Artificial Intelligence: The Melding of Human and Machine
There is a scene in the old movie 2001: A Space Odyssey where astronaut Dave is battling with the malevolent supercomputer Hal 9000 as both tumble through space, with the very existence of mankind hanging in the balance. The computer succumbs, singing the children's song Daisy as it dumbs down. It is a classic man beats machine movie—humanity is saved.
Or is it? Everything Hal could do, computers can now do better, experts say. No computer today can mimic the human personality and malice of the movie character Hal, but for sheer computing power, today's machines have no equal. And, some researchers have predicted that thinking machines will be able to "think" as well as human beings do sometime later in this century.
It's 2001 already—the age of Artificial Intelligence (AI).
AI is cool because it isn't a thing, like robotics or digital imaging, or nanotechnology, but rather a construct, a concept. And, the whole idea of intelligent machines, or inanimate objects that can do some "thinking," has really crept up on us. From the animated pop-up cursor that helps you write letters in some word-processing programs to the synth-voice that walks a teenager through an automated telephone bill payment for their cell phone, the technology is all around us.
Indeed, according to the UN World Robotics Summary released in October 2004, service robots will be everyday tools for mankind one day. "They will not only clean our floors, mow our lawns and guard our homes, but they will also assist elderly and handicapped people with sophisticated interactive equipment, carry out surgery, inspect pipes and sites that are hazardous to people, and fight fires and bombs," the report stated.
Hal's movie character was pretty scary. We got a less sinister, but more complex, portrayal of synthetic intelligence in the 2001 movie, AI: Artificial Intelligence, by no means a typical Steven Spielberg feel-good crowd-pleaser. The picture is a quite dark and disturbing story of an abandoned robotic child, the first in the world programmed to love, searching desperately to reunite with his adoptive human mother.
Strictly defined, AI is the science of building machines that mimic or boost human intelligence. Today's computer programs, like the movie's Hal, have plenty of speed and memory, but their abilities correspond only to the mechanisms the human programmers understand well enough to put in the programs. To complicate things further, scientists still haven't determined how to define human abilities and how to get them into machine intelligence.
It's odd in one way, really. We may have mapped the human genome, but we still haven't directly observed a human thought and don't know exactly how our brain works. We can't really say how it is we learn how to read, for example. But we humans are attempting to teach machines—computers—how to think.
What is consciousness?
At the center of the AI debate is a single, elusive question—what is intelligence? Is it consciousness? Is the brain simply a computer and is consciousness merely the feeling we get when we think? Or is intelligence/consciousness a component of the universe, which the brain latches on to, like satellite TV?
There is no definitive answer.
There are three primary points of view that look at intelligence as an important part of consciousness.
The first, which can be traced back to the founder of modern computing, Alan Turing, is useful. Turing showed that a machine's ability to compute does not depend on what it is made of. If a computer were sufficiently complex, then it, too, would assume consciousness—or at least would seem to be conscious and intelligent. But there is a huge snag in reaching that goal. We can't replace a single brain cell with a functionally equivalent microchip unless we understand the function of the original brain cell, which we currently do not.
Number 2, championed by Oxford mathematician Roger Penrose in his book, The Emperor's New Mind, suggests that intelligence/consciousness is not just a matter of computational complexity. Perhaps the material of which the brain is made matters very much. The human brain has the chemistry required to do the novel physics required to produce consciousness, while, he suggests, silicon chips lack this ability.
Number 3 is the boldest. Perhaps we've misconstrued our reality. In the idealist philosophy of Bishop Berkeley, only thought is real, and matter is an illusion. However, the emerging modern view says that matter and consciousness are not separate entities, but instead are complementary aspects of the universe. Both exist, but neither is primary.
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AI Battlefield
The construct of artificial intelligence has been home to one of the fiercest intellectual battles in science probably in this century. It's a stunning story, full of obscure terms like Turing Machines, probability index and the theory of symbolic manipulation—a precursor to artificial intelligence. It is a battle in which faith and ego play almost as important a part as the science upon which the field is based. Defined as the part of computer science concerned with designing systems that exhibit the characteristics associated with human intelligence—understanding language, learning, reasoning, solving problems—AI has attracted researchers because of its ambitious, if almost impossible, goals.
