries expressed in regular English 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 pouring more money into natural language 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 background of our daily lives. “We’ll definitely get to the point where you will expect to engage your household
The PackBot Explorer from iRobot.
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the human brain—is a no-brainer. In his best-selling book The Sin g u l a r it y I s N e a r , inventor Ray Kurzweil contends that an artifi cial general intelligence (AGI) will necessarily be patterned off the biological processing of a brain and that fully 3-D scans will allow us to reverse-engineer a human brain sometime in the 2020s. Re verse 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
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 systems—voice in, voice and data out.”
Life in SecondLife Like Norvig and Pell, Ben Goertzel, a long-haired, jeans-clad AI superstar and author of From Complexity to Creativity (Plenum, 1997), also sees the birth of AGI as intimately bound up in the Internet. But Goertzel believes 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 catalyst 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 experimenting with artificially intelligent pets for the popular massively multiplayer online role-playing game S econdLife. 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 company hope to launch a commercial
learn from neuroscience, follow ing 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 ma chines 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
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, technologically, 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 computers 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. Information exchange is based on the trading of data. Robots can communicate 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, languageb 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 immediately via binary code and electric current. More communication means faster command execution, and that
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 civilization. If Kurzweil, Hall, and other AI watchers are correct, these systems will eventually learn to behave and process information in a humanistic way. We may be hastening a day when any labor-intensive task can be automated or outsourced to an artificially intelligent entity, a day when such entities might be able to communicate, perform, govern, and even create art more effectively, persuasively, or beautifully than human beings.
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 Kurzweil told reporter Teresa Riordan of the New York Times, “The real power of human thinking is based on recognizing patterns. The better computers get at pattern recognition, the more humanlike they will become.” The program is available from Kurzweil’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
How to Survive a Robot Uprising 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 Hollywood. 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 human 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 sarcasm. And so I wrote a book that very seriously delivers roboticistapproved 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 robot 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 importance. Do we need to understand 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 scientists are trying to create an artificial human-level intelligence—the problem is too unconstrained. Instead, 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 levels to make thousands of decisions in the blink of an eye and for years on end. Unfortunately, humans who are busy trying to spot humanlike intelligence are oblivious
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 growing robot menace. . . . How could so many Hollywood scripts be wrong?” writes robotics engineer Daniel Wilson in his satirical book, How to Stop a Robot Uprising. In it, Wilson captures our half-serious, half-ironic robot phobia with great aplomb. Hollywood has spent the last quarter century turning AI’s worst-case scenario—the robot insurrection—into an absurd cliché. Between the successful Terminator and Matrix fran-
By Daniel Wilson to the very real, very intelligent artificial brains in our cars, our computers, and even our toys. In the future, I foresee more single-purpose AI algorithms infiltrating 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 security systems smart enough to learn the normal patterns of daily life. As the field advances, these little machines will get better at doing more humanlike things—recognizing 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 intelligence will emerge someday. About the Author Daniel Wilson is the author of How to Survive a Robot Uprising (Bloomsbury USA, 2005). Visit www.robotuprising.com
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chises and countless Saturday morning 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 vehicles. But I don’t think malicious intent 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 visions of humanity all could be lost in that bad scenario. In the best scenario, 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 fulfilling, 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 unproductive. 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 successful and the greatest thing to happen 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 13
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the AI Era change human culture and behavior? In the techno-utopia of Kurzweil and others, humans interact effortlessly 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 ability 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 everexpanding 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 prospect. The notion that people would voluntarily choose an antique technology 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 different 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 unimportant. The artisan, the welder, stone-
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 similar to that of the robotically run utopia of Kurzweil and Brooks. But in the Wells scenario, the privileged classes—those with unlimited access to labor-saving devices and services—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 mechanical 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 overnight. 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 governmental 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 market address the effects of our shortsightedness after the fact. This messy, ill-considered process brought us the automobile and, reciprocally, the safety belt; Scotchgard 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 Author Patrick Tucker is the senior editor of THE FUTURIST and director of communications for the World Future Society. E-mail ptucker@ wfs.org. This article draws from several interviews, viewable at www.wfs.org.
