Japanese researchers have revealed artificial intelligence software so smart it can beat most real students on a high school test. Known as To-Robo, the AI software scored higher on the English section of Japan's standardized college entrance test than the average Japanese high school senior, its developers said. It has also managed to double its score in just 12 months - raising hopes it will eventually pass the entrance exam for Tokyo University, Japan's most prestigious college. Man vs machine: Tokyo University helped this robot, Kirobo, which was sent to space. Now researchers hope to create artificial intelligence software so smart it can pass the university entrance exam itself. B: Yes, and he has to have an operation next week.

Will children of the future be taught solely by a computer? That could be a possibility, according to Google's vice-president of research Alfred Spector. He said he thinks apps and technologies that are widely derided as being distracting could actually improve how we learn. And he says this may lead to a future where students do not need to go to school in order to get a formal education. Google's Alfred Spector (shown) says technology could replace teachers in future.

For years scientists have worked to find a way to make it easier for deaf and hearing impaired people to communicate. And now it is hoped that a new intelligent system could be about to transform their lives. Researchers have used image recognition to translate sign language into'readable language' and while it is early days, the tool could one day be used on smartphones. Researchers have used image recognition to translate sign language (pictured) into'readable language' and while it is early days, the tool could one day be used on smartphones Scientists from Malaysia and New Zealand came up with the Automatic Sign Language Translator (ASLT), which can capture, interpret and translate sign language. It has been tested on gestures and signs representing both isolated words and continuous sentences in Malaysian sign language, with what they claim is a high degree of recognition accuracy and speed.

Microsoft's Kinect has already proved its credentials in reading simple hand and body movements in the gaming world. But now a team of Chinese researchers have added sign language to its motion-sensing capabilities. Scientists at Microsoft Research Asia recently demonstrated software that allows Kinect to read sign language using hand tracking. What's impressive is that it can do this in real-time, translating sign language to spoken language and vice versa at conversational speeds. The system, dubbed the Kinect Sign Language Translator, is capable of capturing a conversation from both sides.

Computers and the Internet: Listening to Girls' Voices – Dorothy Ellen Wilcox concludes that "instead of socializing adolescent girls toward docility, non-hierarchical technology like the Internet may provide a discourse for development of higher-level cognitive skills and the ability to unmask inequities in power and politics."

In 2014, the average SAT test taker correctly answered answered 49 percent of the test's math questions. Today, a new software program is now close to doing the same. In a paper published Monday, researchers at the Allen Institute for Artificial Intelligence (AI2) and the University of Washington revealed that their artificial intelligence (AI) system, known as GeoSolver, or GeoS for short, is able to answer "unseen and unaltered" geometry problems on par with humans. According to a report released by College Board, the average SAT math score in 2014 was 513. Though GeoS has only been tested on geometry questions, if the system's accuracy was extrapolated, GeoS would have scored a 500. Using a combination of computer vision and natural language processing, GeoS can interpret diagrams and process text that it then feeds into a geometric solver that analyzes the input and selects the best multiple choice answer.

A report on the future of technology in the workplace predicts that as many as 47 percent of US jobs are at risk in coming years due to increasing computerization, and that one of the best hopes for keeping people employed may come from a dedicated effort to improve a lagging American education system. The study, released in February, is part of a series co-produced by the Oxford Martin School at the University of Oxford and Citigroup that explores the most pressing global challenges of the 21st century. Michael Osborne, Citi Global Perspectives and Solutions project partner and Oxford Associate Professor of Machine Learning, has spoken in recent interviews about his part of the study, examining the characteristics of 702 occupations in the US. According to the report, food service jobs face 87 percent of risk of being replaced by robo burger chefs and counter help. The nearly 4 million Americans who work in the transportation industry are at risk from autonomous cars and trucks; up to 75 percent of jobs could be computerized in coming decades.

A tantalizing development at the Massachusetts Institute of Technology could help boost the popularity ratings of selfie takers and online daters. Scientists at the institute's Computer Science and Artificial Intelligence Laboratory (CSAIL) have taught computers to predict, with near perfect precision, which photos of faces, nature scenes, or other objects people are most likely to remember. Though similar predictive algorithms in the field of machine learning already allow computers to predict information by automatically completing phrases people type into Google search or by recognizing people to tag in photos on Facebook, scientists have not until now been able to use these tools to teach computers to predict what will be memorable to people, a skill that even humans themselves lack, Aditya Khosla, a PhD student in computer science at MIT, told The Christian Science Monitor. For selfies and other portraits, this development means an app might one day tell people which one from a group of photos is likely to get the most likes on Instagram, or to attract more suitors on a dating site. The team in 2013 developed an algorithm that can slightly modify pictures of faces to give them a more memorable flair.

Brandeis has a program in autonomous agents, focusing on multi--agent and multi--robot systems and machine learning, headed by Maja Mataric For details on research directions and a photo of the available robot herd see: http://www.cs.brandeis.edu/dept/faculty/mataric To get more information about the Volen Center for Complex Systems, about the Computer Science Department, and about other faculty, see: http://www.cs.brandeis.edu/dept. For more information about the cognitive science and cognitive neuroscience programs at Brandeis see: http://fechner.ccs.brandeis.edu/cogsci.html The Robotics Institute also offers a Robotics PhD and students from other programs (e.g. Research includes many aspects of mobile robots, computer integrated manufacturing, rapid prototyping, sensors, vision, navigation, learning and architectures.

Warning: This web page was originally constructed to help computer science students who were taking my module on natural language processing. Some terms may be used differently by different authors. Unless otherwise stated, definitions are based on the English language. If you find any errors, please e-mail me at p.coxhead@cs.bham.ac.uk. The verb in an active sentence can be said to be in the active voice. Examples are colourless and green which qualify ideas in Colourless green ideas sleep furiously. Adjectives can also appear after verbs like be, e.g. Examples are furiously which qualifies the verb sleep in Colourless green ideas sleep furiously, or intensely which qualifies stared in He stared at me intensely. Adverbs can also qualify adjectives, e.g. Many English adverbs are formed from an adjective plus the ending -ly. Words like very, which can only qualify adjectives or adverbs but not verbs, are sometimes called adverbs, but are perhaps best put in a separate category. In its broadest sense, an affix can be a prefix, a suffix, or an infix. More narrowly, infixes are sometimes treated separately. The stop and fricative must be produced in a very similar positions in the mouth.