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October 16, 2007: Software saves day. By Cameron England. The Advertiser (via AdelaideNow). "Adelaide firm SolveIT launched its new currency hedging program in time for the recent volatility in the Australian dollar, helping local wool company Michell ride out the turbulence. ... SolveIT chief executive Matthew Michalewicz said the company used 'fuzzy logic' in its software, which could adapt to changing conditions. 'It's based on artificial intelligence so you're looking for really smart algorithms to come up with recommendations, but the easiest way to describe it is that the system creates hedging rules,' Mr Michalewicz said." October 2007 [issue date]: Cracking GO - Brute-force computation has eclipsed humans in chess, and it could soon do the same in this ancient Asian game. By Feng - Hsiung Hsu. IEEE Spectrum Online. "In 1957, Herbert A. Simon, a pioneer in artificial intelligence and later a Nobel Laureate in economics, predicted that in 10 years a computer would surpass humans in what was then regarded as the premier battleground of wits: the game of chess. Though the project took four times as long as he expected, in 1997 my colleagues and I at IBM fielded a computer called Deep Blue that defeated Garry Kasparov, the highest-rated chess player ever. You might have thought that we had finally put the question to rest -- but no. Many people argued that we had tailored our methods to solve just this one, narrowly defined problem, and that it could never handle the manifold tasks that serve as better touchstones for human intelligence. These critics pointed to weiqi, an ancient Chinese board game, better known in the West by the Japanese name of Go, whose combinatorial complexity was many orders of magnitude greater than that of chess. Noting that the best Go programs could not even handle the typical novice, they predicted that none would ever trouble the very best players. Ten years later, the best Go programs still can't beat good human players. Nevertheless, I believe that a world-champion-level Go machine can be built within 10 years, based on the same method of intensive analysis -- brute force, basically -- that Deep Blue employed for chess. I've got more than a small personal stake in this quest. At my lab at Microsoft Research Asia, in Beijing, I am organizing a graduate student project to design the hardware and software elements that will test the ideas outlined here. ..." September / October 2007: Higher Games - On the 10th anniversary of Deep Blue's triumph over Garry Kasparov in chess, a prominent philosopher of mind asks, What did the match mean? By Daniel C. Dennett. Technology Review Magazine. "[F]or a decade, human beings have had to live with the fact that one of our species' most celebrated intellectual summits--the title of world chess champion--has to be shared with a machine, Deep Blue, which beat Garry Kasparov in a highly publicized match in 1997. How could this be? What lessons could be gleaned from this shocking upset? Did we learn that machines could actually think as well as the smartest of us, or had chess been exposed as not such a deep game after all? ... Silicon machines can now play chess better than any protein machines can. Big deal. This calm and reasonable reaction, however, is hard for most people to sustain. They don't like the idea that their brains are protein machines. When Deep Blue beat Kasparov in 1997, many commentators were tempted to insist that its brute-force search methods were entirely unlike the exploratory processes that Kasparov used when he conjured up his chess moves. But that is simply not so. Kasparov's brain is made of organic materials and has an architecture notably unlike that of Deep Blue, but it is still, so far as we know, a massively parallel search engine that has an outstanding array of heuristic pruning techniques that keep it from wasting time on unlikely branches." August 9, 2007: CityU receives international award in artificial intelligence application. By Zoey Tsang. CityU News Center. "Dr Andy Chun Hon-wai, Associate Professor in the Department of Computer Science at City University of Hong Kong (CityU), received an international award in July for the artificial intelligence (AI) system he designed for the Immigration Department in Hong Kong. The Innovative Applications of AI (IAAI) Award was given by the Association for the Advancement of Artificial Intelligence (AAAI), the world’s leading organization for AI academics and practitioners. The IAAI Award is the only well-known award for AI applied research in the world. This award is global recognition for CityU’s excellence in applied research. The award-winning AI technology is an automatic assessment and decision support system that helps the Immigration Department streamline processes for issuing documents. ... 'The AI design for the Immigration Department was challenging. The laws and regulations, as well as the modeling of the decision-making process, were all highly complex,' Dr Chun said. 'To solve this multi-faceted problem, we used multiple AI paradigms, such as business rules, clustering, case-based reasoning and data mining to provide rapid decision support,' he added. ... Many organizations in Hong Kong have benefited from CityU’s AI technologies, such as the Hongkong International Terminals, Hospital Authority, Airport Authority, MTR Corporation, among others."
>>> Law Enforcement, Machine Learning, Case-Based Reasoning, Expert Systems, Data Mining, Applications July 26, 2007: An Emotional Cat Robot. By Duncan Graham-Rowe. Technology Review. "Scientists in the Netherlands are endowing a robotic cat with a set of logical rules for emotions. They believe that by introducing emotional variables to the decision-making process, they should be able to create more-natural human and computer interactions. 'We don't really believe that computers can have emotions, but we see that emotions have a certain function in human practical reasoning,' says Mehdi Dastani, an artificial-intelligence researcher at Utrecht University, in the Netherlands. By bestowing intelligent agents with similar emotions, researchers hope that robots can then emulate this humanlike reasoning, he says. ... In addition to improving interactions, this emotional logic should also help intelligent agents carrying out noninteractive tasks.... 'It's a heuristic that can help make rational decision-making processes more realistic and much more computable,' says Dastani. ... Other robots have been designed to mimic human expressions. But Dastani's focus on how emotions might affect decision makes it different from many of the other projects on emotional, or affective, computing, such as MIT's Kismet robot, developed by Cynthia Breazeal. With Kismet, like other affective robots, the focus is on how to get the robot to express emotions and elicit them from people." July 12, 2007: Arresting developments - Computer science and biological science have a lot to teach each other. The Economist. "Working with Stephen Muggleton of Imperial College, London, [Stephen Emmott of Microsoft Research] is developing an 'artificial scientist' that would be capable of combining inductive logic with probabilistic reasoning. Such a computer would be able to design experiments, collect the results and then integrate those results with theory. Indeed, it should be possible, the pair think, for the artificial scientist to build hypotheses directly from the data, spotting relationships that the humble graduate student or even his supervisor might miss. ... [Luca Cardelli's] colleagues, meanwhile, are examining how the spread of diseases such as malaria and AIDS can be thought of as information systems. They are using what used to be called artificial intelligence and is now referred to as machine learning to explore the relationships between the two. All of which raises some interesting philosophical points. If, say, a computer were used to diagnose a patient's symptoms and recommend treatment, and the result was flawed, could the computer be held responsible? Peter Lipton of the University of Cambridge, who ponders such matters, suggests that such expert systems could indeed be held morally responsible for the consequences of their actions (although the designers of such systems would not necessarily be off the hook). ..." June 21, 2007: Searching Sportscasts - A new way to search video could help fans find footage. By Duncan Graham-Rowe. Technology Review. "A new kind of visual-search engine has been developed to automatically scour sports footage for clips showing specific types of action and events. According to its creators, borrowing a few tricks from the field of machine translation seems to make all the difference in improving the accuracy of video search. ... To cope with growing video repositories, cutting-edge systems are now emerging that use automatic speech recognition (ASR) to try to improve the search accuracy by generating text transcripts. ... [Michael] Fleischman and Deb Roy, director of MIT's Cognitive Machines Group, developed a system that provides a way to associate search terms with aspects of the video, and not just with what is being said as the video plays. ... Using speech and visual information together is a powerful combination for machine learning, [David] Hogg says. 'In machine learning, it is very likely to be easier the more information there is available about each situation.' Speech can help remove ambiguities in visual data, and visual data can help disambiguate speech, says Richard Stern, a professor of electrical and computer engineering at Carnegie Mellon University, in Pittsburgh. It's a natural marriage, he says, but one that's just beginning to emerge." June 18, 2007: City nets £9.5m as conference success gets everyone talking. By Jum Stanton. Edinburgh Evening News. "Edinburgh has scooped ten more conference events that will help pour £9.5 million into the Capital's economy. One meeting alone, the Federated Logic Conference (FLoC), will account more than half of the boost. ... The 13-day FLoC event is made up of a series of conferences which come together every four years to provide an international forum to discuss developments in logic relating to computer science. ... The interaction between logic - which is involved in aspects of computing such as artificial intelligence, artificial intelligence and programming - and computer science has been increasing over the past four decades. Logic is seen as providing computer science with a unifying foundational framework and a tool for modelling, with industry experts referring to it as 'the calculus of computer science'." June 1, 2007: Planning Technology Solves Rescue Missions. By Pia Svejgaard Pedersen. innovations-report. "New Danish research into search algorithms can solve complex planning problems in rescue missions in association with natural catastrophes and military operations. Associate Professor Rune Moeller Jensen from the IT University of Copenhagen has received recognition for combining two different principles within computer science which can be used to rapidly develop emergency plans which take into consideration unforeseen events. Anzeige Rune Moeller Jensen is researching automatic planning at The IT University of Copenhagen. ... Automatic planning is used for example in air traffic control and in the planning of military rescue operations. Automatic planning is part of an area within computer science called artificial intelligence. The goal is to develop algorithms which use descriptions of planning problems, such as a rescue operation, to return a plan of how such complex situations can be solved. ... Rune Moeller Jensen’s research can be seen as providing a way to combine route planning principles with principles for automatic fault finding. 