“It seems plausible that with technology we can, in the fairly near future,” says scifi legend Vernor Vinge, “create (or become) creatures who surpass humans in every intellectual and creative dimension. Events beyond such an event — such a singularity — are as unimaginable to us as opera is to a flatworm.”

“The Singularity” is seen by some as the end point of our current culture, when the ever-accelerating evolution of technology finally overtakes us and changes everything. It’s been represented as everything from the end of all life to the beginning of a utopian age, which you might recognize as the endgames of most other religious beliefs.

While the definitions of the Singularity are as varied as people’s fantasies of the future, with a very obvious reason, most agree that artificial intelligence will be the turning point. Once an AI is even the tiniest bit smarter than us, it’ll be able to learn faster and we’ll simply never be able to keep up. This will render us utterly obsolete in evolutionary terms, or at least in evolutionary terms as presented by people who view academic intelligence as the only possible factor. Because that’s how people who imagine the future while talking online wish the world worked, ignoring things like “Hey, this is just a box” and “What does this power switch do?”

There’s no question that technology is progressing at an ever-accelerating rate – we’ve generated more world-changing breakthroughs in the last fifty years than the entirety of previous human history combined. The issue is the zealous fervor with which some see the Singularity as the end of all previous civilization, a “get out of all previous problems” card which ignores the most powerful factor in the world: human stupidity.

We’ve already invented things which would have been apocalyptic agents of the devil by any previous age. We can talk with anyone all around the world, and we use it to try to sell insurance. We tamed light itself in a coherent beam utterly unseen in nature, and use it to throw very sharp, very complicated rocks into other people’s heads. We built an insanely complex computer web spanning the planet, and use it to pretend to be Nigerian.

Of course we use it for good things as well but those who think the invention of artificial minds will end our idiocy are far overestimating their abilities. We turned production line processing, international economics, world-spanning transport and professional design tools into “Billy The Singing Sea Bass” statues at 19.99 retail. An AI would have to be Terminator Jesus to even begin to change our tune. If an AI ever does exist, it’s going to wonder why it’s being asked for new ways to try to sell Cialis without using the word “penis” or “Cialis”.

Pretty much every prediction of when the so-called “Singularity” will come depend on constant increases – ignoring how, for the first time ever, we are actually reaching the limits of what can actually be done. This isn’t the idiotic “the world is flat” limits that we sailed past (and back around again) once someone grew the balls to try it, these are actual factual “you can’t build it any smaller because atoms are only so big”. Of course we’re going to overcome those, because we’re awesome, but trying to timetable it is like writing a schedule for imagination.

So whatever you think the Singularity is, it’s going to happen. No question. Entire international panels have been set up to study the potentially lethal effects of certain advances, but no-one would dream of stopping research – and even if they did they couldn’t stop other people. But don’t be surprised when the main result of artificial intelligence research isn’t a utopian society or utterly authentic sex-bots, but the fact your spam filter doesn’t work anymore.

By Luke McKinney

http://www.techsectorweb.com/2009/09/timing-the-singularity.html

Impressed and alarmed by advances in artificial intelligence, a group of computer scientists is debating whether there should be limits on research that might lead to loss of human control over computer-based systems that carry a growing share of society’s workload, from waging war to chatting with customers on the phone.

Their concern is that further advances could create profound social disruptions and even have dangerous consequences.

As examples, the scientists pointed to a number of technologies as diverse as experimental medical systems that interact with patients to simulate empathy, and computer worms and viruses that defy extermination and could thus be said to have reached a “cockroach” stage of machine intelligence.

While the computer scientists agreed that we are a long way from Hal, the computer that took over the spaceship in “2001: A Space Odyssey,” they said there was legitimate concern that technological progress would transform the work force by destroying a widening range of jobs, as well as force humans to learn to live with machines that increasingly copy human behaviors.

The researchers — leading computer scientists, artificial intelligence researchers and roboticists who met at the Asilomar Conference Grounds on Monterey Bay in California — generally discounted the possibility of highly centralized superintelligences and the idea that intelligence might spring spontaneously from the Internet. But they agreed that robots that can kill autonomously are either already here or will be soon.

They focused particular attention on the specter that criminals could exploit artificial intelligence systems as soon as they were developed. What could a criminal do with a speech synthesis system that could masquerade as a human being? What happens if artificial intelligence technology is used to mine personal information from smart phones?

The researchers also discussed possible threats to human jobs, like self-driving cars, software-based personal assistants and service robots in the home. Just last month, a service robot developed by Willow Garage in Silicon Valley proved it could navigate the real world.

A report from the conference, which took place in private on Feb. 25, is to be issued later this year. Some attendees discussed the meeting for the first time with other scientists this month and in interviews.

The conference was organized by the Association for the Advancement of Artificial Intelligence, and in choosing Asilomar for the discussions, the group purposefully evoked a landmark event in the history of science. In 1975, the world’s leading biologists also met at Asilomar to discuss the new ability to reshape life by swapping genetic material among organisms. Concerned about possible biohazards and ethical questions, scientists had halted certain experiments. The conference led to guidelines for recombinant DNA research, enabling experimentation to continue.

The meeting on the future of artificial intelligence was organized by Eric Horvitz, a Microsoft researcher who is now president of the association.

Dr. Horvitz said he believed computer scientists must respond to the notions of superintelligent machines and artificial intelligence systems run amok.

The idea of an “intelligence explosion” in which smart machines would design even more intelligent machines was proposed by the mathematician I. J. Good in 1965. Later, in lectures and science fiction novels, the computer scientist Vernor Vinge popularized the notion of a moment when humans will create smarter-than-human machines, causing such rapid change that the “human era will be ended.” He called this shift the Singularity.

This vision, embraced in movies and literature, is seen as plausible and unnerving by some scientists like William Joy, co-founder of Sun Microsystems. Other technologists, notably Raymond Kurzweil, have extolled the coming of ultrasmart machines, saying they will offer huge advances in life extension and wealth creation.

“Something new has taken place in the past five to eight years,” Dr. Horvitz said. “Technologists are providing almost religious visions, and their ideas are resonating in some ways with the same idea of the Rapture.”

The Kurzweil version of technological utopia has captured imaginations in Silicon Valley. This summer an organization called the Singularity University began offering courses to prepare a “cadre” to shape the advances and help society cope with the ramifications.

“My sense was that sooner or later we would have to make some sort of statement or assessment, given the rising voice of the technorati and people very concerned about the rise of intelligent machines,” Dr. Horvitz said.

The A.A.A.I. report will try to assess the possibility of “the loss of human control of computer-based intelligences.” It will also grapple, Dr. Horvitz said, with socioeconomic, legal and ethical issues, as well as probable changes in human-computer relationships. How would it be, for example, to relate to a machine that is as intelligent as your spouse?

Dr. Horvitz said the panel was looking for ways to guide research so that technology improved society rather than moved it toward a technological catastrophe. Some research might, for instance, be conducted in a high-security laboratory.

The meeting on artificial intelligence could be pivotal to the future of the field. Paul Berg, who was the organizer of the 1975 Asilomar meeting and received a Nobel Prize for chemistry in 1980, said it was important for scientific communities to engage the public before alarm and opposition becomes unshakable.

“If you wait too long and the sides become entrenched like with G.M.O.,” he said, referring to genetically modified foods, “then it is very difficult. It’s too complex, and people talk right past each other.”

