Two-thirds of the earth is underwater. We glide over the surface of the oceans, but we still have very little idea what is going on even a few metres down. We spend billions sending craft and people into space, but we do not really know what happens under the waves. One man who finds that more than curious is Graham Hawkes, a beneath-the-sea maverick who has been working on underwater craft for most of his life. It is a lonely, driven quest, relying rather dangerously on the engagement and backing of a few wealthy enthusiasts. Born in London, Mr Hawkes learnt his engineering expertise in the defence industry, working initially on torpedoes in Norfolk, England.

Many web search engines use the concept of a'spider' - automated software which goes out onto the web and trawls through the contents of each server it encounters, indexing documents as it finds them. This approach results in the kinds of databases maintained by services such as Alta Vista and Excite - huge indexes to a vast chunk of what's currently available on the web. However, the problems which users can face when using such databases are beginning to be well documented. A recent JISC-funded investigation [1] into the use of web search engines indicates that users can typically encounter a number of difficulties. These include the issue of finding information relevant to their needs, and the problem of information overload - when far too much information is returned from a search.

From 1966 through 1972, the Artificial Intelligence Center at SRI International (then Stanford Research Institute) conducted research on a mobile robot system nicknamed "Shakey." Endowed with a limited ability to perceive and model its environment, Shakey could perform tasks that required planning, route-finding, and the rearranging of simple objects. Although the Shakey project led to numerous advances in AI techniques, many of which were reported in the literature, much specific information appears only in a series of previously relatively inaccessible SRI technical reports. In 1969, the demonstrations of Shakey were collected in a 24-minute film "SHAKEY: Experimentation in Robot Learning and Planning" (91.7MB The possibilities of Computer Science and Artificial Intelligence caught the public's imagination.

The computer listens to the soloist and classifies the performance according to style. The styles are "lyrical," "syncopated," "pointilistic," and "frantic." The absolute meanings of these terms is not important. What is important is that the player and computer agree so that the computer can understand what the player intends. The computer actually learns about style from examples (see Dannenberg, Thom, and Watson, A Machine Learning Approach to Musical Style Recognition").

With the recent launch of MIT's Institute for Medical Engineering and Science, MIT News examines research with the potential to reshape medicine and health care through new scientific knowledge, novel treatments and products, better management of medical data, and improvements in health-care delivery. At the end of 2012, the National Public Radio show "Fresh Air" featured a segment in which its linguistics commentator argued that "big data" should be the word of the year. The term refers not only to the deluge of data produced by the proliferation of Internet-connected, sensor-studded portable devices but also to innovative techniques for analyzing that data; and big data has received a good deal of credit for Barack Obama's victory in the last presidential election. Certainly, the term was in heavy use around MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), which in 2012 launched a new big-data initiative called bigdata@CSAIL. Several of the researchers affiliated with bigdata@CSAIL are developing new techniques for processing medical data, to make it more accessible to both physicians and patients and to find correlations that could improve diagnosis or choice of therapies.

A central topic in spoken-language-systems research is what's called speaker diarization, or computationally determining how many speakers feature in a recording and which of them speaks when. Speaker diarization would be an essential function of any program that automatically annotated audio or video recordings. To date, the best diarization systems have used what's called supervised machine learning: They're trained on sample recordings that a human has indexed, indicating which speaker enters when. In the October issue of IEEE Transactions on Audio, Speech, and Language Processing, however, MIT researchers describe a new speaker-diarization system that achieves comparable results without supervision: No prior indexing is necessary. Moreover, one of the MIT researchers' innovations was a new, compact way to represent the differences between individual speakers' voices, which could be of use in other spoken-language computational tasks.

Google's search algorithm has been changed over the last year to increasingly reward search results based on how likely you are to click on them, multiple sources tell Business Insider. As a result, fake news now often outranks accurate reports on higher quality websites. The problem is so acute that Google's autocomplete suggestions now actually predict that you are searching for fake news even when you might not be, as Business Insider noted on December 5. There is a common misconception that the proliferation of fake news is all Facebook's fault. Although Facebook does have a fake news problem, Google's ranking algorithm does not take cues from social shares, likes, or comments when it is determining which result is the most relevant, search experts tell Business Insider. The changes at Google took place separately, experts say, to the fake news problem occurring on Facebook.

Although Hollywood films warn of sentient artificial intelligence looking to overthrow and exterminate its human creators, they're mostly a reason for their big-name stars to jump through the air in slow-motion while dual-wielding rocket-propelled grenade launchers while a hovership explodes behind them. Blinky, on the other hand, paints a much more likely reality of the dangers we may soon face as intelligent robotics become as commonplace as smartphones and voice-activated assistants like Siri. Blinky is a short film written, directed, and edited by Ruairi Robinson. The budget was 45,000 euro (approximately $60K U.S.) for the actual shoot, while the extensive visual effects were done almost entirely by Robinson himself. "Visual effects were completed over a period of 9 months, of hell," says Robinson.

The idea is to produce cloud-based software that can understand natural language medical advice questions, and can help doctors make diagnoses. The goal of the current research is to learn more about how artificial intelligence interacts with non-scripted medical questions from the real world. And yes, the key word there is "non-scripted." Rather than programs that understand only coded language, this software would understand speech the way laypeople people talk. The research team from the National Institute of Health (NIH) in Bethesda, Maryland started by accessing 9,000 medical questions from Stanford University and the University of Minnesota.

UBIQUITY: Congratulations on "What the Dormouse Said" it's a fascinating book. MARKOFF: Well, I guess I'd call it a revisionist history. It about things that happened around Stanford University between roughly 1960 and 1975, and is a kind of pre-history of personal computing and the personal computer industry. What I was trying to do was to get at some of the culture through which the technology was developed. MARKOFF: Because technology never happens in a vacuum. The book was an effort to try to pin down how personal computing first emerged around the Stanford campus at two laboratories in the 1960's: one was run by John McCarthy, and was called the Stanford Artificial Intelligence Laboratory; and the other was run by Doug Engelbart and known as the Augmentation Research Center or the Augmented Human Intellect Research Center.