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Showing posts with label Ray Kurzweil. Show all posts
Showing posts with label Ray Kurzweil. Show all posts

Thursday, December 12, 2013

Ray Kurzweil

From Wikipedia, the free encyclopedia

Ray Kurzweil
Raymond Kurzweil Fantastic Voyage.jpg
Born February 12, 1948 (age 65)
Queens, New York, U.S.
Nationality American
Alma mater Massachusetts Institute of Technology (B.S.)
Occupation Author, entrepreneur, futurist and inventor
Employer Google Inc. (Director of Engineering)
Spouse(s) Sonya Rosenwald Fenster (1975–present)[1]
Children Ethan, Amy
Awards Grace Murray Hopper Award (1978)
National Medal of Technology (1999)
Raymond "Ray" Kurzweil (/ˈkɜrzwl/ KURZ-wyl; born February 12, 1948) is an American author, inventor, futurist, and a director of engineering at Google. Aside from futurology, he is involved in fields such as optical character recognition (OCR), text-to-speech synthesis, speech recognition technology, and electronic keyboard instruments. He has written books on health, artificial intelligence (AI), transhumanism, the technological singularity, and futurism. Kurzweil is a public advocate for the futurist and transhumanist movements, as has been displayed in his vast collection of public talks, wherein he has shared his primarily optimistic outlooks on life extension technologies and the future of nanotechnology, robotics, and biotechnology.
Kurzweil was the principal inventor of the first CCD flatbed scanner,[2] the first omni-font optical character recognition,[2] the first print-to-speech reading machine for the blind,[3] the first commercial text-to-speech synthesizer,[4] the first music synthesizer Kurzweil K250 capable of recreating the grand piano and other orchestral instruments, and the first commercially marketed large-vocabulary speech recognition.[5]
Kurzweil received the 1999 National Medal of Technology and Innovation, America's highest honor in technology, from President Clinton in a White House ceremony. He was the recipient of the $500,000 Lemelson-MIT Prize for 2001,[6] the world's largest for innovation. And in 2002 he was inducted into the National Inventors Hall of Fame, established by the U.S. Patent Office. He has received nineteen honorary doctorates, and honors from three U.S. presidents. Kurzweil has been described as a "restless genius"[7] by The Wall Street Journal and "the ultimate thinking machine"[8] by Forbes. PBS included Kurzweil as one of 16 "revolutionaries who made America"[9] along with other inventors of the past two centuries. Inc. magazine ranked him #8 among the "most fascinating" entrepreneurs in the United States and called him "Edison's rightful heir".[10]
Kurzweil has authored seven books, five of which have been national bestsellers. The Age of Spiritual Machines has been translated into 9 languages and was the #1 best-selling book on Amazon in science. Kurzweil's book The Singularity Is Near was a New York Times bestseller, and has been the #1 book on Amazon in both science and philosophy. His latest bestseller is How to Create a Mind: The Secret of Human Thought Revealed.[11] Kurzweil speaks widely to audiences public and private and regularly delivers keynote speeches at industry conferences like DEMO, SXSW and TED. His website catalogs his public speaking, publications and media appearances.[12] He maintains the news website, KurzweilAI.net, which has over two million readers annually.[citation needed]

Life, inventions, and business career

Early life

Ray Kurzweil grew up in the New York City borough of Queens. He was born to secular Jewish parents who had escaped Austria just before the onset of World War II, and he was exposed via Unitarian Universalism to a diversity of religious faiths during his upbringing. Kurzweil is an atheist.[13] His father was a musician, a noted conductor, and a music educator. His mother was a visual artist. By the age of five, Ray wanted to be an inventor. At this age he played with and learned to solve a variety of toy construction sets, including numerous erector sets. In his youth, Ray was an avid reader of science fiction literature. At the age of eight, nine, and ten, Ray read the entire Tom Swift Jr. series. When he was 8 or 9 years old, he built a few things, such as a robotic theater and robotic game. He was involved with computers and built computing devices by the age of 12. These activities collectively impressed upon Ray the belief that nearly any problem could be overcome.[14]
He went to Martin Van Buren High School. During class, he often held onto his class textbooks to seemingly participate, but instead, focused on his own projects which were hidden behind the book. His uncle, an engineer at Bell Labs, taught young Kurzweil the basics of computer science.[15] In 1963, at age fifteen, he wrote his first computer program.[16] He created a pattern-recognition software program that analyzed the works of classical composers, and then synthesized its own songs in similar styles. In 1965, he was invited to appear on the CBS television program I've Got a Secret, where he performed a piano piece that was composed by a computer he also had built.[17] Later that year, he won first prize in the International Science Fair for the invention;[18] he was also recognized by the Westinghouse Talent Search and was personally congratulated by President Lyndon B. Johnson during a White House ceremony.

Mid-life

He obtained a B.S. in computer science and literature in 1970 at MIT. He went to MIT to study with Marvin Minsky who sort of became his mentor. He took all of the computer programming courses offered at MIT in the first year and a half.
In 1968, during his sophomore year at MIT, Kurzweil started a company that used a computer program to match high school students with colleges. The program, called the Select College Consulting Program, was designed by him and compared thousands of different criteria about each college with questionnaire answers submitted by each student applicant. Around this time, he sold the company to Harcourt, Brace & World for $100,000 (roughly $672,841.95 in 2013 dollars) plus royalties.[19]
In 1974, Kurzweil founded Kurzweil Computer Products, Inc. and led development of the first omni-font optical character recognition system, a computer program capable of recognizing text written in any normal font. Before that time, scanners had only been able to read text written in a few fonts. He decided that the best application of this technology would be to create a reading machine, which would allow blind people to understand written text by having a computer read it to them aloud. However, this device required the invention of two enabling technologies—the CCD flatbed scanner and the text-to-speech synthesizer. Development of these technologies was completed at other institutions such as Bell Labs, and on January 13, 1976, the finished product was unveiled during a news conference headed by him and the leaders of the National Federation of the Blind. Called the Kurzweil Reading Machine, the device covered an entire tabletop. It gained him mainstream recognition: on the day of the machine's unveiling, Walter Cronkite used the machine to give his signature soundoff, "And that's the way it is, January 13, 1976." While listening to The Today Show, musician Stevie Wonder heard a demonstration of the device and purchased the first production version of the Kurzweil Reading Machine, beginning a lifelong friendship with Kurzweil.
Kurzweil's next major business venture began in 1978, when Kurzweil Computer Products began selling a commercial version of the optical character recognition computer program. LexisNexis was one of the first customers, and bought the program to upload paper legal and news documents onto its nascent online databases.
Kurzweil sold his company to Lernout & Hauspie. Following the bankruptcy of the latter, the system became a subsidiary of Xerox formerly known as Scansoft and now as Nuance Communications, and he functioned as a consultant for the former until 1995.
Kurzweil's next business venture was in the realm of electronic music technology. After a 1982 meeting with Stevie Wonder, in which the latter lamented the divide in capabilities and qualities between electronic synthesizers and traditional musical instruments, Kurzweil was inspired to create a new generation of music synthesizers capable of accurately duplicating the sounds of real instruments. Kurzweil Music Systems was founded in the same year, and in 1984, the Kurzweil K250 was unveiled. The machine was capable of imitating a number of instruments, and in tests musicians were unable to discern the difference between the Kurzweil K250 on piano mode from a normal grand piano.[20] The recording and mixing abilities of the machine, coupled with its abilities to imitate different instruments made it possible for a single user to compose and play an entire orchestral piece.
Kurzweil Music Systems was sold to Korean musical instrument manufacturer Young Chang in 1990. As with Xerox, Kurzweil remained as a consultant for several years. Hyundai acquired Young Chang in 2006 and in January 2007 appointed Raymond Kurzweil as Chief Strategy Officer of Kurzweil Music Systems.[21]

Later life

Concurrent with Kurzweil Music Systems, Kurzweil created the company Kurzweil Applied Intelligence (KAI) to develop computer speech recognition systems for commercial use. The first product, which debuted in 1987, was an early speech recognition program.
Kurzweil started Kurzweil Educational Systems in 1996 to develop new pattern-recognition-based computer technologies to help people with disabilities such as blindness, dyslexia and ADD in school. Products include the Kurzweil 1000 text-to-speech converter software program, which enables a computer to read electronic and scanned text aloud to blind or visually impaired users, and the Kurzweil 3000 program, which is a multifaceted electronic learning system that helps with reading, writing, and study skills.

