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October 27, 2004: Getting intelligent about the brain. Interview by Richard Shim and Ina Fried. CNET News. "In his first book, 'On Intelligence,' [Jeff] Hawkins explains his theory and how it can be used to build truly smart machines--a question others have tackled, through the study of artificial intelligence and neural networks, but haven't resolved. Hawkins says the main difference between his idea and others is that the other methods try to copy human behavior using the wrong notion of how the brain works. The brain doesn't produce an output for every input, Hawkins says. Instead, it stores experiences and sequences and makes predictions based on those memories. ... [Q] How would a machine that worked more like the brain do a better job? [A] Current computers just don't understand what is being done, and they don't do a good job. The problem with something like speech recognition is that computers are trying to just recognize speech. They take some pattern and try to match it against some template. We understand speech, but with current systems, there is no understanding. So when you have real data coming in that is messy for the most part, you can't match it."
>>> Representation, Reasoning, Machine Learning, Speech, AI Overview, Interviews; also see these related articles: 1 & 2

October 18, 2004: Awarding the Brains Behind AI. By Kari Lynn Dean. Wired News. "[Daphne Koller's] creativity recently garnered a $500,000, no-strings-attached MacArthur Fellowship. ... Koller won the MacArthur 'genius award' because her creativity in resolving uncertainty could benefit society. By addressing fundamental problems with machine learning and exploring the foundations of intelligence, Koller is pushing the limits of present-day scientific understanding of how to build computer programs that learn efficiently and reason intelligently. ... Dealing with information overload 'using sophisticated data management and analysis tools is probably going to be one of the key tasks that (computer science) researchers have to face this decade,' Koller said."
>>> Representation, Probability & Uncertainty, Reasoning, Bioinformatics, AI Overview, Applications

October 4, 2004: Tec gets fuzzy feeling. By Tom Pullar-Strecker. The Dominion Post & Stuff. "Wellington Institute of Technology has reinforced its credentials as a centre for research into fuzzy logic and artificial intelligence, thanks to the efforts of Romanian-born Professor Mircea Negoita. Professor Negoita, director of WelTec's Centre of Computational Intelligence, arranged for Wellington to host this year's International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2004. ... The event attracted 480 academics from 50 countries, including the 'father' of fuzzy logic, Berkeley University Professor Lotfi Zadeh, who has been appointed honorary chairman of the Centre for Computational Intelligence at WelTec."
>>> Fuzzy Logic, Academic Departments & Conferences (@ Resources for Students), Reasoning

October 1, 2004: Decision Evolution. By Tom Davenport. CIO Magazine. "Have you ever known a family in which the child went well beyond the parents? One in which the parents didn't seem to have a lot on the ball, but they bequeathed just enough capabilities to their child for him or her to take off? That's just what happened in the world of automated decision making. The parents -- artificial intelligence (AI) and decision support systems (DSS) -- were ultimately disappointing despite lots of favorable hype. AI and expert systems required those pesky knowledge engineers to create them, and they were very difficult to maintain. Decision support systems also never really flourished, despite being the darling of academics for decades, perhaps because they required too much statistical expertise and too much human analysis for these lean times. But their offspring, a technology called automated decision systems, is taking off, and it embodies the best attributes of each parent. Automated decision systems are rules-based like expert systems. And like DSS, they often involve statistical or algorithmic analysis of data. They typically make decisions in real-time after weighing all the data and rules for a particular customer or case. Sometimes they also carry genes from another ancestor, business process management or workflow, leading some observers to classify them as 'smart BPM' systems. Their most salient characteristic is that they actually make a decision: what price to charge a particular customer, whether to grant a loan or an insurance policy, which delivery truck should be rerouted, what drug to prescribe to a diabetic patient."
>>> Expert Systems, Machine Learning, Banking, Business, Medicine, Scheduling, Reasoning, Applications

September 29, 2004: Koller honored with MacArthur Fellowship for work using computational methods. By Matthew Early Wright. Stanford Report. "Daphne Koller, associate professor of computer science at Stanford, has been named one of this year's MacArthur Fellows. ... Koller's research tackles questions of how complex information with high levels of uncertainty can be approached using algorithms, probabilistic modeling and other computational methods. These tools strive to represent knowledge and reasoning at the intersection of traditional logic and subjective judgment, and have far-reaching implications in the fields of artificial intelligence and biomedical and genetic data analysis. A significant contribution of Koller's work is the expansion of Bayesian networks-reasoning frameworks that deal with uncertainty-by showing how they can be organized into logical, object-oriented hierarchies. She has advanced this concept by implementing 'probabilistic relational models,' which blend logical and statistical representations in ways that employ standard deductive reasoning."
>>> Probability & Uncertainty, Bayes (@ Namesakes), Bioinformatics, Reasoning, Representation; also see thes two related articles: 1 & 2

September 28, 2004: Innovators all - 23 fine MacArthur Fellows. By Rebecca F. Johnson. USA Today. "Lindsey and Martinez are among 23 authors, scientists and others picked by the John D. and Catherine T. MacArthur Foundation as this year's MacArthur Fellows for work that shows 'exceptional merit and promise.' The fellowships come with a no-strings-attached $500,000 grant. The awardees are: ... Daphne Koller is a computer scientist who has developed new computational methods for representing knowledge and reasoning at the intersection of traditional logic, probability, uncertainty, and subjective judgment. Her work bridges a longstanding divide in the field of artificial intelligence between efforts to develop an explicit representation of knowledge (for example, in medical diagnosis) and efforts to categorize data based on statistical properties (such as optical character recognition). ... Naomi Ehrich Leonard is an engineer who develops autonomous underwater vehicles. This work synthesizes elements as diverse as fluid mechanics, robotics, computer science, oceanography, and biology. Leonard's initial efforts focused on single vehicles that have the means to control motion directly in some, but not all, dimensions."
>>> Representation, Reasoning, Autonomous Vehicles, Robots

September 9, 2004: MyVista ready for market. By Charles F. Moreira. The Star Online. "Intelligence Systems Sdn Bhd director See Wan Chee said his company acquired 20 customers for its SmartScan imaging application since it won the regional APICTA 2003 award in the Best in Education category in Bangkok last December.  'Most of them, including Nottingham University's Malaysian campus, use SmartScan to read and mark answers to multiple-choice exam questions, as well as for data collection,' See told In.Tech at ACM2004.  It also indicates students' strengths and weaknesses so that teachers can take remedial action to help students improve in those areas where they are weak.  SmartScan (www.smartscan.com.my) uses artificial intelligence (AI) techniques, including pattern recognition, neural networks and fuzzy logic to analyse answers on an objective test answer sheet."
>>> Pattern Recognition, Neural Networks, Fuzzy Logic, Education, Applications, Machine Learning, Reasoning

September 8, 2004: Artificial Intelligence Creeps into the Commercial Market Despite Initial Hurdles. PhysOrg.com. "When artificial intelligence (AI) was developed to emulate human intelligence, scientists hoped it would be a blockbuster technology. Instead, the inability of end users to deal with its complexity and expensiveness and their lack of understanding of its potential caused these expectations to dwindle. These factors slowed down the adoption rates of AI, but not the efforts of researchers. After a couple of decades, AI, now in the form of applications, is slowly making its way out of laboratories into the mainstream market."
>>> Medicine, Case-Based Reasoning, Scientific Discovery, Bioinformatics, Data Mining & Knowledge Discovery, Customer Relations, Expert Systems, Applications, Reasoning, Machine Learning

September 4, 2004: Robots invade the table football pitch. By Duncan Graham-Rowe. New Scientist Magazine (appears on page 18 with the title: Play table football against a robot). "Fans of table football, or foosball, will no longer have to hang around at the pub waiting for a friend to turn up before they can play. A robotic foosball table will be able to give them just as good a game. ... To allow the control system to track the ball, the base of the table is made of translucent glass, tinted green. A camera underneath photographs the ball 50 times per second, and sends this data to a built-in computer that maps the ball's position. Intelligent software then works out the effect of one of the figures kicking the ball. ... [Bernhard Nebel's University of Freiburg] team is now working on being able to stop the ball and pass it -- a capability that will be essential if the robot is ever going to beat good players."
>>> Sports, Entertainment, Vision, Planning, Machine Learning, Robots, Reasoning, Applications

September 4, 2004: Brain research? Pay it no mind. Mystery of consciousness still outwitting scientists. By Philip Marchand. The Toronto Star. "Scientists who have been trying to understand the brain have recently tried to measure neural activity of Republicans and Democrats to see if political affiliations had anything to do with brain chemistry. The results were inconclusive. ... What really caught my eye about a New York Times Magazine article on the topic was the following statement: 'One of the most celebrated insights of the past 20 years of neuroscience is the discovery -- largely associated with the work of Antonio Damasio -- that the brain's emotional systems are critical to logical decision-making. People who suffer from damaged or impaired emotional systems can score well on logic tests but often display markedly irrational behaviour in everyday life.' I'm sure Damasio has done good work, rooting around the neocortex. But what does it say for neuroscience that one of its 'most celebrated insights' is something we've known for three or four millennia? ... The bravest of the neuroscientists are trying to tackle the toughest nut of all, the mystery of consciousness. ... A professor named Howard Gardner, for example, whose 1985 book The Mind's New Science helped to popularize the field of cognitive science, told Horgan that questions such as consciousness and free will were 'particularly resistant' to the scientific habit of trying to break down a subject into its most elemental parts, like neural pathways in the brain. ... The human brain is so complex it simply defies the same kind of analysis that scientists devote to subatomic particles or human immune systems. 'Like neuroscientists, researchers in evolutionary psychology and artificial intelligence are both bumping up against the Humpty Dumpty dilemma,' [John] Horgan writes. 'They can break the mind into pieces, but they have no idea how to put it back together again.'"
>>> Emotion, Creativity, Cognitive Science, Reasoning, Philosophy, Neural Networks, Machine Learning

September 1, 2004: Turn Search Into Find. By Nathaniel Palmer. Transform Magazine. "Web-based customer self-service is gaining rapid adoption as one of the most promising opportunities for customer-facing firms in all industries to decrease customer transaction costs while maintaining or improving service quality. ... Information retrieval, or search, software is built upon two fundamental components: an indexing engine that maps and categorizes content, and a retrieval engine that deploys algorithms to find and return indexed content. ... A taxonomy refers to structures built to organize information -- a collection of relevant topics and subtopics arranged in a hierarchical structure. Humans use taxonomies to make sense of formerly unstructured information. ... Taxonomy and classification within customer self-service solutions is enabled by software that creates hierarchical structures and defines characteristics throughout the branches of the structures. Once these structures are in place, classification is accomplished by parsing collections of content and assigning individual documents to appropriate categories within the taxonomy structure. This can be done manually (with the aid of software) or automatically based on specific algorithms (see 'Behind the Jargon: Five Approaches to Classification')."

  • sidebar: Behind the Jargon - Five Approaches to Classification. Transform Magazine. "In the statistical analysis approach, subsets of documents are identified manually and presented to the software as 'exemplary' to a given topic or node of the taxonomy. The provided sample content is analyzed and from this the taxonomy is further refined and the rules of classification established. These rules are then used to automate the analysis of new documents and their classification into the taxonomy. This approach is also referred to as 'machine learning.' The Bayesian probability approach attempts a concept-based analysis by learning the probabilities of words being related in a given category. ... Neural networks create a matrix of computational nodes. These nodes track and compare topic similarity. A neural network utilizes artificial intelligence to build an interconnected system of processing elements, each with a limited number of inputs and outputs. Rather than being programmed, these systems learn to recognize patterns. ... Support vector machine algorithms are derived from statistical learning theory.... Semantic analysis and clustering supports both taxonomy creation and content categorization."