Timeline for the Future: Potential Developments and Likely Impacts By Marvin J. Cetron 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. 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 vanishing 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 particular, long-term planning would become increasingly difficult. At that time, the personal computer, 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 nanotechnology 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 prototype). • 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 electronics, 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 technology has in store for us. The timeline presented here offers some basic information to help with planning for the years ahead. Each of the innovations 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 Futurizon GmbH in Ipswich. Forecasting International’s update of the 2005 timeline has been assembled from the work of six contributors. Our panelists were: • Dennis Bushnell, chief scientist at the NASA Langley Research Center. continued on page 17 World Future Society Special Reports
2010-2014 Artificial Intelligence and Artificial Life
Behavior alarms based on human mistake recognition 2010 Software is trained, rather than written 2010 Artificial nervous systems for autonomous robots 2010
Biotechnology, Health, and Medicine
Retinal implants linked to external video cameras 2010 Designer babies 2012
Business and Education
80% of U.S. homes have PCs 2010 Virtual reality used to teach science, art, history, etc. 2012 3-D video conferencing 2014
Optical neurocomputer 2012 DNA computer 2014 Supercomputer as fast as human brain 2014
Environment and Resources
Commercial magma power stations 2011 Clothes collect and store solar power 2012 Effective prediction of most natural disasters 2014
JEFF MILLER / UNIVERSITY OF WISCONSIN–MADISON
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 contains millions of DNA molecules capable, with the help of enzymes that act like software, of solving a relatively complex problem. SCOTTEVEST INC.
Home and Leisure
Fiber-optic plants used in gardens 2010 Smart paint containing computer chips is available 2013
Machine / Human Interface
Voice-activated interface for home appliances 2010 Computer screens in clothes 2010 Tactile sensors, comparable to human sensation 2012 Computers linked to biological sensory organs 2012 LINDEN LAB
2011: First Internet war fought between cybercommunities. A scene from Second Life, a virtual online world inhabited by millions of residents from around the globe.
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.
Security, Law, and War
First Net war fought between cybercommunities 2011 People’s courts on Internet for minor disputes 2012 Virtual reality routinely used in courtrooms for evidence presentation 2013 ID cards replaced by biometric scanning 2014
Near-Earth space tours (suborbital) 2012
Zero point energy engineered/commercialized; all other energy sources become obsolete.
Travel and Transportation Wearable and Personal Technology 15
Portable translation device for s imple conversation available on consumer market 2010
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25% of TV celebrities are synthetic 2015
Machine knowledge exceeds human knowledge 2020 Electronic life-form given basic rights 2020 Artificial insects and small animals with artificial brains 2020
Artificial heart (lab-cultured or entirely synthetic) 2015 Some implants start to be seen as status symbols 2017 Artificial lungs, kidneys 2017
Artificial liver 2020 Nanobots in toothpaste attack plaque 2020 Fully functioning artificial eyes 2020 Artificial peripheral nerves 2020
Quantum computer 2015 All technology imitates thinking processes of human brain 2018
2015: Artificial heart. The AbioCor is a completely self-contained replacement heart designed to sustain the body’s circulatory system. It is intended for endstage 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.
Wild Card: Significant IT attack brings down major country economy.
Library of Congress contents available in sugar-cubesized device 2020 Desktop computer as fast as human brain 2021 DONNA COVENEY / MIT
Insectlike robots used for crop pollination 2015 Carbon-dioxide fixation technologies for environmental protection 2015 Synthetic, nonpetroleum aviation fuel 2018 Living rooms decorated with virtual-reality scenes 2015 Holographic TV 2018
Experience-recording technology developed 2023
Global sensor grid 2018
First Bionic Olympics 2020
Self-diagnostic, self-repairing robots 2015 Houses built by robots 2015 Self-monitoring infrastructures 2015 Robots for almost any job in homes and hospitals 2015
Realistic nanotech toy soldiers are built 2022
2018: All technology imitates thinking processes of human brain. MIT’s Nexi MDS Robot has been designed to effectively convey a wide range of human emotions. PHILIPS
Electromagnetic communications disrupted 2015
Space tugs take satellites into high orbits 2015
2018: Holographic TV. Threedimensional 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.
Reservations required for some key roads 2018
Airplanes 75% more fuel-efficient 2020 Driverless truck convoys using electronic towbar 2022
Spectacles that translate signs, labels 2015
Computer-enhanced dreaming 2020
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2025-2029 Artificial Intelligence and Artificial Life
Genetically engineered electronic toy/pet developed 2025
Biotechnology, Health, and Medicine
Only 15% of deaths worldwide due to infectious diseases 2025 Life extension at one year per year 2025
Business and Education
Molecular manufacturing 2025 Individualized education programs for all students 2025 Wild Card:
Environment and Resources
Discovery of artifacts that force reconsidering significant aspects of common understanding of human history.