Raymond Kurzweil at the Singularity Summit at Stanford in 2006
During the 1990s Kurzweil founded the Medical Learning Company.[22] The company's products included an interactive computer education program for doctors and a computer-simulated patient. Around the same time, Kurzweil started KurzweilCyberArt.com—a website featuring computer programs to assist the creative art process. The site used to offer free downloads of a program called AARON—a visual art synthesizer developed by Harold Cohen—and of "Kurzweil's Cybernetic Poet", which automatically creates poetry. During this period he also started KurzweilAI.net, a website devoted towards showcasing news of scientific developments, publicizing the ideas of high-tech thinkers and critics alike, and promoting futurist-related discussion among the general population through the Mind-X forum.
In 1999, Kurzweil created a hedge fund called "FatKat" (Financial Accelerating Transactions from Kurzweil Adaptive Technologies), which began trading in 2006. He has stated that the ultimate aim is to improve the performance of FatKat's A.I. investment software program, enhancing its ability to recognize patterns in "currency fluctuations and stock-ownership trends."[23] He predicted in his 1999 book, The Age of Spiritual Machines, that computers will one day prove superior to the best human financial minds at making profitable investment decisions. In 2001, Canadian rock band Our Lady Peace released an album, titled Spiritual Machines, based on Kurzweil's book. Kurzweil's voice was featured in the album, reading excerpts from his book.
In June 2005, Kurzweil introduced the "Kurzweil-National Federation of the Blind Reader" (K-NFB Reader)—a pocket-sized device consisting of a digital camera and computer unit. Like the Kurzweil Reading Machine of almost 30 years before, the K-NFB Reader is designed to aid blind people by reading written text aloud. The newer machine is portable and scans text through digital camera images, while the older machine is large and scans text through flatbed scanning.
Kurzweil made a movie called The Singularity Is Near: A True Story About the Future[24] in 2010 based, in part, on his 2005 book The Singularity Is Near. Part fiction, part non-fiction, he interviews 20 big thinkers like Marvin Minsky, plus there is a B-line narrative story that illustrates some of the ideas, where a computer avatar (Ramona) saves the world from self-replicating microscopic robots. In addition to his movie, an independent, feature-length documentary was made about Kurzweil, his life, and his ideas called Transcendent Man. Filmmakers Barry Ptolemy and Felicia Ptolemy followed Kurzweil, documenting his global speaking-tour. Premiered in 2009 at the Tribeca Film Festival,[24] Transcendent Man documents Kurzweil's quest to reveal mankind's ultimate destiny and explores many of the ideas found in his New York Times bestselling book, The Singularity Is Near, including his concept exponential growth, radical life expansion, and how we will transcend our biology. The Ptolemys documented Kurzweil's stated goal of bringing back his late father using AI. The film also features critics who argue against Kurzweil's predictions.
In 2010, an independent documentary film called Plug & Pray premiered at the Seattle International Film Festival, in which Kurzweil and one of his major critics, the late Joseph Weizenbaum, argue about the benefits of eternal life.[25]
Kurzweil frequently comments on the application of cell-size nanotechnology to the workings of the human brain and how this could be applied to building AI. While being interviewed for a February 2009 issue of Rolling Stone magazine, Kurzweil expressed a desire to construct a genetic copy of his late father, Fredric Kurzweil, from DNA within his grave site. This feat would be achieved by exhumation and extraction of DNA, constructing a clone of Fredric and retrieving memories and recollections—from Ray's mind—of his father.[26]
In December 2012 Kurzweil was hired by Google in a full-time position to "work on new projects involving machine learning and language processing".[27] Google co-founder Larry Page and Kurzweil agreed on a one-sentence job description: "to bring natural language understanding to Google".[28]
Kurzweil is married with two children. His wife, Sonya Rosenwald Fenster, whom he married in 1975, is a child psychologist, while his son works as a venture capitalist and his daughter a choreographer.[29]

Books

Kurzweil's first book, The Age of Intelligent Machines, was published in 1990. The nonfiction work discusses the history of computer AI and also makes forecasts regarding future developments. Other experts in the field of AI contribute heavily to the work in the form of essays. The Association of American Publishers' awarded it the status of Most Outstanding Computer Science Book of 1990.[30]
Next, Kurzweil published a book on nutrition in 1993 called The 10% Solution for a Healthy Life. The book's main idea is that high levels of fat intake are the cause of many health disorders common in the U.S., and thus that cutting fat consumption down to 10% of the total calories consumed would be optimal for most people.
In 1999, Kurzweil published The Age of Spiritual Machines, which focuses heavily on further elucidating his theories regarding the future of technology, which themselves stem from his analysis of long-term trends in biological and technological evolution. Much focus goes into examining the likely course of AI development, along with the future of computer architecture.
Kurzweil's next book published in 2004, returned to the subject of human health and nutrition. Fantastic Voyage: Live Long Enough to Live Forever was co-authored by Kurzweil and Terry Grossman, a medical doctor and specialist in alternative medicine.
The Singularity Is Near was published in 2005. The book was made into the movie starring Pauley Perrette from NCIS.[31] In February 2007, Ptolemaic Productions acquired the rights to The Singularity is Near, The Age of Spiritual Machines and Fantastic Voyage including the rights to film Kurzweil's life and ideas for the documentary film Transcendent Man, which was directed by Barry Ptolemy.
Transcend: Nine Steps to Living Well Forever,[32] a follow-up to Fantastic Voyage, was released on April 28, 2009.
Kurzweil's latest book, How to Create a Mind: The Secret of Human Thought Revealed, was released on November 13, 2012.[33] In it Kurzweil describes his Pattern Recognition Theory of Mind, the theory that the neocortex is a hierarchical system of pattern recognizers, and details how duplicating this architecture in machines could lead to an artificial superintelligence.[34]
He is also writing a novel called Danielle, about his imaginary superheroine daughter who solves problems through intelligence.[35]

Views

Encouraging Futurism and Transhumanism

Kurzweil's standing as a futurist and transhumanist has led to his involvement in several Singularity-themed organizations. In December 2004, Kurzweil joined the advisory board of the Singularity Institute for Artificial Intelligence.[36] In October 2005, Kurzweil joined the scientific advisory board of the Lifeboat Foundation.[37] On May 13, 2006, Kurzweil was the first speaker at the Singularity Summit at Stanford.[38] In May 2013, Kurzweil was the keynote speaker at the 2013 proceeding of the Research, Innovation, Start-up and Employment (RISE) international conference in Seoul, Korea Republic.[39]
In February 2009, Kurzweil, in collaboration with Google and the NASA Ames Research Center, announced the creation of the Singularity University training center for corporate executives and government officials. The University's self-described mission is 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".[40] Using Vernor Vinge's Singularity concept as a foundation, the University offered its first nine-week graduate program to forty students in June, 2009.

Stance on the Future of Genetics, Nanotechnology, and Robotics

Kurzweil is working with the Army Science Advisory Board to develop a rapid response system to deal with the possible abuse of biotechnology. He suggests that the same technologies that are empowering us to reprogram biology away from cancer and heart disease could be used by a bioterrorist to reprogram a biological virus to be more deadly, communicable, and stealthy. Fortunately, he believes that we have the scientific tools to successfully defend against these attacks, similar to the way we defend against computer software viruses. He has testified before Congress on the subject of nanotechnology, advocating that nanotechnology has the potential to solve serious global problems such as poverty, disease, and climate change, viz. "Nanotech Could Give Global Warming a Big Chill".[41] In media appearances, Kurzweil has also stressed the extreme potential dangers of nanotechnology[17] but argues that in practice, progress cannot be stopped because that would require a totalitarian system, and any attempt to do so would drive dangerous technologies underground and deprive responsible scientists of the tools needed for defense. He suggests that the proper place of regulation is to ensure that technological progress proceeds safely and quickly, but does not deprive the world of profound benefits. He stated, "To avoid dangers such as unrestrained nanobot replication, we need relinquishment at the right level and to place our highest priority on the continuing advance of defensive technologies, staying ahead of destructive technologies. An overall strategy should include a streamlined regulatory process, a global program of monitoring for unknown or evolving biological pathogens, temporary moratoriums, raising public awareness, international cooperation, software reconnaissance, and fostering values of liberty, tolerance, and respect for knowledge and diversity." [42]

The Law of Accelerating Returns

In his 1999 book The Age of Spiritual Machines Kurzweil proposed "The Law of Accelerating Returns", according to which the rate of change in a wide variety of evolutionary systems (including the growth of technologies) tends to increase exponentially.[43] He gave further focus to this issue in a 2001 essay entitled "The Law of Accelerating Returns", which proposed an extension of Moore's law to a wide variety of technologies, and used this to argue in favor of Vernor Vinge's concept of a technological singularity.[44] Kurzweil suggests that this exponential technological growth is counter-intuitive to the way our brains perceive the world- since our brains were biologically inherited from humans living in a world that was linear and local- and, as a consequence, he believes it has encouraged great skepticism in his future projections.