>>> Information Retrieval, Customer Service, Machine Learning, Probability, Bayes (@ Namesakes), Neural Networks, Natural Language Processing, Representation, Reasoning, Applications

August 30, 2004: Fuzzy Logic. Quickstudy by Russell Kay. Computerworld. "The digital computing world is built on a structure of Boolean logic applied to binary values -- one or zero, yes or no, in or out. But this powerful structure is a gross oversimplification of the real world, where many shades of gray exist between black and white. In everyday life, we use quasimetric notions that are clearly related to numerical concepts or values but lack precision or demarcation. ... The real world simply doesn't map well to binary distinctions, and numerical precision is often unhelpful in making qualitative statements. Fuzzy logic gives us a way to deal with such situations. In fuzzy systems, values are indicated by a number (called a truth value) in the range from 0 to 1, where 0.0 represents absolute falseness and 1.0 represents absolute truth. While this range evokes the idea of probability, fuzzy logic and fuzzy sets operate quite differently from probability."

  • Sidebar: The history of fuzzy logic. By Russell Kay. "In 1965, Lotfi A. Zadeh of the University of California at Berkeley published 'Fuzzy Sets,' which laid out the mathematics of fuzzy set theory and, by extension, fuzzy logic. ... The greatest number of fuzzy researchers today are found in China, with over 10,000 scientists."
  • Sidebar: Seven Truths of Fuzzy Logic. By Russell Kay. "1. Fuzzy logic isn't fuzzy. ... 5. Fuzzy systems aren't neural networks. ..."

>>> Fuzzy Logic, Probability, Boole (@ Namesakes), Neural Networks, Reasoning

August 23, 2004: New software makes debut in tanker sector - Tankers International uses system to manage scheduling across its VLCC fleet. By Hugh O'Mahony. Lloyd's List (subscription req'd.). "Cutting-edge software deployed to accelerate complex decision-making in the logistics sector is being applied for the first time in oil tanker operations to optimise scheduling. ... After two years of trials Tankers International plans to take live a 'multi-agent' software package next month from London developer Magenta to manage scheduling across its very large crude carrier fleet. Multi-agent software uses the artificial intelligence principle of ontology to assess the factors subject to change - 'agents' - that act on a set of assets, devising optimal deployment in relation to prevailing requirements. ... When a new cargo is offered, 'agents', amounting to individual software programmes, 'negotiate' the optimum vessel for the cargo by comparing alternative routes, vessels, ports, costs, freight rates, fuel against propulsion, speed and distance."

  • Also see: Magenta Deploys its Multi-Agent Technology to Optimize One of the World's Largest Oil Tanker Fleets. PRNewswire / available from WQAD (August 13, 2004). "[W]hen a new cargo is offered, agents are created within the database that contains all the data about the cargo - for example freight rates, size and type of cargo, as well as load and discharge ports. The agents then negotiate within the virtual market to decide the optimum vessel for the cargo, based on TI's fleet strategy. The agents do this by competing to find the best solution between supply and demand by comparing alternative routes, vessels, ports, costs, freight rates, fuel for propulsion, speed, distance and even positions of the vessels. This data and the defining concepts upon which the agents base their decisions are stored within the knowledge database, known as the Ontology. Unlike other systems, the agents are also able to resolve conflicts as they are not bound by rigid rules and are able to work around problems."

July 28, 2004: Amplified Intelligence - The AI Problem. Interview with Ken Ford. Astrobiology Magazine. "Astrobiology Magazine (AM): The IMHC [Interdisciplinary Study of Human & Machine Cognition] research agenda broadly seems to cover robotics, cognition and simulations. Are there parts of machine intelligence that your research institute doesn't cover today, but that you see as growth areas? Ken Ford (KF): Don't forget that second letter is 'H'. Although a lot of our research could be categorized as AI, and five of our researchers are AAAI (American Association for Artificial Intelligence) Fellows, IHMC is not a traditional machine intelligence laboratory. The focus and theme of our research is what has become known as human-centered computing which, in a nutshell, is about fitting technology to people instead of fitting people to technology. The human is part of the system, and it is the performance of the whole system, including the human, that we are interested in. This requires that machines should be designed to fit us physically, cognitively, and perhaps even socially. We think of AI as meaning 'Amplified Intelligence.' The interesting thing is that many traditional AI technologies in fact are being used in just this way. We like to refer to it as building cognitive prostheses, computational systems that leverage and extend human intellectual capacities, just as eyeglasses are a kind of ocular prosthesis. Building cognitive prostheses is fundamentally different from AI's traditional Turing Test ambitions -- it doesn't set out to imitate human abilities, but to extend them. ... AM: In your opinion, how well do the machine intelligence problems (like navigation, data-mining, or simulations with agents) map to the basic computer science [CS] problem of efficient 'search'? KF: Wow, efficient search is a 'basic computer science problem'? Not long ago, search was being suggested as a defining characteristic of AI to distinguish it from 'mainstream' CS. But to return to the question: search is certainly a central technique in AI, but the search spaces arising in AI are often impossibly huge, and a more interesting aspect is not so much how to search them efficiently as how to re-cast problems so that the search space itself is reduced in size. Searching is what you do when you can't think of anything smarter."
>>> Interfaces, Search, Robots, Space Exploration, Data Mining, Household Appliances, Interviews, AI Overview, Applications, Reasoning, Machine Learning, Turing Test

July 14, 2004: Attack of the killer vacuum cleaners. By Charles Arthur. The Belfast Telegraph Digital. "Things are about to happen with robots, because the element they need to make them truly useful - the software, which needs to be able to adapt to a wide range of situations - is getting cheaper all the time. Future Horizons, a semiconductor analyst based in Kent, forecasts that by 2010 there will be 55.5 million robots, in a world market worth £30bn - up from £2.4bn last year. 'The electronics industry is on the cusp of a robotics wave, a period in which applications are aimed at labour-saving and extending human skills,' it reports. Of those, it says that 39 million will be domestic robots, and 10.5 million 'domestic intelligent service' robots. That is because there's a growing need for robots to help the elderly and handicapped. ... But the real explosion in robotics is coming among the 'immobots' - or, more simply, just 'bots'. These are bits of software that are incorporated into larger objects, and that remove a lot of the strain of having to decide what to do next. We're getting glimpses of how good these could be at present: the tiny number of Britons with a TiVo personal video recorder have something that decides, based on the programmes they choose to record, what other programmes they might like to see, and records those, too. ... The reason why we can't yet declare 'The Year of the Robot', however, is that researchers are still fundamentally split about how robots should behave and learn. One group favours the 'top-down' approach, in which all the behaviour of the robot is mapped out, and its software is written to fill out that behaviour. The Roomba vacuum cleaner is a classic example.... The alternative is something assembled from smaller, self-contained units, which creates a gestalt of behaviour based on that. Thus the system that controls the legs learns to 'walk' independently.... Sony's Aibo draws on a form of this....
>>> Robots, Science Fiction, Agents, Systems, Assisitive Technologies, Household Appliances, Industry Statistics, Applications, Reasoning, Machine Learning

July 4, 2004: His quest - Do Disney in a day. By Larry Bleiberg. The Dallas Morning News / available from Mickey News. "Rich Vosburgh worked out hard, spending four months with a personal trainer. He scrutinized maps and a detailed timetable. He even deployed a secret weapon: artificial-intelligence research to chart a course through death-defying drops, torrents of water and fiery heat.And when this Texas adventurer clambered out of a floating log a year ago, he had reached his holy grail: visiting - in a single day - each of the 41 operating rides, attractions and shows at the Everest of theme parks, Walt Disney World's Magic Kingdom. His time: a record 10 hours, 40 minutes. ... At heart, the challenge is an enduring and perplexing quandary: What's the most efficient way to route someone to multiple places, taking into account constantly changing conditions? Logistics and timing Mathematicians call it the Time Dependent Traveling Salesman Problem. The answer could help fighter-jet pilots chart bombing targets or freight companies schedule package deliveries."
>>> Traveling Salesperson Problem, Planning and Scheduling, Search, Genetic Algorithms, Machine Learning, Reasoning, Transportation, Applications, Games & Puzzles

June 24, 2004: Informed decisions - CHEO team tests artificial intelligence in neo-natal unit. By Andrew Mayeda. Ottawa Citizen (subscription required). "When a baby is born prematurely, parents must often make a heartbreaking decision of whether to continue care or to simply let go. While that decision will never be easy, a pair of Ottawa researchers have developed artificial-intelligent tools that could at least make it more informed. The result is a software system [Parents Assisting Decision Support] that lets parents know their child's chances of survival, and allows them to weigh the pros and cons of treatment options while consulting their doctor or nurse. ... PADS, as it is called for short, is the brainchild of Dr. Robin Walker and Monique Frize, who have worked together for more than a decade."
>>> Case-Based Reasoning, Neural Networks, Medicine, Applications, Reasoning, Machine Learning

June 10, 2004: Fuzzy logic and neural nets: still viable after all these years? Though no longer headliners, fuzzy logic and neural networks are options in tackling challenging applications. By Graham Prophet. EDN Magazine. "[B]oth still have their place in your engineering tool kit. The two techniques are essentially unrelated, except that they both provide control methodologies to handle highly nonlinear or poorly specified problems, they both came to some prominence at about the same time, and they both faded from view in much the same way. Both neural networks and fuzzy logic aspire to allow electronic systems, built with familiar circuit techniques or employing conventional computing technologies, to attack certain problems in a way that mimics human responses and abilities. ... One of the intimidating aspects of fuzzy logic is the name itself, which has connotations of imprecision. On the contrary, however, fuzzy logic is capable of precise responses. It allows systems built around Boolean logic, handling binary values, to work with imprecisely defined values that you might express verbally as 'more,' 'less,' 'high,' 'low,' and so on. ... Neural networks, unlike fuzzy logic, seek to reproduce the versatility of the human brain in recognizing the end-to-end, input-to-output behavior of a system without understanding all the processes taking place within it. Taking as a fundamental model the interconnections of nervous systems within the brain -- neurons and synapses -- neural networks have the attributes of memory and learning. ... What happens to the expertise built up in neural and fuzzy techniques from their first flush of popularity? If you set about tracking down some of the pioneering companies from as much as a decade ago, you'd find that, although many no longer exist, some have transformed themselves into software-design and consultancy operations. These businesses are applying the same neural and fuzzy techniques but mainly in software simulation running on conventional computers, in areas such as financial modeling, financial services, and data mining."
>>> Fuzzy Logic, Neural Networks, Applications, Banking, Finance & Investing, Data Mining, Reasoning, Machine Learning

June 8, 2004: UCSC man's work earns top award. By David L. Beck. Mercury News (no fee reg. req'd.). "[T]he Association for Computing Machinery honored Santa Cruz's David Haussler on Saturday night at New York's Plaza Hotel.... A mathematician whose contributions to biology are incalculable, Haussler is being given the Allen Newell Award, named for a pioneer in artificial intelligence. ... 'By focusing on scientific interactions between computer scientists and molecular biologists,' the Newell Award announcement noted, 'Dr. Haussler has played a leading role in developing the new field of computational biology.' ... What Haussler and his team at UCSC did was to write computer programs that would take all that DNA -- billions and billions of base pairs, because redundancy is crucial to Haussler's 'probabilistic' approach -- and put them in the right order. Result: the human genome. Some call it a blueprint. Haussler calls it a recipe. ... Haussler shares the Newell Award with Judea Pearl, director of the Cognitive Systems Laboratory at UCLA, whose ideas 'have revolutionized the understanding of causality in statistics, psychology, medicine and the social sciences.'"
>>> Bioinformatics, Reasoning, Applications