'My research has combined two different but complementary principles for handling complex planning problems, developed within the computer science disciplines of artificial intelligence and formal verification', explains Rune Moeller Jensen." June 2007: The Traveler's Dilemma - When playing this simple game, people consistently reject the rational choice. In fact, by acting illogically, they end up reaping a larger reward--an outcome that demands a new kind of formal reasoning. By Kaushik Basu. Scientific American. "Despite their names, Prisoner's Dilemma and the two-choice version of Traveler's Dilemma present players with no real dilemma. Each participant sees an unequivocal correct choice, to wit, 2 (or, in the terms of the prisoner story line, incriminate the other person). That choice is called the dominant choice because it is the best thing to do no matter what the other player does. ... In contrast, the full version of TD has no dominant choice. ... When studying a payoff matrix, game theorists rely most often on the Nash equilibrium, named after John F. Nash, Jr., of Princeton University. ... The game and our intuitive prediction of its outcome also contradict economists' ideas. ... How People Actually Behave - Over the past decade researchers have conducted many experiments with TD, yielding several insights. >>> Reasoning, Games & Puzzles, Cognitive Science, Multi-Agent Systems, Induction May 20, 2007: This Week on Philosophy Talk - Artificial Intelligence (radio broadcast: audio available online). With Ken Taylor and John Perry of Stanford University. KALW, 91.7 FM, San Francisco. "At least some versions of artificial intelligence are attempts not merely to model human intelligence, but to make computers and robots that exhibit it: that have thoughts, use language, and even have free will. Does this make sense? What would it show us about human thinking and consciousness? Join John and Ken [and guest, Marvin Minsky] as they uncover the philosophical issues raised by artificial intelligence." February 20, 2007: Darpa Chief Speaks. Noah Shachtman interviews Tony Tether. Wired's Danger Room blog. "NS: And how about something that maybe isn't on the battlefield right this second, but maybe just on the horizon? TT: Well, we are working hard. One problem is language. We realized that we're either going to have to teach all of our soldiers 16 different languages or come up with the technology to do so, to help them out. When 2001 came we had already been working on a Phraselator, which is a [simple,] one-way [translation] device. One-way in that it has phrases in it that in any of eight different languages -- ... NS: Do you know of anything that Darpa's working on right now that's really game changing? TT: Yes -- our cognitive program. The cognitive program's whole purpose in life is really to increase the tooth-to-tail ratio [military-speak for the number of combat troops to the number of support troops]. ... Our cognitive program's whole aim is to have a computer 'learn you,' as opposed to you having to learn the computer. ... The last real attempt -- other than this attempt now in the last four or five years -- was in the '80s. We had a program called the Strategic Computing Program. And that Strategic Computing Program showed a pyramid. And in that pyramid were many technologies that had to be developed -- microelectronics to get things smaller, memories larger, computers faster. But it all was leading toward coming up with a cognitive computer, although at that time we called it artificial intelligence. We did a great job on the component technology, but the architecture for the cognitive part went down a path that was more neural nets, expert systems. And they were OK for what they did. You know, if you built yourself an expert system or a neural net for a specific situation it worked quite well, but it was very fragile. If you got off of that, it crashed. It was back to the two-way translator for the checkpoint -- don't ask me what your golf game is like. Well, in Darpa fashion, we stopped in the late '80s or early '90s. Since the '90s to now, our ability to create algorithms that can reason -- can more abstractly reason -- about a problem and come up with answers, and also remember what they did using Bayesian techniques and changing values, has really advanced. I mean, it tremendously advanced in the past -- from the '90s to, say, the early 2000s. At the same time, computers became more powerful. ... NS: Let's change gears a little bit and talk about the challenges. TT: Challenges? NS: You know, the prizes [-- like Darpa's $2 million all-robot rally, the Grand Challenge.]. ..." January 8, 2007: Researchers Use Wikipedia To Make Computers Smarter. Science Daily: American Technion Society. "Researchers at the Technion-Israel Institute of Technology have found a way to give computers encyclopedic knowledge of the world to help them 'think smarter,' making common sense and broad-based connections between topics just as the human mind does. ... The program devised by the Technion researchers helps computers map single words and larger fragments of text to a database of concepts built from the online encyclopedia Wikipedia, which has over one million articles in its English-language version. The Wikipedia-based concepts act as 'background knowledge' to help computers figure out the meaning of the text entered into a Web search, for instance. Giving computers this deeper knowledge has been a long-standing problem in artificial intelligence, according to [Shaul] Markovitch. " January 2007: The Discover Interview - Marvin Minsky: The legendary pioneer of artificial intelligence ponders the brain, bashes neuroscience, and lays out a plan for superhuman robot servants. By Susan Kruglinski. Discover (Volume 28, Number 1). "[Q] So as you see it, artificial intelligence is the lens through which to look at the mind and unlock the secrets of how it works? [A] Yes, through the lens of building a simulation. If a theory is very simple, you can use mathematics to predict what it'll do. If it's very complicated, you have to do a simulation. It seems to me that for anything as complicated as the mind or brain, the only way to test a theory is to simulate it and see what it does. ... [Q] Many people feel that the field of AI went bust in the 1980s after failing to deliver on its early promise. Do you agree? [A] Well, no. What happened is that it ran out of high-level thinkers. ... [Q] Has science fiction influenced your work?[A] It's about the only thing I read. ... [Q] What did you do as consultant on 2001: A Space Odyssey? ... " December 13, 2006: Hong Kong's Immigration Department is a paragon of high-tech. Bangkok Post. "Apply for a work permit or residency visa in Hong Kong in the near future, and your application will be processed using artificial intelligence rather than a human being. It's all thanks to the continuing high-tech wave that is sweeping through Hong Kong, particularly with regard to e-government. Assistant director for information systems at Hong Kong S.A.R.'s Immigration Department Raymond Wong explained in an interview how a series of high-tech solutions that had allowed his department's head count to remain constant at around 8,000 over the last 10 years -- despite a four-fold increase in the number of people entering and leaving Hong Kong. ... And by the end of 2006, Wong's team will have rolled out APPLIES -- the Application and Investigation System. All applications for visas and work permits will be filed digitally, with the processing done by an 'e-brain' that uses both artificial intelligence and case-based learning, just like a human brain. Initially, difficult cases will be handled by an officer, but the computer will learn and the next time the case could be approved automatically." November 16, 2006: Not Lost in Translation - Computer programmers use statistics to convert Arabic and Mandarin Chinese texts into English. By Stephen Ornes. Technology Review. "As computer programmers develop new techniques for translating texts between languages with different alphabets, they are increasingly turning to a science that seems to have little in common with the conventions of grammar: statistics. Last week, the National Institute of Standards and Technology (NIST) released the results of its yearly evaluation of computer algorithms that translate Arabic and Mandarin Chinese texts into English. Topping the charts was Google.... 'If you get a good score, you're doing well,' says Peter Norvig, Google's head of research. ... 'We look for matches between texts and find several different translations,' Norvig says. 'You take all these possibilities and ask, What is the most probable in terms of what's been done in the past?' ... Ongoing research at Kansas State University utilizes not only computer scientists, but also anthropologists, modern-language scholars, and psychologists to develop new approaches to machine translations. ..." November 10, 2006: Software devises best plan for tackling forest fires. By Tom Simonite. NewScientist.com news. "Software that generates a plan of action for tackling forest blazes is being tested by fire-fighters in Spain. The system rapidly decides how best to allocate the resources needed to put out a spreading fire. 'The idea is to support people fighting the forest fires that happen in the summer months,' says Luis Castillo, an artificial intelligence researcher at the University of Granada, Spain. He designed the planning system -- known as SIADEX -- with colleagues Juan Fdez-Olivares, Oscar Garcia-Perez and Francisco Palao. ... Austin Tate, who works on emergency planning systems at Edinburgh University in Scotland, UK, says a key strength of SIADEX is that it is 'ready to go now, not in 20 years.' Another strength, he believes, is that it can be used without much training. ... A paper describing SIADEX was presented at The International Conference on Automated Planning & Scheduling in Ambleside, UK, in June 2006, where it won the Best Applications Paper award." November 8, 2006: Women in technology - making an impact In Britain's hi-tech industries, barely one in five workers is female. By Genevieve Roberts. The Independent / also available from the Belfast Telegraph (Through the silicon ceiling). "Genevieve Roberts meets the bright sparks from last week's BlackBerry Women & Technology Awards to hear how they make their presence felt. ... Karen Petrie, 25, information manager at Oxford University: Most Promising Woman in Technology. It's no wonder there are so few women in technology, says Petrie. She is an expert in artificial intelligence, but at school was given little encouragement. ... 'From a young age, girls are simply not exposed to technology in the same way.' Petrie is now a specialist in constraint programming, which is used in scheduling. The computer is told how many trains are leaving the station and is given a set of rules, such as only one train can be on a platform at one time. It can then work through every combination of trains and platforms, and come up with the best timetable. 'These techniques are used for the timetabling of baseball matches in the US, but it should be used more broadly. But the techniques used for constraint programming need simplifying, so you don't need a PhD in pure maths to find the best solution to problems.' Petrie, who was a research fellow at the University of St Andrews, also wrote a program that solves Sudoku puzzles in the same way as humans - but does them instantaneously. ... Shirin Dehghan, 37: Best Woman in Technology Award 2006. ... Her goal is to create self-healing networks, the 'holy grail of the mobile phone industry'. At present, the failure of just one component can take a base station out of service. 'We want to create an intelligent system so the network itself realises that the site has gone down, and other sites automatically cover the lost areas of signal until that site is repaired.'" October 19, 2006: Fuzzy logic comes to life. By Nicholas Sheble. InTech. "The term 'fuzzy logic,' sounds nonsensical. “That’s a problem,” declared R. Russell Rhinehart who heads the chemical engineering school at Oklahoma State University. He gave a couple of tutorials on artificial intelligence as part of a sponsored series of R&D updates Wednesday in the Standards Theater at ISA EXPO 2006 in the Reliant Center. 'The concepts of fuzzy logic are simple, but the jargon obscures that. In fact, fuzzy is absurdly simple.' It is Rhinehart’s contention the chemical industries under appreciate and under utilize fuzzy logic. Fuzzy logic applies like this. Say you have an apple. You take a bite out of the apple. Is it still an apple? You take another bite out of the apple. Now, is it still an apple? And another bite. And then, another bite. At some point, people will no longer perceive it as an apple. 'Fuzzy logic can represent this process,' Rhinehart explained. 