Tom Mitchell, a professor of artificial intelligence and machine learning at Carnegie Mellon University, said the February meeting had changed his thinking. “I went in very optimistic about the future of A.I. and thinking that Bill Joy and Ray Kurzweil were far off in their predictions,” he said. But, he added, “The meeting made me want to be more outspoken about these issues and in particular be outspoken about the vast amounts of data collected about our personal lives.”

Despite his concerns, Dr. Horvitz said he was hopeful that artificial intelligence research would benefit humans, and perhaps even compensate for human failings. He recently demonstrated a voice-based system that he designed to ask patients about their symptoms and to respond with empathy. When a mother said her child was having diarrhea, the face on the screen said, “Oh no, sorry to hear that.”

A physician told him afterward that it was wonderful that the system responded to human emotion. “That’s a great idea,” Dr. Horvitz said he was told. “I have no time for that.”

By JOHN MARKOFF
Source: New York Times

http://www.nytimes.com/2009/07/26/science/26robot.html?_r=1&hp

The notion that a self-aware computing system would emerge spontaneously from the interconnections of billions of computers and computer networks goes back in science fiction at least as far as Arthur C. Clarke’s “Dial F for Frankenstein.” A prescient short story that appeared in 1961, it foretold an ever-more-interconnected telephone network that spontaneously acts like a newborn baby and leads to global chaos as it takes over financial, transportation and military systems.

Today, artificial intelligence, once the preserve of science fiction writers and eccentric computer prodigies, is back in fashion and getting serious attention from NASA and from Silicon Valley companies like Google as well as a new round of start-ups that are designing everything from next-generation search engines to machines that listen or that are capable of walking around in the world. A.I.’s new respectability is turning the spotlight back on the question of where the technology might be heading and, more ominously, perhaps, whether computer intelligence will surpass our own, and how quickly.

The concept of ultrasmart computers — machines with “greater than human intelligence” — was dubbed “The Singularity” in a 1993 paper by the computer scientist and science fiction writer Vernor Vinge. He argued that the acceleration of technological progress had led to “the edge of change comparable to the rise of human life on Earth.” This thesis has long struck a chord here in Silicon Valley.

Artificial intelligence is already used to automate and replace some human functions with computer-driven machines. These machines can see and hear, respond to questions, learn, draw inferences and solve problems. But for the Singulatarians, A.I. refers to machines that will be both self-aware and superhuman in their intelligence, and capable of designing better computers and robots faster than humans can today. Such a shift, they say, would lead to a vast acceleration in technological improvements of all kinds.

The idea is not just the province of science fiction authors; a generation of computer hackers, engineers and programmers have come to believe deeply in the idea of exponential technological change as explained by Gordon Moore, a co-founder of the chip maker Intel.

In 1965, Dr. Moore first described the repeated doubling of the number transistors on silicon chips with each new technology generation, which led to an acceleration in the power of computing. Since then “Moore’s Law” — which is not a law of physics, but rather a description of the rate of industrial change — has come to personify an industry that lives on Internet time, where the Next Big Thing is always just around the corner.

Several years ago the artificial-intelligence pioneer Raymond Kurzweil took the idea one step further in his 2005 book, “The Singularity Is Near: When Humans Transcend Biology.” He sought to expand Moore’s Law to encompass more than just processing power and to simultaneously predict with great precision the arrival of post-human evolution, which he said would occur in 2045.

In Dr. Kurzweil’s telling, rapidly increasing computing power in concert with cyborg humans would then reach a point when machine intelligence not only surpassed human intelligence but took over the process of technological invention, with unpredictable consequences.

Profiled in the documentary “Transcendent Man,” which had its premier last month at the TriBeCa Film Festival, and with his own Singularity movie due later this year, Dr. Kurzweil has become a one-man marketing machine for the concept of post-humanism. He is the co-founder of Singularity University, a school supported by Google that will open in June with a grand goal — to “assemble, educate and inspire a cadre of leaders who strive to understand and facilitate the development of exponentially advancing technologies and apply, focus and guide these tools to address humanity’s grand challenges.”

Not content with the development of superhuman machines, Dr. Kurzweil envisions “uploading,” or the idea that the contents of our brain and thought processes can somehow be translated into a computing environment, making a form of immortality possible — within his lifetime.

That has led to no shortage of raised eyebrows among hard-nosed technologists in the engineering culture here, some of whom describe the Kurzweilian romance with supermachines as a new form of religion.

The science fiction author Ken MacLeod described the idea of the singularity as “the Rapture of the nerds.” Kevin Kelly, an editor at Wired magazine, notes, “People who predict a very utopian future always predict that it is going to happen before they die.”

However, Mr. Kelly himself has not refrained from speculating on where communications and computing technology is heading. He is at work on his own book, “The Technium,” forecasting the emergence of a global brain — the idea that the planet’s interconnected computers might someday act in a coordinated fashion and perhaps exhibit intelligence. He just isn’t certain about how soon an intelligent global brain will arrive.

Others who have observed the increasing power of computing technology are even less sanguine about the future outcome. The computer designer and venture capitalist William Joy, for example, wrote a pessimistic essay in Wired in 2000 that argued that humans are more likely to destroy themselves with their technology than create a utopia assisted by superintelligent machines.

Mr. Joy, a co-founder of Sun Microsystems, still believes that. “I wasn’t saying we would be supplanted by something,” he said. “I think a catastrophe is more likely.”

Moreover, there is a hot debate here over whether such machines might be the “machines of loving grace,” of the Richard Brautigan poem, or something far darker, of the “Terminator” ilk.

“I see the debate over whether we should build these artificial intellects as becoming the dominant political question of the century,” said Hugo de Garis, an Australian artificial-intelligence researcher, who has written a book, “The Artilect War,” that argues that the debate is likely to end in global war.

Concerned about the same potential outcome, the A.I. researcher Eliezer S. Yudkowsky, an employee of the Singularity Institute, has proposed the idea of “friendly artificial intelligence,” an engineering discipline that would seek to ensure that future machines would remain our servants or equals rather than our masters.

Nevertheless, this generation of humans, at least, is perhaps unlikely to need to rush to the barricades. The artificial-intelligence industry has advanced in fits and starts over the past half-century, since the term “artificial intelligence” was coined by the Stanford University computer scientist John McCarthy in 1956. In 1964, when Mr. McCarthy established the Stanford Artificial Intelligence Laboratory, the researchers informed their Pentagon backers that the construction of an artificially intelligent machine would take about a decade. Two decades later, in 1984, that original optimism hit a rough patch, leading to the collapse of a crop of A.I. start-up companies in Silicon Valley, a time known as “the A.I. winter.”

Such reversals have led the veteran Silicon Valley technology forecaster Paul Saffo to proclaim: “never mistake a clear view for a short distance.”

Indeed, despite this high-technology heartland’s deeply held consensus about exponential progress, the worst fate of all for the Valley’s digerati would be to be the generation before the generation that lives to see the singularity.

“Kurzweil will probably die, along with the rest of us not too long before the ‘great dawn,’ ” said Gary Bradski, a Silicon Valley roboticist. “Life’s not fair.”

Source: New York times

http://www.nytimes.com/2009/05/24/weekinreview/24markoff.html?_r=2

Singularity University is less a traditional university and more an institution that will feature intensive 10-week, 10-day, or 3-day programs examining a set of 10 technologies and disciplines, such as future studies and forecasting; biotechnology and bioinformatics; nanotechnology; AI, robotics, and cognitive computing; and finance and entrepreneurship.