May 21, 2004: Supply chain by numbers - Complex mathematics helps business managers think outside the box. By Ephraim Schwartz. InfoWorld. "Long the exclusive domain of AI (artificial intelligence) research, statistical probability analysis got its real start during World War II, when military intelligence wanted a way to predict possible outcomes. Throughout the 1950s, the field was continued by mathematicians such as John Nash, and now this complex discipline is becoming the stuff of ordinary business applications. From a common-sense point of view, the way statistical analysis works seems illogical. The more complex a data set becomes, for instance, the easier it is to make predictions. For example, take NLU (natural language understanding) research. ... NLU program designers input millions of sentences into their software. So, statistically, they know that there might be a 90-percent chance that the next word in my sentence is going to be "is." In similar fashion, supply-chain management companies such as G-Log have begun using a form of statistical analysis called stochastic optimization, first as a guidance tool for managers and later to actually automate the decision-making process."
>>> Probability, Natural Language Understanding, Business, Applications, Reasoning, Natural Language Processing

May 20, 2004: A Design Epiphany - Keep It Simple. By Jessie Scanlon. The New York Times (no fee reg. req'd.). "Dr. [John] Maeda says the solution is not better design or better technology but a better partnership between the two. Hence the Simplicity Design Workshop, which could leverage the lab's understanding of emerging technologies and the real-world experience of the designers into a series of concrete, well tested principles. ... In January Mr. [Bill] Moggridge of Ideo met with a Media Lab group led by Cynthia Breazeal, an assistant professor of media arts and sciences, to try to define simplicity. It was easy to embrace the concept, with its connotations of beauty and elegance and its promise of a better way, but what did it mean in practical terms? ... 'We started with the big picture: what does simplicity mean in the context of our work?' said Dr. Breazeal, a pioneer of social robotics whose current project is building a learning companion robot called RoCo. 'But the real value is to see how Bill approaches the problem of design.'... A third arm of research focuses on making computers smarter. One method, a new branch of artificial intelligence, aims to give computers common sense in the form of a vast database of mundane truths about the world (the sky is blue, coffee wakes you up). A second approach, affective computing, gathers information about the state of the user through a range of sensors, enabling the computer to adapt by, say, holding delivery of all but high-priority e-mail when it detects stress."
>>> Robots, Interfaces, Commonsense, Emotion, Cognitive Science, Reasoning, Representation

May 5 - 12, 2004: Chaos seems to aid learning. By Kimberly Patch. Technology Research News. " Researchers from Core Research for Evolutional Science and Technology (CREST) in Japan have built a computer simulation of the inferior olive, a portion of the brain that probably relays errors in movement to the cerebellum. It has been difficult to explain the mechanics of this relationship because inferior olive cells that connect to the cerebellum fire slowly, and this does not fit well with the common hypothesis that high-fidelity error signals are needed for efficient learning. ... In addition to allowing researchers to better understand the mechanics of the brain, the researchers' theory of chaotic resonance could speed electronic communications and improve robotics. 'In communications, our work [could] maximize the information transmitted in networks,' he said. 'In robotics, chaos could be used to explore the environment to optimize learning,' [Nicholas Schweighofer] said."
>>> Fuzzy Logic, Probablility, Logic, Reasoning, Robots, Machine Learning

May 10, 2004: Tahoka student takes byte out of competition. By P. Christine Smith. The Lubbock Avalance-Journal /available from LubbockOnline.com. "Brandon Jackson is the kind of student his science teacher would like to clone. A bright, articulate student who ranks fifth in his sophomore class, Brandon recently won a regional science fair and now is in Portland, Ore., competing in the International Science and Engineering Fair. He began his study on artificial intelligence as an eighth-grader. Brandon uses two computers, which he built, in his project 'Computers at War: How Far Can Artificial Intelligence Go?' He programmed the two computers to spar against each other in a chess-type game of his own design. Through the neuro-network, the two computers can 'learn' from each other, he said last week. ... [H]e took the project a step further and added additional programming using case-based reasoning. ... He is going up against 3,000 students from around the world at the Intel-sponsored fair in Portland, which began Sunday and runs to Friday."
>>> Competitions (@ Resources for Students), Neural Networks, Case-Based Reasoning, Chess, Machine Learning, Reasoning

April 27, 2004: Association for Computing Machinery Honors Innovators Who Changed Scientific World; David Haussler, Judea Pearl Built Bridges Beyond Computer Science. AScribe Newswire. "The Association for Computing Machinery (ACM) has recognized Dr. David Haussler and Dr. Judea Pearl for separate groundbreaking contributions that have changed the scientific world beyond computer science and engineering. Dr. Haussler was cited as possibly the most influential contributor to the field of computational biology. Dr. Pearl made seminal contributions to the field of artificial intelligence that extend to philosophy, psychology, medicine, statistics, econometrics, epidemiology and social science. As the recipients of the 2003 Allen Newell Award, they demonstrate the remarkable influence that computer science and artificial intelligence can have on other sciences, on practical tools, and on human thought. The Allen Newell Award, which is cosponsored by ACM and the American Association for Artificial Intelligence (AAAI), comes with a cash prize of $10,000."
>>> Uncertainty & Probability, Bioinformatics, Reasoning, Applications, Associations & Organizations (@ Resources for Students), AI Overview

April 27, 2004: NASA Develops Decision Support Software For Mars Mission. SpaceDaily. "'This is mission-critical software and the first application of an artificial intelligence-based system for operating a platform on the surface of another planet,' [Kanna Rajan] said, adding that MAPGEN plans out a whole day of activities for the rovers in advance. MAPGEN even decides when the rovers wake up from their nightly slumbers to begin the next 'Sol,' or martian day, of activities. MAPGEN is actually a combination of two previously built planning systems: the Activity Plan Generator (APGEN), a manually operated planner developed by JPL and EUROPA, an automated planning and scheduling system developed at Ames Research Center. An earlier version of EUROPA was flown as part of NASA's Deep Space One Remote Agent experiment in 1999."
>>> Space Exploration, Planning & Scheduling, Applications, Reasoning

April 27, 2004: Cognitive Rascal in the Amorous Swamp: A Robot Battles Spam. Essay by George Johnson. The New York Times (no fee reg. req'd.). "In Richard Powers's postmodern science fiction 'Galatea 2.2,' a young novelist, very much like the author, returns from the Netherlands to a Midwestern university, where he teaches a computer called Implementation H, or Helen, the meaning of beauty. By feeding it example after example of the world's great literature and music and engaging it in conversation, researchers hope to imbue the machine with so deep a grasp of human culture that it can pass a comprehensive master's degree examination. Instead it prefers to sing. Galatea was the name of the statue brought to life by Pygmalion, and the novel, published in 1995 by Farrar, Straus & Giroux, captures the dream of artificial intelligence: the creation of a computer so smart and engaging that you might want to keep it as a friend. Efforts nearly as ambitious continue to plod on. ... Most A.I. researchers content themselves with narrower, more practical tasks: machines that can diagnose a certain type of illness or an ailing stock portfolio, that can crawl through the World Wide Web or across the surface of Mars. Recently I've become acquainted with one of these idiot savants, a software robot called SpamProbe. Its one modest talent is learning by example to recognize junk e-mail messages and keep them from my in-box. At the heart of this and similar programs is a statistical method called Bayesian inference, a simple learning procedure that works so well in this limited domain that perhaps something like the fictional Helen is not so far-fetched after all. Within minutes, the program had discovered rules of spam identification that had taken me years to acquire. ... Bayesian statistics were invented in the 18th century by Thomas Bayes, a theologian and mathematician.... The system has been a staple of A.I. research for years. Based on what has happened in the past, a Bayesian-savvy computer can estimate the odds that it will happen again. It learns from experience through something that seems very much like the process of induction."
>>> Filtering, Probability, Commonsense, Machine Learning, Reasoning, Bayes (@ Namesakes), Applications, SciFi

April 21, 2004: Teaching Robots to Herd Cats. By Michelle Delio. Wired News. "Robots designed for emergency rescue work can survive a six-story drop onto collapsed, jagged concrete. They can be thrown 100 feet into a disaster site. They can even cope with poisonous chemicals, fires, freezing temperatures and floods. But, like most rugged individualists, they don't play well with others. ... To translate the human concept of teamwork into electronics, three teams of university researchers are working together to develop technology that would turn a pack of robots into a single machine. Led by Nikos Papanikolopoulos, researchers at the University of Minnesota, the University of Pennsylvania and Caltech are working on software that will allow small robots to coordinate their actions, carry out commands from a single human operator or take directions from a larger, smarter robot. ... Robots have to do much of this work on their own. Humans usually can't control more than three or four robots at one time. 'We've tried it -- anything over four robots and the rescuers are overwhelmed with too much information,' says Papanikolopoulos."
>>> Robots, Hazards & Disasters, Multi-Agent Systems, Vision, Planning, Reasoning, Applications

April 19, 2004: DARPA tech chief envisions the future - Sci-fi inspires Brachman to use computers in creative ways. By Frank Tiboni. Federal Computer Week. "Ron Brachman's curiosity about robots programmed to think on their own dates back to his childhood in New Jersey. It was the 1960s, 'Star Trek' first appeared on television and putting a man on the moon became a remarkable reality. ... Now Brachman works at the Defense Advanced Research Projects Agency as director of its Information Processing Technology Office, where he oversees programs that study and develop cognitive computing. He wants to solve the same problem he pondered as a teenager watching 'Star Trek' -- how to get people and computers to collaborate. Military officials think robots, with their superior memory, can aid generals in command and control centers, Brachman said. 'My sense of what it takes to put together a cognitive agent that is successful, like a really good executive assistant, is that you just don't put all these [technologies] in a pot and stir and hope that it all adds up,' he said. ... Brachman's team will take an eclectic approach to building a robot similar to Data. 'The challenge we have asked people to look at is how do we put all of these pieces together,' Brachman said. 'Maybe we don't need the world's best computer vision or speech-understanding technology. But what would happen if they both work together?'"
>>> SciFi, Reasoning, Representation, Agents, Robots, Military, Applications, Careers in AI (@ Resources for Students)

April 11, 2004: Machine rage is dead ... long live emotional computing. Consoles and robots detect and respond to users' feelings. By Robin McKie. The Observer. "Computer angst - now a universal feature of modern life - is an expensive business. But the days of the unfeeling, infuriating machine will soon be over. Thanks to break throughs in AI (artificial intelligence), psychology, electronics and other research fields, scientists are now creating computers and robots that can detect, and respond to, users' feelings. The discoveries are being channelled by Humaine, a £6 million programme that has just been launched by the EU to give Europe a lead in emotional computing. As a result, computers will soon detect our growing irritation at their behaviour and respond - by generating more sympathetic, human-like messages or slowing down the tempo of the games they are running. Robots will be able to react in lifelike ways, though we may end up releasing some unwelcome creations - like Hal, the murderous computer of the film 2001: A Space Odyssey . 'Computers that can detect and imitate human emotion may sound like science fiction, but they are already with us,' said Dr Dylan Evans, of the University of the West of England and a key Humaine project collaborator. ... A key breakthrough has been the discovery that cool, unemotional decision-making is not necessarily a desirable attribute. In fact, humans cannot make decisions unless they are emotionally involved. 'The cold, unemotional Mr Spock on Star Trek simply could not have evolved,' said artificial intelligence expert Professor Ruth Aylett of Salford University, another Humaine project leader."
>>> Emotion, Interfaces, Applications, Cognitive Science, Assistive Technologies, Robotic Pets, Video Games, Robots, Reasoning, Systems