'It becomes less of an apple as you move along. Fuzzy logic can assign percentages of belongingness to the process. It’s not a digital sort of situation where it’s either a one (1) or a zero (0). It’s either an apple, or it’s not.' ... Degrees of truth are often confused with probabilities. However, they are conceptually distinct; fuzzy truth represents membership in vaguely defined sets, not the likelihood of some event or condition. ... Fuzzy logic controls household appliances such as washing machines, which sense load size and detergent concentration and adjust their wash cycles accordingly, and refrigerators. ... 'You can use fuzzy to incorporate anticipatory behavior into a process, and it’s the type of prediction that is like the intuitive knowledge that a human operator of that process has,' concluded Rhinehart." October 17, 2006: CMU research professor made the world say :-) By Tim Stienstraw. The Pitt News. "Carnegie Mellon University researcher Scott Fahlman is the inventor of the emoticon, a popular way to express voice tone in an electronic message, but he also works with Artificial Intelligence and mentors students who work for him. 'I know this is going to be the first line in my obituary, "Scott Fahlman, the guy who created the smiley and did some other stuff,"' he said. ... Fahlman's real passion is his AI research. Since the 1970s, he has worked on making computers think more like humans Within the last five years, he has worked on the challenge of creating software that compiles information and experience to simulate common sense. He said he hopes one day his research will lead to a program that can actually create its own text about a subject, as opposed to simply reading and understanding things people have put into the computer, which is easier. He likened this to illiteracy, citing the fact that many more people can speak English than can read and write it. ... 'Really the Holy Grail is text comprehension and text generation.' [Cinar Sahin] Sahin inputs data from sources like the CIA fact book into the software, called SCONE. Ben Lambert, another of Fahlman's students, said SCONE has two definitions, depending upon Fahlman's mood. It either stands for "Scalable Ontology Engine" or "Scott's Ontology Engine." September 8, 2006: Eugene firm wins Navy contract. By Sherri Buri McDonald. The Register-Guard. "In the next nine months, On Time Systems will evaluate the potential cost savings if the Navy used the firm's shipyard scheduling software at its major repair yards. ... This is the second major contract On Time Systems has secured with the Navy. Two years ago, the Navy awarded a $1.38 million contract to On Time Systems to create a simulated shipyard to test the potential cost savings if the firm's shipyard scheduling software, called ARGOS, was used to build new vessels. On Time Systems projected that the software could save taxpayers $500 million annually. ... On Time Systems is a 1998 spin-off of CIRL, the UO's Computational Intelligence Research Laboratory. On Time Systems also has developed software to route all U.S. Air Force noncombat flights - typically 700 to 900 flights a day. The technology prevents 20 million gallons of jet fuel a year from being burned in the upper atmosphere, [Matt] Ginsberg said." August 17, 2006: Brainpower in a box - MSNBC.com's Tech Tour Across America stops in Austin, TX to learn about a machine that has common sense. Watch Gina Smith interview Cycorp's Doug Lenat. July 28, 2006: Artificial Intelligence Researcher Revels in the Impossible - Summer Research at Bowdoin. Collegenews.org. "Stephen Majercik has a problem. A big problem. It is so difficult, in fact, it belongs to an actual class of complexity. In computer sciencespeak it is what's known as PSPACE-complete. That translates, roughly, as 'impossible,' laughs Majercik, assistant professor of computer science. 'I've been working on it for a long time; it's extremely, extremely difficult.' Problems are the stuff of computer programming, and like most problems that ultimately relate to artificial intelligence, Majercik's computation is an attempt to mathematically codify certain aspects of human behavior and reasoning. In this case, Majercik - and student research assistant Mark McGranaghan '09 - are working on a probabilistic planning problem. They are trying to come up with a program that could help robots make planning decisions in an environment in which there are uncertainties." July 27, 2006: For Syria's envoy, no calls from the White House. By Thom Shanker. The New York Times / available from the International Herald Tribune. [Appeared in The New York Times on July 29, 2006: For Syria’s Voice in U.S., Isolation but Not Silence.] "Syria's ambassador to the United States, Imad Moustapha.... In keeping with the times - and as would befit the former dean of information technology at the University of Damascus - the ambassador is a blogger. ... Middle Eastern politics is a world of shaded language where issues rarely are black and white, in stark contrast to Moustapha's prior professional universe of computers, where the language is a binary code, written in zeros and ones. 'We are trained to think in a logical way,' the ambassador said. 'However, there is one particular science in artificial intelligence that is entitled "fuzzy logic," in which you deal with the nuances of uncertainty.'" July 13, 2006: Marvin Minsky on Common Sense and Computers That Emote - As artificial intelligence research celebrates its 50th birthday, the MIT icon asks what makes the minds of three-year-olds tick. By Wade Roush. Technology Review. "Top computer scientists from around the world are meeting today at Dartmouth College in Hanover, NH, to mark the 50th anniversary of 'artificial intelligence.' Back in 1956, John McCarthy, then a member of Dartmouth's mathematics faculty, invented the term for the field's seminal gathering, the Dartmouth Summer Research Project on Artificial Intelligence. McCarthy and four other participants in the 1956 project, including MIT's Marvin Minsky, are participating in this week's meeting, which focuses on AI's next 50 years. ... Minsky, who will open the Dartmouth conference with McCarthy, admired [Terry] Winograd's work. But he's long eschewed reductionistic demonstrations in favor of exploring the real mechanisms behind human thought. Working with Seymour Papert in the MIT AI Lab, for instance, Minsky began in the 1970s to develop the 'Society of Mind' theory, which posits that consciousness arises from layers of purposeful yet mindless 'agents' that work together to generate consciousness. ... TR: So, what are your thoughts about the state of AI research today, compared to where it was in 1956? MM: What surprises me is how few people have been working on higher-level theories of how thinking works. That's been a big disappointment. I'm just publishing a big new book on what we should be thinking about: How does a three- or four-year-old do the common-sense reasoning that they're so good at and that no machine seems to be able to do? ... TR: Why do people shy away from the common-sense problem? MM: I think people look around to see what field is currently popular, and then waste their lives on that. If it's popular, then to my mind you don't want to work on it. ... TR: As people have realized how difficult it is to get a computer to understand even simple common-sense situations, would you say that some of the optimism around the possibilities for AI in the 1950s and 1960s has dissipated? MM: I don't think optimism is the right word. I think we were asking good questions, but somehow most of the people working on what they called AI started looking for one of these universal solutions. In physics, that worked; there were Newton's equations and then Maxwell's and then relativity and quantum theory. Most AI people are trying to imitate that and find a general theory. But humans have 100 different brain centers that all work in slightly different ways. You shouldn't be working on a single solution; you should be working on a host of gadgets. ... TR: What are some of the main arguments or research recommendations in your upcoming book, The Emotion Machine? MM: The main idea in the book is what I call resourcefulness. Unless you understand something in several different ways, you are likely to get stuck. So the first thing in the book is that you have got to have different ways of describing things. I made up a word for it: 'panalogy.' When you represent something, you should represent it in several different ways, so that you can switch from one to another without thinking. The second thing is that you should have several ways to think. " July 12, 2006: Artificial Intelligence. [Radio broadcast; audio available.] Reported by Shay Zeller for The Front Porch. New Hampshire Public Radio. "Dartmouth College is celebrating 50 years of Artificial Intelligence this week with a special conference that takes a look forward and a look back at the field. We'll find out how AI has evolved since its inception and how far scientists have come to creating the technological brain that's been depicted in science fiction for decades. We'll also look at the philosophical and ethical questions that go along with creating machines that emulate the human mind. Our guest are: Eugene Charniak, professor of Computer Science at Brown University. Charniak's expertise is in language development, and he's presenting a speech at the conference entitled 'Why Natural Language Processing is Now Statistical Natural Language Processing.' James H. Moor, professor of Philosophy at Dartmouth. He's the conference's main organizer." July 11, 2006: Computers learn common sense. The Engineer Online. "US advanced technology solutions firm BBN Technologies has been awarded $5.5 million in funding from the Defense Advanced Research Projects Agency (DARPA) for the first phase of the Integrated Learning Program. Over the next four years BBN will develop an artificial intelligence capability called 'Integrated Learner' that will learn plans or processes after being shown a single example. ... The goal is to combine specialised domain knowledge with common sense knowledge to create a reasoning system that learns as well as a person and can be applied to a variety of complex tasks. ... 'This programme attacks one of the biggest problems in AI,' said Mark Berman, vice president, BBN Technologies, 'The Integrated Learner will combine traditional machine learning techniques with an AI reasoning system capable of understanding behaviour it observes only once. ...'" July 3, 2006: Getting machines to think like us. Newsmaker interview with John McCarthy. By Jonathan Skillings. CNET News.com. "In 1956, a group of computer scientists gathered at Dartmouth College to delve into a brand-new topic: artificial intelligence. ... It was [John] McCarthy who put the name 'artificial intelligence' to the field of study, just ahead of the conference. With Dartmouth hosting a 50th anniversary conference this month, McCarthy--now a professor emeritus at Stanford University--spoke with CNET News.com about the early expectations for AI, the accomplishments since then and what remains to be done. [Q;] You're credited with coining the term "artificial intelligence" just in time for the 1956 conference. Were you just putting a name to existing ideas, or was it something new that was in the air at that time? ... [Q;] And looking back, do you think that that's the right term? It seems fairly self-evident, but would there be a better way to describe this kind of research? ... [Q;] What are some of the big things that have been learned over the last 50 years that have helped shape research in artificial intelligence? ... [Q:] What's the next big thing, then, to accomplish? ... " July 2006: The Search for Artificial Intelligence - For 50 years the finest minds have been telling computers what to do. What they haven't been able to instill in them is common sense. By Tom Bethell. The American Spectator (subscription req'd). "In a semi-official way, the search for artificial intelligence began 50 years ago. In the summer of 1956, a two-month conference at Dartmouth College set out to explore 'the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.' Computers could do what the mind does, in other words. An attempt would be made 'to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.' The four authors of the grant proposal added -- optimistically it turned out: 'We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer.' The Rockefeller Foundation put up the money. The conjecture that machines could be built with the ability to think had been made by the British mathematician Alan Turing in the 1930s. By the end of the 20th century, he believed, 'one will be able to speak of machines thinking without