Humanoid robot "Posy," was unveiled in Tokyo in 2002. (Silicon Graphics of Japan)
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But, let's start at the beginning. At its core, AI is no more than a desire to implant our animated image onto inanimate objects. Back in ancient Egypt, animated statues moved under the control of hidden priests. There is a story that in Thebes, the figure of the god Ammon, whose arms were under the secret control of a priest, chose a new king by waving at one member of the assembled royal family. In the 18th century, Pierre Jacquet-Droz built a number of life-sized dolls that were remarkably life-like. But, ultimately they were no more than the robotic figures at a Chuck E. Cheese restaurant or at Disneyland's Pirates of the Caribbean.
Neat stuff, artificial surely—but hardly intelligent.
The birth of artificial intelligence would have to wait until 1900 when a noted mathematician laid out the principles that formed the basis for AI and a World War II British code-breaker built the first computer. The mathematician, David Hilbert, gave a brilliant speech at the Second International Congress of Mathematicians in Paris in August 1900, in which he laid down the foundation of many of the ideas that would later form the basis of AI. In 1940, Alan Turing, a Cambridge scholar, worked with the code-breakers at Bletchley Park to develop a series of primitive computing devices called bombes. They had no memory, but were invaluable in breaking the Nazi's "unbreakable" Enigma code, enabling British intelligence to read Hitler's orders to the troops even before his own generals—a first step to thinking machines.
The Infancy of AI
Norbert Wiener
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Although the computer provided some of the technology necessary for AI, it was not until the early 1950s that the link between human intelligence and machines was made. Norbert Wiener was one of the first Americans to make observations on the principle of feedback theory. The best example is a house thermostat because it controls the temperature by measuring the actual temperature of the house, comparing it to the desired temperature, and responding. Wiener theorized that all intelligent behavior was the result of feedback mechanisms and that machines could possibly simulate these same mechanisms. This observation influenced much of the early development of AI.
If the whole idea of feedback theory sounds a little dense, try a suite of works that pays "homage" to Wiener and provides a playful exercise that touches the very heart of the "human - machine" dialectic. Each work consists of an electronic circuit that elects only one of two possible paths at the split moment when the user presses a button. The circuit "decision" cannot be predetermined.
In 1956, Newell and Herbert Simon developed The Logic Theorist, considered by many to be the first AI program. The program, based on the heuristic principles of George Polya, represents each problem as a tree model and attempts to solve it by selecting the branch that would most likely result in the correct conclusion. Later, researchers dubbed this approach to AI symbolic manipulation.
Turing Test
The most well-known test of cognitive sophistication for AI is the Turing Test, based on an idea by famous wartime code breaker Alan Turing. In this scenario a central human examiner is in communication with a computer and a human, though he can't see them and doesn't know which is which. The examiner poses questions that are answered by both the computer and the human. The idea is that for the computer to pass, the examiner must be unable to tell them apart. So far, no computer has passed the Turing Test.
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McCarthy, AI's Father
A domestic android, Valerie is the most advanced android in the world having more degrees of freedom than any android shown up to now. She uses the AT&T speech synthesizer giving the most human-sounding voice available today. She is also easily the most anthropomorphic android available. She will have a high degree of artificial intelligence. (Chris Willis, Androidworld)
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In 1956, researcher John McCarthy organized a conference to draw on the talent and expertise of others interested in machine intelligence. He called it "The Dartmouth Summer Research Project On Artificial Intelligence." It was the first time the term artificial intelligence had been used in public and it stuck. Participants were a bit reluctant, citing the informal appearance of the gathering. The Dartmouth conference did bring together the founders in AI, however, and served to lay the groundwork for the future of AI research.
After the conference, AI research sped forward. Although still undefined, ideas formed at the conference were re-examined and built upon. Centers for AI research began forming at Carnegie Mellon and Massachusetts Institute of Technology, and new challenges were faced: further research was needed on creating systems that could efficiently solve problems by limiting the search, such as the Logic Theorist, and on making systems that could learn by themselves. In 1957, the first version of a new program—the General Problem Solver (GPS), a follow-up to the Logic Theorist—was tested. It extended Wiener's feedback principle and was capable of solving common sense problems to a greater extent. Later, IBM contracted a team to research artificial intelligence.
In 1958, McCarthy announced his new development: the LISP language, which is still used today. LISP, which stands for LISt Processing, was soon adopted as the language of choice among most AI developers.