April 7, 2004: Flood Risk Management Research Consortium (FRMRC) launched by Environment Minister Elliot Morley and Environment Agency chair Sir John Harman. Department for Environment, Food and Rural Affairs (Defra) news release. "A new floods consortium staffed by some of Britain's leading engineers and scientists and launched today by Environment Minister Elliot Morley and Environment Agency chair Sir John Harman, will invest more than £5.5m to develop more accurate flood forecasting and warning and modelling systems and improve flood management infrastructure. Its work will help reduce risk to people, their property and the environment. The new group, known as the Flood Risk Management Research Consortium (FRMRC) will pull in staff from a number of universities to work with industrial partners and operational bodies on integrated research projects, including: ... Using intelligent systems, neural networks, fuzzy set theory, artificial intelligence evolution computation (genetic algorithms), decision support tools and expert systems - to help predict the likelihood of flooding."
>>> Natural Resource Management and the Environment, Applications, Expert Systems, Neural Networks, Genetic Algorithms, Fuzzy Logic, Machine Learning, Reasoning

March 31, 2004: Conversational interface aids robot navigation. By Chappell Brown. EE Times. "Recognizing the difficulty of fully autonomous navigation, a scientist at the University of Missouri-Columbia is designing a semiautonomous approach that might have a better chance of producing useful machines in the near term. Marjorie Skubic, a computer scientist at the University of Missouri's College of Engineering, has demonstrated a prototype robot that can read sketches drawn on a PDA and then execute a proposed path through a room. Skubic is conducting the research with Missouri colleague James Keller, an expert in fuzzy-logic-based pattern recognition, and with Pascal Matsakis of the computing and information science department at Ontario's University of Guelph. ... The human director would supply the high-level cognitive understanding of the space, and the robot would execute low-level distance and motion calculations. 'It turns out that both maps and everyday conversations share a simple set of spatial elements and relationships that are used to navigate around obstacles,' said Skubic. Skubic's group is creating a robotic AI system that can understand those basic terms so that human operators would be able to direct robots through a room in an intuitive conversational mode. The goal is to create a more practical and flexible means of directing robots and autonomous vehicles. ... The underlying spatial language is based on constructing histograms representing the distance relationships between objects."
>>> Autonomous Vehicles, Fuzzy Logic, Pattern Recognition, Languages & Structures, Robots, Reasoning, Interfaces, Representation, Machine Learning, Natural Language Processing

March 27, 2004: The Isaac Newton of logic - It was 150 years ago that George Boole published his classic The Laws of Thought, in which he outlined concepts that form the underpinnings of the modern high-speed computer. By Siobhan Roberts. The Globe and Mail (page F9). "It was 150 years ago that George Boole published his literary classic The Laws of Thought, wherein he devised a mathematical language for dealing with mental machinations of logic. It was a symbolic language of thought -- an algebra of logic (algebra is the branch of mathematics that uses letters and other general symbols to represent numbers and quantities in formulas and equations). In doing so, he provided the raw material needed for the design of the modern high-speed computer. His concepts, developed over the past century by other mathematicians but still known as 'Boolean algebra,' form the underpinnings of computer hardware, driving the circuits on computer chips. And, at a much higher level in the brain stem of computers, Boolean algebra operates the software of search engines such as Google. ... The most basic and tangible example is the machinations of Boolean searches, which operate on three logical operators: and, or, not. Algebra gets factored in to this logical equation when Boole designates a multiplication sign (x) to represent 'and,' an addition sign (+) to represent 'or,' and a subtraction sign (-) to represent 'not.' ... The same 'and' gates and 'or' gates drive computer circuitry, with streams of electrons performing Boole's algebraic operations -- a computer's bits and bytes operate on the binary system, as does Boole's algebra. He employs the number 1 to represent the universal class of everything (or true) and 0 to represent the class of nothing (false). ... With his PhD in artificial intelligence, it might appear that ['Geoffrey Hinton, a computer-science professor at the University of Toronto and his great-great-grandson'] followed after Boole. But in fact, he says, 'I'm entirely on the other side.' The field of artificial intelligence, in its early years circa 1950-60, was committed to the Boolean idea that symbols effectively represent human reasoning. Since the eighties, however, artificial intelligence has come to see human reasoning as not purely logical. Rather, it is more about what is intuitively plausible. 'Boole thought the human brain worked like a pocket calculator or a standard computer,' Prof. Hinton says. 'I think we're more like rats.'"
>>> Systems & Languages, History, Logic, Boole (@ Namesakes), Reasoning, Web-Searching Agents, Cognitive Science, Information Retrieval

March 22, 2004: Sharp unit to license IP from U.S. labs. By R. Colin Johnson. EE Times. "Artificial-intelligence technology that could change the way busy sports fans get their fix will be among the licensable intellectual property unveiled here Tuesday (March 23) by the newly formed Sharp Technology Ventures. ... One technology that could find a wide audience is Sharp's HiMpact Sports, which applies a set of algorithms that understand the semantics of baseball, football and soccer (for starters) and can boil down a three-hour game to 45 minutes without skipping a single play. ... How can Sharp Labs teach a computer to recognize a base hit regardless of whether it's a grounder, a line drive or a bunt? Traditional AI would extract features from the video stream, then use handwritten rules to infer the meaning (base hit) from the features. After extensive testing, however, Sharp Labs concluded that its requirement that HiMpact provide 100 percent accuracy could only be met by probabilistic methods that directly learn from experience. ... The best probabilistic method Sharp Labs has tried thus far is the hidden Markov model (HMM), which has previously been successful in learning how to recognize spoken voices. Just as HMM is 'taught' words by training it with samples of different people speaking the same word, Sharp Labs trained its HMM on video clips it categorized into a training set."
>>> Information Retrieval, Image Understanding, Probability, Reasoning, Machine Learning, Sports, Markov (@ Namesakes), Vision, Speech, Applications

March 17, 2004: RFID chips watch Grandma brush teeth. By Celeste Biever. New Scientist News. "Tiny computer chips that emit unique radio-frequency IDs could be slapped on to toothbrushes, chairs and even toilet seats to monitor elderly people in their own homes. Data harvested from the RFID chips would reassure family and care-givers that an elderly person was taking care of themselves, for example taking their medication. Unusual data patterns would provide an early warning that something was wrong. A group of Intel researchers demonstrated the technology to US government officials in Washington DC on Tuesday. ... Algorithms on the PC use 'probabilistic' reasoning to infer what the person is doing. For some tasks, merely picking up an object such as a toothbrush is enough. But to determine that someone is making a cup of tea, a series of objects and their order must also be known. Concerned relatives can then check on their loved one over the internet. The computer could even be programmed to pick up on unusual patterns automatically and alert relatives through an email or SMS message. ... Other companies and universities also showcased wireless healthcare technologies including a bed that monitors a person's weight and movements. Larson's team at MIT demonstrated embedded systems that rely on a network of embedded cameras and temperature sensors to make inferences about behaviour."
>>> Assistive Technologies, Uncertainty/Probability, Machine Learning, Reasoning, Applications

February 19, 2004: Know your imitations - From cameras and fridges to business solutions, artificial intelligence is at work. By Mary Branscombe. The Guardian. "Computers like things precise: on or off, one or zero, yes or no. The real world is rarely precise or exact; information is partial and uncertain and people make judgment calls. Artificial intelligence is about making computers act more like humans and you might be surprised at how many places it's showing up - from cameras and fridges to spam filters and Microsoft's forthcoming BizTalk Server 2004. If AI makes you think of robots and artificial brains, think again. Researchers are still arguing about whether a computer could actually think or just respond as if it can, but the results of their research are too useful to stay in the lab. Software can act in ways we think of as intelligent, can deliver results we'd only expect from a human, and can let us work in ways that feel more natural."
>>> AI Overview, Applications, Video Games, Household Appliances, Fuzzy Logic, Bayes (@ Namesakes), Probability, Filtering, Interfaces, Expert Systems, Banking, Business, Reasoning

February 12, 2004: Vulcan project aims to build 'Digital Aristotle'. By Luke Timmerman. The Seattle Times. "[Paul] Allen's private investment company, Vulcan, is announcing today that it is willing to bankroll three competing research teams from around the world for what it calls 'Project Halo,' a quest over the next 30 months to create a computerized tutor that's smart enough to pass college-level Advanced Placement (AP) tests in chemistry, biology and physics. Vulcan is trying to avoid being linked with forays into artificial intelligence -- colossally hyped flops since the 1980s that crumbled under sci-fi dreams of mimicking human motivation or emotion. This effort, they say, is more about 'knowledge representation and reasoning,' synthesizing existing information to produce not just a yes/no answer but a lucid explanation. ... 'This is not going to be a sentient computer or have self-awareness or emotion or anything like that,' [Noah] Friedland said. 'We're going to have a hard-enough time with common-sense issues. But this is going to reason about science and be used as a tool for learning.'"
>>> Reasoning, Representation, Commonsense, Emotion, AI Overview, Turing Test

February 8, 2004: Reasoning on the Web with rules and semantics. By Ibn Campusino. The Sunday Times (Malta). "In the framework of its sixth Framework Programme, the European Commission has decided to launch a research 'Network of Excellence' on 'Reasoning on the Web' entitled REWERSE (for REasoning on the WEb with Rules and SEmantics). ... The objective of REWERSE is to strengthen Europe in the area of reasoning languages for Web systems and applications, especially Semantic Web systems and applications aiming at enriching the current Web with so-called intelligent capabilities for data and service retrieval, composition, and processing."
>>> Reasoning, Representation, Languages & Structures, Ontologies, Web-Searching Agents, Information Retrieval, Applications

January 19, 2004: Follow The Water - Robotic Mission To Martian Soil Is A Search For Life. By Nivedita Mookerji. The Financial Express. "There's also a strong Indian connection, when we talk about Mission Mars. Soon after its landing, Spirit began its scientific tasks. And many of these tasks are being controlled, operated and monitored by the software developed by a NASA team led by Kanna Rajan, a 1990 computer science and engineering master's graduate from the University of Texas at Arlington (UTA). Mr Rajan is now a principal investigator and senior scientist at NASA Ames Research Center in Moffett Field, California. According to reports published by NASA on its home page, Mr Rajan and his team's software, called 'MAPGEN (Mixed-Initiative Activity Plan GENerator), for building activity plans with complex constraints and dependencies, is the first published artificial intelligence-based system to command a rover on another planet.' Mr Rajan, while speaking to eFE (see interview), said: 'MAPGEN produces an initial plan automatically, using state-of-the-art Artificial Intelligence (AI) techniques that we've been working on at NASA Ames Research Center over the last 10 years, to produce a conflict free plan. It is conflict free, in terms of activities that do not clash.'"

  • January 19, 2004: Interview with Kanna Rajan, 'We Must Continue The Quest To Outer Planets To Discover Our Origins.' By Nivedita Mookerji. The Financial Express. "Principal investigator and project lead for the Mars Mission's on-ground software effort, Kanna Rajan, in an interview to eFE, talks about the IT initiatives in MER, role of artificial intelligence in it, relevance of such missions and more."