expecting to be contradicted.' In 1950 he devised what became known as the Turing test. If a human behind a screen cannot distinguish human from machine responses, then the machine must be considered intelligent. Fifty years after the Dartmouth conference, the computer science people are still working on these problems. Computers have not yet passed the Turing test. A 'significant advance' has been made in solving some problems 'now reserved for humans.' But the advance belongs in the realm of what is called 'applied' artificial intelligence. Computers can do useful things like multiplication and division, and they are also very good at chess. An IBM program beat the world chess champion. As to machines forming abstractions on their own, there has been no progress. ... In the past half century, an important distinction has emerged: between 'strong' and 'weak' AI. It divides what is often called 'cognitive' and 'applied' artificial intelligence. It distinguishes between computers on the one hand actually knowing and thinking -- the still unattained goal of 'strong' AI; and on the other hand performing a programmed sequence of tasks in order to achieve a well-defined goal -- 'applied' AI." June 30, 2006: BACS breathes perception into robots. ETH Zürich. "The ETH Zurich is coordinating the integrated research project BACS (Bayesian Approach to Cognitive Systems), which is being sponsored by the EU and will run until 2010. In this project, researchers are investigating the extent to which Bayes' theorem can be used in artificial systems capable of managing complex tasks in a real world environment. ... The basis of this research is Bayes' theorem. Thomas Bayes was an English mathematician and Presbyterian monk who lived in the 18th century. The theorem named after him describes alternatives for calculating the likelihood of events occurring using conditional probability. It is a model for rational judgment when only uncertain and incomplete information is available. ... The scientific work being carried out under BACS makes robots with new capabilities a real prospect: robots capable of handling incomplete information, analyzing their environment, acquiring context-specific knowledge, interpreting the data and, together with humans, taking decisions. Specific implementations with market potential are already planned." June 2006: Dependable Software by Design - Computers fly our airliners and run most of the world's banking, communications, retail and manufacturing systems. Now powerful analysis tools will at last help software engineers ensure the reliability of their designs. By Daniel Jackson. Scientific American. "Now a new generation of software design tools is emerging. Their analysis engines are similar in principle to tools that engineers increasingly use to check computer hardware designs. A developer models a software design using a high-level (summary) coding notation and then applies a tool that explores billions of possible executions of the system, looking for unusual conditions that would cause it to behave in an unexpected way. This process catches subtle flaws in the design before it is even coded, but more important, it results in a design that is precise, robust and thoroughly exercised. One example of such a tool is Alloy, which my research group and I constructed. Alloy (which is freely available on the Web) has proved useful in applications as varied as avionics software, telephony, cryptographic systems and the design of machines used in cancer therapy. Alloy and related design-checking tools build on a quarter of a century of existing research into ways to prove mathematically whether programs are correct. But rather than requiring proofs to be done by hand, they employ automated reasoning techniques that treat a software design problem as a giant puzzle to be solved. These analyzers operate on designs, not program code, so they cannot guarantee that a program will not crash. But they potentially offer software engineers the first practical tools to ensure that designs are robust and free from conceptual flaws and thus provide a firm foundation on which to build reliable software systems." May 26, 2006: The next wave of the web. By Declan Butler. news @ nature. "Web gurus and geeks descended on Edinburgh, UK, this week for www2006. Chairing the panel 'The Next Wave of the Web' was Nigel Shadbolt, an artificial intelligence researcher at the University of Southampton, UK, and deputy president of the British Computer Society. Declan Butler asks him about the Web's progress. ... [Q:] Your background is in artificial intelligence. How is AI fitting into the Web? [A:] I did my PhD here in Edinburgh in the late 1970s. We had interesting problems in trying to emulate human expertise and knowledge acquisition. But we couldn't get network effects going like what is happening on the Web. One of the problems of AI is that we've often been trying to do too good a job of emulating classic inductive reasoning; we've picked problems that are too hard. So AI hasn't really delivered on providing sentience in a box. But, though most people don't realize it, the Web is already full of knowledge-intensive [AI] components. The Web is a brilliant place to get AI out there. Take Bayesian methods.... [Q:] The idea of a 'semantic web' -- this notion of adding machine-readable tags to web pages so that a computer can read and 'understand' the text and data -- has been around for years. But, like nuclear fusion, it always seems to be 'just around the corner'. Is it ever going to happen?" April 27, 2006: Shared Theories on Thought Could Lead to Smart Machines. NIST Tech Beat. "Ontologists, researchers who make it their business to understand the thought process, hope to end the age of stupid machines. Last month, ontologists who have created some of the most advanced logic systems, agreed at a National Institute of Standards and Technology (NIST) workshop to share their leading-edge concepts on such comprehensive ideas as time, space and process. The promise to cooperate, expressed in a 10-item communiqué* issued at the end of the two-day workshop, eventually could lead to software programs that will equip machines with mutually compatible frames of reference, enabling them to interpret and act on commands with near human common sense. Efforts to equip machines with artificial intelligence capacity have, up to now, been relatively rudimentary. Software programs might, for instance, give machines used to make furniture considerable 'understanding' of terms and frames of reference used in the furniture business. But such collected knowledge known as a 'lower ontology' is of limited use, and human operation is necessary at virtually every step in the manufacturing process. A machine empowered by programs that incorporate expanded frames of reference of such 'higher ontologies' as space and cost might be able to begin making design and shipping decisions virtually on its own. 'We believe we have planted an historic stake in the ground by enabling the leading upper ontologists throughout the world to come together and sign this agreement to cooperate,' says Steven Ray...." April 27, 2006: Shared Theories on Thought Could Lead to Smart Machines. NIST Tech Beat. "Ontologists, researchers who make it their business to understand the thought process, hope to end the age of stupid machines. Last month, ontologists who have created some of the most advanced logic systems, agreed at a National Institute of Standards and Technology (NIST) workshop to share their leading-edge concepts on such comprehensive ideas as time, space and process. The promise to cooperate, expressed in a 10-item communiqué* issued at the end of the two-day workshop, eventually could lead to software programs that will equip machines with mutually compatible frames of reference, enabling them to interpret and act on commands with near human common sense. Efforts to equip machines with artificial intelligence capacity have, up to now, been relatively rudimentary. Software programs might, for instance, give machines used to make furniture considerable 'understanding' of terms and frames of reference used in the furniture business. But such collected knowledge known as a 'lower ontology' is of limited use, and human operation is necessary at virtually every step in the manufacturing process. A machine empowered by programs that incorporate expanded frames of reference of such 'higher ontologies' as space and cost might be able to begin making design and shipping decisions virtually on its own. 'We believe we have planted an historic stake in the ground by enabling the leading upper ontologists throughout the world to come together and sign this agreement to cooperate,' says Steven Ray...." April 15, 2006: The word - Common sense. New Scientist (Issue 2547; page 54). "This is the big headache for artificial intelligence (AI) researchers: they can design a computer that might beat Garry Kasparov at chess, but you couldn't have an intelligent conversation with it because it has no grasp of ordinary life.How, then, do you teach a computer common sense? Researchers at a company called Cycorp in Austin, Texas, are trying to find out. Since 1984, they have been incorporating a huge collection of everyday knowledge in an AI project named Cyc. ... Cycorp has also just launched a trivia game for the public that will help fill in gaps in Cyc's knowledge...." February 16, 2006: Speed thrills with neural networks. By Christine Evans-Pughe. ElectronicsWeekly.com. "Conventional computing methods can solve most data processing and control tasks as long as you throw enough high-speed silicon at the problem. Our brains, though, can complete some remarkably complex tasks, faster than a room full of computers, and yet we achieve this with neurons that do not respond in much less than a millisecond. ... Anyone working in electronics a decade ago will remember the excitement, followed by disappointment, generated by fuzzy logic microcontrollers that used artificial neural network algorithms and machine-learning to 'revolutionise' embedded systems. There was no revolution. But the idea has not disappeared and today, driven by increasingly stringent emissions regulations, software and hardware-based neural network-based techniques are being successfully applied to engine control and diagnostics in automotive embedded systems. In the Aston Martin DB9, for example, Ford has used a software neural network running on the main ECU for detecting misfires in the car’s high revving V12 engine. ... Artificial neural networks work well where there is a causal relationship between one or more inputs and a physical quantity, but where there is no straightforward analytical relationship between the inputs and the output." February 2006: Sudoku Science - A popular puzzle helps researchers dig into deep math. By Lauren Aaronson. IEEE Spectrum Online. "Millions of people around the world are tackling one of the hardest problems in computer science --- without even knowing it. The logic game Sudoku is a miniature version of a longstanding mathematical challenge, and it entices both puzzlers, who see it as an enjoyable plaything, and researchers, who see it as a laboratory for algorithm design. ... As a member of the NP-complete subset, Sudoku is an ideal tool for investigating the whole class of NP problems: an efficient algorithm for any NP-complete problem --- the toughest of NP problems --- automatically provides an efficient algorithm for solving all. Although most experts believe that no such algorithm exists, they continually search for improved algorithms that provide shorter, if not the very shortest, paths to solutions. Sudoku has already led some researchers to concrete advances in algorithm design. At the Intelligent Information Systems Institute at Cornell University, Ithaca, N.Y., director Carla Gomes experiments with Latin Squares, a version of Sudoku without subgrids. ... Sudoku follows in a long tradition of artificial intelligence research on games, most notably chess. But some of AI's most important advances stem from more modest games. The route-finding algorithm that powers car navigation systems, for instance, was first demonstrated on the Sliding Tile puzzle...." January 16, 2006: Fujitec eases bottlenecks. By Anna Guido. The Cincinnati Enquirer. "A new elevator system developed by Fujitec America Inc. alleviates passenger bottlenecks in lobbies and in other high-traffic areas. The Destination Floor Guidance System - which was