Machine / Human Interface
Full direct brain link 2025
Robot population surpasses human population in the developed world 2025
Security, Law, and War
Emotion-control chips used to control criminals 2025
Space hotel accommodates 350 guests 2025 Wild Card:
Travel and Transportation
Teleportation at the particle level 2025 FAA approves autonomous drone airliners 2026 Hydrogen-fueled executive jets (cryoplanes) 2028
continued from page 14 • Ian Pearson, the forecaster most familiar with this timeline. • William Halal of George Washington University, whose company, TechCast LLC, periodically devises a similar timeline (see page 39). • Murray Smith, Professional Pilot’s publisher and resident expert on the future of aviation. • A senior R&D expert at the Department of Defense who chose to remain anonymous. • The staff of Forecasting International. The six wild cards were provided by John L. Petersen, president and 17
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founder of The Arlington Institute, a research institute that specializes in thinking about global futures. The wild cards do not necessarily represent 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 anticipations of fully functional artificial eyes and peripheral nerves have been pushed ahead from the 2030s and beyond to the 2020s.
Bio / nano experiment gets out of control; regional or global impact.
In choosing target dates for the timeline, we assume that the item will be readily available, but not yet a commodity item. Consumer products 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 median date. If we at Forecasting International felt especially strongly about the issue, we may have had our thumbs on the scale when making the final decision, but it did not
2040 and Beyond ESA / DLR / FU BERLIN (G. NEUKUM)
Robots are physically and mentally superior to humans 2032
Artificial brain implants 2030
Renewable energy replaces fossil carbon 2030
2040: First manned mission to Mars. A perspective view of Hebes Chasma on Mars. A chasma is a deep valley with steep sides.
Robots replace humans in workforce completely 2035 Asteroid diversion technology used as weapon 2040
Space factories for commercial production 2035
Moon base the size of a small village is built 2040 First manned mission to Mars 2040 Start of construction of manned Mars laboratory 2048 Use of human hibernation in space travel 2052 Teleportation of a human being 2040
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 acceptable. The space program is one obvious example. The space-related events on our timeline all assume that putting human beings into space will remain a priority, but that is not guaranteed. 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 technical innovation can be decided by a very small group of managers. In other cases, the decision must be reached by a much broader consensus. Sometimes it is a matter of political will. However, business and life both require management that is becoming 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 important in itself,” says Pearson. “What matters is what this change enables or destroys.” ❑ About the Author Marvin J. Cetron is president of Forecasting International Ltd. and a member of the World Future Society board of directors. E-mail glomar@ tili.com. This article draws from an earlier version published by Professional Pilot magazine (October 2008) and is used with permission. World Future Society Special Reports
Emerging Technologies and the Global Crisis of Maturity By William E. Halal
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. 19
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clear now that a technology revolution is under way as ever more sophisticated information systems create unprecedented gains in knowledge, leading to breakthroughs everywhere. The latest forecasts from the TechCast Project are presented here to show that modern societies can realistically envision renewable energy replacing oil, medical control over the genetic 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 accelerating pace of this progress; the unique power of the infotech, biotech, and nanotech fields; and artificial intelligence becoming good enough to
spread smart machines throughout the nooks and crannies of life. The buzz over this wave of breakthroughs is growing at such a fevered 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 dotcom boom. The Singularity and transhumanist movements, for in-
stance, expect to achieve immortality through nanotech medicine, to upload and download the mind, and to see humans eclipsed by intelligent machines. Pioneering computer scientist Vernor Vinge has said that intelligent machines “would use [people] the way we’ve used oxen and donkeys.” Is it possible to sort out exaggerations from realistic forecasts? Previous claims of the “paperless office,” “nuclear energy too cheap to meter,” and “excessive leisure with nothing to do” come to mind. This article presents an authoritative forecast of technology breakthroughs, showing that relentless ad-
