Do they really not have any understanding of the differences between the role of money and the role of deep analysis of problems combined with careful research and experiment to find good solutions? Insofar as many of those ministers have university degrees, I suppose that is just another manifestation of the inadequacies of the educational policies of previous governments, alongside the inadequacies of the processes of selection of ministers? There are four concepts of freewill (two of them incoherent and the other two compatible with determinism). Why Asimov's "laws of robotics" are unethical. Why Computing Education has Failed and How to Fix it Comments on the NHS IT disaster and suggestions for an alternative approach.

Note added 3 Mar 2007: I and some others had some reservations about the document because we felt it did not adequately distinguish degrees that were mainly concerned with training for particular vocations involving use of computers and development of software systems from degrees that were more concerned with teaching the foundations of the subject and preparing future researchers to extend both the foundations and the techniques. This meant, in particular, that some of us felt that there was insufficient emphasis on the mathematical content of computer science and the possibility of doing theoretical (e.g. I personally felt then, and still feel that that expressed a narrowness of vision in the majority of members of the computer science community. The more detailed overview is given in section C, as an indication of the scope of the field. This might be used by reviewers considering degrees in AI, not in order to define what should or should not be in such a degree, but in order to provide some background that might be useful when assessing an AI degree course, or possibly when designing one. There is no implication that everything mentioned here must be included in a degree with "Artificial Intelligence" in its title, or that topics not included here are excluded.

When the work began in 1991 it was a continuation of work begun in the 1960s in the School of Social Sciences at The University of Sussex, and later continued in the School of Cognitive and Computing Sciences (COGS). Chapter 7 on "Intuition and analogical reasoning", including reasoning with diagrams, and Chapter 8 "On Learning about Numbers" were specially closely related to the 1962 DPhil work on the nature of mathematical knowledge. The first PhD thesis completed in the project was by Luc Beaudoin (funded by major scholarships from: Quebec's FCAR, The Association of Commonwealth Universities (UK), and the Natural Sciences and Engineering Research Council (NSERC) of Canada). The this is online here, along with others. Among other things, it offered a new, unusually detailed analysis of aspects of motives that can change over time, and introduced the important distinction between deliberative mechanisms (which can represent, explore, hypothesise, plan and select possible situations, processes and future actions) and meta-management mechanisms which can can monitor, and to some extent control internal processes (including deliberative processes).

Seeing the significance in a collection of experimental results, grasping a character in a play or novel, and diagnosing an illness on the basis of a lot of ill-defined symptoms, all require this ability to make a'Gestalt' emerge from a mass of information. A much simpler example is our ability to see something familiar in a picture like Figure 1. How does a'Gestalt', a familiar word, emerge from all those dots? Close analysis shows that this kind of ability is required even for ordinary visual perception and speech understanding, where we are totally unaware that we are interpreting untidy and ambiguous sense-data. In order to appreciate these unconscious achievements, try listening to very short extracts from tapes of human speech (about the length of a single word), or looking at manuscripts, landscapes, street scenes and domestic objects through a long narrow tube.

Google's Hartmut Neven demonstrates his visual-search app by snapping a picture of a Salvador Dali clock in his office building. Google and other tech companies are racing to improve image-recognition software Computers can recognize some objects in images, but not all Google's engineering director predicts the technology will fully mature in 10 years Google's engineering director predicts the technology will fully mature in 10 years Santa Monica, California (CNN) -- Computers used to be blind, and now they can see. Thanks to increasingly sophisticated algorithms, computers today can recognize and identify the Eiffel Tower, the Mona Lisa or a can of Budweiser. Still, despite huge technological strides in the last decade or so, visual search has plenty more hurdles to clear. At this point, it would be quicker to describe the types of things an image-search engine can interpret instead of what it can't.

Raffaello D'Andrea heads ETH Zurich's Flying Machine Arena Arena is at forefront of research into autonomous flying robots Quadrocopters learn amazing throwing and catching maneuvers D'Andrea says technology education needs to promote "unconstrained creation" D'Andrea says technology education needs to promote "unconstrained creation" Professor Raffaello D'Andrea isn't short of admirers for his autonomous flying robots and the amazing tricks they perform. Every week, he receives a flood of e-mails from excited people telling him how to use them, he says. "Folks have contacted me about using them to deliver burritos and pizzas, paint walls, do search and rescue, monitor the environment, flying cameras for movies ... It's just endless," D'Andrea says. "I'm not going to pass judgment on whether they are good or bad ... my role is to show people what is possible." It appears those possibilities are growing by the day at the Swiss Federal Institute of Technology in Zurich (ETH Zurich) where D'Andrea leads a team of researchers at the Flying Machine Arena (FMA).

Go ahead and try to hide how you're feeling. Your keystrokes could rat you out. Researchers in Bangladesh have designed a computer program that analyzes typing and text patterns to identify emotional states. While both methods have been used before to detect computer users' emotions, the researchers combined them, for the first time, they say, and saw promising results -- software that nails mood as much as 87 percent of the time. They say the findings could be significant to the development of emotionally aware computer systems as their approach relies on less expensive, and less intrusive, methods than tools like voice analysis, facial sensors, thermal imaging, and gesture tracking.

Remember those kids in high school who got all A's, never had a bad hair day and never broke a rule but always called you out when you did? They got on your nerves for being so perfect, didn't they? Given that meaningful human interaction often hinges on relatability, we often tend to prefer the company of those who show very real humanness. If they have imperfections -- not misplaced screws or actuators, but human-like behavioral flaws -- people are more prone to forge successful working relationships with them, according to a new study out of the UK's University of Lincoln. The findings could have significant implications as people increasingly rely on social robots for tasks like helping seniors stay active and aiding autistic kids in the classroom.

The "communication android", as Toshiba is calling its creation, was unveiled this week at the Cutting-Edge IT & Electronics Comprehensive Exhibition (CEATEC), Japan, and has been designed for a maximum of movement fluidity in its hands and arms, employing 43 actuators in its joints, in order to speak in Japanese sign language. At this point, its range is fairly limited: it can mimic simple movements, such as greetings, but the company has plans to develop the robot -- named Aiko Chihira -- into a full communications robot by 2020. This will include speech synthesis, speech recognition, robotic control and other sensors. The end goal, the company said, is a robot that can serve as a "companion for the elderly and people with dementia, to offer telecounseling in natural speech, communicate through sign language and allow healthcare workers or family members to keep an eye on elderly people." If the robot looks familiar, that's because it was developed in collaboration with Osaka University, which has been developing humanoid robots for some time.

They sniff, wag their tails, fetch and run in packs. But no one minds if these canines stick their noses into some pretty dirty stuff. That's because they are robotic dogs, modified by engineering students at Yale University to sniff out toxic materials. Equipped with just about everything but a wet nose, the plastic and metallic-skinned robots have spurred toxic search projects in the United States, Europe and Australia. They are the brainchild of Natalie Jeremijenko, a lecturer in engineering at Yale and self-described technoartist.