Health and aging

Kurzweil admits that he cared little for his health until age 35, when he was found to suffer from a glucose intolerance, an early form of type II diabetes (a major risk factor for heart disease). Kurzweil then found a doctor (Terry Grossman, M.D.) who shares his non-conventional beliefs to develop an extreme regimen involving hundreds of pills, chemical intravenous treatments, red wine and various other methods to attempt to live longer. Kurzweil was ingesting "250 supplements, eight to 10 glasses of alkaline water and 10 cups of green tea" every day and drinking several glasses of red wine a week in an effort to "reprogram" his biochemistry.[45] Lately, he has cut down the number of supplement pills to 150.[46]
Kurzweil joined the Alcor Life Extension Foundation, a cryonics company. In the event of his declared death, Kurzweil will be perfused with cryoprotectants, vitrified in liquid nitrogen, and stored at an Alcor facility in the hope that future medical technology will be able to repair his tissues and revive him.[47]
He has authored three books on the subjects of nutrition, health and immortality: The 10% Solution for a Healthy Life, Fantastic Voyage: Live Long Enough to Live Forever and Transcend: Nine Steps to Living Well Forever.[48] In all, he recommends that other people emulate his health practices to the best of their abilities. Kurzweil and his current "anti-aging" doctor, Terry Grossman, MD., now have two websites promoting their first[49] and second book.[50]
He has stated that he believes that in the future, everyone will live forever.[51] In a 2013 interview, Kurzweil said that in 15 years, medical technology could add more than a year to one's remaining life expectancy for each year that passes, and we could then "outrun our own deaths". He has been an extreme advocate of SENS Research Foundation for the successful defeating of aging, and has encouraged acts of donation to hasten their rejuvenation research.[28][52]

Kurzweil's view of the human neocortex

According to Kurzweil, technologists will be creating synthetic neocortexes based on the operating principles of the human neocortex with the primary purpose of extending our own neocortexes. He believes that the neocortex of an adult human consists of approximately 300 million pattern recognizers. He draws on the commonly accepted belief that the primary anatomical difference between humans and other primates that allowed for superior intellectual abilities was the evolution of a larger neocortex. He claims that the six-layered neocortex deals with increasing abstraction from one layer to the next. He says that at the low levels, the neocortex may seem cold and mechanical because it can only make simple decisions, but at the higher levels of the hierarchy, the neocortex is likely to be dealing with concepts like being funny, being sexy, expressing a loving sentiment, creating a poem or understanding a poem, etc. He believes that these higher levels of the human neocortex were the enabling factors to permit the human development of language, technology, art, and science. He stated, "If the quantitative improvement from primates to humans with the big forehead was the enabling factor to allow for language, technology, art, and science, what kind of qualitative leap can we make with another quantitative increase? Why not go from 300 million pattern recognizers to a billion?”[53]

Predictions

Past predictions

Kurzweil's first book, The Age of Intelligent Machines, presented his ideas about the future. It was written from 1986 to 1989 and published in 1990. Building on Ithiel de Sola Pool's "Technologies of Freedom" (1983), Kurzweil claims to have forecast the demise of the Soviet Union due to new technologies such as cellular phones and fax machines disempowering authoritarian governments by removing state control over the flow of information.[54] In the book Kurzweil also extrapolated preexisting trends in the improvement of computer chess software performance to predict that computers would beat the best human players "by the year 2000".[55] In May 1997 chess World Champion Garry Kasparov was defeated by IBM's Deep Blue computer in a well-publicized chess tournament.[56]
Perhaps most significantly, Kurzweil foresaw the explosive growth in worldwide Internet use that began in the 1990s. At the time of the publication of The Age of Intelligent Machines, there were only 2.6 million Internet users in the world,[57] and the medium was unreliable, difficult to use, and deficient in content. He also stated that the Internet would explode not only in the number of users but in content as well, eventually granting users access "to international networks of libraries, data bases, and information services". Additionally, Kurzweil claims to have correctly foreseen that the preferred mode of Internet access would inevitably be through wireless systems, and he was also correct to estimate that the latter would become practical for widespread use in the early 21st century.
Kurzweil also claims to have accurately forecast that, by the end of the 1990s, many documents would exist solely in computers and on the Internet, and that they would commonly be embedded with sounds, animations, and videos that would inhibit their transfer to paper format. Moreover, he claims to have foreseen that cellular phones would grow in popularity while shrinking in size for the foreseeable future.
Ray Kurzweil's predictions for 2009 were mostly inaccurate, claims Forbes magazine. For example Ray predicted that "The majority of text is created using continuous speech recognition". This is not the case.[58]

Future predictions

In 1999, Kurzweil published a second book titled The Age of Spiritual Machines, which goes into more depth explaining his futurist ideas. The third and final part of the book is devoted to predictions over the coming century, from 2009 through 2099. While in The Singularity Is Near he makes fewer concrete short-term predictions, but includes many longer-term visions. He believes that with radical life extension will come radical life enhancement.
He is confident that within 10 years we will have the option to spend some of our time in 3D virtual environments that appear just as real as real reality, but these will not yet be made possible via direct interaction with our nervous system. He believes that 20 to 25 years from now, we will have millions of blood-cell sized devices, known as nanobots, inside our bodies fighting against diseases, improving our memory, and cognitive abilities. He believes that a machine will pass the turing test by 2029, and that around 2045, "the pace of change will be so astonishingly quick that we won't be able to keep up, unless we enhance our own intelligence by merging with the intelligent machines we are creating". He stresses that "AI is not an intelligent invasion from Mars. These are brain extenders that we have created to expand our own mental reach. They are part of our civilization. They are part of who we are. So over the next few decades our human-machine civilization will become increasingly dominated by its non-biological component." [59]
In 2008, Kurzweil said in an expert panel in the National Academy of Engineering that solar power will scale up to produce all the energy needs of Earth's people in 20 years. According to Kurzweil, we only need to capture 1 part in 10,000 of the energy from the Sun that hits Earth's surface, in order to meet all of humanity's energy needs.[60]

Reception

Recognition and awards

Kurzweil was referred to by Forbes as "the ultimate thinking machine."[8] He has received many awards and honors, including:
  • First place in the 1965 International Science Fair[18] for inventing the classical music synthesizing computer.
  • The 1978 Grace Murray Hopper Award from the Association for Computing Machinery. The award is given annually to one "outstanding young computer professional" and is accompanied by a $35,000 prize.[61] Kurzweil won it for his invention of the Kurzweil Reading Machine.[62]
  • The 1990 "Engineer of the Year" award from Design News.[63]
  • The 1994 Dickson Prize in Science. One is awarded every year by Carnegie Mellon University to individuals who have "notably advanced the field of science." Both a medal and a $50,000 prize are presented to winners.[64]
  • The 1998 "Inventor of the Year" award from the Massachusetts Institute of Technology.[65]
  • The 1999 National Medal of Technology.[66] This is the highest award the President of the United States can bestow upon individuals and groups for pioneering new technologies, and the President dispenses the award at his discretion.[67] Bill Clinton presented Kurzweil with the National Medal of Technology during a White House ceremony in recognition of Kurzweil's development of computer-based technologies to help the disabled.
  • The 2000 Telluride Tech Festival Award of Technology.[68] Two other individuals also received the same honor that year. The award is presented yearly to people who "exemplify the life, times and standard of contribution of Tesla, Westinghouse and Nunn."
  • The 2001 Lemelson-MIT Prize for a lifetime of developing technologies to help the disabled and to enrich the arts.[69] Only one is meted out each year to highly successful, mid-career inventors. A $500,000 award accompanies the prize.[70]
  • Kurzweil was inducted into the National Inventors Hall of Fame in 2002 for inventing the Kurzweil Reading Machine.[71] The organization "honors the women and men responsible for the great technological advances that make human, social and economic progress possible."[72] Fifteen other people were inducted into the Hall of Fame the same year.[73]
  • The Arthur C. Clarke Lifetime Achievement Award on April 20, 2009 for lifetime achievement as an inventor and futurist in computer-based technologies.[74]
  • Kurzweil has received eighteen honorary doctorates.[75]
  • In 2011, Kurzweil was named a Senior Fellow of the Design Futures Council.[76]