>>> Planning, Space Exploration, Reasoning, Applications, Interviews

January 17, 2004: A robot that likes to play with test tubes. By David Akin. The Globe & Mail. "[I]f one were to design an artificial scientist -- say, a robot that likes to play with test tubes -- one would have to tell it to play a hunch every now and again. At some point in its investigations, the robot would have to throw away the logic of its memory chips and engage in that uniquely human activity of asking, 'What if?' Well, that has now happened. This week, a group of researchers in Britain unveiled the Robot Scientist, a device five years in the making. Not only can it ask, 'What if?' it can also design some experiments to test its hypothesis, carry out those experiments and, finally, analyze the data collected before confirming or altering its hypothesis. 'Putting all that altogether in a working system is a major scientific achievement,' said Alan Mackworth, a computer science professor at the University of British Columbia and the president-elect of the American Association of Artificial Intelligence. ... There is a 40-year history of robotics researchers designing and building systems that do some of the things the Robot Scientist does. But in almost all of those cases, the robot or the AI software program was built to operate in pristine, laboratory conditions. One of the significant innovations with the British project is that the Robot Scientist is built to work in the real world. It can handle the kind of 'noise,' to use the researchers' term, that human investigators so easily deal with."
>>> Scientific Discovery, Robots, AI Overview, Bioinformatics, Abduction, Reasoning, Applications

January 14, 2004: Auto-Eureka! British scientists develop robot to take their place. By Alex Dominguez. Associated Press / available from the Khaleej Times Online / also available from the Star-Telegram (Scientists Develop Experimenting Robot). "A new robotic system they developed can, for the first time, independently design and carry out a genetics experiment, and then interpret the results. No difference was found between the lab bench results generated by the robot scientist and those gathered by graduate students doing similar work, the researchers report in Thursday's issue of the journal Nature. ... 'The sort of grunt research can be done this way, and more creative stuff humans will have more time to do,' said study author Stephen Oliver of the University of Manchester. ... The robot scientist uses a type of reasoning called abduction. [Ross] King said it is the kind of reasoning police use to reconcile clues when investigating a crime. ... 'It is now possible to design artificial intelligence systems that are able to reason well enough to be effective partners in scientific research,' [Larry] Hunter said."
>>> Bioinformatics, Scientific Discovery, Abduction, Reasoning, Robots, Applications

January 13-19, 2004: Making Sense of Common Sense Knowledge - Benjamin Kuipers on using commonsense reasoning to make useful conclusions, or, finding gold nuggets in a pan of sand. Ubiquity (Volume 4, Number 45). "UBIQUITY: Would it be wrong-headed to suggest that 'common sense' is a very squishy term, since John McCarthy, Joe McCarthy, Eugene McCarthy and Charlie McCarthy would have had radically different and incompatible views of what is 'commonsensical'? What is common about commonsense knowledge if there's no real agreement on what commonsense knowledge actually means in ordinary life? And if commonsense knowledge is undeterminable, how can you build on it?"
>>> Commonsense, Qualitative Reasoning, Reasoning, Interviews

January 11, 2004: Indian American leads Mars rover's software team. By Vasantha Arora. Indo-Asian News Service / available from the Hindustan Times. "Indian American computer engineer Kanna Rajan led a team of scientists to develop software that enabled America's Spirit rover to land on the Red Planet. As soon as the Mars Exploration Rovers leave their landers, they will be confronted with completing hundreds of manoeuvres and scientific tasks. The order in which they do these tasks will be decided by computer software developed by a National Aeronautics and Space Administration (NASA) team led by Rajan. ... Their software, called MAPGEN (Mixed-Initiative Activity Plan Generator), for building activity plans with complex constraints and dependencies will be the first published artificial intelligence-based system to command a rover on another planet."
>>> Planning, Space Exploration, Reasoning, Applications

January 1, 2004: SVM Analysis -A Closer Look. By Ernie Schell. Catalog Age. "While far from a household word in direct marketing circles, SVM [support vector machine] is not a brand-spanking new concept. It is a statistical modeling methodology that has proved effective in a variety of fields, primarily those encompassing a large number of variables, such as face-recognition, bioinformatics (gene classifications), and the classification of Web pages for search engine companies such as Google. ... SVM is a form of data mining; it's also a form of artificial intelligence applied to data mining. By combining the two, bringing 'pattern recognition' and 'statistical probability' into the picture. SVM can handle a much larger number of inputs than traditional mining as well. ... SVM allows you to make accurate predictions of future behavior based on a practical analysis of past behavior. ... SVM happens to be based on logic that follows the precepts of Ockham's Razor, or the so-called Law of Economy, stated by William of Ockham in the 14th century, which says that a simpler solution is preferable to a more complex one if the results are the same."
>>> Marketing, Data Mining, Pattern Recognition, Machine Learning, Uncertainty/Probability, Ockham (@ Namesakes), Biometrics (@ Image Understanding), Bioinformatics, Information Retrieval, Applications, Reasoning

December 24 - January 6, 2004: Stuart Russell on the Future of Artificial Intelligence. Ubiquity (Volume 4, Issue 43). "AI may not take over the world but it will provide new and powerful tools. Smart microwave ovens? No big deal. Full-size humanoid robots that walk, climb stairs, open and close doors, and pick things up? Now that gets our attention."
>>> AI Overview, The AI Effect, Cognitive Science, Reasoning, Applications, Chess, Academic Departments (@ Resources for Students), Interviews, But is it AI?

December 25, 2003: Puzzles provide brain insight. Column by Walter Witschey. Richmond Times-Dispatch. "The first modern crossword puzzle appeared 90 years ago in the New York World Sunday paper. ... Solving crosswords has been a test of artificial-intelligence programs since 1977. Recently, researchers wrote a program to solve a crossword based on its clues as well as its diagram structure. Researchers at Duke created a program called 'Proverb' for 'probabilistic cruciverbalist.' A cruciverbalist is a crossword-puzzle solver, and probabilistic refers to using a computer to calculate how probable or likely a given answer is among many choices. ... There doesn't seem to be anything artificial about intelligence such as this. In fact, if we are ever to have robots and machines around us that respond as intelligently as humans, working crossword puzzles is a dandy first step."
>>> Crossword Puzzles, Probability, Constraint-Based Reasoning, Reasoning, Games & Puzzles

December 15, 2003: Blossoming business. Mark Fox generates ideas - then pursues them to fruition. By Nicolle Wahl. News @ University of Toronto. "Westinghouse took an interest in his work and asked him to create a role for robotics and artificial intelligence in their complex manufacturing process. Fox identified a lack of awareness of what was happening on the factory floor as interfering with the company's ability to develop accurate manufacturing schedules and decided to build an 'expert' system that used artificial intelligence to represent the knowledge of a human scheduler. He realized that the computer needed to view the scheduling in terms of constraints such as the availability of a material, worker or tool, and preference (as in preferring to meet a due date). Fox called this approach 'knowledge-based simulation.' His growing expertise caught the attention of the U.S. army, which was looking for ways to schedule the movement of available resources before the first Gulf War. ... His research now looks at enterprise modelling, which includes designing systems involving both terminology and the meaning, or semantics, of that terminology. 'The Holy Grail for us in that area is to create a computer-based description of an enterprise -- the terminology along with the semantics -- with a natural language front end where any person in an organization can type in a question about the enterprise.'"
>>> Expert Systems, Planning & Scheduling, Constraint-Based Reasoning, Reasoning, Representation, Natural Language Processing, Business & Manufacturing, Military, Information Retrieval, Careers in AI (@ Resources for Students), Applications

December 13, 2003: Cracker joke or two to win a £500 prize from Asda and be a laugh next year. By David Williamson. The Western Mail / available from ic Wales. "One of the reasons such groan-inducing favourites are still attracting interest is that pioneers of artificial intelligence are teaching computers to tell Q&A jokes. So far, computers have learned how puns work and how to match them with nouns and verbs. Tests show that the jokes they have told are almost as funny as those told by humans. And researchers at the University of Edinburgh are hoping to create a 'language playground' where children will be able to experiment with words. Graeme Ritchie said, 'We are aiming it at children with disabilities becausethey are mainly deprived of the thrusting swapping of jokes with their peer group.' In scientific studies their Jape (Joke Analysis and Production Engine) system has amused children."
>>> Assistive Technologies, Humor Research (@ AI toons), Why did the chicken cross the road (@ NewsToons), Natural Language Processing, Reasoning, Applications

December 10, 2003: Rice goes digital cooked the fuzzy logic way. Side-by-side tests show appliance makes a difference. By Olivia Wu. San Francisco Chronicle / available from SF Gate. "And when [Chris Chen] says 'the fuzzy logic cooker has wisdom,' he grabs your attention. ... As it turns out, pioneer fuzzy logicians do evoke higher powers and a great deal of wisdom when describing its function. Bart Kosko, professor of electrical engineering, author and expert on artificial intelligence and neural networks, has claimed that Buddha was really the world's first fuzzy theorist. Fuzzy logic recognizes more than simple true and false values; it sees degrees of truthfulness, for example, in the statement, 'There is a 25 percent chance of rain today.' Fuzzy logic deals with complex real systems. The Japanese learned exactly how well it worked when they used fuzzy logic to operate subway cars, which then ran and stopped more smoothly than when they were human-operated or automated. Fuzzy logic balanced out the complex components of acceleration, deceleration and braking. Rice cooks in basically four stages: It stands in water, it boils, it absorbs (the "steamed stage") and then it rests. Heat is accelerated or decelerated for each stage and in different ways for each variety of rice."
>>> Fuzzy Logic, Transportation, Smart Houses, Reasoning, Applications

November 18, 2003: Humanity counts in chess battle. By Robert Plummer. BBC. "Artificial intelligence experts and computer programmers agree that chess-playing computers have tended to be better at tactics than strategy. The classic way to design a chess program relies on what experts call 'brute force' - that is, using massive processing power to work out as many potential moves as possible and analyse their consequences. The problem is that even for a computer, chess is a fiendishly complex game. ... Deep Junior and Mr Kasparov's latest opponent, X3D Fritz, represent a change of approach. They pack less processing power, but use 'smart' software to pick out the moves that have most potential. The difference is noticeable."
>>> Chess, Search, Reasoning, Games & Puzzles

November 2003: Contexts of Paradox. Devlin's Angle column by Keith Devlin. The Mathematical Association of America. "In one popular version, Russell's Paradox asks us to imagine a village where the barber shaves all men who do not shave themselves. The question then is, who shaves the barber? ... Issues of context are rarely significant in present day mathematics -- indeed, a crucial aspect of the methodology of mathematics is to study various phenomena in a de-contextualized fashion, removed from the contextual complexities that surround them in the real world. But context is fundamental in many areas in which mathematicians find themselves getting involved these days, such as linguistics, communications, HCI, artificial intelligence, information systems design, and business process modeling. There are a number of annual conferences that address issues of context from different perspectives. AI pioneer John McCarthy is just one of several researchers who are trying to develop a logic for contextual inference."
>>> Reasoning, Logic, Fuzzy Logic (also see this article from October 2, 2003), FAQ
s

October 28, 2003: People Are Robots, Too. Almost. JPL News. "Biologically inspired robots aren't just an ongoing fascination in movies and comic books; they are being realized by engineers and scientists all over the world. While much emphasis is placed on developing physical characteristics for robots, like functioning human-like faces or artificial muscles, engineers in the Telerobotics Research and Applications Group at NASA's Jet Propulsion Laboratory, Pasadena, Calif., are among those working to program robots with forms of artificial intelligence similar to human thinking processes. ... [Dr. Homayoun] Seraji's group at JPL focuses on two of the many approaches to implementing behavior-based control: fuzzy logic and neural networks. The main difference between the two systems is that robots using fuzzy logic perform with a set knowledge that doesn't improve; whereas, robots with neural networks start out with no knowledge and learn over time. ... With continuous advances in robotic methods like behavior-based control, future space missions might be able to function without relying heavily on human commands. On the home front, similar technology is already used in many practical applications such as digital cameras, computer programs, dishwashers, washing machines and some car engines. The post office even uses neural networks to read handwriting and sort mail."
>>> Neural Networks, Fuzzy Logic, Space Exploration, Reasoning, Robots, Machine Learning, Applications

October 15, 2003: Computer researchers on the prowl for human "common sense". The Associated Press / available from NEPA News / also available from CNN (Teaching computers to think. October 17, 2003). "Two Carnegie Mellon University researchers using a Web site called the ESP Game [www.espgame.org] are among a growing number nationwide tapping into human brains for common knowledge that can be programmed into computers to improve artificial intelligence. Grad student Luis von Ahn and his mentor, computer science professor Manuel Blum, hope that search engines such as Google and Alta Vista someday will adopt word labels generated by their ESP Game to help computers see images more like the way humans do. ... 'Computers don't know very much about the world - like that clouds are fluffy, and the sky is blue, and people sleep at night. It's called common-sense knowledge; that's actually a technical term,' said Push Singh, a graduate student researcher at the Massachusetts Institute of Technology. ... The Open Mind project, begun three years ago (openmind.media.mit.edu) by Singh and others at MIT, solicits common-sense information from visitors by asking them to supply underlying "common-sense" facts that fit a given scenario."
>>> Commonsense, Representation, Information Retrieval, Reasoning