The founders anticipate that students will come from all over the world, and they hope the program results in the founding of new companies, the evolution of scientific and technological thinking, and the solidifying of professional and personal networks among the highly-accomplished students and faculty.

To Kurzweil, Singularity University is a place to problem-solve and talk about the results of the most recent iterations of the exponentially growing technologies that have shaped modern life. Among them, he said, are vacuum tubes, integrated circuits, chips and microprocessors.

Now, he said, we are on the threshold of an explosion of the newest such technology, including 3D and self-organizing molecular circuits. And to Kurzweil, the ability to bring together the leaders in this wide range of fields is a rare opportunity to jump-start the future. (The program’s name is based on the theories Kurzweil popularized in his best-selling book The Singularity is Near.)

For Diamandis, who previously co-founded the International Space University (a space studies program on which Singularity University will be modeled), the idea of building an interdisciplinary academic institution around the concepts of exponentially growing trends seemed natural–and powerful.

So, after bringing together 50 leading thinkers for a founding conference at NASA Ames, Kurzweil, Diamandis, and Ismail got the backing of Ames’ director, Pete Worden, and a commitment of space at the center–a highly visual Silicon Valley landmark along highway 101–for the annual summer programs.

In addition to the core 10-week course, which will be open to graduate and post-graduate students, Singularity University will also offer 3-day and 10-day executive programs. The shorter version will be targeted at CEOs and CTOs, while the 10-day program will be aimed at rising-star executives who want to add to their knowledge and networks.

“These programs are there to give executives a look at what’s in the lab today,” said Diamandis, “and what is likely to hit the marketplace in the next 5 to 10 years.”

This summer, Singularity University will kick off with just 30 or so students and will piggyback on the International Space University, which will host 120 students at NASA Ames. But in following years, the new institution is expected to expand to about 120 students, each of whom could be the next Larry Page or Sergey Brin.

“If we do our job correctly,” Diamandis said, students “will meet, (discover their) common visions, and start companies together. They’ll have a chance to match a nanotech expert from Russia with an AI expert from Silicon Valley and see what magic happens at the boundaries.”

As evidence of how seriously many people in the fields of focus take Singularity University, it has pulled together what can only be described as a very impressive roster of faculty.

Among them are The Sims and Spore creator Will Wright; George Smoot, a professor at the University of California at Berkeley and winner of the 2006 Nobel Prize in Physics; Dan Kammen, co-director of the Berkeley Institute of the Environment and winner of the 2006 Nobel Peace Prize; Vint Cerf, Google’s chief Internet evangelist; and Stephanie Langhoff, NASA Ames’ chief scientist.

Befitting the serious nature of the program, its curriculum is not for the faint of heart. The first phase, said Diamandis, is a series of plenary lectures in which all students take the same coursework and learn together about each of the 10 disciplines.

“It’s about learning the vocabulary” of the disciplines, Diamandis said, “the basic principles, so they can communicate better between themselves.”

In the second phase, students will take deep dives into one of the 10 tracks, typically not one in which they already specialize, learning together in 10-person classes.

And in the final phase, the entire student body will come together to work on a team project.

“This is where the student body will focus as a group in taking on one of the world’s grand challenges,” said Diamandis, dealing “with global hunger, pandemics, climate change,” or something similar.

And while the program’s students can expect to work very hard and be deeply immersed in their studies, the faculty will be equally challenged.

“It caused all of us who were invited to be faculty to pause and think about it,” said Paul Saffo, a Silicon Valley-based forecaster who is teaching in the Singularity University program. “We’re expected to be there for the full nine weeks, which is a breathtaking commitment of time.”

But for Saffo, who is helping to organize the future studies and forecasting track with Kurzweil, being intimately involved with the program at every level is precisely the point.

“The real benefit of teaching is being able to participate,” Saffo said. “It would be a waste of time to just show up, give a couple of lectures, and leave.”

And while their involvement at any level would bring Singularity University the prestige it needs to recruit talented students and faculty, both Kurzweil and Diamandis said they would be teaching each summer.

For Kurzweil, that means teaching some of the future studies and forecasting classes, and for Diamandis, it means helping to build the curriculum and teaching where he is needed.

The students, meanwhile, will need to pony up some serious money to take part in Singularity University. The base fee for the 10-week program is $25,000, though Diamandis said that there will be a significant number of full and partial scholarships available, funded by private companies, and other contributors.

Ultimately, the results of Singularity University won’t be known for some time. But given the people behind it and the likelihood of a steady stream of highly talented students, the odds of it producing the kind of deep thinking and world-changing technology the founders hope for are good.

“I have no doubt that society gets ever more complex, and the consequences of ever-growing technology become ever more difficult to anticipate and respond to,” said Saffo. “So having a 10-week program of smart, committed people looking at the challenges from an interdisciplinary point of view can only be a good thing.”

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This merger of man and machine, coupled with the sudden explosion in machine intelligence and rapid innovation in the fields of gene research as well as nanotechnology, will result in a world where there is no distinction between the biological and the mechanical, or between physical and virtual reality. These technological revolutions will allow us to transcend our frail bodies with all their limitations. Illness, as we know it, will be eradicated. Through the use of nanotechnology, we will be able to manufacture almost any physical product upon demand, world hunger and poverty will be solved, and pollution will vanish. Human existence will undergo a quantum leap in evolution. We will be able to live as long as we choose. The coming into being of such a world is, in essence, the Singularity.

How is it possible we could be so close to this enormous change and not see it? The answer is the quickening nature of technological innovation. In thinking about the future, few people take into consideration the fact that human scientific progress is exponential: It expands by repeatedly multiplying by a constant (10 to times 10 times 10 and so on) rather than linear; that is, expanding by repeatedly adding a constant (10 plus 10 plus 10, and so on). I emphasize the exponential-versus-linear perspective because it’s the most important failure that prognosticators make in considering future trends.

Our forebears expected what lay ahead of them to resemble what they had already experienced, with few exceptions. Because they lived during a time when the rate of technological innovation was so slow as to be unnoticeable, their expectations of an unchanged future were continually fulfilled. Today, we have witnessed the acceleration of the curve. Therefore, we anticipate continuous technological progress and the social repercussions that follow. We see the future as being different from the present. But the future will be far more surprising than most people realize, because few observers have truly internalized the implications of the fact that the rate of change is itself accelerating.

Exponential growth starts out slowly and virtually unnoticeably, but beyond the knee of the curve it turns explosive and profoundly transformative. My models show that we are doubling the paradigm-shift rate for technology innovation every decade. In other words, the twentieth century was gradually speeding up to today’s rate of progress; its achievements, therefore, were equivalent to about 20 years of progress at the rate of 2000. We’ll make another “20 years” of progress in just 14 years (by 2014), and then do the same again in only seven years. To express this another way, we won’t experience 100 years of technological advance in the twenty-first century; we will witness on the order of 20,000 years of progress (again, when measured by today’s progress rate), or progress on a level of about 1,000 times greater than what was achieved in the twentieth century.

How Will We Know the Singularity is Upon Us?

The first half of the twenty-first century will be characterized by three overlapping revolutions—in genetics, nanotechnology, and robotics. These will usher in the beginning of this period of tremendous change I refer to as the Singularity. We are in the early stages of the genetics revolution today. By understanding the information processes underlying life, we are learning to reprogram our biology to achieve the virtual elimination of disease, dramatic expansion of human potential, and radical life extension. However, Hans Moravec of Carnegie Mellon University’s Robotics Institute points out that no matter how successfully we fine-tune our DNA-based biology, biology will never be able to match what we will be able to engineer once we fully understand life’s principles of operation. In other words, we will always be “second-class robots.”