Criticism

Kurzweil's ideas have generated criticism within the scientific community and in the media.
Although the idea of a technological singularity is a popular concept in science fiction, some authors such as Neal Stephenson[77] and Bruce Sterling have voiced skepticism about its real-world plausibility. Sterling expressed his views on the singularity scenario in a talk at the Long Now Foundation entitled The Singularity: Your Future as a Black Hole.[78][79] Other prominent AI thinkers and computer scientists such as Daniel Dennett,[80] Rodney Brooks,[81] David Gelernter[82] and Paul Allen[83] also criticized Kurzweil's projections.
Daniel Lyons, writing in Newsweek, criticized Kurzweil for some of his predictions that turned out to be wrong, such as the economy continuing to boom from the 1998 dot-com through 2009, a US company having a market capitalization of more than $1 trillion, a supercomputer achieving 20 petaflops, speech recognition being in widespread use and cars that would drive themselves using sensors installed in highways; all by 2009.[84] To the charge that a 20 petaflop supercomputer was not produced in the time he predicted, Kurzweil responded that he considers Google a giant supercomputer, and that it is indeed capable of 20 petaflops.[84]
In the cover article of the December 2010 issue of IEEE Spectrum, John Rennie criticizes Kurzweil for several predictions that failed to become manifest by the originally predicted date. "Therein lie the frustrations of Kurzweil's brand of tech punditry. On close examination, his clearest and most successful predictions often lack originality or profundity. And most of his predictions come with so many loopholes that they border on the unfalsifiable."[85]
Bill Joy, cofounder of Sun Microsystems, agrees with Kurzweil's timeline of future progress, but thinks that technologies such as AI, nanotechnology and advanced biotechnology will create a dystopian world.[86] Mitch Kapor, the founder of Lotus Development Corporation, has called the notion of a technological singularity "intelligent design for the IQ 140 people...This proposition that we're heading to this point at which everything is going to be just unimaginably different—it's fundamentally, in my view, driven by a religious impulse. And all of the frantic arm-waving can't obscure that fact for me."[87]
Some critics have argued more strongly against Kurzweil and his ideas. Cognitive scientist Douglas Hofstadter has said of Kurzweil's and Hans Moravec's books: "It's an intimate mixture of rubbish and good ideas, and it's very hard to disentangle the two, because these are smart people; they're not stupid."[88] Biologist P. Z. Myers has criticized Kurzweil's predictions as being based on "New Age spiritualism" rather than science and says that Kurzweil does not understand basic biology.[89][90] VR pioneer Jaron Lanier has even described Kurzweil's ideas as "cybernetic totalism" and has outlined his views on the culture surrounding Kurzweil's predictions in an essay for Edge.org entitled One Half of a Manifesto.[91]
In a critical review of Kurzweil's book How to Create a Mind: The Secret of Human Thought Revealed, philosopher Colin McGinn refers to "the hype so blatantly brandished in its title" and asks: "He is clearly a man of many parts—but is ultimate theoretician of the mind one of them?" McGinn calls Kurzweil's claim that pattern recognition is the key to mental phenomena "obviously false" and concludes that the book is "interesting in places, fairly readable, moderately informative, but wildly overstated".[92]
John Gray, the British philosopher, argues that contemporary science is what magic was for ancient civilizations. It gives a sense of hope for those who are willing to do almost anything in order to achieve eternal life. He quotes Kurzweil's Singularity as another example of a trend which has almost always been present in the history of mankind.[93]

Wednesday, December 11, 2013

Ray Kurzweil: This is your future


By futurist Ray Kurzweil, Special to CNN
December 10, 2013 -- Updated 1610 GMT (0010 HKT)

Editor's note: Ray Kurzweil is one of the world's leading inventors, thinkers, and futurists, with a 30-year track record of accurate predictions. Called "the restless genius" by The Wall Street Journal and "the ultimate thinking machine" by Forbes magazine, Kurzweil was selected as one of the top entrepreneurs by Inc. magazine, which described him as the "rightful heir to Thomas Edison." Ray has written five national best-selling books. He is Director of Engineering at Google. Below are five ways he predicts our lives will change.
(CNN) -- By the early 2020s, we will have the means to program our biology away from disease and aging.
Up until recently, health and medicine was basically a hit or miss affair. We would discover interventions such as drugs that had benefits, but also many side effects. Until recently, we did not have the means to actually design interventions on computers.
All of that has now changed, and will dramatically change clinical practice by the early 2020s.
Ray Kurzweil
Ray Kurzweil
We now have the information code of the genome and are making exponential gains in modeling and simulating the information processes they give rise to.
We also have new tools that allow us to actually reprogram our biology in the same way that we reprogram our computers.
RNA interference, for example, can turn genes off that promote disease and aging. New forms of gene therapy, especially in vitro models that do not trigger the immune system, have the ability to add new genes.
Stem cell therapies, including the recently developed method to create "induced pluripotent cells" (IPCs) by adding four genes to your own skin cells to create the equivalent of an embryonic stem cell but without use of an embryo, are being developed to rejuvenate organs and even grow then from scratch.
There are now hundreds of drugs and processes in the pipeline using these methods to modify the course of obesity, heart disease, cancer, and other diseases and aging processes.
Company fights to keep monopoly on gene
As one of many examples, we can now fix a broken heart -- not (yet) from romance -- but from a heart attack, by rejuvenating the heart with reprogrammed stem cells.
The minds behind the Brain Activity Map
Health and medicine is now an information technology and is therefore subject to what I call the "law of accelerating returns," which is a doubling of capability (for the same cost) about each year that applies to any information technology.
As a result, technologies to reprogram the "software" that underlie human biology are already a thousand times more powerful than they were when the genome project was completed in 2003, and will again be a thousand times more powerful than they are today in a decade, and a million times more powerful in two decades.
Clinical applications are now at the cutting edge and will be routine in the early 2020s.
By 2030 solar energy will have the capacity to meet all of our energy needs. The production of food and clean water will also be revolutionized.
If we could capture one part in ten thousand of the sunlight that falls on the Earth we could meet 100% of our energy needs, using this renewable and environmentally friendly source.
As we apply new molecular scale technologies to solar panels, the cost per watt is coming down rapidly. Already Deutsche Bank, in a recent report, wrote "The cost of unsubsidized solar power is about the same as the cost of electricity from the grid in India and Italy. By 2014 even more countries will achieve solar 'grid parity.'"
The total number of watts of electricity produced by solar energy is growing exponentially, doubling every two years. It is now less than seven doublings from 100%.
Similar approaches will address other resource needs. Once we have inexpensive energy we can readily and inexpensively convert the vast amount of dirty and salinated water we have on the planet to usable water.
We are also headed towards another agriculture revolution, from horizontal agriculture to vertical agriculture, where we grow very high quality food in AI controlled buildings.
These will recycle all nutrients and end the ecological disaster that constitutes contemporary factory farming. This will include hydroponic plants for fruits and vegetables and in vitro cloning of muscle tissue for meat, that is meat without animals, thereby ending animal suffering.
3-D printing enters the metal age
By the early 2020s we will print out a significant fraction of the products we use including clothing as well as replacement organs.
Schumer takes aim at 3-D printed guns
3D printing is getting a lot of attention. There are niche applications such as printing our replacement parts for machinery, but the opportunity to begin replacing significant portions of manufacturing is still about five years away.
If we look at the life cycle of technologies we see an early period of over-enthusiasm, then a "bust" when disillusionment sets in, followed by the real revolution.
3-D printing buildings of the future
Remember the Internet boom of the 1990s followed by the Internet bust around the year 2000?
That was around the time Google was getting started, and now we have multi-hundred billion dollar Internet companies.
We're in the early boom phase of 3D printing enthusiasm and hopefully we've learned enough to avoid a period of undue disillusionment, but I do see the early 2020s as the golden era of 3D printing.
For example, in the early 2020s, you'll have a choice of many thousands of cool clothing designs that are open source and that can be printed out for pennies a pound.
But that will not mean the end of the fashion industry. Look at other industries that have already been transformed from physical products to digital ones, such as books, movies and music.
Despite enormous changes in business models (and the availability of many free open source products) the overall revenues for proprietary forms of these products remains strong.
We can already experimentally print out organs by printing a biodegradable scaffolding and then populating it with a patient's own stem cells, all with a 3D printer.
By the early 2020s, this will reach clinical practice.
Can a computer diagnose, treat cancer?
Within five years, search engines will be based on an understanding of natural language.
How tech helps beat social barriers
Consider that IBM's Watson got a higher score on the American television game of Jeopardy than the best two human players combined.
Test-driving Google Glass
Jeopardy is a broad task involving complicated natural language queries which include puns, riddles, jokes and metaphors.
For example, Watson got this query correct in the rhyme category: "A long tiresome speech delivered by a frothy pie topping." It correctly responded "What is a meringue harangue."
What is not widely appreciated is that Watson got its knowledge by reading Wikipedia and several other encyclopedias, a total of 200 million pages of natural language documents.
I does not read each page as well as you or I. It might read one page and conclude that there is a 56% chance that Barack Obama is President of the United States.
You could read that page, and if you didn't happen to know that ahead of time, conclude that there is a 98% chance.
So you did a better job than Watson at reading that page. But Watson makes up for this relatively weak reading by reading more pages, a lot more, and it can combine its inferences across everything it has read and conclude that there is a 99.9% chance that Obama is president.
At Google, we are creating a system that will read every document on the web and every book for meaning and provide a rich search and question answering experience based on the true meaning of natural language.
Virtual tour guides 'creep out' travelers
For example, it will engage you in dialogue to clarify questions and discuss answers that are ambiguous or complex.
Console wars: PS4 vs Xbox One
By the early 2020s we will be routinely working and playing with each other in full immersion visual-auditory virtual environments. By the 2030s, we will add the tactile sense to full immersion virtual reality.
Hologram madness
 