AI Research: The Revolution Will Be Computerized
In 1963, MIT received a $2.2 million grant from the United States government to be used in researching Machine-Aided Cognition (artificial intelligence). The grant by the Department of Defense's Advanced Research Projects Agency (DARPA) to ensure U.S. technological supremacy was the beginning of a long-term funding commitment by the federal government.
Robonaut is a humanoid robot designed by the Robot Systems Technology Branch at NASA's Johnson Space Center in a collaborative effort with DARPA.
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In the 1960s, a nasty split began to divide this infant science: the optimists, who believed that thinking machines were right around the corner, were on one side, and the realists, who insisted scientists were over-estimating their own engineering abilities, were on the other.
The optimists were people like Turing—who believed thinking machines would evolve by the 21st century—and like entrepreneur Raymond Kurzweil, who is best known for his books on the coming AI revolution and his work on voice-to-text conversion science. The optimists believed that once computers became complex enough, probably by the year 2030, making them think would be a snap. The realists were led by AI critic Hubert Dreyfus, who argued in 1972 that AI was more of a philosophical viewpoint than a science. Later, MIT professor Joseph Weizenbaum branded most AI research as tinkering and questioned the need for such work. "Since we do not now have ways of making computers wise, we ought not to give computers tasks that demand wisdom," he wrote.
The 1980s were not the best of times for AI. In 1986-87 the demand in AI systems decreased, and the industry lost almost half a billion dollars. Teknowledge and Intellicorp together lost more than $6 million, about a third of their total earnings. This led to research cutbacks. A major disappointment was the DARPA "smart truck," a robot designed to perform many battlefield tasks. In 1989, the Pentagon cut funding for the project. Despite these events, AI slowly recovered. New technology in Japan was being developed. Fuzzy logic, first pioneered in the U.S., showed it had the unique ability to make decisions under uncertain conditions.
There were some significant successes. DARPA began funding speech recognition research on a large scale in 1984. The leading commercial speech-recognition program on the market today, the Dragon Systems software, traces its roots directly back to the early work. Other examples of commercial success abound. Charles Schwab and Co. adopted DARPA technology to develop its VoiceBroker system, which provides stock quotes over the telephone. The system can recognize the names of 13,000 different securities spoken in a variety of regional U.S. accents. On the military side, DARPA provided translingual communication devices for use in Bosnia.
AI: Where Is It Lurking Now Near You?
The biggest success story is that the AI-driven advances in rule-based reasoning systems (such as expert systems) proved to be extremely valuable for the emerging national information infrastructure and electronic commerce. These advances, including probabilistic reasoning systems (a type of expert system), brought AI out of the laboratory and into the marketplace. Paradoxically, the major commercial successes are mostly hidden from view today because they are embedded in larger software systems. AI technologies help industry diagnose machine failures, design new products, and plan, simulate, and schedule production. They help scientists search large databases and decode DNA sequences, and they help doctors make more informed decisions about diagnosis and treatment of particular ailments.
Because the AI technologies are embedded, their benefits are relatively easy to identify, but measuring them is difficult. A good example is the animated "office assistant" paper clip that monitors the behavior of users of Microsoft's Office programs and assists them with applications. It started as the Lumiere project initiated at Microsoft Research in 1993. Lumiere monitors a computer user's actions to determine when assistance may be needed. It continuously follows the user's goals and tasks as software programs run, using Bayesian networks to generate a probability distribution over topic areas that might pose difficulties and calculating the probability that the user will not mind being bothered with assistance.
But, as AI moved into the marketplace, it stumbled across integration problems. Both the software and the hardware developed by the AI research were so advanced that their integration into older, more conservative computer and organizational systems proved to be an enormous challenge. As one observer has noted, "Because AI was a leading-edge technology, it arrived in this world too early."
A word about Expert Systems
These days, the poster child of AI's successes is the Expert System (ES), software programs that encapsulate specialist knowledge. First developed in the 1980s, ES is a computer system intended to perform at the level of a human expert in whatever domain it aspires to deal with. Early expert systems included systems aimed at medical diagnosis. All expert systems must confront the issue of knowledge.
If that sounds a bit complicated, consider ES as simply custom-written computer programs that are "expert" in some narrow problem area. Expert Systems have been used in many problem areas, such as medicine, chemistry, geology, meteorology, and computer systems. Expert Systems can generally be used in problem areas that do not require common sense, are well understood, involve objective data, and are scarce in human expertise.