put into operation Friday in the Metropolitan Park West Tower in downtown Seattle - minimizes stops by grouping together passengers with common destinations. ... The Neuros Logic program that runs the system rationalizes and manages the elevator traffic patterns as they change throughout the day using technology such as artificial intelligence, fuzzy logic and genetic algorithms. ... Fujitec says only its system incorporates artificial intelligence to learn the building's traffic flow." January 5, 2006: Bayes rules - A once-neglected statistical technique may help to explain how the mind works. The Economist. "Science, being a human activity, is not immune to fashion. For example, one of the first mathematicians to study the subject of probability theory was an English clergyman called Thomas Bayes, who was born in 1702 and died in 1761. His ideas about the prediction of future events from one or two examples were popular for a while, and have never been fundamentally challenged. But they were eventually overwhelmed by those of the 'frequentist' school, which developed the methods based on sampling from a large population that now dominate the field and are used to predict things as diverse as the outcomes of elections and preferences for chocolate bars. Recently, however, Bayes's ideas have made a comeback among computer scientists trying to design software with human-like intelligence. Bayesian reasoning now lies at the heart of leading internet search engines and automated 'help wizards'. That has prompted some psychologists to ask if the human brain itself might be a Bayesian-reasoning machine. They suggest that the Bayesian capacity to draw strong inferences from sparse data could be crucial to the way the mind perceives the world, plans actions, comprehends and learns language, reasons from correlation to causation, and even understands the goals and beliefs of other minds." January 5, 2006: Finding the Decisive Factor. Science Today feature by Dick Ahlstrom. The Irish Times (subscription req'd). "Those having difficulty choosing this year's summer holiday might consider talking to a research group at University College Cork where computers with artificial intelligence are making important decisions. The Cork Constraint Computation Centre (4C) develops computer software and the underlying science to help businesses and individuals make good decisions, explains 4C director Prof Gene Freuder. 'Our motto is "making hard decisions easier" and that is what we try to do,' he says. The idea is to solve complex decision-making problems using computers 'taught' to weigh up the pros and cons while including the constraints operating on any given decision. ... 'I come from an AI background so I use inference and other AI techniques. One of the active research areas is integrating AI and OR [operations research] techniques to solve problems.' ... 4C conducts extensive research but has also established links with companies here to work on real-world problems. Projects have included work on supply logistics with Cork University Hospital and on the optimisation of floor plans at Bausch & Lomb in Waterford." January 2, 2006: Over the holidays 50 years ago, two scientists hatched artificial intelligence. By Byron Spice. The Pittsburgh Post-Gazette & post-gazette.com. [Also available from the Scripps Howard News Service: Fiftieth anniversary of invention of artificial intelligence (January 5, 2006).] "Fifty years ago, Herbert A. Simon and Allen Newell had a Christmas break story that would top them all. 'Over the Christmas holiday,' Dr. Simon famously blurted to one of his classes at Carnegie Institute of Technology, 'Al Newell and I invented a thinking machine.' It was another way of saying that they had invented artificial intelligence -- in fact, the only way of saying it in the winter of 1955-56 because no one had gotten around to inventing the term 'artificial intelligence.' ... It would be eight more months before their program, called Logic Theorist, would successfully run on a computer, the Rand Corp.'s JOHNNIAC. But they had helped invent artificial intelligence and their work 'inspired generations of researchers to work in that area,' said Randal E. Bryant, dean of the School of Computer Science at Carnegie Tech's successor institution, Carnegie Mellon University. ... Though many of the specific methods used by the pair have been superseded, 'a huge fraction of what we do today ties back to Newell and Simon's work,' he added. Language translation by machine, speech recognition, robotics -- all embody or depend heavily on artificial intelligence. In his 1991 autobiography 'Models of My Life,' Dr. Simon noted he became involved in the work almost by happenstance, after first coming in contact with computers at the Rand think tank in California in the early '50s. ... The symbolic view of artificial intelligence -- that knowledge and information could be programmed into a computer -- was one of two camps that came to dominate AI research, Dr. Bryant said. The other approach, championed by John McCarthy of Stanford, was to express intelligence as formal logic. In the last decade or so, however, AI has achieved great success with a radically different approach, which uses statistical tools rather than human-like reasoning." January 2006: Say Hello to Stanley - Stanford's souped-up Volkswagen blasted through the Mojave Desert, blew away the competition, and won Darpa's $2 million Grand Challenge. Buckle up, human - the driverless car of the future is gaining on you. By Joshua Davis. Wired (Issue 14.01). "The 128-mile race is a success. Four other vehicles, including both of CMU's entries, complete the course behind Stanley. The message is clear: Autonomous vehicles have arrived, and Stanley is their prophet. 'This is a watershed moment - much more so than Deep Blue versus Kasparov,' says Justin Rattner, Intel's R&D director. 'Deep Blue was just processing power. It didn't think. Stanley thinks. We've moved away from rule-based thinking in artificial intelligence. The new paradigm is based on probabilities. It's based on statistical analysis of patterns. It is a better reflection of how our minds work.' The breakthrough comes just as carmakers are embracing a host of self-driving technologies, many of them barely recognizable as robotic. ... But even as vehicles are being produced with sensors that perceive the world, they have, until now, lacked the intelligence to comprehensively interpret what they see. Thanks to [Sebastian] Thrun, that problem is being solved. Computers are nearly ready to take the wheel. But are humans ready to let them?"
>>> Autonomous Vehicles, Transportation, Machine Learning, Uncertainty / Probablility, Vision, Robots, Reasoning, Expert Systems, Applications, History, Chess, Careers in AI (@ Resources for Students) December 25, 2005: Fuzzy logic to fog rescue - Scientists work on forecast plan that could reduce chaos. By G.S. Mudur. The Telegraph. "Scientists at the Centre for Mathematical Modelling and Computer Simulation in Bangalore have teamed up with a software company to produce fog forecasts 12 to 24 hours in advance, much earlier than those currently available. ... While fog physics is well understood, reliable forecasts have eluded weather scientists. Now, with a Rs 1-crore grant from the Council of Scientific and Industrial Research (CSIR), the Bangalore centre has worked out a new technique that combines an element of artificial intelligence --- fuzzy logic --- with conventional weather forecasting. ... [Prashant ] Goswami is trying to reduce uncertainties in fog prediction with the help of fuzzy logic -- a mathematical technique to handle imprecise concepts." December 21, 2005: Knowledge Acquisition and Projection Lab completes Navy project. Indiana University press release. "Researchers in Indiana University's Knowledge Acquisition and Projection Lab -- part of Pervasive Technology Labs -- along with computer scientists from the IU School of Informatics, have completed a project for the U.S. Navy in which they developed key components of the Navy's maintenance Knowledge Projection System. This project, a three-year joint undertaking with Crane Naval Surface Warfare Division and Purdue University, was aimed at developing next-generation diagnostics and maintenance capabilities for shipboard systems. 'The need for tele-maintenance and distance support technologies for today's battleships, aircraft carriers and submarines is compelling,' said Donald F. (Rick) McMullen, director of the Knowledge Acquisition and Projection Lab (KAPLab). McMullen, along with IU Professor of Computer Science David Leake, served as principal investigator for the KPS project. ... This system uses an artificial intelligence technique known as 'case-based reasoning,' which draws upon solutions to previous, similar problem scenarios to help engineers and crew diagnose and solve new problems. The system also serves as a 'recording system' for engineering expertise within the Navy's widely distributed maintenance organization, capturing engineering expertise as it is expressed during problem-solving sessions." November 14, 2005: David's delight at £1m deals. Manchester Evening News. "The group said its latest American contract would improve efficiency and reduce the cost of processing claims at a major insurance company, by scheduling jobs for 450 staff. The Finnish contract, with the country's second-largest telecoms company Elisa Corporation, is to manage the schedules of 400 technicians who install the Internet in homes and businesses. ... ServicePower, which is quoted on the stock market, employs around 110 people and sells licences for its artificial intelligence software that enables clients to schedule jobs based on skills level, geographical location and customer availability...." November 6, 2005: Driving force -- the robocar that won. How AI team from Stanford aced rugged desert race. By Tom Abate. San Francisco Chronicle & SFGate.com. "On Oct. 8, a robotic car run by Stanford researchers rode into history by winning a race of driverless vehicles. How a robocar nicknamed 'Stanley' crossed 132 miles of desert near Las Vegas, beating 22 rivals, is a tale that began in the summer of 2004, when Stanford artificial intelligence specialist Sebastian Thrun, now 38, and postdoctoral scholar Mike Montemerlo, 30, hatched a plan to beat the field, including the odds-on favorite, Carnegie Mellon University. ... In the early 1990s, [Thrun] was a graduate student at the University of Bonn. But he wanted to pursue a career in the United States. So he wrote to various American universities, offering to fly over and talk about his work. 'This is the type of thing you'd never do in Germany, but in America, it works,'' Thrun said. In Pittsburgh, he met [Tom] Mitchell and another Carnegie Mellon computer scientist, Alex Waibel. They found a way to bring Thrun over as a visiting scholar. ... How did Stanford's robocar know whether an object dead ahead was a harmless tumbleweed or a dangerous boulder? It couldn't really tell the difference, Thrun explained. Instead, the algorithms in Stanley's brain calculated probabilities. Say it was 95 percent certain an object was tumbleweed. It then balanced this with an analysis of the consequences of being wrong -- a 100 percent chance of being wiped out -- which made swerving its best option. 'The computer is calculating the odds of different outcomes, and it understands the consequences of these outcomes,' Thrun said. 'All of my research for the last 10 years has been on this one sentence'... There also looms the possibility that self-directing software could one day be mounted on offensive military robots -- a future envisioned in the 'Terminator' movies. That has some people worried. 'Certainly, it's a debate people should have,' said Ron Kurjanowicz, DARPA's race manager." September 13, 2005: Six degrees. The Engineer Online. "University of Massachusetts Amherst researchers have invented a new algorithm that solves a network-searching conundrum that has puzzled computer scientists and sociologists for years. The scientists created an algorithm that helps explain the sociological findings that led to the theory of 'six degrees of separation,' and could have broad implications for how networks are navigated, from improving emergency response systems to preventing the spread of computer viruses. ... Dubbed expected-value navigation, the algorithm describes an efficient way of searching a particular class of networks.... The work was inspired by research pioneered in the late 1960s that focused on navigating social networks, explains [Ozgur] Simsek." September 11, 2005: Robo-justice - Do we have the technology to build a better legal system? By Drake Bennett. The Boston Globe. "Computer judges, of course, aren't going to be ascending to the bench in the foreseeable future. 