The telephone is virtual reality in that you can meet with someone as if you are together, at least for the auditory sense.
We've now added the visual sense with video conferencing -- although not yet 3D and full immersion.
The visual sense will become full immersion over the next decade. We'll also be able to augment real reality so that I could see you sitting on the coach in my living room and you could see me sitting on your back porch, even though we're hundreds of miles apart.
Your augmented reality glasses will also be able to make suggestions to you for an interesting joke or anecdote that you could slip into a conversation you're having.
There will be limited ways of adding the tactile sense to virtual and augmented reality by the early 2020s, but full immersion virtual tactile experiences will require tapping directly into the nervous system.
We'll be able to do that in the 2030s with nanobots traveling noninvasively into the brain through the capillaries and augmenting the signals coming from our real senses.
The opinions expressed in this commentary are solely those of Ray Kurzweil

Wednesday, November 6, 2013

Artificial intelligence

From Wikipedia, the free encyclopedia

Artificial intelligence (AI) is technology and a branch of computer science that studies and develops intelligent machines and software. Major AI researchers and textbooks define the field as "the study and design of intelligent agents",[1] where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success.[2] John McCarthy, who coined the term in 1955,[3] defines it as "the science and engineering of making intelligent machines".[4]
AI research is highly technical and specialised, and is deeply divided into subfields that often fail to communicate with each other.[5] Some of the division is due to social and cultural factors: subfields have grown up around particular institutions and the work of individual researchers. AI research is also divided by several technical issues. Some subfields focus on the solution of specific problems. Others focus on one of several possible approaches or on the use of a particular tool or towards the accomplishment of particular applications.
The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, communication, perception and the ability to move and manipulate objects.[6] General intelligence (or "strong AI") is still among the field's long term goals.[7] Currently popular approaches include statistical methods, computational intelligence and traditional symbolic AI. There are an enormous number of tools used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics, and many others.
The field was founded on the claim that a central ability of humans, intelligence—the sapience of Homo sapiens—can be so precisely described that it can be simulated by a machine.[8] This raises philosophical issues about the nature of the mind and the ethics of creating artificial beings, issues which have been addressed by myth, fiction and philosophy since antiquity.[9] Artificial intelligence has been the subject of tremendous optimism[10] but has also suffered stunning setbacks.[11] Today it has become an essential part of the technology industry and many of the most difficult problems in computer science.[12]

History

Thinking machines and artificial beings appear in Greek myths, such as Talos of Crete, the bronze robot of Hephaestus, and Pygmalion's Galatea.[13] Human likenesses believed to have intelligence were built in every major civilization: animated cult images were worshiped in Egypt and Greece[14] and humanoid automatons were built by Yan Shi, Hero of Alexandria and Al-Jazari.[15] It was also widely believed that artificial beings had been created by Jābir ibn Hayyān, Judah Loew and Paracelsus.[16] By the 19th and 20th centuries, artificial beings had become a common feature in fiction, as in Mary Shelley's Frankenstein or Karel Čapek's R.U.R. (Rossum's Universal Robots).[17] Pamela McCorduck argues that all of these are examples of an ancient urge, as she describes it, "to forge the gods".[9] Stories of these creatures and their fates discuss many of the same hopes, fears and ethical concerns that are presented by artificial intelligence.
Mechanical or "formal" reasoning has been developed by philosophers and mathematicians since antiquity. The study of logic led directly to the invention of the programmable digital electronic computer, based on the work of mathematician Alan Turing and others. Turing's theory of computation suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate any conceivable act of mathematical deduction.[18][19] This, along with concurrent discoveries in neurology, information theory and cybernetics, inspired a small group of researchers to begin to seriously consider the possibility of building an electronic brain.[20]
The field of AI research was founded at a conference on the campus of Dartmouth College in the summer of 1956.[21] The attendees, including John McCarthy, Marvin Minsky, Allen Newell and Herbert Simon, became the leaders of AI research for many decades.[22] They and their students wrote programs that were, to most people, simply astonishing:[23] Computers were solving word problems in algebra, proving logical theorems and speaking English.[24] By the middle of the 1960s, research in the U.S. was heavily funded by the Department of Defense[25] and laboratories had been established around the world.[26] AI's founders were profoundly optimistic about the future of the new field: Herbert Simon predicted that "machines will be capable, within twenty years, of doing any work a man can do" and Marvin Minsky agreed, writing that "within a generation ... the problem of creating 'artificial intelligence' will substantially be solved".[27]
They had failed to recognize the difficulty of some of the problems they faced.[28] In 1974, in response to the criticism of Sir James Lighthill and ongoing pressure from the US Congress to fund more productive projects, both the U.S. and British governments cut off all undirected exploratory research in AI. The next few years would later be called an "AI winter",[29] a period when funding for AI projects was hard to find.
In the early 1980s, AI research was revived by the commercial success of expert systems,[30] a form of AI program that simulated the knowledge and analytical skills of one or more human experts. By 1985 the market for AI had reached over a billion dollars. At the same time, Japan's fifth generation computer project inspired the U.S and British governments to restore funding for academic research in the field.[31] However, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, longer lasting AI winter began.[32]
In the 1990s and early 21st century, AI achieved its greatest successes, albeit somewhat behind the scenes. Artificial intelligence is used for logistics, data mining, medical diagnosis and many other areas throughout the technology industry.[12] The success was due to several factors: the increasing computational power of computers (see Moore's law), a greater emphasis on solving specific subproblems, the creation of new ties between AI and other fields working on similar problems, and a new commitment by researchers to solid mathematical methods and rigorous scientific standards.[33]
On 11 May 1997, Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov.[34] In 2005, a Stanford robot won the DARPA Grand Challenge by driving autonomously for 131 miles along an unrehearsed desert trail.[35] Two years later, a team from CMU won the DARPA Urban Challenge when their vehicle autonomously navigated 55 miles in an urban environment while adhering to traffic hazards and all traffic laws.[36] In February 2011, in a Jeopardy! quiz show exhibition match, IBM's question answering system, Watson, defeated the two greatest Jeopardy champions, Brad Rutter and Ken Jennings, by a significant margin.[37] The Kinect, which provides a 3D body–motion interface for the Xbox 360, uses algorithms that emerged from lengthy AI research[38] as does the iPhone's Siri.

Goals

The general problem of simulating (or creating) intelligence has been broken down into a number of specific sub-problems. These consist of particular traits or capabilities that researchers would like an intelligent system to display. The traits described below have received the most attention.[6]

Deduction, reasoning, problem solving

Early AI researchers developed algorithms that imitated the step-by-step reasoning that humans use when they solve puzzles or make logical deductions.[39] By the late 1980s and 1990s, AI research had also developed highly successful methods for dealing with uncertain or incomplete information, employing concepts from probability and economics.[40]
For difficult problems, most of these algorithms can require enormous computational resources – most experience a "combinatorial explosion": the amount of memory or computer time required becomes astronomical when the problem goes beyond a certain size. The search for more efficient problem-solving algorithms is a high priority for AI research.[41]
Human beings solve most of their problems using fast, intuitive judgements rather than the conscious, step-by-step deduction that early AI research was able to model.[42] AI has made some progress at imitating this kind of "sub-symbolic" problem solving: embodied agent approaches emphasize the importance of sensorimotor skills to higher reasoning; neural net research attempts to simulate the structures inside the brain that give rise to this skill; statistical approaches to AI mimic the probabilistic nature of the human ability to guess.

Knowledge representation

An ontology represents knowledge as a set of concepts within a domain and the relationships between those concepts.
Knowledge representation[43] and knowledge engineering[44] are central to AI research. Many of the problems machines are expected to solve will require extensive knowledge about the world. Among the things that AI needs to represent are: objects, properties, categories and relations between objects;[45] situations, events, states and time;[46] causes and effects;[47] knowledge about knowledge (what we know about what other people know);[48] and many other, less well researched domains. A representation of "what exists" is an ontology: the set of objects, relations, concepts and so on that the machine knows about. The most general are called upper ontologies, which attempt to provide a foundation for all other knowledge.[49]
Among the most difficult problems in knowledge representation are:
Default reasoning and the qualification problem
Many of the things people know take the form of "working assumptions." For example, if a bird comes up in conversation, people typically picture an animal that is fist sized, sings, and flies. None of these things are true about all birds. John McCarthy identified this problem in 1969[50] as the qualification problem: for any commonsense rule that AI researchers care to represent, there tend to be a huge number of exceptions. Almost nothing is simply true or false in the way that abstract logic requires. AI research has explored a number of solutions to this problem.[51]
The breadth of commonsense knowledge
The number of atomic facts that the average person knows is astronomical. Research projects that attempt to build a complete knowledge base of commonsense knowledge (e.g., Cyc) require enormous amounts of laborious ontological engineering — they must be built, by hand, one complicated concept at a time.[52] A major goal is to have the computer understand enough concepts to be able to learn by reading from sources like the internet, and thus be able to add to its own ontology.[citation needed]
The subsymbolic form of some commonsense knowledge
Much of what people know is not represented as "facts" or "statements" that they could express verbally. For example, a chess master will avoid a particular chess position because it "feels too exposed"[53] or an art critic can take one look at a statue and instantly realize that it is a fake.[54] These are intuitions or tendencies that are represented in the brain non-consciously and sub-symbolically.[55] Knowledge like this informs, supports and provides a context for symbolic, conscious knowledge. As with the related problem of sub-symbolic reasoning, it is hoped that situated AI, computational intelligence, or statistical AI will provide ways to represent this kind of knowledge.[55]