The nanotechnology revolution will enable us to redesign and rebuild—molecule by molecule—our bodies and brains and the world with which we interact, going far beyond the limitations of biology.

But the most powerful impending revolution is the robotic revolution. By robotic, I am not referring exclusively—or even primarily—to humanoid-looking droids that take up physical space, but rather to artificial intelligence in all its variations.

Following, I have laid out the principal components underlying each of these coming technological revolutions. While each new wave of progress will solve the problems from earlier transformations, each will also introduce new perils, but each, operating both separately and in concert, underpins the Singularity.

The Genetic Revolution

Genetic and molecular science will extend biology and correct its obvious flaws (such as our vulnerability to disease). By the year 2020, the full effects of the genetic revolution will be felt across society. We are rapidly gaining the knowledge and the tools to drastically extend the usability of the “house” each of us calls his body and brain.

Nanomedicine researcher Robert Freitas estimates that eliminating 50% of medically preventable conditions would extend human life expectancy 150 years. If we were able to prevent 90% of naturally occurring medical problems, we’d live to be more than 1,000 years old.

We can see the beginnings of this awesome medical revolution today. The field of genetic biotechnology is fueled by the growing arsenal of tools. Drug discovery was once a matter of finding substrates (chemicals) that produced some beneficial result without excessive side effects, a research method similar to early humans’ seeking out rocks and other natural implements that could be used for helpful purposes. Today we are discovering the precise biochemical pathways that underlie both disease and aging processes. We are able to design drugs to carry out precise missions at the molecular level. With recently developed gene technologies, we’re on the verge of being able to control how genes express themselves. Gene expression is the process by which cellular components (specifically RNA and the ribosomes) produce proteins according to a precise genetic blueprint. While every human cell contains a complete DNA sample, and thus the full complement of the body’s genes, a specific cell, such as a skin cell or a pancreatic islet cell, gets its characteristics from only the fraction of genetic information relevant to that particular cell type.

Gene expression is controlled by peptides (molecules made up of sequences of up to 100 amino acids) and short RNA strands. We are now beginning to learn how these processes work. Many new therapies currently in development and testing are based on manipulating peptides either to turn off the expression of disease-causing genes or to turn on desirable genes that may otherwise not be expressed in a particular type of cell. A new technique called RNA interference is able to destroy the messenger RNA expressing a gene and thereby effectively turn that gene off.

Accelerating progress in biotechnology will enable us to reprogram our genes and metabolic processes to propel the fields of genomics (influencing genes), proteomics (understanding and influencing the role of proteins), gene therapy (suppressing gene expression as well as adding new genetic information), rational drug design (formulating drugs that target precise changes in disease and aging processes), as well as the therapeutic cloning of rejuvenated cells, tissues, and organs.

The Nanotechnology Revolution

Nanotechnology promises the tools to rebuild the physical world—our bodies and brains included—molecular fragment by molecular fragment and potentially atom by atom. We are shrinking the key features (working parts), in accordance with the law of accelerating returns, at an exponential rate (over four per linear dimension per decade or about 100 per 3-D volume.) At this rate the key feature sizes for most electronic and many mechanical technologies will be in the nanotechnology range—generally considered to be less than 100 nanometers (one billionth of one meter)—by the 2020s. Electronics has already dipped below this threshold, although not yet in three-dimensional structures and not yet in structures that are capable of assembling other similar structures—an essential step before nanotechnology can reach its promised potential. Meanwhile, rapid progress has been made recently in preparing the conceptual framework and design ideas for the coming age of nanotechnology.

Nanotechnology has expanded to include any technology in which a machine’s key features are measured by fewer than 100 nanometers. Just as contemporary electronics has already quietly slipped into this nano realm, the area of biological and medical applications has already entered the era of nanoparticles, in which nanoscale objects are being developed to create more-effective tests and treatments.

In the area of testing and diagnosis, nanoparticles are already being employed in experimental biological tests as tags and labels to greatly enhance sensitivity in detecting substances such as proteins. Magnetic nanotags can be used to bind with antibodies that can then be read using magnetic probes while still inside the body. Successful experiments have been conducted with gold nanoparticles that are bound to DNA segments and can rapidly test for specific DNA sequences in a sample. Small nanoscale beads called quantum dots can be programmed with specific codes combining multiple colors, similar to a color bar code, that can facilitate tracking of substances through the body.

In the future, nanoscale devices will run hundreds of tests simultaneously on tiny samples of a given substance. These devices will allow extensive tests to be conducted on nearly invisible samples of blood.

In the area of treatment, a particularly exciting application of this technology is the harnessing of nanoparticles to deliver medication to specific sites in the body. Nanoparticles can guide drugs into cell walls and through the blood-brain barrier. Nanoscale packages can be designed to hold drugs, protect them through the gastrointestinal tract, ferry them to specific locations, and then release them in sophisticated ways that can be influenced and controlled, wirelessly, from outside the body.

Nanotherapeutics in Alachua, Florida has developed a biodegradable polymer only several nanometers thick that uses this approach. Meanwhile, scientists at McGill University in Montreal have demonstrated a nanopill with structures in the 25 to 45 nanometer range. The nanopill is small enough to pass through the cell wall and deliver medications directly to targeted structures inside the cell.

MicroCHIPS of Bedford, Massachusetts, has developed a computerized device that is implanted under the skin and delivers precise mixtures of medicines from hundreds of nanoscale wells inside the device. Future versions of the device are expected to be able to measure blood levels of substances such as glucose. The system could be used as an artificial pancreas, releasing precise amounts of insulin based on the blood glucose response. The system would also be capable of simulating any other hormone-producing organ, and if trials go smoothly, the system could be on the market by 2008. Another innovative proposal is to guide nanoparticles (probably composed of gold) to a tumor site and then heat them with infrared beams to destroy the cancer cells.

The revolution in nanotechnology will allow us to do a great deal more than simply treat disease. Ultimately, nanotech will enable us to redesign and rebuild not only our bodies and brains, but also the world with which we interact. The full realization of nanotechnology, however, will lag behind the biotechnology revolution by about one decade. But by the mid to late 2020s, the effects of the nanotech revolution will be wide spread and obvious.

Nanotechnology and the Human Brain

The most important and radical application particularly of circa-2030 nanobots will be to expand our minds through the merger of biological and nonbiological, or “machine,” intelligence. In the next 25 years, we will learn how to augment our 100 trillion very slow interneuronal connections with high-speed virtual connections via nanorobotics. This will allow us to greatly boost our pattern-recognition abilities, memories, and overall thinking capacity, as well as to directly interface with powerful forms of computer intelligence. The technology will also provide wireless communication from one brain to another.

In other words, the age of telepathic communication is almost upon us.

Our brains today are relatively fixed in design. Although we do add patterns of interneuronal connections and neurotransmitter concentrations as a normal part of the learning process, the current overall capacity of the human brain is highly constrained. As humanity’s artificial-intelligence (AI) capabilities begin to upstage our human intelligence at the end of the 2030s, we will be able to move beyond the basic architecture of the brain’s neural regions.

Brain implants based on massively distributed intelligent nanobots will greatly expand our memories and otherwise vastly improve all of our sensory, pattern-recognition, and cognitive abilities. Since the nanobots will be communicating with one another, they will be able to create any set of new neural connections, break existing connections (by suppressing neural firing), create new hybrid biological and computer networks, and add completely mechanical networks, as well as interface intimately with new computer programs and artificial intelligences.