'Nobody thinks that's a good idea,' says Carole D. Hafner, a Northeastern University computer scientist and pioneer in using artificial intelligence to study the law. Judging, and most especially Supreme Court judging, is a complex and subtle mix of imagination, acuity, and political calculation. Still, at a time when doctors are starting to use software to aid in their diagnoses and when hedge funds are using computer models to make multibillion-dollar investment decisions, there is growing interest -- even in an American legal establishment usually resistant to change -- in finding ways to incorporate artificial intelligence into the law. ... Put simply, artificial intelligence is the branch of computer science that deals with getting machines to think like human beings. Its application to the law dates, in its earliest form, to the 1950s, when mathematicians first tried to use formal logic to model legal reasoning. ... Some of the fruits of this fascination, however, have been decidedly practical, from intelligent document retrieval systems that use fuzzy logic to search not just by keyword but by concept (the only AI application widely used in American law firms) to programs that predict the outcomes of court cases or evaluate potential clients." August 28, 2005: Predicting how you're going to shop online. By Tania Hershman. Israel21c. "'What we are dealing with is a model for Customer Lifetime Value,' says Amit Fisher, a researcher at IBM's Haifa Laboratories. 'Normally customer value is calculated by looking at the purchases up til now and assuming that that is what they will carry on doing. It is very simplistic. But you can't assume that what happened in the past is what will happen in the future.' Fisher decided to take algorithms from the field of data mining, operations research and artificial intelligence and combine them with economics in order to take a more complex view of predicting how human beings are likely to behave in the 'long term' - a phrase which to a site like Amazon.com may mean ten years, but to another website could mean one year." August 2, 2005: Teaching Common Sense to Computers - An exhibit developed by members of USC's Information Sciences Institute sets out to prove that a computer can think on its own -- given the proper piece of information. By Eric Mankin. University of Southern California press release. " People are helping computers become independent thinkers through a traveling exhibit that gives the machines something distinctly human: common sense. The exhibit, part of the 'Robots and Us' show on display through August at the California Science Center, is the brainchild of scientists at USC’s Information Sciences Institute. ... Yolanda Gil, also of ISI, said that researchers in the field of artificial intelligence have learned over the past 50 years that 'any intelligent system needs to be able to learn new things all the time. You cannot predict in advance all the things they need to know in order to perform a task, nor can you count on ... engineers or programmers to be able to describe all the things they know about the objects being used in an application,' Gil said. The problem is the so-called 'knowledge acquisition' bottleneck that makes intelligent systems 'brittle' because they cannot reason even slightly beyond the knowledge they start with, she said. 'Having a system that [continuously] learns new things about the world … from volunteers [who] have a lot of time on their hands … is a very promising approach to address brittleness,' she said." July 22 - 28, 2005: I Think, Therefore I Am -- Sorta. The belief system of a virtual mind. Quark Soup column by Margaret Wertheim. LA Weekly. "Far more than mere cartoons, these virtual people have each been endowed with a virtual mind complete with its own internal 'desires' and 'goals.' Technically known as 'agents,' they are driven by a revolutionary software system known as PsychSim that enables programmers to simulate the cognitive faculties of human minds. Dr. Stacy Marsella, a leading agent researcher and one of the primary architects of PyschSim, declares that agents actually 'think for themselves.' Indeed, the ultimate goal of agent research is to create autonomous self-determining minds capable of a full spectrum of human behavior. A small, dark-haired man with a doctorate in artificial intelligence, Marsella is a project leader at USC’s Information Sciences Institute in Marina del Rey, one of the world’s top centers for agent research. ... Last year, Marsella and his colleague Dr. David Pynadath developed an agent-based game [Carmen’s Bright Ideas] in which parents of childhood cancer patients engage in virtual counseling sessions with a virtual therapist. ... But what does it mean to talk about a virtual mind? What, indeed, is a mind of any variety? ... Until very recently, artificial-intelligence researchers believed that modeling the mind was simply a matter of simulating rational cognition, an activity that was seen to be epitomized by strategical games such as chess and go -- but over the past decade, computer scientists have come to understand that a virtual mind needs a virtual psychology. To 'think' requires not just an ability to carry through a chain of logical inferences; it also requires a mental environment, or psychic context, in which such rationalizations can be given meaning. " July 1, 2005: 125 Big Questions. Science (Vol 309, Issue 5731, 79). "In a special collection of articles published beginning 1 July 2005, Science Magazine and its online companion sites celebrate the journal's 125th anniversary with a look forward -- at the most compelling puzzles and questions facing scientists today. A special, free news feature in Science explores 125 big questions that face scientific inquiry over the next quarter-century; accompanying the feature are several online extras including a reader's forum on the big questions." Start with the editorial, 125, by Donald Kennedy, Editor-in-Chief, and then explore questions such as:
>>> The Future, Systems, Cognitive Science, Philosophy, Reasoning, Grand Challenges July 2005: AI In Control - Artificial intelligence, expert systems, fuzzy logic, neural nets, and rules-based algorithms for factory control. Although the buzz is quieted, all of it is still around. You just don't notice it. Automotive Manufacturing & Production. "'Real-time rule engines' and 'adaptive control' are two of today's monikers for artificial intelligence (AI), fuzzy logic, and similar information technologies that were so widely touted in the 1980s. ... Toyota Motor Corp. uses Gensym G2 to plan its final assembly line. ... Volkswagen (VW) Group (Madrid, Spain) uses the inference engine from ILOG Inc. for new-car sequencing and production planning at the group's SEAT Martorell and the VW Navarra plants. ... In reality, rules-based technology 'gets embedded in solutions so that the end user doesn't even know there's AI inside,' says [David] Siegel. 'I don't know of many total standalone AI/expert system-type applications. They're almost always a part of the larger picture.' ... The IMS [Intelligent Maintenance Systems] Center has developed a toolbox of algorithms. Of particular interest is the Watchdog Agent. This agent, explains Lee, 'can assess and predict the process or equipment performance based on the inputs from the sensors mounted on it. ... A second IMS project is the Device-to-Business (D2B) platform, basically an autonomous intelligent agent that links factory floor devices directly to a business system, such as enterprise resource planning (ERP), thereby circumventing traditional factory supervisory control systems, such as programmable controllers." June 29, 2005: Your brain - Search engine, or calculator? By Michael Kanellos. CNET News.com. "For years, cognitive theorists have likened the human brain to a computer that completes tasks by breaking down complex problems into a series of small yes/no decisions. A recent study, however, shows that the brain adjusts its thinking as more data arrives. In a study published online this week in Proceedings of the National Academy of Sciences, Michael Spivey, a psycholinguist and associate professor of psychology at Cornell University, tracked the mouse movements of 42 undergraduate students while working at a computer. ... Interestingly, the whole field of artificial intelligence has moved from a Boolean model, in which systems guide themselves through a series of embedded rules, to a Bayesian model, in which machines guide themselves by studying past experiences. Bayesian probability also underlies search engines." June 27, 2005: KlearVision Software Now Downloadable. By E&P Staff. Editor & Publisher. "Photo-D relies on a rule-based Intelligent Expert System to automatically optimize and enhance digital image files, preparing them for printing or viewing. Using fuzzy logic and artificial intelligence algorithms, it emulates the decision-making expertise of a traditional color expert or digital-imaging professional to analyze, correct, and produce high-quality image files." June 1, 2005: Pentagon envisions electronic office assistant for busy human bosses. By Robert S. Boyd. Knight Ridder Newspapers / Knight Ridder Washington Bureau. "With a strong push from the Pentagon, computer scientists are trying to create an artificial 'personal office assistant' that's smart enough to handle routine tasks for a human boss, military or civilian. The researchers aim to build an electronic system that understands human language, takes and remembers instructions, learns from its experiences and copes with unexpected situations. ... The office assistant program is sponsored by the Defense Advanced Research Projects Agency, a Pentagon unit that pioneered such once blue-sky developments as the Internet, stealth aircraft and microelectronic machines. DARPA Director Anthony Tether told the House Science Committee last month that his agency is moving into the field of 'cognitive computing,' meaning computer systems that 'perceive, reason and learn,' not just crunch numbers and manipulate data. The Pentagon project is called PAL, an acronym for 'personalized assistant that learns.' 'Cognitive systems that learn to adapt to their users could dramatically improve a wide range of military operations,' said Ronald Brachman, the director of DARPA's Information Processing Technology Office. 'They could learn and even improve on their own.'" May 19, 2005: "Machine learning" is Beal’s focus - Computer scientist adapts "hot" technology to bioinformatics, artificial intelligence. By Irene Liguori. UB Reporter. "MIT calls it one of the hot 10 emerging technologies that will change your world: Bayesian Machine Learning. It also happens to be the focal point of research for Matthew J. Beal, who last fall joined the faculty in the Department of Computer Science and Engineering in the School of Engineering and Applied Sciences. Bayesian Machine Learning is a head-spinning concept based on a mathematical basis for probability inference discovered by 18th-century mathematician and clergyman Thomas Bayes. Today it is used in applications such as tracking the time evolution of cells, gene expression and interaction, and drug development. 'Students like courses where they are trying to build intelligent algorithms,' says Beal, who won the UB Graduate Student Association's Distinguished Teacher Award after his very first semester as an assistant professor in the fall of 2004 teaching 'Introduction to Machine Learning.'" April 27, 2005: HK scientists win int'l award with AI software. Xinhua News Agency & China View. "An Artificial Intelligence (AI) software system jointly developed by City University of Hong Kong (CityU) and the Hong Kong MTR Corporation received an international award, announced CityU Wednesday. The system has been honored by the American Association for Artificial Intelligence (AAAI) with the 'Innovative Application of Artificial Intelligence' Award 2005 (IAAI) in the category of 'Deployed Applications'. The system, 'AI Engine', which has been in daily use since July 2004, is a component of the MTR's Engineering Works and Traffic Information Management System."