Planning

A hierarchical control system is a form of control system in which a set of devices and governing software is arranged in a hierarchy.
Intelligent agents must be able to set goals and achieve them.[56] They need a way to visualize the future (they must have a representation of the state of the world and be able to make predictions about how their actions will change it) and be able to make choices that maximize the utility (or "value") of the available choices.[57]
In classical planning problems, the agent can assume that it is the only thing acting on the world and it can be certain what the consequences of its actions may be.[58] However, if the agent is not the only actor, it must periodically ascertain whether the world matches its predictions and it must change its plan as this becomes necessary, requiring the agent to reason under uncertainty.[59]
Multi-agent planning uses the cooperation and competition of many agents to achieve a given goal. Emergent behavior such as this is used by evolutionary algorithms and swarm intelligence.[60]

Learning

Machine learning is the study of computer algorithms that improve automatically through experience[61][62] and has been central to AI research since the field's inception.[63]
Unsupervised learning is the ability to find patterns in a stream of input. Supervised learning includes both classification and numerical regression. Classification is used to determine what category something belongs in, after seeing a number of examples of things from several categories. Regression is the attempt to produce a function that describes the relationship between inputs and outputs and predicts how the outputs should change as the inputs change. In reinforcement learning[64] the agent is rewarded for good responses and punished for bad ones. These can be analyzed in terms of decision theory, using concepts like utility. The mathematical analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory.[65]
Within developmental robotics, developmental learning approaches were elaborated for lifelong cumulative acquisition of repertoires of novel skills by a robot, through autonomous self-exploration and social interaction with human teachers, and using guidance mechanisms such as active learning, maturation, motor synergies, and imitation.[66][67][68][69]

Natural language processing

A parse tree represents the syntactic structure of a sentence according to some formal grammar.
Natural language processing[70] gives machines the ability to read and understand the languages that humans speak. A sufficiently powerful natural language processing system would enable natural language user interfaces and the acquisition of knowledge directly from human-written sources, such as Internet texts. Some straightforward applications of natural language processing include information retrieval (or text mining) and machine translation.[71]
A common method of processing and extracting meaning from natural language is through semantic indexing. Increases in processing speeds and the drop in the cost of data storage makes indexing large volumes of abstractions of the users input much more efficient.

Motion and manipulation

The field of robotics[72] is closely related to AI. Intelligence is required for robots to be able to handle such tasks as object manipulation[73] and navigation, with sub-problems of localization (knowing where you are, or finding out where other things are), mapping (learning what is around you, building a map of the environment), and motion planning (figuring out how to get there) or path planning (going from one point in space to another point, which may involve compliant motion - where the robot moves while maintaining physical contact with an object).[74][75]

Perception

Machine perception[76] is the ability to use input from sensors (such as cameras, microphones, sonar and others more exotic) to deduce aspects of the world. Computer vision[77] is the ability to analyze visual input. A few selected subproblems are speech recognition,[78] facial recognition and object recognition.[79]

Social intelligence

Kismet, a robot with rudimentary social skills[80]
Affective computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects.[81][82] It is an interdisciplinary field spanning computer sciences, psychology, and cognitive science.[83] While the origins of the field may be traced as far back as to early philosophical inquiries into emotion,[84] the more modern branch of computer science originated with Rosalind Picard's 1995 paper[85] on affective computing.[86][87] A motivation for the research is the ability to simulate empathy. The machine should interpret the emotional state of humans and adapt its behaviour to them, giving an appropriate response for those emotions.
Emotion and social skills[88] play two roles for an intelligent agent. First, it must be able to predict the actions of others, by understanding their motives and emotional states. (This involves elements of game theory, decision theory, as well as the ability to model human emotions and the perceptual skills to detect emotions.) Also, in an effort to facilitate human-computer interaction, an intelligent machine might want to be able to display emotions—even if it does not actually experience them itself—in order to appear sensitive to the emotional dynamics of human interaction.

Creativity

A sub-field of AI addresses creativity both theoretically (from a philosophical and psychological perspective) and practically (via specific implementations of systems that generate outputs that can be considered creative, or systems that identify and assess creativity). Related areas of computational research are Artificial intuition and Artificial imagination.

General intelligence

Most researchers think that their work will eventually be incorporated into a machine with general intelligence (known as strong AI), combining all the skills above and exceeding human abilities at most or all of them.[7] A few believe that anthropomorphic features like artificial consciousness or an artificial brain may be required for such a project.[89][90]
Many of the problems above may require general intelligence to be considered solved. For example, even a straightforward, specific task like machine translation requires that the machine read and write in both languages (NLP), follow the author's argument (reason), know what is being talked about (knowledge), and faithfully reproduce the author's intention (social intelligence). A problem like machine translation is considered "AI-complete". In order to solve this particular problem, you must solve all the problems.[91]

Approaches

There is no established unifying theory or paradigm that guides AI research. Researchers disagree about many issues.[92] A few of the most long standing questions that have remained unanswered are these: should artificial intelligence simulate natural intelligence by studying psychology or neurology? Or is human biology as irrelevant to AI research as bird biology is to aeronautical engineering?[93] Can intelligent behavior be described using simple, elegant principles (such as logic or optimization)? Or does it necessarily require solving a large number of completely unrelated problems?[94] Can intelligence be reproduced using high-level symbols, similar to words and ideas? Or does it require "sub-symbolic" processing?[95] John Haugeland, who coined the term GOFAI (Good Old-Fashioned Artificial Intelligence), also proposed that AI should more properly be referred to as synthetic intelligence,[96] a term which has since been adopted by some non-GOFAI researchers.[97][98]

Cybernetics and brain simulation

In the 1940s and 1950s, a number of researchers explored the connection between neurology, information theory, and cybernetics. Some of them built machines that used electronic networks to exhibit rudimentary intelligence, such as W. Grey Walter's turtles and the Johns Hopkins Beast. Many of these researchers gathered for meetings of the Teleological Society at Princeton University and the Ratio Club in England.[20] By 1960, this approach was largely abandoned, although elements of it would be revived in the 1980s.

Symbolic

When access to digital computers became possible in the middle 1950s, AI research began to explore the possibility that human intelligence could be reduced to symbol manipulation. The research was centered in three institutions: Carnegie Mellon University, Stanford and MIT, and each one developed its own style of research. John Haugeland named these approaches to AI "good old fashioned AI" or "GOFAI".[99] During the 1960s, symbolic approaches had achieved great success at simulating high-level thinking in small demonstration programs. Approaches based on cybernetics or neural networks were abandoned or pushed into the background.[100] Researchers in the 1960s and the 1970s were convinced that symbolic approaches would eventually succeed in creating a machine with artificial general intelligence and considered this the goal of their field.
Cognitive simulation
Economist Herbert Simon and Allen Newell studied human problem-solving skills and attempted to formalize them, and their work laid the foundations of the field of artificial intelligence, as well as cognitive science, operations research and management science. Their research team used the results of psychological experiments to develop programs that simulated the techniques that people used to solve problems. This tradition, centered at Carnegie Mellon University would eventually culminate in the development of the Soar architecture in the middle 1980s.[101][102]
Logic-based
Unlike Newell and Simon, John McCarthy felt that machines did not need to simulate human thought, but should instead try to find the essence of abstract reasoning and problem solving, regardless of whether people used the same algorithms.[93] His laboratory at Stanford (SAIL) focused on using formal logic to solve a wide variety of problems, including knowledge representation, planning and learning.[103] Logic was also the focus of the work at the University of Edinburgh and elsewhere in Europe which led to the development of the programming language Prolog and the science of logic programming.[104]
"Anti-logic" or "scruffy"
Researchers at MIT (such as Marvin Minsky and Seymour Papert)[105] found that solving difficult problems in vision and natural language processing required ad-hoc solutions – they argued that there was no simple and general principle (like logic) that would capture all the aspects of intelligent behavior. Roger Schank described their "anti-logic" approaches as "scruffy" (as opposed to the "neat" paradigms at CMU and Stanford).[94] Commonsense knowledge bases (such as Doug Lenat's Cyc) are an example of "scruffy" AI, since they must be built by hand, one complicated concept at a time.[106]
Knowledge-based
When computers with large memories became available around 1970, researchers from all three traditions began to build knowledge into AI applications.[107] This "knowledge revolution" led to the development and deployment of expert systems (introduced by Edward Feigenbaum), the first truly successful form of AI software.[30] The knowledge revolution was also driven by the realization that enormous amounts of knowledge would be required by many simple AI applications.

