"The basic premise is that human space exploration must be highly efficient, cost effective, and autonomous as placing humans beyond low Earth orbit is fraught with political economic, and technical difficulties," John D. Mathews, professor of electrical engineering, reported in the current issue of the Journal of the British Interplanetary Society. If aliens are out there, they have the same problems we do, they need to conserve resources, are limited by the laws of physics and they may not even be eager to meet us, according to Mathews. He suggests that "only by developing and deploying self-replicating robotic spacecraft -- and the incumbent communications systems -- can the human race efficiently explore even the asteroid belt, let alone the vast reaches of the Kuiper Belt, Oort Cloud, and beyond." Mathews assumes that any extraterrestrial would need to follow a similar path to the stars, sending robots rather than living beings, which would explain why SETI has not succeeded to date. "If they are like us, they too have a dysfunctional government and all the other problems plaguing us," said Mathews.

UC Berkeley researchers are tapping the cognitive smarts of babies, toddlers and preschoolers to program computers to think more like humans. "Children are the greatest learning machines in the universe. Imagine if computers could learn as much and as quickly as they do," said Alison Gopnik a developmental psychologist at UC Berkeley and author of "The Scientist in the Crib" and "The Philosophical Baby." In a wide range of experiments involving lollipops, flashing and spinning toys, and music makers, among other props, UC Berkeley researchers are finding that children -- at younger and younger ages -- are testing hypotheses, detecting statistical patterns and drawing conclusions while constantly adapting to changes. "Young children are capable of solving problems that still pose a challenge for computers, such as learning languages and figuring out causal relationships," said Tom Griffiths, director of UC Berkeley's Computational Cognitive Science Lab. "We are hoping to make computers smarter by making them a little more like children."

Some argue that robots do not have free will and therefore cannot be held morally accountable for their actions. But UW psychologists are finding that people don't have such a clear-cut view of humanoid robots. The researchers' latest results show that humans apply a moderate amount of morality and other human characteristics to robots that are equipped with social capabilities and are capable of harming humans. In this case, the harm was financial, not life-threatening. But it still demonstrated how humans react to robot errors.

Scientists are developing artificial intelligence solutions for image processing, which have applications in many areas including advertising, entertainment, education and healthcare. They have, for example, developed computer algorithms for facial age classification -- the automated assignment of individuals to predefined age groups based on their facial features as seen on video captures or still images. Improving the accuracy of facial age classification, however, is not easy. A person can teach a computer to make better guesses by running its algorithm through a large database of facial images of which the age is known using sets of labeled images, but acquiring such a database can be both time-consuming and expensive. The process might even breach privacy in certain countries.

Facial characteristics play a central role in our everyday assessments of other people. "The perception of dominance has been shown to be an important part of social roles at different stages of life, and to play a role in mate selection," said Mr. Rojas. If the information on which the evaluation of faces is based could be automatically learned, it could be modeled and used as a tool for designing better interactive systems. The team studied to what extent this information is learnable from the point of view of computer science. Specifically, the task was formulated with the intention of predicting 9 facial trait judgments (attractive, competent, trustworthy, dominant, mean, frightening, extroverted, threatening, and likable) using Machine learning techniques (a branch of artificial intelligence that uses examples to teach a program how to work).

Each robot is 1.3 meters, or about 4-feet, 3-inches, tall. They are fully actuated, which means that they have similar joints and movement capabilities to that of a human, including arms, legs and hands with fully functional fingers and an opposable thumb. "This is an historic event," said Dr. Youngmoo Kim, an associate professor and assistant dean of media technologies in the College of Engineering and the director of the Music and Entertainment Technology (MET) Lab. "Never before have seven adult-sized, fully actuated humanoids appeared on stage together, so it's truly a milestone in robotics research." This gathering of robots is the fruition of seeds planted in 2008 when Drexel received a five-year grant from the National Science Foundation's Partnership for International Research and Education (PIRE) Program with the goal of training engineers to work in global multi-disciplined design teams.

Dr Dyer is one of Australia's leading bee experts and his latest research shows that honeybees can learn to recognise human faces even when seen from different viewpoints. Dr Dyer said the research could be applied in the areas of new technology, particularly the development of imaging systems. "What we have shown is that the bee brain, which contains less than 1 million neurons, is actually very good at learning to master complex tasks. Computer and imaging technology programmers who are working on solving complex visual recognition tasks using minimal hardware resources will find this research useful," Dr Dyer said. "Most current artificial intelligence (AI) recognition systems perform poorly at reliably recognising faces from different viewpoints. However the bees have shown they can recognise novel views of rotated faces using a mechanism of interpolating or image averaging previously learnt views."

"As my father lost the ability to do things for himself, my mother would give him gentle prompts to keep him on track," recalled Kautz, associate professor in the University of Washington's Department of Computer Science & Engineering. "So at a stage of the disease where, according to the clinical scales, it would seem he couldn't do anything for himself, he could still perform many of the functions of life. He could shower, get dressed, and so forth because my mother would monitor him and give a prompt when needed." It's a recollection that has guided Kautz in initiating a research effort at the UW to explore ways in which computer science can compensate for diminished mental capacity. The Assisted Cognition Project is a collaborative effort by the UW, Intel Computers and Elite Care, a private company developing a state-of-the-art retirement community in the Portland area that utilizes so-called ubiquitous computing to keep tabs on residents' needs.

The Leibniz Center for Law has its roots in the former department of Computer Science & Law of the Law Faculty of the University of Amsterdam, and currently houses about 15 researchers. The Leibniz Center conducts research and provides education in the field of Artificial Intelligence and law. In the tradition of Leibniz, we focus on the development and application of techniques from Artificial Intelligence to the field of Law for the purpose of supporting legal practice, and bringing new insights to legal theory. The Leibniz Center for Law has longstanding experience on legal ontologies, automatic legal reasoning and legal knowledge-based systems, (standard) languages for representing legal knowledge and information, user-friendly disclosure of legal data, and the application of ICT in education and legal practice (e.g. It plays an important role in the development of eGovernment on both national and international level. The center provides advice on change-management issues of knowledge-intensive legal processes and the improvement of knowledge-productivity in legal organisations.

"Researchers photograph dolphins in their natural surroundings and compare new dorsal fin photographs against a catalogue of previously identified dolphins," explains Kelly Debure, professor of computer science at Eckerd College in St. Petersburg, Florida. "These catalogs are often organized into categories based on either distinct fin shape or location of predominant damage. The manual photo-identification process, although effective, is extremely time consuming and visually stressful, particularly with large collections of known dolphins." It was time to bring dolphin identification into the digital age. Debure, along with Eckerd students, developed DARWIN, or Digital Analysis and Recognition of Whale Images on a Network, a computer program that simplifies photo-identification of bottlenose dolphins by applying computer vision and signal processing techniques to automate much of the tedious manual photo-id process.