>>> Scheduling, Business, Transportation, Reasoning, Applications, Conferences (@ Resources for Students) April 23, 2005: Whatever happened to machines that think? By Justin Mullins. New Scientist (Issue 2496; pages 32 - 37). "The first chatbot appeared in the 1960s. Back then, the very idea of chatting to a computer astounded people. Today, a conversation with a computer is viewed more on the level of talking to your pet pooch - cute, but ultimately meaningless. The problem with chatbots is a symptom of a deeper malaise in the field of artificial intelligence (AI). For years researchers have been promising to deliver technology that will make computers we can chat to like friends, robots that function as autonomous servants, and one day, for better or worse, even produce conscious machines. Yet we appear to be as far away as ever from any of these goals. But that could soon change. In the next few months, after being patiently nurtured for 22 years, an artificial brain called Cyc (pronounced 'psych') will be put online for the world to interact with. And it's only going to get cleverer. Opening Cyc up to the masses is expected to accelerate the rate at which it learns, giving it access to the combined knowledge of millions of people around the globe as it hoovers up new facts from web pages, webcams and data entered manually by anyone who wants to contribute. ... [Doug] Lenat's optimism about Cyc is mirrored by a reawakening of interest in AI the world over. In Japan, Europe and the US, big, well-funded AI projects with lofty goals and grand visions for the future are once again gaining popularity. The renewed confidence stems from a new breed of systems that can deal with uncertainty - something humans have little trouble with, but which has till now brought computer programs grinding to a halt. ... Where could the secret to intelligence lie? According to [Tom] Mitchell, the human brain is the place to look." April 20 / 27, 2005: Overly smart buildings. By Ted Smalley Bowen. Technology Research News. "The notion of buildings as 'machines for living in, 'as pioneering modernist architect Le Corbusier put it in the 1920s, morphs to fit the technologies and issues of the day. In the '70s, it was energy efficiency. In the '80s, computer technology spawned 'smart' buildings sporting automated controls and pre-configured information systems. The latest crop of technologies [footnotes] include microelectromechanical systems that combine sensors and actuators, wireless sensor networks, and fuzzy logic control schemes, and has the makings of a sophisticated nervous system. ... One recent experiment measured the effectiveness of a decentralized control scheme in regulating the indoor environment of a Swiss office building. The researchers focused on a single performance metric -- user comfort -- putting aside energy use and security. The complexity of meeting multiple, sometimes conflicting criteria is one of the main challenges of designing intelligent buildings. The researchers' fuzzy logic software for controlling the building's heat and lighting is self-tuning, using feedback from sensors and switches. Measuring comfort in terms how often users changed the settings themselves, the researchers found that the building system gradually took over the controls and made the majority of adjustments." April 19, 2005: Computer generates verifiable mathematics proof. By Will Knight. NewScientist.com news. "A computer-assisted proof of a 150-year-old mathematical conjecture can at last be checked by human mathematicians. ... A proof of the theorem was announced by two US mathematicians, Kenneth Appel and Wolfgang Haken, in 1976. But a crucial portion of their work involved checking many thousands of maps - a task that can only feasibly be done using a computer. So a long-standing concern has been that some hidden flaw in the computer code they used might undermine the overall logic of the proof. But now Georges Gonthier, at Microsoft's research laboratory in Cambridge, UK, and Benjamin Werner at INRIA in France have proven the theorem in a way that should remove such concerns. They translated the proof into a language used to represent logical propositions - called Coq - and created logic-checking software to confirm that the steps put forward in the proof make sense." April 15, 2005: Automated mining still a dream. By Denis St. Pierre. Sudbury Star / available from Canoe. "The use of artificial intelligence to create truly-automated machines remains decades away, an expert on the subject told a mining conference Monday. 'Why can't we make a machine that's a miner?' Marvin Minsky said in his address to the International Symposium on Mine Mechanization and Automation and Telemin 1 Conference at Laurentian University. 'We can make these big, powerful machines...but we cannot get the complete automation we'd like to get,' said Minsky. New approaches and greater resources are needed to develop computerized machines that can mimic the human capacity for common sense reasoning, he said. ... Increased automation will allow Inco to develop deeper ore bodies and possibly lower-grade ore that currently cannot be mined economically, [Peter] Jones said." April 13, 2005: MusicStrands uses artificial intelligence to recommend music to site visitors. innovations report. "The Universitat Autònoma de Barcelona Research Park has a new company: a spin-off of the UAB and the Higher Council for Scientific Research (CSIC). MusicStrands uses artificial intelligence techniques to provide people with music recommendations. ... The artificial intelligence techniques used for the recommender systems are based on statistical learning, Bayesian estimation, probabilistic reasoning and visualisation techniques." March 28, 2005: Gene Finding with Hidden Markov Models- The application of phylogeny to HMMs is improving gene annotation. By Karen Heyman. The Scientist (Volume 19, Issue 6). "HMMs are special instances of graphical models, which were originally developed by computer scientists studying machine learning and speech recognition. In technical parlance, says [Sean] Eddy, HMMs 'describe a probability distribution over an infinite number of sequences.' To the uninitiated, they resemble a cross between a flow chart and a doodle. In order to understand conceptually how HMMs work, consider their origin in speech recognition, says [David] Haussler. In that field, a computer is asked, given a speech wave, what are the phonemes (sounds) that it encodes. The wave is the measured signal; the phonemes are the 'hidden' signals that give the HMM its name. 'There is a probabilistic relationship between phonemes,' Haussler explains. 'After a 'th' sound can easily come an 'r' or an 'ah' or several other types of sounds, but not, for example, a 'k' sound. A hidden Markov model for speech incorporates all possible phonemes, and for each phoneme the probability that it's followed by any other phoneme.' Haussler says the HMM also 'models the stochastic relationship between each phoneme and the speech wave one might measure for it. In this way it can be used to infer the sequence of phonemes that best fits a given segment of recorded speech.' Translating that to molecular biology, he explains, the measured signal is the sequence of nucleotides, while the hidden signal is their function. 'Biology is trying to speak a language to us, and the HMM model is helping us to distinguish the phonemes of that language.'" March 26, 2005: Everest climber aims high with city software. By Fiona McGlynn. Edinburgh Evening News & Scotsman.com. "A mountaineer is set to conquer Mount Everest with the help of pioneering software created in a Capital lab. Dr Rob Milne will be the first climber to use the life-saving technology in his attempt to tackle the world’s highest peak next month. Designed by Edinburgh University engineers, the IM-PACs (intelligent messaging, planning and collaboration) system will help Dr Milne to make critical choices during his trek. The system is designed to help climbers adversely affected by altitude sickness to make life or death decisions about their journey. ... Professor Austin Tate, the technical director of Edinburgh University’s Artificial Intelligence Applications Institute and a friend of Dr Milne’s, devised the IM-PAC. He said: 'Any attempt on Everest requires a lot of co-ordination and planning before, during and after the expedition. This makes such extreme expeditions good examples of the kind of thing we wish to support with IM-PACs and AI planning technology.'" March 23 / 30, 2005: Common sense boosts speech software. By Eric Smalley. Technology Research News. "Speech recognition software matches strings of phonemes -- the sounds that make up words -- to words in a vocabulary database. The software finds close matches and presents the best one. The software does not understand word meaning, however. This makes it difficult to distinguish among words that sound the same or similar. The Open Mind Common Sense Project database contains more than 700,000 facts that MIT Media Lab researchers have been collecting from the public since the fall of 2000. These are based on common sense like the knowledge that a dog is a type of pet rather than the knowledge that a dog is a type of mammal. The researchers used the phrase database to reorder the close matches returned by speech recognition software." March 21, 2005: Artificial
intelligence - Solving problems for the real world. By Billy
Defrain. Daily Nebraskan (Editor's note: This is the second part
in an occasional series in which the fantastic realms of science
fiction are compared to those of real world science fact.). "For
a typical American, the mention of artificial intelligence may
conjure up nasty images of a robot wielding a plasma rifle atop
a pyramid of human skulls. Mention artificial intelligence to
Berthe Choueiry, though, and she thinks of problems. Choueiry,
an associate professor of computer science and engineering, conducts
artificial intelligence research at the University of Nebraska-Lincoln.