October 2, 2003: Fuzzy logic - This headline is (half) false. The Economist. "Epimenides the Cretan, a philosopher of the 6th century BC , is said to have uttered the sentence, 'All Cretans are liars'. As he himself was a Cretan, this gave rise to a paradox-if he were telling the truth, then he would be a liar. Depending on how one defines a liar, the paradox is resolvable; he could have been a habitual liar who was telling the truth in this one instance. However, a stronger version of the paradox, known as the Liar paradox -- 'this sentence is false' -- is not resolvable in conventional logic systems. Indeed, the circular loop that the sentence induces -- if it is false, it must be true, and if true, false -- has been used more than once in science-fiction movies to cause marauding computers to lose their sanity and explode. But in a new paper, Kostis Vezerides of the American College of Thessaloniki, and Athanasios Kehagias of the Aristotle University of Thessaloniki, in Greece, show that, in almost all cases, paradoxes such as the Liar are resolvable with the use of 'fuzzy logic'. ... In the 1960s, Lotfi Zadeh of the University of California at Berkeley came up with the catchy innovation of 'fuzzy logic'."
>>> Fuzzy Logic, Reasoning

September 22, 2003: Smart Steps for Eliminating Spam - If you're drowning in unsolicited e-mail, find out what you can do to stop it before it strikes your mailbox. By Bob Parsons. Entrepreneur.com. "Spam, or unsolicited bulk mail, is an inexpensive and easy way to advertise to a vast audience. Today, an estimated 50 percent of all e-mails handled by Internet service providers (ISPs) are spam. It's also estimated that the average U.S.-based office worker sifts through as many as 100 pieces of spam every day. ... The best way to clean up your inbox is to download an intelligent spam filter that recognizes and separates the good from the bad. To date, the best on the market is the 'Bayesian' spam filter, which uses artificial intelligence to detect spam according to your personal e-mail preferences. This self-learning filter uses a probability-based mathematical theory developed by Thomas Bayes, an 18th Century British clergyman. Bayes' theory is based on the number of times an event has or has not occurred, and the likelihood that it will occur in the future."
>>> Uncertainty/Probability, Filtering, Bayes (@ Namesakes), Reasoning, Applications, Industry Statistics

September 13, 2003 [issue date]: Smart software makes sense of rough sketches. By Celeste Biever. New Scientist. "Intelligent software that brings rough sketches to life in a virtual world is promising to revolutionise the way children learn and to help engineers visualise their designs. ... The MIT software monitors the image as it is being drawn on to a computer screen and allocates probabilities to various interpretations of what it might represent. As the user adds more detail, the software adjusts these weightings. To do this, it uses a technique known as Bayesian analysis, which is normally used to compute the likelihood of specific causes, given certain effects. 'With our software, the 'causes' are what the user had in mind to draw, and the 'effects' are what was actually drawn,' says Randall Davis, who has developed the code with Christine Alvarado at MIT's Artificial Intelligence Laboratory."
>>> Engineering,
Architecture & Design, Education, Uncertainty/Probability, Thomas Bayes (@ Namesakes), Reasoning, Applications

September 1, 2003: Genoa II - Man and Machine Thinking as One. Can software algorithms predict a terrorist's next move before he makes it? Future Watch by Dan Verton. Computerworld. "Some of the technology shown in last year's blockbuster movie Minority Report may soon be a reality and a centerpiece of the intelligence community's war on terrorism. ... In Genoa II, the Defense Advanced Research Projects Agency (DARPA) is studying potential IT that may not only enable new levels of collaboration among teams of intelligence analysts, policy-makers and covert operators, but could also make it possible for humans and computers to 'think together' in real time to 'anticipate and preempt terrorist threats,' according to official program documents ... [P]rivate-sector researchers are studying cognitive amplifiers that can enable software to model current situations and predict 'plausible futures.' Researchers are also on the verge of creating practical applications to support cognitive machine intelligence, associative memory, biologically inspired algorithms and Bayesian inference networks, which are based on a branch of mathematical probability theory that says uncertainty about the world and outcomes of interest can be modeled by combining common sense with evidence observed in the real world."
>>> Law Enforcement, Uncertainty/Probability, Commonsense, Neural Networks, Reasoning, Machine Learning, Agents, Applications; also see the Fall 2002 AI in the news column in AI Magazine

August 23, 2003: They are proud of the `fuzzy logic.' By K. Ramachandran. The Hindu. "Two students of the Crescent Engineering College today form a proud pair. They are just back from Leige, Belgium, after participating in an international conference where, to their own surprise, they won the 'Best Paper Award' for a presentation on 'fuzz-nhanced manoeuvrability law for tactical missile guidance'. In simple terms, it is a numerical model of an intelligent guidance system, which uses all three known laws on guidance systems. ... V. Ananth Kumar and V.P. Deepak (both aged 21 studying in fourth year B.E Computer Science) are doubly thrilled that they got a commendation from Lotfi A. Zadeh, considered the father of the concept of 'fuzzy logic', a branch of artificial intelligence."
>>> Fuzzy Logic, Reasoning

August 2003: Why A.I. Is Brain-Dead. Marvin Minsky sits in the "hot seat" and responds to a series of questions from Josh McHugh. Wired Magazine. "Wired: The biggest name in artificial intelligence declares AI research 'brain-dead' since the 1970s. What gives? Minsky: There is no computer that has common sense. We're only getting the kinds of things that are capable of making an airline reservation. No computer can look around a room and tell you about it. But the real topic of my talk was overpopulation."
>>> AI Overview, Interviews, Commonsense, Reasoning, Ethical & Social Implications

July 29, 2003: Helping Machines Think Different. By Noah Shachtman. Wired News. "In recent months, the Defense Advanced Research Projects Agency has launched a series of seemingly disparate programs -- all designed, the agency says, to help computers deal with the complexities of life, so they finally can begin to think. 'Our ultimate goal is to build a new generation of computer systems that are substantially more robust, secure, helpful, long-lasting and adaptive to their users and tasks. These systems will need to reason, learn and respond intelligently to things they've never encountered before,' said Ron Brachman , the recently installed chief of Darpa's Information Processing Technology Office, or IPTO. ... 'LifeLog is about forcing computers into the real world,' said leading artificial intelligence researcher Doug Lenat, who's bidding on the project. What LifeLog is not, Brachman asserts, is a program to track terrorists. By capturing so much information about an individual, and by combing relationships and traits out of that data, LifeLog appears to some civil libertarians to be an almost limitless tool for profiling potential enemies of the state. ... Human beings don't dump their experiences into some formless database or tag them with a couple of keywords. They divide their lives into discreet installments -- 'college,''my first date,''last Thursday.' Researchers call this 'episodic memory.' LifeLog is about trying to install episodic memory into computers, Brachman said. It's about getting machines to start "'remembering experiences in the commonsensical way we do -- a vacation in Bermuda, a taxi ride to the airport.'"
>>> Commonsense, Reasoning, Representation, Ethical & Social Implications, Applications; also see the following article ->

July 29, 2003: AI Depends on Your Point of View. By Noah Shachtman. Wired News. "Even the dumbest people can look at a situation from several different angles. But that's still a problem for even the smartest computer systems. The Real-World Reasoning project, a Defense Advanced Research Projects Agency program, is designed to get computers to start examining situations in more than one way. It's part of a larger effort, spearheaded by the Agency's Information Processing Technology Office, or IPTO, to move toward machines that can think for themselves. ... The project also is supposed to help computers learn from their experiences. If machines are ever going to have minds of their own, they must put what they know into context, as people do. When human beings learn things, [Ron] Brachman said, 'we don't just stick it into a database. It's got to jive with what we know already. Or we've got (to) adjust our previous understanding.'"
>>> Reasoning

July 28, 2003: A veritable cognitive mind. By R.Colin Johnson EE Times. " Marvin Minsky, MIT professor and AI's founding father, says today's artificial-intelligence methods are fine for gluing together two or a few knowledge domains but still miss the 'big' AI problem. Indeed, according to Minsky, the missing element is something so big that we can't see it: common sense. 'To me the problem is how to get common sense into computers,' said Minsky. 'And part of that, it seems to me, is not how to solve any particular problem but how to quickly think of a new way to solve it-perhaps through a change in emotional state-when the usual method doesn't work.' In his forthcoming book, The Emotion Machine, Minsky shares his accumulated knowledge on how people make use of common sense in the context of discovering that missing cognitive glue. ... Reasoning by analogy is a way of adapting old knowledge, which almost never perfectly matches the present situation, by following a recipe of detecting differences and tweaking parameters. It all happens so quickly that no 'thinking' seems to be involved."
>>> Commonsense, Analogy, Emotion, Reasoning, Representation, Cognitive Science, AI Overview

July 8, 2003: DARPA seeks thinking computers. By Matthew French. Federal Computer Week. "Officials at the Defense Advanced Research Projects Agency are hoping to find an individual or company that can develop a computer that thinks, a major stepping stone in the arena of artificial intelligence. DARPA's Information Processing Technology Office is engaging in a program called Real-World Reasoning, the objective of which is to 'explore and develop foundations, technology and tools to enable effective, practical automated reasoning of the scale and complexity required for computers to perform complex tasks in the real world requiring intelligence.'"
>>> Reasoning

June 2003: Robo Space - How Space Perception Seperates Man From Machine. By Luc Steels. Wired Magazine. "For a robot coming fresh into the world, there is at first total confusion. What is "above"? What is 'behind'? To the newborn android, all sensory input is a blur. Blobs float into view, the occasional sound drifts by, 3-D space is a mass of contradictory coordinates. The problem isn't the hardware. Autonomous bots like Honda's Asimo and Sony's SDR-4X II have cameras for depth perception and microphones to help pinpoint a sound source. And in the lab, researchers in artificial intelligence have made strides in symbolic reasoning, allowing machines to make inferences based on definitions of spatial concepts. But combining sensory perception and spatial reasoning remains elusive, which explains why robots lack a true sense of space. ... Figuring out how to teach spatial cognition is precisely what's going on in current robotics research, including in my own laboratory. We are trying to create robots and robot cultures that develop an autonomous approach to space, time, and action."
>>> Robots, Reasoning, Representation, Vision

May 28, 2003: Pentagon seeks to sort, store lifetime experience. By Jim Wolf. Reuters. "The Pentagon is shopping for ways to capture everything a person sees, says and hears as part of a project it says is meant to help create smarter robots. The projected system called Lifelog would suck in all of a subject's experience -- from phone numbers dialed and emails viewed to every breath taken, step made and place gone. The idea is to index the material, and make patterns easily retrievable in an effort to make machines think more like people, learning from experience. ... The LifeLog goal is to create a searchable database of human lives -- initially those of the developers -- to promote artificial intelligence, the agency said. ... Perhaps eager to avoid any comparisons with George Orwell's all-seeing 'Big Brother' in the classic novel 1984, DARPA said respondents must address 'human subject approval, data privacy and security, copyright and legal considerations that would affect the LifeLog development process.'"
>>> Reasoning, Machine Learning, Ethical & Social Implications