The advantages of ES over their human counterparts are clear: human skills and knowledge can deteriorate over time, training human experts is expensive, and humans are susceptible to emotional and psychological factors that can impair decision making. And, human expertise, say in an area like medicine, is relatively rare.
Prominent ES systems include: XCON, a commercial expert system that configures some computers for hardware manufacturer DEC and is the biggest and most mature rule-based expert system in operation; and PROSPECTOR, which aids geologists in their search for ore deposits.
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Moving The Goal Posts
One of the persistent difficulties in AI research is a phenomenon called "moving the goal posts." Think of a football field. Just when the kicker for the Philadelphia Eagles makes a 50-yard field goal to win the game, the referee throws a yellow flag, they move the goal posts back another 10 yards and tell him to re-kick. Likewise, just as soon as an AI technique truly succeeds, in the minds of many it ceases to be AI, becoming something like engineering. For example, when the IBM Deep Blue supercomputer program defeated world chess champion Garry Kasparov in 1997, many said Deep Blue wasn't AI, but just brute computing power. Such an opinion does not reflect the fact that Deep Blue's search/decision patterns were based on AI research.
Robosapien is a humanoid robot about 14" tall. Fully programmable from ergonomic remote control, has over 60 built-in functions for fast and flexible movements. (www.Androidworld.com)
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Enter the pragmatists. They are the engineers who are less concerned with the overarching questions of Man vs. Machine and are more worried about making things work. Maybe AI's greatest attribute is the willingness of its researchers to soldier on, even without a clear long-term vision. Alan Harmon, a researcher at the U.S. Naval Academy in Annapolis, MD, is a good example of the mainstream pragmatist.
Harmon, working under a contract with Computer Associates, is developing a predictive analysis tool using neugents or neural networks, a form of AI. The Naval Academy receives anywhere from 10,000 — 12,000 applications a year for 1,000 student openings. The predictive tool will help academy admissions officials make better choices of who to accept. The tool combines multiple academic components, such as SAT scores, extracurricular activities, and recommendations of school officials. The tool also uses an algorithm to process admissions data from successful academy graduates to gain further insight into what makes a good applicant.
Into the future
Harmon ponders the eternal AI question: "Will it ever be possible to create a human-like machine from, say, a neural network?"
Robos, a company in Japan, is now offering a 90cm (36") tall robot called Kozoh. It weighs 16 Kg (35 pounds) and can operate for 1 hour on its battery. (www.Androidworld.com)
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"I do think that there will eventually be machines that can learn (build new rules from experiences) and will 'think' from these new associations and rules," Harmon says. "Is that thinking, as a human thinks? Yes and no. Yes, from the perspective that we, as humans, learn by doing and by building rules and rule sets in our heads. But no, if you consider thinking to be pondering the universe and more conceptual ideas and issues. That relates to a different part of the brain and brain activity."
The philosophers are more hopeful. In his recent book Tools for Thought, Howard Rheingold makes a case that we have yet to see the real AI revolution. "Because mass production of sophisticated electronic devices can lag 10 years or more behind the state of the art in research prototypes, the first effects of the astonishing achievements in computer science since 1960 have only begun to enter our lives. Word processors, video games, educational software, and computer graphics were unknown terms to most people only 10 years ago, but today they are the names for billion-dollar industries," he says.
So where does that leave us if the experts can't agree? Some things are apparent. Don't expect to see the blinking red eye of Hal 9000 staring ominously at you over the lab table any time soon. Moreover, don't expect to engage Star Trek-TNG's Lt. Commander Data over a cup of coffee at Starbucks or see Star Wars' C3P0 in computer-science class.
But don't let this lapse into science fiction fool you. Every sci-fi author will tell you that most stories set in the future are merely carefully camouflaged examinations of the present. If we accept the fact that the science of AI is inextricably linked to the future, then science fiction may be our best interpretation of these emerging technologies that are just around the corner.
XtraReal People
Name: Anthony Friday
Age: 36
Title: Customer Advocate
Affiliations: Computer Associates International (Western Australia); Member-Australian Institute of Company Directors.