The TechCast Project’s Research Method The TechCast Project’s scientific approach is empirical in nature, gathering the best background data available and organizing it into a careful analysis of each technology. Experts are taken through these analyses online and instructed to estimate the most likely year when each technology will enter mainstream use, the potential size of the economic market when it matures, and their confidence in the forecast. To keep the analysis honest, TechCast includes opposing trends that hinder technology, such as political obstacles, social resistance, or other barriers. More than snapshots in time, the technology forecasts are a continuous tracking process that improves as technologies arrive. Comments from the experts and new data are also used to update the analyses periodically. The project has used this method for 15 years, and the average variance of all forecasts is plus or minus three years. Some technologies vary widely because they are controversial, while others show little variance because they are well understood. We have also recorded arrivals of several tech-
nologies roughly within this error 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, according to the Journal of Economic Perspectives. It is often thought that methods like this are subjective, whereas quantitative methods are precise. However, quantitative methods also involve uncertainty because they require underlying assumptions that often are doubtful. This approach subsumes quantitative forecasts into the background data and allows the judgment of experts to resolve the uncertainty that remains. Experts may have their own bias, naturally, but it is usually distributed normally, washing out in the aggregate results. If the present level of uncertainty is defined as 100%, we have found that this process reduces uncertainty to about 20%–30%. Good enough to get you in the right ballpark. — William E. Halal
v a n c e s a re d r i v i n g a c re a t i v e transformation of business, society, the global order, and even what it means to be human. First I briefly o u t l i n e t h e Te c h C a s t re s e a rc h method, which pools the knowledge of 100 experts online. Then I integrate the forecasts into longitudinal scenarios that “macro-forecast” the most likely path civilization will follow over the next 20 years — a virtual trip through time. The major conclusion from this analysis is that the world is facing a global crisis of maturity, the most salient example being the near-collapse of the global banking system in October 2008. Warnings of massive transformations have been anticipated for decades by the Club of Rome and many others. Today, however, the acceleration of change seems to be producing a mounting series of severe global disruptions — energy shortages as oil supplies peak, impending climate change and environmental decline in general, spreading of weapons of mass destruction, continuing terrorism, 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 suggest that the crisis of maturity can be best understood as part of a “life c ycle of evolution.” The path of global development has been driven by successive waves of increasingly powerful technology frontiers — agriculture, 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, common 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 maturity offers bold ideas and thoughtprovoking policies for making a historic passage to a world that works. World Future Society Special Reports
Hardly a perfect world, of course, but a functioning global order. A Virtual Trip through Time The TechCast Project at George Washington University has develo p e d a s o p h i s t i c a t e d We b s i t e (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 technology. Think of it as an online research system, a scientific version of Wikipedia, social networks, and endless other participative Web 2.0 sites that are raising global awareness dramatically. Our studies show that technological advances, their adoption patterns, and social impacts follow well-defined cycles that can be forecast rather accurately. The TechCast Project strives to be the most complete forecasting system available, covering the entire span of technological innovation and updated constantly. 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 discussed 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 scenarios 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 responses unimaginable by present standards. World GDP should double by 2020 and almost quadruple by 2030, producing commensurate increases in all of the threats noted above. In global power politics, the system of 21
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Alternative Paths through the Crisis of Maturity • Pessimistic. If the world reacts slowly or half-heartedly, the result will likely prove disastrous. Climate change could destroy life as we know it, energy shortages would render societies impotent, ecological systems might collapse, and declining law and order could encourage 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, pessimism 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
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 megac risis seems insurmountable because the present world order is not sustainable. Some new form of global order is needed to avert disaster. 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: “Pessimistic,” “Optimistic,” and “Most Likely.” Although “paths” are similar to scenarios, scenarios differ in representing one possible outcome.