May 26, 2003: Guess who's smarter. As sophisticated as computing has become, machines still lack the common sense of a 3-year-old. But MIT artificial intelligence researchers are tackling ways to start building that basic breadth of knowledge into programs and applications. By D.C. Denison. The Boston Globe. "[N]ow there are signs that 'common sense' artificial intelligence research may be making a comeback, sparked by projects like [Push] Singh's Open Mind database. For the first time, after decades of theoretical research, researchers and programmers have begun using a freely distributed, natural language common sense database to start the process of building common sense into products, programs, and applications. In fact, as Singh sits in his cramped office in the Media Lab, he's able to point in the direction of a number of MIT researchers using his database for applications that may soon bring common sense AI to consumers. A few doors down to the right, Barbara Barry, a graduate student in the Media Lab's Interactive Cinema group, is working with Singh to build common sense into video cameras. On the other side of the Media Lab, Henry Lieberman, a research scientist who works with the Software Agents Group, is using common sense to enhance e-mail programs, language translation software, even a search engine. And just outside Singh's office, the Media Lab's 'wearable computing' group is building common sense into the devices and sensors they believe many of us will be wearing in the future. ... Marvin Minsky, one of the cofounders of the Lab (and Singh's adviser at MIT), recently caused a stir in the field when Wired News reported that he told a Boston University audience that 'AI has been brain-dead since the 1970s.' The article went on to quote Minsky attacking the current artificial intelligence 'fad' of making 'stupid little robots.' Minsky, who is now less actively involved with the field after working at the intersection of computing and human intelligence since the early 1950s, said his remarks sounded more extreme taken out of context."
>>> Commonsense, Reasoning, Representation, AI Overview, Applications,; also see the related article from Wired News

May 5, 2003: Man or Machine? (Part 1 of 3): Human or Robot? Ivanhoe Newswire / available from HealthCentral.com. "Movies like 'Artificial Intelligence: AI' and 'The Matrix' offer glimpses of what artificial intelligence holds for the future. Some say it will have a great impact on medical research. Today, artificial intelligence flies airplanes, makes financial decisions, and aids in medical diagnoses. Futurist and Inventor Ray Kurzweil says the key to artificial intelligence is pattern recognition. ... SONY has already made commercially available AIBO, an artificially intelligent robot companion with owner recognition capabilities that follows commands. But emotion is one quality still separating man from machines like AIBO. Computer scientist Eric Chown, Ph.D., is working to narrow even this gap. He's reprogramming AIBO to feel emotions. ... At MIT's Artificial Intelligence Lab in Cambridge, Mass., Charlie Kemp is programming his computer to have common sense. ... In the future these experts predict humans and machines will actually merge. Humans will think using non-biological intelligence."
>>> Pattern Recognition, Medicine, Commonsense, Nature of Intelligence, SciFi, Emotion, Robots, Machine Learning, AI: the movie, Assistive Technologies, Robotic Pets, Reasoning, AI Overview, Applications, Newstoons

April 15, 2003: NASA Offers Glimpse Into Cutting-Edge Research. NASA Release 03-141. "NASA is opening its doors to high school and college students Thursday for a free online educational opportunity focusing on the exciting world of information technology research. ... The course will be Web cast on Tuesdays and Thursdays through May 15, 2003. Sessions will cover high-speed networking and communications technologies for Earth and space applications; artificial intelligence and telepresence; breakthroughs in nanotechnology and biotechnology; advanced systems development; and simulation technology. The course is free to all registrants."

>>> Reasoning, Resources for Students, Resources for Educators, Space Exploration

April 14, 2003: Carnegie Mellon psychologist helps build a better mine sweeper - In a low-tech solution, soldiers are taught an expert's technique and detection soars. By Byron Spice. Post-Gazette. "[T]he more significant advance in demining, a practice that must continue for years after the war ends, may be the revamped training that U.S. soldiers have received since last spring. Devised by Jim Staszewski, a cognitive psychologist at Carnegie Mellon University, the training program teaches soldiers to use the thought patterns and techniques honed by an expert, a 30-year mine-detection veteran. It builds on the work of the late CMU scientists Herbert Simon and Allan Newell, pioneers in the study of the nature of human expertise. ... What was it that made them experts? How do experts differ from everybody else? These were the questions being asked by the Army and the same sort of questions that mesmerized CMU's Newell and Simon. The CMU researchers were popularly known for creating the first thinking machine, launching the field of artificial intelligence, in the mid-1950s. But this quest to make computers think like humans went hand-in-hand with their efforts to understand how humans think. As a result, the pair had as much impact on the field of psychology as on computer science. "Herb Simon and Allan Newell pretty much got cognitive science off the ground," Staszewski said. 'I'd like to think this work is a direct descendant of them' and other CMU psychologists, including the late William Chase. How to be an expert One of the things they learned is that you don't have to be a genius to be an expert. In his 1991 book, 'Models of My Life,' Simon wrote: 'Experts, human and computer, do much of their problem solving not by searching selectively, but simply by recognizing relevant cues in situations similar to those they have experienced before.'"
>>> Cognitive Science, Reasoning, Expert Systems, Landmines (@Hazards & Disasters), AI Overview, History

April 7, 2003: Cyc-ing out the terrorists. By Dana Hawkins. U.S. News & World Report. "Only sheer brilliance or a crystal ball could foretell the next terrorist attack, right? Nope, says the inventor of a computer program that predicted anthrax might be sent through the mail six months before it happened. Cyc (as in encyclopedia), a project that is now part of the Pentagon's research on sniffing out terrorist plots, comes up with scenarios that could help focus screening efforts. It works by applying common sense to an extensive knowledge of terrorism. 'It's not like beating Kasparov at chess,' says creator Doug Lenat, head of the Austin-based firm Cycorp. He compares it to 'a person with average intelligence but a vast amount of time and patience.'"
>>> Commonsense, Law Enforcement, Military, Reasoning, Chess

March 14, 2003: Mind of the company - Science is finding that mimicking living systems to produce robots is about understanding biology, not physics. There are lessons here for the way we run our corporations. By Tim Wallace. Financial Review Boss. "The phrase 'fast, cheap and out of control' was coined by Australian-born scientist Rodney Brooks and a colleague for an article published in 1989 advocating the use of robots in space exploration. Internet guru Kevin Kelly later adapted it for the title of his 1994 book on new modes of thinking in artificial intelligence, while filmmaker Errol Morris used it for his 1997 documentary film featuring the robotics scientist. ... Brook's work on AI challenges us to rethink OI (organisational intelligence) and to smash the machine, rebuilding it from the bottom up - fast, cheap and out of control. ... The most celebrated of all early efforts to create a robot that could do childish things resulted in Shakey, built at the Stanford Research Institute in the late 1960s to early 1970s, and so named because of the way its camera and TV transmitter mast shook when it moved. ... The designers of Shakey, and of the projects following it, believed that for a robot to act intelligently in the world it first needed an accurate model of that world. ... What must be happening in insects, Brooks realised, was sensing connected to action - sensors to actuators - very quickly. The key to building a similarly efficient robot, he concluded, was to have it react to its sensors in the same way, so it did not need a detailed computational model of the world. 'If the building and maintaining of the internal world model was hard and a computational drain, then get rid of the internal model. Every other robot had had one. But it was not clear that insects had them, so why did our robots necessarily need them?'"
>>> Nature of Intelligence, Robots, Reasoning, History, Cognitive Science

March 1, 2003: The Meaning of Computers and Chess - What Deep Junior, Deep Blue, and Garry Kasparov teach us about intelligence, human and artificial. By Philip Ross. IEEE Spectrum Web Only News. "The original chess-playing algorithm, proposed more than 50 years ago by Claude Shannon, the electrical engineer who founded information theory, begins with the search function, which generates all possible move sequences to a certain depth, set by the computer's speed and memory. Then each end position is graded, numerically, by an evaluation function that assigns value to such aspects of chess knowledge as material.... Deep Junior seems to have played about as well as Deep Blue, although its hardware was perhaps only 1 or 2 percent as powerful. Blue had 480 processors, each specialized totally for the generation and evaluation of chess positions; Junior had up to eight general-purpose Pentium 4 processors with up to 8 GB of RAM. Processor performance has of course improved enormously in the intervening years, but not enough to account for Deep Junior's matching Deep Blue's performance. Rather, it is improved chess-playing software that mainly explains Deep Junior's success, in part because programmers, working with grandmaster advisers, have learned how to encode many aspects of chess knowledge that had previously been unmanageable. Deep Junior's handlers could not hope to match Deep Blue's search capabilities, so they concentrated on tweaking Deep Junior's evaluation function."
>>> Chess, History, Search, Reasoning, Representation, Systems, Tributes, Games & Puzzles

March 2003: Look under the Hood - New ERP system in. Application integration? Done. Data backed up? Check. So what's that rattling noise coming from your enterprise? Time to pop the hood and take a look at your business processes. By Raoul Le Blond. CIO Asia. "The next time you order that Big Mac and Coke at a McDonald's Singapore restaurant, a new crew scheduling solution recently deployed by the company may just make the wait for your food a little shorter. After all, it is about fast food. ... 'McScheduler takes a lot of the guesswork from our HR planning and provides management with greater visibility and a more objective way to schedule crews to serve our customers in the fastest time and the best way possible,' says [Davy] Wee. Using operations research, constraint programming and artificial intelligence technologies, the Web-enabled solution enables managers to speed up crew scheduling and assignments. ... Labour and resource costs have also been improved. The solution also brought more objectivity to the whole scheduling process...."
>>> Planning & Scheduling, Business, Applications, Reasoning

February 25, 2003: Two Computer Scientists Recognized for Their Work. Duke News & Communications. "Two Duke professors were among only 16 Sloan Research Fellowship winners in computer science awarded this year in the United States and Canada. ... [Ronald] Parr's work on decision-making processes can be applied to problems ranging from 'deciding the speed at which one drives one's car, the way a company manages its distribution channels, or the best way to treat a medical condition.' The first step in solving these problems by computer is converting the relevant problems into a format that computers can manage. That step, Parr said, was taken decades ago. ... 'My research is trying to reduce the number of calculations needed for the computer to obtain a good answer, bringing us closer to the point where computers will help us work through such difficult decisions on a regular basis.'... Parr said he is intrigued by the recent interest within the artificial intelligence and robotics communities in robotic search-and-rescue missions. In such missions, robots would enter a burning or damaged building, assess the situation and return to provide rescue workers with the information they need to save lives. 'Along these lines, we're developing new mapping techniques that will allow robots to explore an area and return with a very detailed and accurate floor plan,' Parr said. 'Eventually, I expect that robots will help the injured find exits and even provide rudimentary first aid.'"
>>> Representation, Reasoning, Robots, Hazards & Disasters

February 19, 2003: 18th century theory is new force in computing. By Michael Kanellos. ZDNet. "Thomas Bayes, one of the leading mathematical lights in computing today, differs from most of his colleagues: He has argued that the existence of God can be derived from equations. His most important paper was published by someone else. And he's been dead for 241 years. Yet the 18th-century clergyman's theories on probability have become a major part of the mathematical foundations of application development. Search giant Google and Autonomy , a company that sells information retrieval tools, both employ Bayesian principles to provide likely (but technically never exact) results to data searches. Researchers are also using Bayesian models to determine correlations between specific symptoms and diseases, create personal robots, and develop artificially intelligent devices that 'think' by doing what data and experience tell them to do. ... 'Bayesian research is used to make the best gambles on where I should flow with computation and bandwidth,' said Eric Horvitz, senior researcher and group manager of the Adaptive Systems & Interaction Group at Microsoft Research. 'I personally believe that probability is at the foundation of any intelligence in an uncertain world where you can't know everything.' ... Bayesian theory can roughly be boiled down to one principle: To see the future, one must look at the past."
>>> Uncertainty/Probability, Natural Language, Reasoning, Namesakes, Information Retrieval, Filtering, Agents, Representation, Applications, Medicine