His real job: "My real job is making people happy. I use these skills for Computer Associates (CA), a company that is passionate about customer satisfaction. The role involves ensuring that customer expectations are met and exceeded. This means having a deep understanding of my customers and their business, then coordinating the resources of CA." As part of his job, Tony works with a racing car team that uses AI sensors that make their cars "smart," allowing them to automatically adjust their steering and drive systems for differing road conditions.
Why he chose this career: "People are fascinated with the high-profile aspects of AI, but many fail to realize just how integrated this technology has become in our everyday life. As well as cutting-edge development, our company leverages technology to assist real people in solving real problems ranging from reducing the incidence of medical fraud to increasing the competitive edge of the McLaren F-1 automobile racing team. The career is exciting because the possibilities are limitless."
School: Certificate (Financial Administration) from Canning College; Bachelor Degree (Mathematics Education) from Edith Cowan University; Graduate Diploma (Business) from Curtin University of Technology; MBA from Curtin University of Technology; currently preparing for PhD at Curtin University of Technology.
Was he a math/science wiz in high school? "I wasn't particularly drawn to mathematics or the sciences in high school.
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I can remember questioning my teachers at high school with: "When will I ever use this knowledge once I leave school?" Of course, 20 years later, I now realize they were teaching me a way of thinking, not just a collection of numbers and facts. Eventually, I began to appreciate the elegance and beauty in pure mathematics and to understand the value of an analytical mind and scientific rigor."
What he does for fun: "Travel, travel and more travel. I love visiting different countries and understanding the differences between their culture and my own. It is now possible to fly right around the world in less than a day. Given that we live so closely together, I am constantly amazed at the diversity of cultures —each with their own traditions and idiosyncrasies. Favorite places to visit include Strasbourg in France, Mae Hong Son in Thailand, the Cinque Terre in Italy and Prague in the Czech Republic. Each of these are special places."
Favorite sport: "Because I spend so much time interacting with other people both at work and socially, I tend to prefer individual sports such as aerobics."
Advice: "Meet people. Interact. Engage. Although it has become increasingly possible to retreat into a virtual world and shut out real people, it is the soft (people) skills that count the most toward our personal development."
Gazing at the crystal ball: "We can already engage in virtual travel through our television screens and video conferencing. I suspect that we will soon be able to completely immerse ourselves in the virtual travel experience. While pictures of the Mekong River in Laos are interesting in themselves, they lose so much impact without the sound of the rushing water and children playing at the edges, the smell of the jungle crowding the river's edge and the feel of the heavy cloying atmosphere. Rather than long aircraft flights, painful inoculations and endless immigration queues, I look forward to relaxing in my living room with a virtual reality helmet for a 'day trip' to visit friends on the other side of the globe."
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XTRA PLACES TO EXPLORE AI
Interested in AI? Just want to poke around with artificial intelligence, or try exploring some of the activities that offer real-world experiences?
At CyberArt advancements with AARON, the Cybernetic Artist, www.kurzweilcyberart.com you can download the first fine art screensaver to utilize artificial intelligence to continuously create original paintings on your PC.
Or, if you would like to talk to an animated self-made bot, go to www.pandorabots.com/pandora/talk-oddcast?botid=bedb7e221e340ab1. And, there is always the www.talklikeapirate.com Web site to assist would-be pirate talkers. It even contains a simple English-to-Pirate translator in the form of an interactive Web page.
Also, check out NSTEP'S TechXplore® program and competition that connects teams of students with scientists and high-tech companies to explore the world of technology. Go to www.techxplore.org. If you want to create a TechXplore team at your school to explore AI, send an email to TechXplore@nationalstep.org.
A number of colleges, MIT, Stanford, Carnegie Mellon University, and the University of Southern California's Information Sciences Institute, to name just a few, have active research programs looking at AI.
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Artificial Intelligence Links
Artificial Intelligence Glossary
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Artificial Intelligence (AI) The part of computer science concerned with designing systems that exhibit the characteristics associated with human intelligence—understanding language, learning, reasoning and solving problems. The field has been controversial because of its social, ethical, and philosophical implications.
AI Agent Anything that can be viewed as perceiving its environment through sensors and acting upon that environment. AI Agents have also been defined as any program that operates on behalf of a human, similar to its use in the phrase "travel agent." Researcher Marvin Minsky offers another definition in the book Society of Mind—which hypothesizes that a large number of seemingly mindless agents can work together to create an intelligent society of mind. Minsky theorized that not only will this be the basis of computer intelligence, but it is also an explanation of how human intelligence really works.