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 challenges and the natural inclination to procrastinate, TechCast rates this alternative as quite unlikely, about 10%–20% probability or less. • Most Likely. With a 20%– 30% probability of global disaster and a 10%–20% probability of a smooth transition, the remaining 50%–80% describes the “Most Likely Path” forward. Action may start slowly in this case, but the threats are so massive that they spur continued efforts, and far more powerful technical capabilities are available. The sense of urgency builds as threats increase, pushing humanity to find solutions, as we are struggling to do even now. There may be minor disasters along the way but little that is catastrophic, making the transition in the nick of time at about 2030. — William E. Halal
Paths define an entire string of outcomes 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-forecasting 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-
certainty surrounding each forecast. But I think these scenarios identify the dominant themes of each period and thereby lay a pretty solid foundation for understanding the emerging global order. • Scenario 2010: The World Online. The waning first decade of the twenty-first century should continue to see powerful advances in information 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, setting the stage for the breakthroughs to come. About 2014, for example, it should be common for most people around the world to interact via intelligent 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 conscious thought directing life on the planet. • Scenario 2020: High Tech Arrives. This decisive period should see major technological breakthroughs. 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, virtual education, and e-government. Biotech should provide personalized medicine, genetic therapy, cancer cures, and other advanced healthcare. 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. Industrialization will reach most developing nations at this point, with as many as 5 billion people living at modern levels of consumption toward the end of the decade, escalating all the crises we have focused on
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 forecasts also suggest that routine human thought should increasingly be automated by far more sophisticated IT networks, a second generation of more powerful computers, smart robots that think and talk, and other forms of artificial intelligence that approach human skills. For example, the advent of GPS navigation systems means that the problem of getting from point A to point B has been solved. The Information Age should mature by about 2020, leading to an era focusing our attention beyond knowledge. As even better machine intelligence takes over common mental 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 Consciousness. Advances in information technology pave the way for an emerging global consciousness, which rises mainly out of the necessity to tackle this global crisis of maturity. It’s impossible to really grasp the reality of a different era, but something like a global consciousness is likely to emerge, focusing on higher-level understanding, productive 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, collaborative problem solving, diplomacy, conflict resolution, ceremonies, mediation, prayer, and other yet unknown “technologies of consciousness” may offer the next logical step in this evolutionary process. As General David Petraeus explained to the Washington Post about gaining the support of 70,000 Sunni leaders in Iraq: “We cannot kill our way to victory. Tribal engagement and local reconciliation work.”
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 enlightened 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 collaboration to bring radical states into the modern world where conflict is transcended. The development of new approaches for such conflict resolution may be viewed as advanced social technologies, as Futuring author Edward Cornish has termed them. Things look especially bleak because 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 foolhardy in a world that celebrates today’s culture of ruthless capitalism, power politics, money, glamour, consumerism, and “me.” The 2008 financial crisis, however, is widely understood 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, professional pollster John Zogby has analyzed 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 limited resources.” It is especially noteworthy 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. continued on page 25 World Future Society Special Reports
World Future Society Special Reports NUVERA / PRNEWSFOTO / NEWSCOM
= Energy and Environment er
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Technology Revolution: 2010: The World Goes Online
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= Information Technology
Fuel-Cell Cars, 2013
Source: © TechCast LLC, graph designed by Evan M. H. Faber
= Manufacturing and Robotics
2030: Global Crisis of Maturity
THE MULTIVERSE NETWORK / PRNEWSFOTO / NEWSCOM
Virtual Reality, 2017
CARLA THOMAS / NASA
STEPHEN AUSMUS / USDA-ARS
Genetic Food, 2022
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= Medicine and Biogenetics
World Future Society Special Reports
continued from page 22 Other prescient voices are advocating global unity. Strobe Talbott — former U.S. ambassador to the United Nations, deputy secretary of state in the Clinton administration, and now president of the Brookings Institution — 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 system.” 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 governance.” Today’s emerging global order seems to possess a life cycle all its own that is unfolding rapidly, provoking a series of mental shifts to address this crisis. The obstacles are enormous, but it is precisely because so many people are so deeply concerned that a change in consciousness is under way. We have accepted women in power, transformed planned economies into free markets, and begun to protect the environment. 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 virtual time travel through progressive longitudinal scenarios is not intended 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 crisis of maturity and its resolution is rather clearly marked. I realize this runs counter to much prevailing pessimism; however, Arthur C. Clarke noted that a failure of imagination can easily obscure our vision, and a lack of courage can prevent accepting new realities that are quite apparent. At this point, readers are asked to 25
World Future Society Special Reports
make a shift in consciousness themselves. The previous discussion focused on a science-based, objective view in order to forecast how the crisis is likely to be resolved. While this may be accurate in the abstract, countless people must take very difficult actions based on commitment, values, and tough choices at the personal level to make forecasts a reality. From this personal or strategic view, we now address what can be done to avert calamity and encourage successful passage through the crisis of maturity. Here’s my best thinking about the policy implications for energy and the environment, 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 governments to direct unused labor, capital, and knowledge toward the crucial challenge of sustainability and even pull the global economy out of recession. Not only is the energy and environment issue an opportunity in disguise, but also the intertwined problems facing corporations and governments encourage the type of collaboration badly needed today. There is a unifying purpose to serving this higher calling of protecting the earth, and the prospects are so great that they justify a Green Manhattan 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 suggest the move to sustainability is beginning, and we have a rough timetable of how and when it will occur, although with the normal level of doubt that accompanies historic change.