And out of the wide field of artificial intelligence, her research
focuses on constraint processing. This involves developing techniques
to solve decision problems and applying them to real world uses,
Choueiry said. ... She works to generate 'solutions that hopefully
apply to real world problems like resource allocation, airline
times and natural language processing,' Choueiry said. ... Constraint propagation, which
is Choueiry's speciality, is just one method of reasoning for
artificial intelligence. Constraint-based reasoning is a deductive
process in which the program looks at a group of data by considering
which responses are not acceptable. These unacceptable responses
are constraints.... Choueiry said one of the most difficult aspects
of constraint processing is the concept of combinatorial explosion."
March 2005: Cycorp
- The Cost of Common Sense. By Lamont Wood. Technology Review.
"The 10-year-old company cares about the services it sells
-- but mainly because they bankroll its true quest: creating a
'knowledge base' called Cyc that can endow computers with something
approaching common sense. This quest has been so time-consuming
that most venture capitalists would long ago have written off
their investments -- or demanded the CEO’s head on a platter.
That Doug Lenat and his 54 employees have avoided this fate is
a lesson in managing long-term, visionary R&D projects. ... After
reaching a certain level of sophistication, Cyc began to help
direct its own education by asking questions based on what it
already knew. (Lenat hopes that Cyc will eventually be able to
read unassisted.) The result: a computer that doesn’t have
to be told that parents are older than their children and that
people stop subscribing to magazines after they die. ... [I]t
licenses Cyc for use in third-party software packages. A slimmed-down
Cyc is available free to research organizations, and OpenCyc,
an even smaller version suitable for desktop computers, is available
as a free download." January 29, 2005: Google's search for meaning. By Duncan Graham-Rowe. New Scientist Magazine (Issue 2484; page 21). "Computers can learn the meaning of words simply by plugging into Google. The finding could bring forward the day that true artificial intelligence is developed. ... Paul Vitanyi and Rudi Cilibrasi of the National Institute for Mathematics and Computer Science in Amsterdam, the Netherlands, realised that a Google search can be used to measure how closely two words relate to each other. ... From this a computer can infer meaning, says Vitanyi. 'This is automatic meaning extraction. It could well be the way to make a computer understand things and act semi-intelligently,' he says. ... The pair's results do not surprise Michael Witbrock of the Cyc project in Austin, Texas, a 20-year effort to create an encyclopaedic knowledge base for use by a future artificial intelligence. ... 'The web might make all the difference in whether we make an artificial intelligence or not,' says Witbrock." December 31, 2004: Simplicity sets tone for cell phones. Editorial by Nicholas Negroponte. The Straits Times (Singapore) Asia News Network / available from The Korea Times. "A scenario even more futuristic than the tooth telephone is a new class of device, one with reasoning and common sense. An example might be a mobile phone that neither rings nor vibrates: instead it answers itself or reads the message and takes appropriate action, like a well-trained butler who knows when and how to interrupt you. This level of intelligence, which probably will not be available for another 10 to 20 years, requires familiarity with you, your life and your moods, the kind you would expect to find in the world's best human secretary. But this artificial intelligence also requires a familiar understanding of the world around us and how we live in it. None of these advances will happen tomorrow. Instead we will evolve through a handful of smaller changes that can be expected with greater speed and certainty." December 15, 2004: Building thinking robotics for the real world. IST Results. "Researchers at the Bayesian Inspired Brain and Artefacts (BIBA) project are using a novel application of Bayesian reasoning to design artefacts (objects produced or shaped by human craft) that can learn to act rationally with incomplete information. ... BIBA project researchers use Bayesian reasoning to understand the behaviour of animals and then apply this same logic to create artefacts for the 'real world'. Pierre Bessière, Scientific Manager of the IST programme-funded BIBA project at INRIA’s GRAVIR laboratory in France explains: 'Both living organisms and robotic systems face the difficulty of how to use an incomplete model of their environment to perceive, infer, decide and act efficiently.' ... BIBA researchers developed probabilistic programming methods for the Cycab that use biologically plausible techniques to define the obstacle avoidance system as a survival instinct. The goal is to create a completely automatic car that doesn’t need a human driver and can safely navigate streets that are beset with unpredictable occurrences." December 10, 2004: Flying eyes. By Helen Knight. The Engineer. "A fleet of unmanned aerial vehicles will co-operate with a ground robot on surveillance tasks in the Australian Outback, in trials to be held next year by BAE Systems. The series of trials are being organised by researchers at the company's Advanced Technology Centre (ATC), to demonstrate its autonomous systems, data fusion and artificial intelligence technologies. ... 'We hope to deploy a land vehicle in some preliminary experiments, where we would have air vehicles gathering information, and we will look at how they would interact with something on the ground, perhaps by giving it information it can use to decide where it should move to, to participate in the sensing task,' [Dr Phil Greenway] said. ... To allow the system to deal with uncertainties such as incomplete observations, problems with sensors or deliberate attempts to fool it by enemy forces, the team is using Bayesian network technology. These networks, based on statistical pattern recognition, use probability theory to cope with such uncertainties." December 2004: AI Revisited - Pieces of the AI Puzzle are Already Deployed, but Much Remains to be Done. Bart Eisenberg's Pacific Connection series in Software Design Magazine. "'There's a joke in the AI community that as soon as AI works, it is no longer called AI,' says Sara Hedberg, a spokeswoman for the American Association for Artificial Intelligence. Hedberg, who has written about AI for the past 20 years or so, has done her share of trying to enlighten reporters who are ready to declare AI dead. 'Once a technology leaves the research labs and gets proven, it becomes ubiquitous to the point where it is almost invisible,' she says. ... The American Association for Artificial Intelligence serves as a kind of crossroads for AI researchers. Ahead of its 2004 conference, the organization identified a slew of emerging fields where AI research is going strong, starting with counter-terrorism, crisis management and defense. One big project funder is DARPA, the Defense Advanced Research Projects Agency, the same U.S. government agency that first funded the Internet. Other research areas include space exploration, robotics, Web search engines and agents, healthcare, and manufacturing. And what do all of these areas have in common? AI applications have grown so diverse that the shared term 'artificial intelligence' may be the only thing these applications share. If you declare that your research is AI-related-then, ipso facto, it is. 'AI has splintered into various isolated sub-fields,' says Bill Havens, the chief technology officer for Actenum, a Vancouver-based startup tackling difficult scheduling problems. ... To get a sense of what AI looks like in the year 2004, I spoke with researchers in a variety of fields. ..." November 23, 2004: Bayesian networks made easy - Q&A with Zach Cox, Java coder and chief developer of BNET Builder. Interview by Rich Seeley. ADTmag.com. "ADT: What’s a Bayesian network? Cox: Not many people know about the technology. It’s an artificial intelligence technology and it’s very similar to expert systems, rule systems, if-then rules. But instead of working with true and false logic, like if-then rules, it works with probability theory. So, you basically say, if A is true, then the probability that B is true is X. So, instead of saying, if it’s cloudy outside, then it will rain, you can say things like, if it’s cloudy outside, then the probability of rain is 85%. ADT: How is this used? Cox: It has a lot of different applications. One of the earlier applications that drove a lot of the research was medical diagnosis. So, a doctor or medical researchers can build a Bayesian network that specifies how different diseases cause symptoms. That’s a big part of Bayesian networks from reasons and causes to effects. ... ADT: How about business uses? Cox: Another big application of Bayesian networks is in the financial industry for credit scoring." November 22, 2004: Driven by logic. By Jessie Hui. Sourth China Morning Post (subscription req'd.) "Driven by the desire to use technology to help the needy, a group of secondary school students won a competition with their communication system that makes life a little easier for blind people. The Intelligence @ Society Contest, organised by Hong Kong Baptist University's (HKBU) Computer Science Department earlier this month, was divided into secondary school and university categories. It aimed to arouse students' interest in artificial intelligence and how the 'fuzzy logic' can be applied in our daily lives." November 10, 2004: Birmingham in €6m AI project. By Harry Yeates. ElectronicsWeekly.com. "Researchers in artificial intelligence (AI) at the University of Birmingham are participating in a €6.25m, four-year European project to develop a cognitive robot. One of the project's aims is to help throw some light on human cognition. The plan is to take the various AI systems that have so far been realised in some form or other ('natural language' systems that process human voice inputs and can use bits of our grammar and machine vision) and create a robot that combines those cognitive abilities. 'The idea is to put it all back together, and that's what's hard,' said Dr Jeremy Wyatt, a lecturer in computer science at Birmingham." THERE'S MORE ! SEE THE REASONING NEWS ARCHIVE
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