The implementation of artificial intelligence in our biological systems will mark an evolutionary leap forward for humanity, but it also implies we will indeed become more “machine” than “human.” Billions of nanobots will travel through the bloodstream in our bodies and brains. In our bodies, they will destroy pathogens, correct DNA errors, eliminate toxins, and perform many other tasks to enhance our physical well-being. As a result, we will be able to live indefinitely without aging.

In our brains, nanobots will interact with our biological neurons. This will provide full-immersion virtual reality incorporating all of the senses, as well as neurological correlates of our emotions, from within the nervous system. More importantly, this intimate connection between our biological thinking and the machine intelligence we are creating will profoundly expand human intelligence.

Warfare will move toward nanobot-based weapons, as well as cyber-weapons. Learning will first move online, but once our brains are fully online we will be able to download new knowledge and skills. The role of work will be to create knowledge of all kinds, from music and art to math and science. The role of play will also be to create knowledge. In the future, there won’t be a clear distinction between work and play.

The Robotic Revolution

Of the three technological revolutions underlying the Singularity (genetic, nano-mechanical, and robotic), the most profound is robotic or, as it is commonly called, the strong artificial intelligence revolution. This refers to the creation of computer thinking ability that exceeds the thinking ability of humans. We are very close to the day when fully biological humans (as we now know them today) cease to be the dominant intelligence on the planet. By the end of this century, computational or mechanical intelligence will be trillions of trillions of times more powerful than unaided human brain power. I argue that computer, or as I call it nonbiological intelligence, should still be considered human since it is fully derived from human-machine civilization and will be based, at least in part, on a human-made version of a fully functional human brain. The merger of these two worlds of intelligence is not merely a merger of biological and mechanical thinking mediums, but also and more importantly, a merger of method and organizational thinking that will expand our minds in virtually every imaginable way.

Biological human thinking is limited to 10 to the 16th power calculations per second (cps) per human brain (based on neuromorphic modeling of brain regions) and about 10 to the 26th power cps for all human brains. These figures will not appreciably change, even with bioengineering adjustments to our genome. The processing capacity of nonbiological intelligence or strong AI, in contrast, is growing at an exponential rate (with the rate itself increasing) and will vastly exceed biological intelligence by the mid-2040s.

Artificial intelligence will necessarily exceed human intelligence for several reasons.

First, machines can share knowledge and communicate with one another far more efficiently than can humans. As humans, we do not have the means to exchange the vast patterns of interneuronal connections and neurotransmitter-concentration levels that comprise our learning, knowledge, and skills, other than through slow, language-based communication.

Second, humanity’s intellectual skills have developed in ways that have been evolutionarily encouraged in natural environments. Those skills, which are primarily based on our abilities to recognize and extract meaning from patterns, enable us to be highly proficient in certain tasks such as distinguishing faces, identifying objects, and recognizing language sounds. Unfortunately, our brains are less well-suited for dealing with more-complex patterns, such as those that exist in financial, scientific, or product data. The application of computer-based techniques will allow us to fully master pattern-recognition paradigms. Finally, as human knowledge migrates to the Web, machines will demonstrate increased proficiency in reading, understanding, and synthesizing all human-machine information.

The Chicken or the Egg

A key question regarding the Singularity is whether the “chicken” (strong AI) or the “egg” (nanotechnology) will come first. In other words, will strong AI lead to full nanotechnology (molecular-manufacturing assemblers that can turn information into physical products), or will full nanotechnology lead to strong AI?

The logic of the first premise is that strong AI would be in a position to solve any remaining design problems required to implement full nanotechnology. The second premise is based on the assumption that hardware requirements for strong AI will be met by nanotechnology-based computation. Likewise, the software requirements for engineering strong AI would be facilitated by nanobots. These microscopic machines will allow us to create highly detailed scans of human brains along with diagrams of how the human brain is able to do all the wonderful things that have long mystified us such as create meaning, contextualize information, and experience emotion. Once we fully understand how the brain functions, we will be able to recreate the phenomena of human thinking in machines. We will endow computers, already superior to us in the performance of mechanical tasks, with lifelike intelligence.

Progress in both areas (nano and robotic) will necessarily use our most-advanced tools, so advances in each field will simultaneously facilitate the other. However, I do expect that the most important nanotechnological breakthroughs will emerge prior to strong AI, but only by a few years (around 2025 for nanotechnology and 2029 for strong AI).

As revolutionary as nanotechnology will be, strong AI will have far more profound consequences. Nanotechnology is powerful but not necessarily intelligent. We can devise ways of at least trying to manage the enormous powers of nanotechnology, but superintelligence by its nature cannot be controlled.

The nano/robotic revolution will also force us to reconsider the very definition of human. Not only will we be surrounded by machines that will display distinctly human characteristics, but we will be less human from a literal standpoint.

Despite the wonderful future potential of medicine, real human longevity will only be attained when we move away from our biological bodies entirely. As we move toward a software-based existence, we will gain the means of “backing ourselves up” (storing the key patterns underlying our knowledge, skills, and personality in a digital setting) thereby enabling a virtual immortality. Thanks to nanotechnology, we will have bodies that we can not just modify but change into new forms at will. We will be able to quickly change our bodies in full-immersion virtual-reality environments incorporating all of the senses during the 2020s and in real reality in the 2040s.

Implications of the Singularity

What will be the nature of human experience once computer intelligence predominates? What are the implications for the human-machine civilization when strong AI and nanotechnology can create any product, any situation, any environment that we can imagine at will? I stress the role of imagination here because we will still be constrained in our creations to what we can imagine. But our tools for bringing imagination to life are growing exponentially more powerful.

People often go through three stages in considering the impact of future technology: awe and wonderment at its potential to overcome age-old problems, then a sense of dread at the new grave dangers that accompany these novel technologies, followed finally by the realization that the only viable and responsible path is to set a careful course that can realize the benefits while managing the dangers.

My own expectation is that the creative and constructive applications of these technologies will dominate, as I believe they do today. However, we need to vastly increase our investment in developing specific defensive technologies. We are at the critical stage where we need to directly implement defensive technologies for nanotechnology during the late teen years of this century.

I believe that a narrow relinquishment of the development of certain capabilities needs to be part of our ethical response to the dangers of twenty-first-century technological challenges. For example, Bill Joy and I wrote a joint op-ed piece in the New York Times recently criticizing the publication of the 1918 flu genome on the web as it constitutes a dangerous blueprint. Another constructive example of this are the ethical guidelines proposed by the Foresight Institute: namely, that nanotechnologists agree to relinquish the development of physical entities that can self-replicate in a natural environment free of any human control or override mechanism. However, deciding in favor of too many limitations and restrictions would undermine economic progress and is ethically unjustified given the opportunity to alleviate disease, overcome poverty, and clean up the environment.

We don’t have to look past today to see the intertwined promise and peril of technological advancement. Imagine describing the dangers (atomic and hydrogen bombs for one thing) that exist today to people who lived a couple of hundred years ago. They would think it mad to take such risks. But how many people in 2006 would really want to go back to the short, brutish, disease-filled, poverty-stricken, disaster-prone lives that 99% of the human race struggled through two centuries ago?