Modern economies are adapting to new realities with a wave of innovative 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 billion 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 governments to work together on improving environmental management, alternative energy, and other sustainable 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 environmental problems more efficiently. We also need to encourage innovative solutions, like sequestering carbon dioxide, planting trees, and using industrial ecology. With hard work and good leadership, the world could realize the benefits of ecologically safe living during the next 10 to 20 years. A rising interest in protecting the environment is starting to integrate industries, energy systems, farming, homes, and offices into a living tapestry that sustains life. Authors Paul Hawken, Amory Lovins, and L. Hunter Lovins call it a “natural capitalism,” in which the environment is recognized as a valuable asset that produces $33 trillion of econ o m i c b e n e f i t s a n n u a l l y. T h e 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, Technology’s Promise, suggest possibilities for transforming social institutions using a combination of enterprise and community. For example, that’s how
For Further Reading • Jerome C. Glenn, Theodore J. Gordon, and Elizabeth Florescu, 2008 State of the Future (Millennium Project/World Federation of UN Associations, 2008). • William E. Halal, “The Life Cycle of Evolution: A Macro-Technological Analysis of Civilization’s Progress,” Journal of Future Studies (August 2004) Vol. 9, No. 1, pp. 59-74. • William E. Halal, Technology’s Promise: Expert Knowledge on the Transformation of Business and Society (Palgrave Macmillan, 2008). • Paul Hawken, Amory Lovins, and L. Hunter Lovins, Natural Capitalism: Creating the Next Industrial Revolution (Little, Brown and Company, 1999). • Strobe Talbott, The Great Experiment: The Story of Ancient Empires, Modern States, and the Quest for a Global Nation (Simon & Schuster, 2008). • John Zogby, The Way We’ll Be: A Zogby Report on the Transformation of the American Dream (Random House, 2008).
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 system, 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 require all to have basic health-care insurance, and it might organize “exchanges” 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 being self-insured. • Employers relieved of responsibility. Corporations and other employers would be freed of the responsibility for health care. Business could then become more competitive by avoiding the $500 billion they now spent annually on health insurance. • Providers evaluated on results. One of the great flaws in the present system is that there is little or no information to help make sound decisions. But plans are under way to require hospitals and physicians to be evaluated for providing results. Patients could then make wiser choices
and thereby allow market forces to improve the system. • Minimal added cost or bureauc racy. This solution would simply shift costs from employers to individuals, resulting in little added cost or federal programs. The costs of vouchers for the poor could be offset by higher tax revenue as corporations are better able to drive robust growth and as market forces improve efficiency of the entire system. Time to Grow Up or Perish Technological, economic, and political projections make it clear that the world must mature if it is to survive. 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 industrialization, energy shortages, climate change, environment collapse, nuclear holocaust, spreading terrorism, global conflict, and other unknown crises reach critical levels about 2020. The transition could happen anytime, 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 recall our discussion of how technological evolution drives a life cycle of the planet, much like the life cycle of any organism but infinitely larger.
Whether a teenager shedding the baggage of youth to become a responsible adult or a civilization facing the crisis of maturity, the imperative 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 lithiumion batteries. GM could still fail, obviously, but Maryann Keller, a longtime 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 social systems — helps us see that the world is undergoing a natural process of maturity, with global intelligence and awareness increasing dramatically. Our great challenge now is to assure that social institutions evolve and mature along with the material technologies. It will be necessary to replace today’s cumbersome social systems, religious dogmas, heated emotions, partisan ideologies, and other commonly outmoded forms of thought and consciousness that now form the major obstacles to progress. ❑ About the Author William E. Halal is professor emeritus of science, technology, and innovation at George Washington University, Washington, D.C., cofounder of the Institute for Knowledge & Innovation, and President of TechCast LLC. He may be contacted at Halal@gwu.edu. Portions of this article are adapted from his book Technology’s Promise: Expert Knowledge on the Transformation of Business 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 International Affairs at George Washington University. World Future Society Special Reports
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