Sub-symbolic

By the 1980s progress in symbolic AI seemed to stall and many believed that symbolic systems would never be able to imitate all the processes of human cognition, especially perception, robotics, learning and pattern recognition. A number of researchers began to look into "sub-symbolic" approaches to specific AI problems.[95]
Bottom-up, embodied, situated, behavior-based or nouvelle AI
Researchers from the related field of robotics, such as Rodney Brooks, rejected symbolic AI and focused on the basic engineering problems that would allow robots to move and survive.[108] Their work revived the non-symbolic viewpoint of the early cybernetics researchers of the 1950s and reintroduced the use of control theory in AI. This coincided with the development of the embodied mind thesis in the related field of cognitive science: the idea that aspects of the body (such as movement, perception and visualization) are required for higher intelligence.
Computational intelligence
Interest in neural networks and "connectionism" was revived by David Rumelhart and others in the middle 1980s.[109] These and other sub-symbolic approaches, such as fuzzy systems and evolutionary computation, are now studied collectively by the emerging discipline of computational intelligence.[110]

Statistical

In the 1990s, AI researchers developed sophisticated mathematical tools to solve specific subproblems. These tools are truly scientific, in the sense that their results are both measurable and verifiable, and they have been responsible for many of AI's recent successes. The shared mathematical language has also permitted a high level of collaboration with more established fields (like mathematics, economics or operations research). Stuart Russell and Peter Norvig describe this movement as nothing less than a "revolution" and "the victory of the neats."[33] Critics argue that these techniques are too focused on particular problems and have failed to address the long term goal of general intelligence.[111] There is an ongoing debate about the relevance and validity of statistical approaches in AI, exemplified in part by exchanges between Peter Norvig and Noam Chomsky.[112][113]

Integrating the approaches

Intelligent agent paradigm
An intelligent agent is a system that perceives its environment and takes actions which maximize its chances of success. The simplest intelligent agents are programs that solve specific problems. More complicated agents include human beings and organizations of human beings (such as firms). The paradigm gives researchers license to study isolated problems and find solutions that are both verifiable and useful, without agreeing on one single approach. An agent that solves a specific problem can use any approach that works – some agents are symbolic and logical, some are sub-symbolic neural networks and others may use new approaches. The paradigm also gives researchers a common language to communicate with other fields—such as decision theory and economics—that also use concepts of abstract agents. The intelligent agent paradigm became widely accepted during the 1990s.[2]
Agent architectures and cognitive architectures
Researchers have designed systems to build intelligent systems out of interacting intelligent agents in a multi-agent system.[114] A system with both symbolic and sub-symbolic components is a hybrid intelligent system, and the study of such systems is artificial intelligence systems integration. A hierarchical control system provides a bridge between sub-symbolic AI at its lowest, reactive levels and traditional symbolic AI at its highest levels, where relaxed time constraints permit planning and world modelling.[115] Rodney Brooks' subsumption architecture was an early proposal for such a hierarchical system.[116]

Tools

In the course of 50 years of research, AI has developed a large number of tools to solve the most difficult problems in computer science. A few of the most general of these methods are discussed below.

Search and optimization

Many problems in AI can be solved in theory by intelligently searching through many possible solutions:[117] Reasoning can be reduced to performing a search. For example, logical proof can be viewed as searching for a path that leads from premises to conclusions, where each step is the application of an inference rule.[118] Planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis.[119] Robotics algorithms for moving limbs and grasping objects use local searches in configuration space.[73] Many learning algorithms use search algorithms based on optimization.
Simple exhaustive searches[120] are rarely sufficient for most real world problems: the search space (the number of places to search) quickly grows to astronomical numbers. The result is a search that is too slow or never completes. The solution, for many problems, is to use "heuristics" or "rules of thumb" that eliminate choices that are unlikely to lead to the goal (called "pruning the search tree"). Heuristics supply the program with a "best guess" for the path on which the solution lies.[121] Heuristics limit the search for solutions into a smaller sample size.[74]
A very different kind of search came to prominence in the 1990s, based on the mathematical theory of optimization. For many problems, it is possible to begin the search with some form of a guess and then refine the guess incrementally until no more refinements can be made. These algorithms can be visualized as blind hill climbing: we begin the search at a random point on the landscape, and then, by jumps or steps, we keep moving our guess uphill, until we reach the top. Other optimization algorithms are simulated annealing, beam search and random optimization.[122]
Evolutionary computation uses a form of optimization search. For example, they may begin with a population of organisms (the guesses) and then allow them to mutate and recombine, selecting only the fittest to survive each generation (refining the guesses). Forms of evolutionary computation include swarm intelligence algorithms (such as ant colony or particle swarm optimization)[123] and evolutionary algorithms (such as genetic algorithms, gene expression programming, and genetic programming).[124]

Logic

Logic[125] is used for knowledge representation and problem solving, but it can be applied to other problems as well. For example, the satplan algorithm uses logic for planning[126] and inductive logic programming is a method for learning.[127]
Several different forms of logic are used in AI research. Propositional or sentential logic[128] is the logic of statements which can be true or false. First-order logic[129] also allows the use of quantifiers and predicates, and can express facts about objects, their properties, and their relations with each other. Fuzzy logic,[130] is a version of first-order logic which allows the truth of a statement to be represented as a value between 0 and 1, rather than simply True (1) or False (0). Fuzzy systems can be used for uncertain reasoning and have been widely used in modern industrial and consumer product control systems. Subjective logic[131] models uncertainty in a different and more explicit manner than fuzzy-logic: a given binomial opinion satisfies belief + disbelief + uncertainty = 1 within a Beta distribution. By this method, ignorance can be distinguished from probabilistic statements that an agent makes with high confidence.
Default logics, non-monotonic logics and circumscription[51] are forms of logic designed to help with default reasoning and the qualification problem. Several extensions of logic have been designed to handle specific domains of knowledge, such as: description logics;[45] situation calculus, event calculus and fluent calculus (for representing events and time);[46] causal calculus;[47] belief calculus; and modal logics.[48]

Probabilistic methods for uncertain reasoning

Many problems in AI (in reasoning, planning, learning, perception and robotics) require the agent to operate with incomplete or uncertain information. AI researchers have devised a number of powerful tools to solve these problems using methods from probability theory and economics.[132]
Bayesian networks[133] are a very general tool that can be used for a large number of problems: reasoning (using the Bayesian inference algorithm),[134] learning (using the expectation-maximization algorithm),[135] planning (using decision networks)[136] and perception (using dynamic Bayesian networks).[137] Probabilistic algorithms can also be used for filtering, prediction, smoothing and finding explanations for streams of data, helping perception systems to analyze processes that occur over time (e.g., hidden Markov models or Kalman filters).[137]
A key concept from the science of economics is "utility": a measure of how valuable something is to an intelligent agent. Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using decision theory, decision analysis,[138] information value theory.[57] These tools include models such as Markov decision processes,[139] dynamic decision networks,[137] game theory and mechanism design.[140]

Classifiers and statistical learning methods

The simplest AI applications can be divided into two types: classifiers ("if shiny then diamond") and controllers ("if shiny then pick up"). Controllers do however also classify conditions before inferring actions, and therefore classification forms a central part of many AI systems. Classifiers are functions that use pattern matching to determine a closest match. They can be tuned according to examples, making them very attractive for use in AI. These examples are known as observations or patterns. In supervised learning, each pattern belongs to a certain predefined class. A class can be seen as a decision that has to be made. All the observations combined with their class labels are known as a data set. When a new observation is received, that observation is classified based on previous experience.[141]
A classifier can be trained in various ways; there are many statistical and machine learning approaches. The most widely used classifiers are the neural network,[142] kernel methods such as the support vector machine,[143] k-nearest neighbor algorithm,[144] Gaussian mixture model,[145] naive Bayes classifier,[146] and decision tree.[147] The performance of these classifiers have been compared over a wide range of tasks. Classifier performance depends greatly on the characteristics of the data to be classified. There is no single classifier that works best on all given problems; this is also referred to as the "no free lunch" theorem. Determining a suitable classifier for a given problem is still more an art than science.[148]

Neural networks

A neural network is an interconnected group of nodes, akin to the vast network of neurons in the human brain.
The study of artificial neural networks[142] began in the decade before the field AI research was founded, in the work of Walter Pitts and Warren McCullough. Other important early researchers were Frank Rosenblatt, who invented the perceptron and Paul Werbos who developed the backpropagation algorithm.[149]
The main categories of networks are acyclic or feedforward neural networks (where the signal passes in only one direction) and recurrent neural networks (which allow feedback). Among the most popular feedforward networks are perceptrons, multi-layer perceptrons and radial basis networks.[150] Among recurrent networks, the most famous is the Hopfield net, a form of attractor network, which was first described by John Hopfield in 1982.[151] Neural networks can be applied to the problem of intelligent control (for robotics) or learning, using such techniques as Hebbian learning and competitive learning.[152]
Hierarchical temporal memory is an approach that models some of the structural and algorithmic properties of the neocortex.[153]