Automatic Programming The task of describing what a program should do and having the AI system "write" the program.
Backward Chaining A means of utilizing a set of rules. In backward chaining, researchers work back from possible conclusions of the system to the evidence. Forward Chaining In a logic system, reasoning goes from facts to conclusions.
Bayesian Networks A model for representing uncertainty in our knowledge.
Belief Network (also Bayesian Network) A mechanism for representing knowledge. Inference algorithms in belief networks use the structure of the network to generate inferences.
Certainty Factor A number, often in the range -1 to +1, which is associated with a condition or an action of a rule. A certainty factor of 1 means that the fact (or proposition) is highly certain.
Computational Linguistics The branch of AI that deals with understanding human language. Also called natural language processing.
Data Mining Also known as Knowledge Discovery in Databases (KDD). A process in which scientists extract nontrivial, previously unknown, and potentially useful information from data. It uses machine learning, statistical and visualization techniques to discover and present knowledge in a form that is easily comprehensible to humans.
Embodiment An approach to Artificial Intelligence that maintains that the only way to create artificial intelligence is to use programs with "bodies" in the real world (i.e., robots).
Expert System A computer system intended to perform at the level of a human expert. Early expert systems included those aimed at medical diagnosis. In a recent AI success, a computerized Leukemia diagnosis system did a better job checking for blood disorders than human experts.
Heuristic A fancy name for a "rule of thumb" or approach that doesn't always work or doesn't always produce completely optimal results, but does
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go toward solving a particularly difficult problem for which no perfect solution is available.
Inference Engine A computer program that triggers a rule-based system into taking an action.
Language Acquisition A relatively new sub-branch of AI. Traditionally computer linguists tried to make computers understand human language by giving them grammar rules. Language acquisition is a technique for the computer to generate the grammar rules itself.
LISP Language LISP/LISt Processing LISP is an acronym for LISt Processor. The LISP language, designed primarily for symbolic data processing, has been used for symbolic calculations in differential and integral calculus, electrical circuit theory, mathematical logic, game playing, and other fields of artificial intelligence. It is a formal mathematical language.
Logic Theorist Considered by many to be the first AI program. The program, representing each problem as a tree model, would attempt to solve it by selecting the branch that would most likely result in the correct conclusion.
Machine Learning A field of AI concerned with programs that learn. It includes Reinforcement Learning and Neural Networks, among many other fields.
Natural Language (NL) Evolved languages that humans use to communicate with one another. Related to Natural Language Queries, which use human language to get information from a database.
Neural Networks Programs that function in a manner similar to how animal brains function.
Situatedness The property of an AI program being located in an environment that it senses. Through its actions, the program can select its sensation input as well as change its environment. Some researchers claim it is key to understanding general intelligence (see Embodiment).
Strong AI The claim computers can be made to actually think, just like human beings do. More precisely, the claim that there can be developed a class of computer programs that actually will mimic human thought (examples of S-AI include robots and Data on Star Trek). Weak AI The belief that computers are important tools in the modeling and simulation of human activity, but probably won't be good at replicating the complexity of human thought (examples of W-AI include expert systems, drive-by-wire cars and speech recognition software).
Validation The process of confirming that an AI model uses measurable inputs and produces output that can be used to make decisions in the real world. Related to Verification, which is the process of confirming that an implemented model works as intended.
Working Memory A store of information used by a rule-based system to decide which of the condition-action rules can be fired. The contents of the working memory when the system was started up would normally include the input data.
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NSTEP is grateful for the support provided for this issue by:
Computer Associates
Editorial Advisory Committee
Jennifer Martino, PhD, science teacher, Governor Livingston High School
John E. Riley, Radiation Safety Consultant, Just-In-Time Industrial Hygiene
Gary Ybarra, PhD, Director of Undergraduate Studies, Duke University
Guest Technical Advisor
for this issue:
Thomas Howell, PhD, Computer Associates
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TechXtra, a free e-newsletter published periodically from September through May by the National Science & Technology Education Partnership (NSTEP), brings new technology to life for students and their science, technology and math teachers. And, it brings life to technology with a close-up look at the jobs, career paths and education of the people who make it all happen.
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Sony's walking/dancing android called SDR-4X. (