January 31, 2003: A cure for the call centre queue blues. By Alan Cane.The Financial Times. "So would it not be better (and cheaper), the argument goes, if call centres could be done away with and replaced by computers? This is the kind of thinking which has informed the work of Transversal, a start-up spun out of Cambridge University, and which now has Fujifilm UK, Sony Computer Entertainment Europe and furniture retailer MFI among the customers for its Metafaq system. Transversal's founders, the physicists David MacKay and Davin Yap, started from the premise that 80 per cent of enquiries made to call centres have been made before and therefore should be susceptible to being answered without human intervention. Establishing a database is not difficult. The complexity comes in developing a system which can 'understand' inquiries couched in natural language from individuals who are not computer specialists. The number of answers may be finite but the number of ways the questions could be asked is infinite. ... MacKay and Yap tackled the problem with a combination of computational linguistics and Baysian networks, a pair of mathematical techniques which are giving 'artificial intelligence' back some of the respectability it lost when much ballyhooed systems in the 1980s and 1990s failed to deliver. Computational linguistics breaks down the elements of natural language into units that a computer can process. Baysian networks organise discrete bodies of knowledge by tracing cause-and-effect among the key variables and assigning numeric values to them. Essentially, a Baysian system can predict a likely outcome from incomplete data."
>>> Natural Language, Customer Relations, Namesakes, Uncertainty / Probability, Applications, Reasoning, AI Overview

January 20, 2003: IBM aims to get smart about AI. By Michael Kanellos. CNET News. "In the coming months, IBM will unveil technology that it believes will vastly improve the way computers access and use data by unifying the different schools of thought surrounding artificial intelligence. The Unstructured Information Management Architecture (UIMA) is an XML-based data retrieval architecture under development at IBM. ... Although it's been alternately touted and debunked, the era of functional artificial intelligence may be dawning. For one thing, the processing power and data-storage capabilities required for thinking machines are now coming into existence. Researchers also have refined more acutely the algorithms and concepts behind artificially intelligent software. Additionally, the explosive growth of the Internet has created a need for machines that can function relatively autonomously. ... Artificial intelligence in a sense will function like a filter. Sensors will gather data from the outside world and send it to a computer, which in turn will issue the appropriate actions, alerting its human owners only when necessary. ... IBM's approach to artificial intelligence has been decidedly agnostic. There are roughly two basic schools of thought in artificial intelligence. Statistical learning advocates believe that the best guide for thinking machines is memory. ... By contrast, rules-based intelligence advocates, broken down into syntactical and grammatical schools of thought, believe that machines work better when more aware of context."
>>> AI Overview, Information Retrieval, Natural Language, Machine Learning, Namesakes (Bayes), Representation, Reasoning, Web-Searching Agents, Agents, Systems & Languages, Applications

December 22, 2002: Saul Amarel, 74, an Innovator in Artificial Intelligence, Is Dead. By Eric Nagourney. The New York Times (no fee reg. req'd). "Dr. Saul Amarel, who helped develop the field of artificial intelligence and founded the computer science department at Rutgers University, died on Wednesday in Princeton, N.J., where he lived. ... Among his peers, Dr. Amarel was perhaps best known for a paper he wrote in 1968, which put him at the vanguard of the artificial intelligence movement. Decades later, the importance of the paper may be hard to understand. It concerned the way one might program a computer to solve a brain-teaser well known to mathematicians that involves three cannibals, three missionaries and a boat that seats only two. The challenge for the missionaries is to transport the cannibals across a river without ever letting any of their party be outnumbered -- and eaten. Solving the problem was not really the point. That had already been done. What Dr. Amarel set out to do was to create an approach that did not rely on a mechanical crunching of numbers, but instead used an algorithm that allowed the computer to figure out a solution in a manner more akin to human reasoning."
>>> Tributes, Applications, History, Reasoning

December 13, 2002: Will technology ever be as intelligent as us? By Liz Simpson. Computing / vnunet.com. " Ask any stranger, 'Do you have the time?' and they look at their watch. Not many of us would be fazed by that request, or the reply 'Time for what?' Our brains cope with understanding and responding to such ambiguities of communication, while computers, so far, do not. But one day they will, thanks to artificial intelligence pioneer Doug Lenat. At the Austin, Texas offices of Cycorp, Lenat and his team have been working on machines that are smart, in the way that humans using common sense are smart. ... artificial intelligence provided the perfect platform for a man who once said: 'How many people have in their lives a two to 10 per cent chance of dramatically affecting the way the world works? When one of those chances comes along, you should take it.'Lenat's contribution to the world is a program called Cyc (as in 'en-cyc-lopaedia'), said to be the world's largest extra-sentient body of common sense and perhaps, one day, this planet's first digital consciousness."
>>> Commonsense, Ontologies, Careers in AI (@ Resources for Students), Reasoning, Representation

December 13, 2002: Digital Actors in Rings Can Think. By Courtney Macavinta. Wired News. "[Stephen] Regelous created Massive, the special-effects program behind the colossal battles in The Lord of the Rings film trilogy. Using Massive, the Oscar-winning Weta Digital team pulled off anticipated scenes for the latest installment, The Two Towers -- such as the battle at Helm's Deep -- by digitally generating smart crowds to supplement the live action. The computer-generated characters, called agents, have minds of their own. 'Every agent has its own choices and a complete brain,' Regelous said. 'The most important thing about making realistic crowds is making realistic individuals.' ... Agents aren't robots, though. Each makes subtle responses to its surroundings with fuzzy logic rather than yes-no, on-off decisions. ... For inspiration, Regelous didn't watch war movies as you might expect. Instead he experimented with artificial intelligence by growing digital plants, and studied how people avoided each other on crowded streets."
>>>Multi-Agent Systems, Drama, Fuzzy Logic, Agents, Applications, Reasoning

December 2002/January 2003: Immobots Take Control. By Wade Roush. Technology Review. "From photocopiers to space probes, machines injected with robotic self-awareness are reliable problem solvers. ... But Deep Space One had something Mars Polar Lander lacked: an onboard robot able to think autonomously and handle the unexpected. Using its engineering knowledge, the robot tried to repair the switch by toggling it on and off. When this failed, it devised a successful plan to complete the navigation maneuver, and the craft proceeded unharmed. The robot that saved Deep Space One was in the vanguard of a new breed of machines poised to have a big impact in space and here on Earth. Quite unlike the metallic contraptions that march stiffly through sci-fi movies or the mindless, stripped-down devices that heft parts on our assembly lines, the new robots have more brain than brawn. Each possesses a detailed picture of its own inner workings—encoded in software-based models—that gives it the ability to respond in novel ways to events its programmers might not have anticipated. Because many of these inward-focused, self-reconfiguring machines don’t move, some computer scientists call them immobile robots, or 'immobots.' ... A deep-space probe obviously requires much more autonomy than, say, a photocopier. But heavily used office machines must meet a similar demand for reliability and efficiency... 'This distinction between telling a system how to do its job and telling the system the end result you want is very fundamental,' says Robert Morris, director of IBM’s Almaden Research Center in San Jose, CA. IBM is working to build what it calls 'autonomic' characteristics -- model-based features, as well as others that employ classic heuristic programming -- into products such as Web servers and storage networks. These features will allow the products to reconfigure themselves for optimal performance, depending on what’s being asked of them."
>>> Reasoning, Space Exploration, Autonomous Vehicles, Qualitative Reasoning, Engineering, Commonsense, Applications, Networks, Transportation

October 24, 2002: What is neuro-fuzzy logic? By Surjit Singh Bhatti. The Tribune (Chandigarh, India). "It is common now-a-days to come across electronic gadgets marketed by multinational companies that claim the use of 'fuzzy logic' control systems. Nissan, for instance, has fixed fuzzy anti-lock brakes in their vehicles. ... Samsung washing machines, among others, are examples of consumer products that use the fuzzy control devices. Besides, automation is being achieved in factories and process industries using sophisticated fuzzy controls which are inexpensive and easier to maintain compared to the conventional 'digital logic' control systems. Fuzzy logic models itself on the pattern of human reasoning in its use of approximate information and uncertainty to generate decisions. ... [F]uzzy controls avoid the conventional rigidity of computers and allow them to use parameters based on 'common sense.' Fuzzy logic application to a problem involves three steps: converting crisp (numerical) values to a set of fuzzy values, an inference system (based on fuzzy if-then rules) and de-fuzzification. ... While fuzzy logic uses approximate human reasoning in knowledge-based systems, the neural networks aim at pattern recognition, optimisation and decision making. A combination of these two technological innovations delivers the best results. This has led to a new science called neuro-fuzzy logic in which the explicit knowledge representation of fuzzy logic is augmented by the learning power of simulated neural networks.
>>> Fuzzy Logic, Neural Networks, Applications, Reasoning, Machine Learning

October 21, 2002: British Concern to Help U.S. Track Terrorists. By John Markoff. The New York Times (no-fee reg. req'd). "Autonomy, a British developer of sophisticated information retrieval software, plans to announce on Monday that it has been chosen to provide an analysis system to help the United States government track suspected terrorists. ... Autonomy's software uses statistical techniques to search for patterns of information across large masses of data. Mr. Cooper has said publicly on several occasions that the domestic security effort will require technology that will allow government agencies to share and analyze information, and that data-mining technologies will be a central part of the operation. ... One early application for the Autonomy software will be as part of a consolidated watch list for suspected terrorists that the agencies will maintain, according to Mr. Cronin of Autonomy. He described the possibility that dozens of separate data repositories would be accessible by Autonomy software known as the Intelligent Data Operating Layer, which is designed to integrate unstructured text documents and traditional database information. ... The Autonomy software has the flexibility to search names and words with variable spellings as well as to retrieve information based on patterns that are related but may not match exactly. The software is based on Bayesian statistical techniques, which are used to match patterns and are gaining favor among software designers and artificial-intelligence researchers."
>>> Uncertainty/Probability, Data Mining, Law Enforcement, Applications, Machine Learning, Namesakes, Information Retrieval, Reasoning, Knowledge Management

April 17, 2002: Mohammed approves by-laws. Gulf News Online. "General Sheikh Mohammed bin Rashid Al Maktoum, Crown Prince of Dubai, UAE Minister of Defence and Head of Dubai Police and Public Security, yesterday approved by-laws and organisational structure of the Dubai Decision Support Centre (DDSC). ... Following is the text of Sheikh Mohammed's decision approving by-laws of the DDSC: We, Mohammed bin Rashid Al Maktoum, Dubai Crown Prince and Head of Dubai Police and Public Security, aiming to set into motion the mechanism of decision making, rationalise decisions, support decision makers in various sectors particularly under uncertainty and risk and to make greater outcome of decisions, decides as follows: ... Article (4) The main tasks of the DDSC shall be as follows: ... 12: Establishing quantity programme bases and artificial intelligence methods to enhance ability to take decisions under different uncertainty levels."
>>> Reasoning, Law Enforcement

April 11, 2002: Firm aims to 'computerize' common sense. Computerworld / available from CNN.com/Sci-Tech. "Austin, Texas-based Cycorp claims to be 'the leading supplier of formalized common sense.' CEO and founder Doug Lenat has labored 17 years to codify facts such as 'Once people die, they stop buying things.' He uses a form of symbolic logic called 'predicate calculus' to classify and show the properties of information in a standard way. ... Is Cyc like the human genome project, where eventually you will be done, or will it grow forever? I refer to it as the human 'memome' project. A typical person knows about 100 million things about the world. I see us crossing that point in five years. It's difficult to predict the course thereafter."
>>> Commonsense, Reasoning, Representation

December 4, 2001: Cyc-ed Up for Open Source. IT-Director.com "Cyc is about as close to true artificial Intelligence that you can get and has, typically, been living off Defence Department contracts for several years. Both the Feds and the company are mute on the nature of the research but have admitted to its being in the area of anti-terrorism. ... Lenat's vision hinges on this statement: 'If we can write down knowledge and wisdom and rules of thumb for the computer to follow and use, it can apply rules of reasoning to the knowledge that we give it and produce the same kinds of conclusions.'"
>>> Commonsense, Military, Reasoning, Representation

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