Control theory

Control theory, the grandchild of cybernetics, has many important applications, especially in robotics.[154]

Languages

AI researchers have developed several specialized languages for AI research, including Lisp[155] and Prolog.[156]

Evaluating progress

In 1950, Alan Turing proposed a general procedure to test the intelligence of an agent now known as the Turing test. This procedure allows almost all the major problems of artificial intelligence to be tested. However, it is a very difficult challenge and at present all agents fail.[157]
Artificial intelligence can also be evaluated on specific problems such as small problems in chemistry, hand-writing recognition and game-playing. Such tests have been termed subject matter expert Turing tests. Smaller problems provide more achievable goals and there are an ever-increasing number of positive results.[158]
One classification for outcomes of an AI test is:[159]
  1. Optimal: it is not possible to perform better.
  2. Strong super-human: performs better than all humans.
  3. Super-human: performs better than most humans.
  4. Sub-human: performs worse than most humans.
For example, performance at draughts is optimal,[160] performance at chess is super-human and nearing strong super-human (see computer chess: computers versus human) and performance at many everyday tasks (such as recognizing a face or crossing a room without bumping into something) is sub-human.
A quite different approach measures machine intelligence through tests which are developed from mathematical definitions of intelligence. Examples of these kinds of tests start in the late nineties devising intelligence tests using notions from Kolmogorov complexity and data compression.[161] Two major advantages of mathematical definitions are their applicability to nonhuman intelligences and their absence of a requirement for human testers.
An area that artificial intelligence had contributed greatly to is Intrusion detection.[162]

Applications

An automated online assistant providing customer service on a web page – one of many very primitive applications of artificial intelligence.
Artificial intelligence techniques are pervasive and are too numerous to list. Frequently, when a technique reaches mainstream use, it is no longer considered artificial intelligence; this phenomenon is described as the AI effect.[163]

Competitions and prizes

There are a number of competitions and prizes to promote research in artificial intelligence. The main areas promoted are: general machine intelligence, conversational behavior, data-mining, robotic cars, robot soccer and games.

Platforms

A platform (or "computing platform") is defined as "some sort of hardware architecture or software framework (including application frameworks), that allows software to run." As Rodney Brooks[164] pointed out many years ago, it is not just the artificial intelligence software that defines the AI features of the platform, but rather the actual platform itself that affects the AI that results, i.e., there needs to be work in AI problems on real-world platforms rather than in isolation.
A wide variety of platforms has allowed different aspects of AI to develop, ranging from expert systems, albeit PC-based but still an entire real-world system, to various robot platforms such as the widely available Roomba with open interface.[165]

Philosophy

Artificial intelligence, by claiming to be able to recreate the capabilities of the human mind, is both a challenge and an inspiration for philosophy. Are there limits to how intelligent machines can be? Is there an essential difference between human intelligence and artificial intelligence? Can a machine have a mind and consciousness? A few of the most influential answers to these questions are given below.[166]
Turing's "polite convention"
We need not decide if a machine can "think"; we need only decide if a machine can act as intelligently as a human being. This approach to the philosophical problems associated with artificial intelligence forms the basis of the Turing test.[157]
The Dartmouth proposal
"Every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it." This conjecture was printed in the proposal for the Dartmouth Conference of 1956, and represents the position of most working AI researchers.[167]
Newell and Simon's physical symbol system hypothesis
"A physical symbol system has the necessary and sufficient means of general intelligent action." Newell and Simon argue that intelligences consist of formal operations on symbols.[168] Hubert Dreyfus argued that, on the contrary, human expertise depends on unconscious instinct rather than conscious symbol manipulation and on having a "feel" for the situation rather than explicit symbolic knowledge. (See Dreyfus' critique of AI.)[169][170]
Gödel's incompleteness theorem
A formal system (such as a computer program) cannot prove all true statements.[171] Roger Penrose is among those who claim that Gödel's theorem limits what machines can do. (See The Emperor's New Mind.)[172]
Searle's strong AI hypothesis
"The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds."[173] John Searle counters this assertion with his Chinese room argument, which asks us to look inside the computer and try to find where the "mind" might be.[174]
The artificial brain argument
The brain can be simulated. Hans Moravec, Ray Kurzweil and others have argued that it is technologically feasible to copy the brain directly into hardware and software, and that such a simulation will be essentially identical to the original.[90]

Predictions and ethics

Artificial intelligence is a common topic in both science fiction and projections about the future of technology and society. The existence of an artificial intelligence that rivals human intelligence raises difficult ethical issues, and the potential power of the technology inspires both hopes and fears.
In fiction, artificial intelligence has appeared fulfilling many roles.
These include:
Mary Shelley's Frankenstein considers a key issue in the ethics of artificial intelligence: if a machine can be created that has intelligence, could it also feel? If it can feel, does it have the same rights as a human? The idea also appears in modern science fiction, including the films I Robot, Blade Runner and A.I.: Artificial Intelligence, in which humanoid machines have the ability to feel human emotions. This issue, now known as "robot rights", is currently being considered by, for example, California's Institute for the Future, although many critics believe that the discussion is premature.[175] The subject is profoundly discussed in the 2010 documentary film Plug & Pray.[176]
Martin Ford, author of The Lights in the Tunnel: Automation, Accelerating Technology and the Economy of the Future,[177] and others argue that specialized artificial intelligence applications, robotics and other forms of automation will ultimately result in significant unemployment as machines begin to match and exceed the capability of workers to perform most routine and repetitive jobs. Ford predicts that many knowledge-based occupations—and in particular entry level jobs—will be increasingly susceptible to automation via expert systems, machine learning[178] and other AI-enhanced applications. AI-based applications may also be used to amplify the capabilities of low-wage offshore workers, making it more feasible to outsource knowledge work.[179]
Joseph Weizenbaum wrote that AI applications can not, by definition, successfully simulate genuine human empathy and that the use of AI technology in fields such as customer service or psychotherapy[180] was deeply misguided. Weizenbaum was also bothered that AI researchers (and some philosophers) were willing to view the human mind as nothing more than a computer program (a position now known as computationalism). To Weizenbaum these points suggest that AI research devalues human life.[181]
Many futurists believe that artificial intelligence will ultimately transcend the limits of progress. Ray Kurzweil has used Moore's law (which describes the relentless exponential improvement in digital technology) to calculate that desktop computers will have the same processing power as human brains by the year 2029. He also predicts that by 2045 artificial intelligence will reach a point where it is able to improve itself at a rate that far exceeds anything conceivable in the past, a scenario that science fiction writer Vernor Vinge named the "singularity".[182]
Robot designer Hans Moravec, cyberneticist Kevin Warwick and inventor Ray Kurzweil have predicted that humans and machines will merge in the future into cyborgs that are more capable and powerful than either.[183] This idea, called transhumanism, which has roots in Aldous Huxley and Robert Ettinger, has been illustrated in fiction as well, for example in the manga Ghost in the Shell and the science-fiction series Dune. In the 1980s artist Hajime Sorayama's Sexy Robots series were painted and published in Japan depicting the actual organic human form with life-like muscular metallic skins and later "the Gynoids" book followed that was used by or influenced movie makers including George Lucas and other creatives. Sorayama never considered these organic robots to be real part of nature but always unnatural product of the human mind, a fantasy existing in the mind even when realized in actual form. Almost 20 years later, the first AI robotic pet (AIBO) came available as a companion to people. AIBO grew out of Sony's Computer Science Laboratory (CSL). Famed engineer Dr. Toshitada Doi is credited as AIBO's original progenitor: in 1994 he had started work on robots with artificial intelligence expert Masahiro Fujita within CSL of Sony. Doi's, friend, the artist Hajime Sorayama, was enlisted to create the initial designs for the AIBO's body. Those designs are now part of the permanent collections of Museum of Modern Art and the Smithsonian Institution, with later versions of AIBO being used in studies in Carnegie Mellon University. In 2006, AIBO was added into Carnegie Mellon University's "Robot Hall of Fame".
Political scientist Charles T. Rubin believes that AI can be neither designed nor guaranteed to be friendly.[184] He argues that "any sufficiently advanced benevolence may be indistinguishable from malevolence." Humans should not assume machines or robots would treat us favorably, because there is no a priori reason to believe that they would be sympathetic to our system of morality, which has evolved along with our particular biology (which AIs would not share).
Edward Fredkin argues that "artificial intelligence is the next stage in evolution", an idea first proposed by Samuel Butler's "Darwin among the Machines" (1863), and expanded upon by George Dyson in his book of the same name in 1998.[185]

See also