Powerset, Inc., based in San Francisco, is on the verge of offering an innovative natural-language search engine, based on linguistic research at the Palo Alto Research Center (PARC). The engine does more than merely accept queries asked in the form of a question. The company claims that the engine finds the best answer by considering the meaning and context of the question and related Web pages. "Powerset extracts deep concepts and relationships from the texts, and the users query and match them efficiently to deliver a better search," Powerset CEO Barney Pell says. Even though attempts have been made at natural-language search for decades, Powerset says that its system is different because it has solved some of the fundamental technological problems that have existed with this kind of search.

The 3-D research was seminal in the fields of computer vision and artificial intelligence. Known as "constraint propagation," the technique is now used in industry for solving problems like route scheduling, package routing and construction scheduling. At M.I.T., Dr. Waltz was taught by Marvin Minsky, a pioneer in artificial intelligence. Dr. Waltz graduated in 1972, then taught computer science at the University of Illinois at Urbana-Champaign and, later, at Brandeis University in Massachusetts. But it was as a member of a group of researchers at the Thinking Machines Corporation, in Cambridge, Mass., that Dr. Waltz made his breakthrough in information retrieval.

With the advent of the World-Wide Web and the huge increase in the use of the Internet, there has been a corresponding increase in demand for text retrieval systems that can work in distributed, wide-area network environments. This demand also comes from groupware applications such as Lotus Notes, which facilitate the rapid creation of databases distributed throughout an organization. One problem of this type is addressed by Web search engines, such as Infoseek and Lycos, which index Web pages and provide access to them. The more general problems are locating the best databases to search in a distributed environment that may contain hundreds or even thousands of databases, and merging the results that come back from the distributed search. The results must be merged in order to produce the overall ranking of retrieved items, instead of a collection of individual rankings.

Tony Rose, vice-chair of the BCS Information Retrieval Specialist Group, looks at the management problems associated with the huge volumes of data that society produces. It has often been said that we are currently living through an information revolution. The internet age has undoubtedly brought with it a massive increase in the volume of data being produced and stored worldwide, driven by an ever-increasing demand for communication networks and online information access. For example Computing magazine reports that global information storage grew by about 30 per cent between 1999 and 2003, and that during 2002 about five exabytes of new, unique data was stored on print, film magnetic and optical storage (a volume roughly equivalent to 37,000 times the size of the book collection in the Library of Congress). Moreover when we examine the implications of this for individuals across the globe, the consequence is that almost 800Mb of recorded information is produced for each person on earth in a single year.

Computer scientist extraordinaire Karen Spärck Jones, professor emeritus of computer and information at the University of Cambridge, died last month of cancer. Shortly before that, she got to see her life's work in natural language processing and information retrieval receive even more acclaim than ever from major computer science institutions around the globe. The Association for Computing Machinery (ACM) had chosen her to receive both the ACM/AAAI Allen Newell Award and the ACM-W Athena Lecturer Award. And only weeks before that, she was also awarded the prestigious Lovelace Medal by the British Computer Society (BCS). The woman they honored pioneered techniques that allow people to work with computers using ordinary words instead of equations or codes, a breakthrough that was important in the subsequent development of search engines. According to the ACM, she also discovered term weighting, a statistical method used to evaluate how important any given word is in a set of documents, and thus the word's significance for an individual document.

Oulu, Finland based Start-up Valossa has released its new video search tool. The technology analyses video streams in real-time to identify more than one thousand concepts such as places and objects from any video stream. Valossa technology understands the contents of video files themselves through a combination of natural language processing and pattern recognition AI. It has been designed for service and content providers enabling users to can reach down into their video content, identify it and make it searchable. Sometimes the most profound solution is to change the entire problem.

You can spend a long time searching satellite images for interesting locations. Now imagine a tool that can not only show you the location, but scans large geographical areas to find specific features that are similar, and then it presents these results in a pattern-like format. In 2002, Carnegie Mellon's (CMU) School of Computer Science launched what it calls the world's "first PhD program in Machine Learning". It attempted to learn how to program systems to automatically learn and use its experience to improve its results. A group of CMU professors and students have now created a visual search tool for satellite imagery called Terrapattern. In a landmark project, Google Earth has teamed up with locals to provide a fully immersive experience of the Sherpa community and their mountain home in the Sagarmatha region in Nepal, home to the tallest mountain in the world, Everest.

Analyzing queries made to Google, Bing, and other search engines can reveal the potentially dangerous consequences of mixing prescriptions before they are known to the Food and Drug Administration (FDA), according to a new study. Such data mining could even expose medical risks that slip through clinical trials undetected. Pharmaceuticals often have side effects that go unnoticed until they're already available to the public. This is especially true of side effects that emerge when two drugs interact, largely because drug trials try to pinpoint the effects of one drug at a time. Physicians have a few ways to hunt for these hidden risks, such as reports to FDA from doctors, nurses, and patients.

Remember Watson, IBM's Jeopardy champion? A couple years ago, Watson beat the top two human champions Ken Jennings and Brad Rutter at a game where even interpreting the cue is complex with language nuances. Yet after the initial excitement, most people -- except for a notable few -- forgot about Watson. But we need to pay attention, and now. This could be the beginning of a serious challenge to Google, whose most ambitious initiatives -- from wearables to cars to aging -- are funded through its thriving advertising business.

SAN FRANCISCO -- Google is putting its search engine in the hands of its artificial intelligence chief. John Giannandrea will take over from Amit Singhal who is leaving Google this month after 15 years. The move shows the ascendance and growing importance of artificial intelligence at the major Silicon Valley technology companies. Search is the engine that drives Google's lucrative advertising business. Artificial intelligence is increasingly behind advances in how Google answers search queries.