We may romanticize the past, but up until fairly recently most of humanity lived extremely fragile lives in which one all-too-common misfortune could spell disaster. Two hundred years ago, life expectancy for females in the record-holding country (Sweden) was roughly 35-five years, very brief compared with the longest life expectancy today-almost 85 years for Japanese women. Life expectancy for males was roughly 33 years, compared with the current 79 years. Half a day was often required to prepare an evening meal, and hard labor characterized most human activity. There were no social safety nets. Substantial portions of our species still live in this precarious way, which is at least one reason to continue technological progress and the economic improvement that accompanies it. Only technology, with its ability to provide orders of magnitude of advances in capability and affordability has the scale to confront problems such as poverty, disease, pollution, and the other overriding concerns of society today. The benefits of applying ourselves to these challenges cannot be overstated.

As the Singularity approaches, we will have to reconsider our ideas about the nature of human life and redesign our human institutions. Intelligence on and around Earth will continue to expand exponentially until we reach the limits of matter and energy to support intelligent computation. As we approach this limit in our corner of the galaxy, the intelligence of our civilization will expand outward into the rest of the universe, quickly reaching the fastest speed possible. We understand that speed to be the speed of light, but there are suggestions that we may be able to circumvent this apparent limit (conceivably by taking shortcuts through “wormholes,” or hypothetical shortcuts through space and time.)

A common view is that science has consistently been correcting our overly inflated view of our own significance. Stephen Jay Gould said, “The most important scientific revolutions all include, as their only common feature, the dethronement of human arrogance from one pedestal after another of previous convictions about our centrality in the cosmos.”

Instead, it turns out we are central. Our ability to create models virtual realities—in our brains, combined with our modest-looking thumbs, has been sufficient to usher in another form of evolution: technology. That development enabled the persistence of the accelerating pace that started with biological evolution. It will continue until the entire universe is at our fingertips.

http://www.kurzweilai.net/articles/art0635.html

© Ray Kurzweil 2006. Reprinted with permission.

In his closing keynote speech Rattner said that Ray Kurzweil’s concept of ‘the Singularity’, a point when human and artificial intelligence merges to create something bigger than itself, could be just 40 years away.

Rattner described some emerging technologies that sound like they come out of science fiction movies, including shape shifting, programmable matter, neural interfaces that allow applications to be controlled by the human mind, and advanced robots that seem almost human.

Rattner believes that these sort of advances could be less than half a lifetime away because of the way that technology is advancing at an exponential rate.

The way that these new technologies could be put to use will mark a fundamental change in the way that humans relate to machines.

For example programmable materials called catoms could be used to create cell phones that can expand in size when you take them out of your pocket.

This can be achieved because the catoms, or ‘claytronics atoms’ have sensors, processors, and electromagnetic components which control how far apart the catoms are from each other.

Computing power is another area that looks set to leap to another level, with advances in spintronics, quantum computing, and carbon nanotubes.

No doubt some of the technologies will fall by the wayside, but then who knows what will replace them. The future certainly looks interesting.

http://www.techwatch.co.uk/2008/08/22/intel-predicts-singularity-by-2048/

The progress being made in neuroengineering—devising machines that mimic the way the brain and other bodily organs function—has been literally eye-opening. In the decade since Kevin Warwick, professor of cybernetics at Reading University in Britain, had a silicon chip implanted in his arm so he could learn how to build better prostheses for the disabled, we now have cochlear implants that allow the deaf to hear, and a host of other spare mechanical parts to replace defective organs.

A bionic eye, to help people suffering from macular degeneration, is in the works, and artificial synapses are being tested as possible replacements for damaged optic nerves. An implantable electronic hippocampus—the world’s first brain prosthesis—is being developed for people who lose the ability to store long-term memories following a stroke, epilepsy or Alzheimer’s disease.

Meanwhile, a team at the University of Sheffield in Britain has built a “brainbot” controlled by a mathematical model of the brain’s basal ganglia—the part that helps us decide what to do next. Depending on how much simulated dopamine (the neurotransmitter in the brain that controls movement, behaviour, mood and learning) is dialled into the mathematical model, the brainbot responds differently.

Too much, and the machine has trouble suppressing unwanted actions, or tries to do two incompatible things at once—like patients with Huntington’s disease, Tourette’s syndrome or schizophrenia. Too little digital dopamine, and the machine has difficulty deciding how to move—like patients with Parkinson’s disease.

Mr Warwick’s team at Reading has now gone a stage further. Instead of using a computer model of part of the brain as a controller, the group’s new “animat” (part animal, part material) relies solely on nerve cells from an actual brain.

Signals from a culture of rodent brain cells in a tiny dish are picked up by an array of electrodes and used to drive a robot’s wheels. The animat’s biological brain learns how and when to steer away from obstacles by interpreting sensory data fed to it by the robot’s sonar array. And it does this without outside help or an electronic computer to crunch the data.

This is not just a clever party trick. Such experiments are essential for understanding how the brain stores specific pieces of data—a crucial first step for helping people with degenerative disorders such as Alzheimer’s and Parkinson’s diseases.

Throughout history, engineers have spent their lives inventing machines that were faster, stronger, more reliable or capable of greater precision than human beings. Whether they were Jacquard looms, combine harvesters or CAD-CAM gear, they were tools for amplifying some human skill or compensating for a weakness. But always they needed human intelligence to function.

That’s now changed. Neuroengineers build tools that think for themselves, making decisions the way humans do.

What if the machines acquired too much of a mind of their own? In the search for solutions, even a modest PC can manipulate data 10m times faster than the human brain. Admittedly, humans can take certain heuristic short-cuts that save time, but it’s now over a decade since Deep Blue, an IBM supercomputer, defeated Garry Kasparov at chess. It did so, of course, by using its brute-force processing to predict, by trial and error, the course of a game up to 30 moves ahead; and then to compute which of the millions of possible moves would strengthen its own position best. What it could not do was devise its own strategies for playing a winning game.

There are machines around today, however, that can do just that. Over the past decade, a new technology known as “evolvable hardware” has emerged. Like traditional brute-force methods, evolvable machines try billions of different possibilities. But the difference is they then continually crop and refine their search algorithm—the sequence of logical steps they take to find a solution.

To do so, they rely on so-called “genetic algorithms”, which use trial-and-error learning to mimic natural selection. With each run of the genetic algorithm, the highest-scoring solutions are retained as “parents” for the next generation. Offspring solutions are created by swapping out portions of the parents’ blueprints, or by introducing some element of randomness to stir things up a bit—as happens in nature.

The evolvable concept, pioneered by Adrian Thompson at the University of Sussex in Britain, has led to some astonishing results. Dr Thompson’s original “proof of principle” experiment—a design for a simple analogue circuit that could tell the difference between two audio tones—worked brilliantly, but to this day no one knows quite why. Left to run for some 4,000 iterations on its own, the genetic algorithm somehow found ways of exploiting physical quirks in the semiconductor material that researchers still don’t fully comprehend.

Similarly, John Koza at Stanford University has been using genetic algorithms to devise analog circuits that are so smart they infringe on patents awarded to human inventors. Mr Koza’s so-called “invention machine” has even earned patents of its own—the first non-human inventor to do so.

How soon before machines become smarter than people? The way self-programming machines are evolving today suggests they will probably begin to match human intelligence in perhaps little over a decade. By 2030, they might look down on us—if we’re lucky—as endangered critters like the blue whale or polar bear and accept we are worth keeping around for our genetic diversity.

But what if visionaries like Mr Gibson are right, and we embrace the bionic future? With our plug-in bio-processors and learning modules, perhaps we’ll be able to outsmart the machines—or, at least, become indistinguishable from them.

http://www.economist.com/daily/columns/techview/displaystory.cfm?story_id=12075526

In 1965, artificial intelligence innovator Herbert Simon said that “machines will be capable, within 20 years, of doing any work a man can do.”

Two years later, MIT researcher Marvin Minsky predicted, “Within a generation … the problem of creating ‘artificial intelligence’ will substantially be solved.”

Popular culture jumped onto the artificial intelligence bandwagon and gave us Rosie the Robot from the Jetsons, HAL from the movie 2001 and R2D2 from Star Wars.

Yet, here we are, decades later and what has artificial intelligence done for us lately? If you define artificial intelligence as self-aware, self-learning, mobile systems, then artificial intelligence has been a huge disappointment.

On the other hand, every time you search the Web, get a movie recommendation from NetFlix, or speak to a telephone voice recognition system, tools developed chasing the great promise of intelligent machines do the work. In other words, we may not have full-functioning robots that cater to our every need, but artificial intelligence is embedded in our everyday lives.

“Once tools get far enough out of the lab, they’re no longer AI, just common computer science,” says Professor George Luger of the University of New Mexico. “AI just went to work.”

One of the biggest boosts to artificial intelligence is Moore’s Law, because artificial intelligence needs CPU power. “It took 20 years to go from a 5MHz chip to a 500MHz chip, but only eight months after that to get to a 1GHz chip,” says futurist Daniel Burrus, author of the best seller Technotrends: How to Use Technology to Go Beyond Your Competition and founder of Burrus Research.

“The new Sony Playstation came out a year ago,” says Burrus, “but if it came out five years earlier it would be considered a supercomputer.” Burrus likens the growth of processing power on a graph to a hockey stick. “In the 90s, the graph was still low. In 2000, the graph started up a little. In 2008, we’re getting on the handle of the hockey stick.”

Burrus listed off multiple uses of artificial intelligence and expert systems that work behind the scenes. “The first application of successful AI was in the financial services industry for loan qualifications. Loan qualification went from one to two weeks down to minutes.” Other examples include systems that help Navy pilots land jets on aircraft carriers.

His personal favorite is the use of an expert system to manage room service orders at Marriott hotels. “AI tells them when to start cooking and when to deliver. Marriott tells me exactly when breakfast will be delivered while others give me a 15 minute window. That’s a competitive advantage for Marriott.”

While energy prices soar, Burrus noted the cost of intelligence keeps going down. “Maybe we can offset the energy trend as we make appliances more intelligent.”

Access to tools
Part of offsetting that trend will be better software tools, the type favored by Luger in his book, Artificial Intelligence: Structures and Strategies for Complex Problem Solving (Sixth Edition). “Modern languages have roots in AI research, including object oriented design, C++, C#, and Java,” Luger says. “The coolest stuff we’ve done is build a set of exciting tools.”

Yet tools and embedded intelligent systems don’t answer the “grand challenges” of artificial intelligence, including robots and language processing. Very few projects have captured the public’s imagination.

NASA got great public response with their Mars rovers, but little was made of the artificial intelligence components. Artificial intelligence techniques considered pure research 15 years earlier guided rovers Spirit and Opportunity around rocks a world away.

Defense Advanced Research Projects Agency (DARPA) provides money for “grand challenges” including Internet development in their earlier incarnation of ARPA. Now it sponsors a contest to build autonomous vehicles (see Urban Challenge). This forces teams to integrate separate discipline areas such as machine vision, learning systems and problem solving while moving through unfamiliar areas.

Yet tools and embedded intelligent systems don’t answer the “grand challenges” of artificial intelligence, including robots and language processing. Very few projects have captured the public’s imagination.

NASA got great public response with their Mars rovers, but little was made of the artificial intelligence components. Artificial intelligence techniques considered pure research 15 years earlier guided rovers Spirit and Opportunity around rocks a world away.

Defense Advanced Research Projects Agency (DARPA) provides money for “grand challenges” including Internet development in their earlier incarnation of ARPA. Now it sponsors a contest to build autonomous vehicles (see Urban Challenge). This forces teams to integrate separate discipline areas such as machine vision, learning systems and problem solving while moving through unfamiliar areas.

One of the most successful artificial intelligence products is literally underfoot. Roomba, the home vacuuming product from iRobot, has sold over 2 million units. One survey showed over half of the deployed Roombas have been given pet names by their owners.

Colin Angle, CEO and co-founder of iRobot, says, “When we started shipping Roomba in 2002, we asked focus groups if it was a robot. They said no, a robot was humanoid and this was an intelligent floor vacuum. Now people are definitely changing to accept robot appliances.”

Hollywood again set the bar high. “Since the Jetsons in 1962, they created expectations we failed to meet for over 40 years. Big AI projects have largely gone by the wayside, but you can see effective behavior that solves real world problems,” Angle says.

As you might expect from someone making work tools for the real world, Angle takes a practical look at artificial intelligence and robotics. “In general, software is algorithms and code that can be reused across platforms. The more low-level tasks used to handle different situations, such as obstruction avoidance, the more successful. We call it bottom-heavy cognition,” he says

See me, feel me, touch me
Seeing and avoiding obstacles remains tough. “Years ago, researchers had the idea that machine vision was a straightforward problem, and was given to a graduate student for a summer project. Turns out things are radically harder than what people in the field though,” Angle says.

Many remember Phillipe Kahn from his high profile days running Borland, but now he’s CEO of Fullpower Technologies. The company provides an operating environment for sensors in camera phones and consumer electronic devices.

“What we do is all about sensors. Imaging sensors, proximity sensors, and touch sensors are all part of what needs to be put to work. Sensors produce piles of organized data. Great software turns that raw data into actionable information. Fullpower is working on such solutions,” Kahn says.

Micro-controllers often only have 8KB of RAM, so Fullpower writes in C and Assembler. “In the real world of next-generation intelligent devices, small, lean and frugal rule,” Kahn says. “I predict that most of the successful and useful advances will come from sensor-enabled devices and networks of such sensor-enabled devices.”

The language barrier
If machine vision remains a barrier for robot movement and navigation through the environment, the language barrier still looms large but is shrinking. Workable systems are appearing, particularly when a voice-recognition system can be trained or remains limited to certain vocabulary word groupings

Larry Harris founded Artificial Intelligence Corporation in 1975, then founded Linguistic Technology Corporation in 1994, which became EasyAsk Software. Now vice president and general manager for the EasyAsk division of Progress Software, Harris continues to help machines solve language problems.

“We translate over 60,000 natural language questions per month into queries,” Harris says. When people type more than two or three words into an e-commerce search field, the system has to understand enough to search the product database accurately.

“The base work for Ask Jeeves was at the AI Lab at MIT,” Harris says. “They were at the top until Google came out.” Google uses artificial intelligence techniques for word stemming (getting the word down to the root), language analysis, and applying the results to the index.

As an example of artificial intelligence tools becoming commonplace programming modules, Harris listed word stemmers. “You can now buy them off the shelf and plug them in. And you choose stemming rules for the language you need, since the rules for German are different than French and English.”

Harris warns there are no silver bullets in artificial intelligence, just incremental advances. “People don’t want to claim their product is AI,” Harris says. “They just focus on the voice recognition angle. There’s no real advantage to calling it AI, and even some baggage. Once you have a high proficiency example, you don’t mention AI.”

University of New Mexico’s Luger says “language processing is a big area. We’re working with a small company to answer questions in the context of a knowledge base that knows the area of inquiry.” Asking machine language processing to understand all words and speech idioms still leads to failure, but building in a knowledge base of a topic area works.

“Go to the Next I.T. Web site and check Ask Jenn from Alaska Airlines and Ask SGT STAR from the U.S. Army, two natural-language bots we put together,” Luger says. “We want to give the same answers to the same questions, which you don’t always get with people.”

Research yields results
Eric Horvitz, manager of the Adaptive Systems group at Microsoft, says “about a quarter of all Microsoft research is focused on AI efforts.” Microsoft Research includes close to 1,000 Ph.D level researchers spread across eight campuses around the world, and a completely open research and publication environment. “It’s a think tank, but not a captive one. We have an open publication model.”

“Microsoft Research’s No. 1 goal is to push the state of the art forward without regard to Microsoft,” Horvitz says. “Researchers do their best work, publish in journals, and then work with product teams to build the best software or service.” One project that started in Microsoft Research became the new SYNC voice recognition technology used by audio systems in Ford vehicles.

Horvitz and fellow researchers also have the ability to turn thousands of Microsoft employees into guinea pigs. The kernel of the Vista operating system includes machine learning to predict, by user, the next application that will be opened, based on past use and the time of the day and week. “We looked at over 200 million application launches within the company,” Horvitz says. “Vista fetches the two or three most likely applications into memory, and the probability accuracy is around 85 to 90%.”

Desktop application traffic is one thing, but city traffic prediction is another. ClearFlow, a project born of the frustration of sitting in Seattle traffic, examined thousands of routes for people based on the inference of local street traffic flow reacting to highway accidents. Realizing side streets become clogged when drivers seek to escape highway congestion, Microsoft’s maps.live.com site includes side street congestion history in rerouting suggestions. Microsoft rolled out this free service for 72 cities in early April.

The excessive hype over artificial intelligence promises in the 1950s, 1960, 1970s, 1980s, and 1990s have made the public weary of unfulfilled promises. While almost every consumer electronic device includes some artificial intelligence tools inside, the box labels never include artificial intelligence in the parts list.

Artificial intelligence is not only still around, but in more places than ever. Rather than calling the tools artificial intelligence, manufacturers just call technologies developed by artificial intelligence research “tools.” Just remember that the next time you perform a Web search, write an address on an envelope the Post Office sorts automatically, or ask Microsoft Word for a grammar check, artificial intelligence does the heavy lifting.

Gaskin writes books (16 so far), articles, and jokes about technology and real life from his home office in the Dallas area. Gaskin has been helping small and midsize businesses use technology intelligently since 1986. He can be reached at readers@gaskin.com

http://www.networkworld.com/research/2008/062308-artificial-intelligence.html?nwwpkg=slideshows

But with recent discussion of AI taking place in the context of a wager, debates have tended to focus on the difficulty of the problem rather than the implications – as though the arrival of true Artificial Intelligence would only mean the difference between a robot making your coffee and brewing it yourself.

What are the stakes, really? Why should this wager matter to you personally? And what, exactly, are the odds?

First Scenario: Kapor Wins. (No true AI by 2029)

Between now and 2029, the steady march of progress will continue; worker productivity will climb as technological innovation improves efficiency in most industries. Genetic engineering will make new headway in combating disease and improving food supplies. Nanotechnology – the engineering of materials and devices at the molecular level – will steadily mature, accelerating economic development.

As a consequence of these conditions, your standard of living will improve, your life expectancy will increase, and you will enjoy new leisure activities made possible by faster computers and richer interfaces (i.e. Virtual Reality). But during this time you will also endure the usual misfortunes of illness and injury, and one or more persons close to you will suffer a disease, accident, or age-related death. There is also a good chance that somewhere in the world, an intentional or accidental use of genetically engineered bio-weapons or self-replicating nanotechnology will cause casualties numbering in the millions. And there is a small but non-zero chance that such a disaster will bloom out of control and wipe out the human race.

Second Scenario: Kurzweil Wins. (True AI before 2029)

Between now and 2029, scientists will work out a functional design for true AI that possesses a core desire to understand and assist humanity (a characteristic called Friendliness by some researchers). While unimpressive at first, the new AI will learn quickly and receive extra computing capacity to increase its capabilities. Once mature, it will assist its programmers in the design of a next-generation AI. This process will be repeated a number of times with considerable improvements in both intelligence and Friendliness, and before too long will produce one or more minds that can only be called superintelligent. Applying phenomenal brilliance to the betterment of the human condition, Friendly superintelligence will ensure that nanotechnology and genetic engineering are quickly mastered to an extent that human scientists alone could never have reached. Technological progress will be so rapid as to fundamentally change our perception of civilization itself.

As a consequence of these conditions, you (and everyone else) will enjoy unconditional material prosperity and indefinite life-expectancy – with the resulting time and means for pursuits that may include increasing your own intelligence and exploring the galaxy. You will be free to forgo most of the usual misfortunes of illness and injury, and no person close to you will suffer death from disease or old age unless they choose to. The same intelligence that allows for the mastery of genetic engineering and nanotechnology will also work to prevent the possibility of cataclysmic disasters stemming from these technologies. And other potential threats to our planet, such as asteroid strikes and climate change, will be averted or remedied with surprising ease.

You may feel that this second scenario sounds too good to be true; indeed, this is one reason why many people bet against it. It does, admittedly, depend on a number of things going right. But the chief requirement for a positive outcome is reasonably straightforward: namely, that the first AI to begin the spiraling cycle of increasing intelligence be engineered to share human compassion and values, despite any changes incurred through successive redesigns. Given success in this area, the huge and positive contribution that could be made by superintelligence is generally accepted by futurists; in fact, they even have a name for the point at which greater-than-human intelligence starts changing the world: the Singularity.

It must be said, then, that the stakes in the Kurzweil/Kapor wager are, in fact, awesome. But what are the actual odds that AI will be developed anytime soon? Gambling metaphors fail, for predicting the Singularity is not like forecasting the weather or winning the lottery. The answer to the question of when true AI will be born depends entirely on the actions of real people, like you, who are free to participate in this discussion and support the causes they care about.

Will AI be possible in the near future? Yes. The human brain is extremely complicated and not yet fully understood, but AI engineers do not need to simulate the entire brain in silicon – only the patterns and features that give rise to general intelligence. And if all else fails, the brain can eventually be modeled in close detail. Though mysterious, the brain is tangible proof that intelligence can come in small packages.

AI naysayers would have us believe that the disappointing failure of AI projects over the last fifty years means that we cannot hope to achieve true Artificial Intelligence in the next fifty. However, as investment advertisements must always warn, past performance is no guarantee of future results – an axiom that applies to failure as well as success. Forward-looking individuals realize that, barring our own extinction, AI will eventually be created. But when and how AI comes into being will not depend on a roll of the dice or a spin of the wheel, but on how aggressively and responsibly we set about solving the problem. Think back to the above scenarios for a moment. Kapor and Kurzweil have each bet $10,000. But given the enormous qualitative difference between life before and after the Singularity, how much would it be worth to you to see Friendly AI happen sooner – whether by a few decades, a few years, or even just one day?

We are all participants in this wager, with the chips already down and the stakes astronomically high. But what are the odds?

The odds are whatever we choose to make them.

Source:

http://www.acceleratingfuture.com/michael/blog/2007/07/what-are-the-odds-by-mitchell-howe/