In Silicon Valley, Nikolas Janin rises for his 40-minute commute to work just like everyone else. The shop manager and fleet technician at Google gets dressed and heads out to his Lexus RX 450h for the trip on California's clotted freeways. That's when his chauffeur – the car – takes over. One of Google's self-driving vehicles, Mr. Janin's ride is equipped with sophisticated artificial intelligence technology that allows him to sit as a passenger in the driver's seat. Its first real job, expected later this year, will be as a telemedicine robot, allowing a specialist thousands of miles away to visit patients' hospital rooms via a video screen mounted as its "head." When the physician is ready to visit another patient, he taps the new location on a computer map: Ava finds its own way to the next room, including using the elevator. In Pullman, Wash., researchers at Washington State University are fitting "smart" homes with sensors that automatically adjust the lighting needed in rooms and monitor and interpret all the movements and actions of its occupants, down to how many hours they sleep and minutes they exercise.

Max's comment remains relevant to a great deal of 21st Century AI research in machine vision (i.e. up to 2014 at least), focusing on training machines to attach labels as opposed to understanding structures (I would now add "and processes involving interacting structures"). One of the points he could have made but nowhere seems to have made, is that natural vision systems are mostly concerned with motion and change, including change of shape, and change of viewpoint. The emphasis on static scenes and images may therefore conceal major problems e.g.

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.

Welcome to the November 2004 Newsletter. Dennis has asked me to take over a few issues, so I'd like to introduce myself. Like Dennis, I like Prolog, and have used it as my language of choice for various applications, including expert systems, analysing financial data, and teaching AI at Oxford University. My most recent project is an algebraic manipulation system for safe spreadsheet construction, to try and reduce some of the billions of pounds lost to spreadsheet errors every year. Having declared a bias to Prolog and logic-based AI, I must add that this is not the only way to go.

Jim Doran will first talk about his time, from 1964 to 1969, as a Junior Research Fellow in the Edinburgh Experimental Programming Unit directed by Donald Michie, and give some insight into the scientific objectives of the group, and into the adventurous and combative atmosphere of those early days. Then he will describe the early development of the Essex AI group, founded by Tony Brooker in about 1972, which also became one of the leading UK AI centres. Finally he will offer some remarks about the history of AI in the UK from the thirties until today. What were the key developments, what caused them, and how much scientific progress have we really made?' Presentation PDF (920KB), PowerPoint (912KB).

What if machines could think like us -- comprehending social cues, visual prompts and spoken words just like a human would? For Computer Science and Artificial Intelligence Laboratory (CSAIL) Professor Patrick Winston, the Ford Professor of Artificial Intelligence and Computer Science and leader of the Genesis Group at CSAIL, uncovering the true nature of human intelligence is the next grand challenge. To solve the puzzle of how humans think, Winston is employing classic engineering methodology to build systems that think and comprehend as people do using computational methods. Motivated by a desire to advance artificial intelligence and create systems that operate in a manner consistent with high-level human thinking, Winston feels there is a substantial difference between machines that actually display human-like intelligence and those that possess superb computational powers such as IBM's Watson system. For Winston, understanding what makes us different leads to questioning our uniquely symbolic nature, our ability to build descriptions using an inner language, and especially our ability to construct and tell stories, from fairy tales to case studies.

Since the 1960s, politicians and pundits have predicted the imminent arrival of a digital utopia in which robots would do the washing up and we would live in peace and harmony in an electronically connected, global village, thanks to the net. So why are the utopian visions of 40 years ago strangely similar to the ones we hold today? Because business and political leaders have consistently pushed a carefully orchestrated fantasy of the future to distract us from the present, says Richard Barbrook, who explores the subject in Imaginary Futures - From Thinking Machines to the Global Village. Barbrook, a senior lecturer in politics at the University of Westminster, has been researching this topic for more than four years. What he wants is to show how ideology is used to warp time.

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.

The day we can't tell the difference between a human and robot just got a little bit closer. A Turing Test of sorts has been put to humans to see if they could differentiate between their fellow human and non-human combatants in a first-person shooter game. For the first time in the five years that the contest has run, humans couldn't tell the difference. The contest, conceived of and organized by Philip Hingston, Associate Professor of Computer Science at Edith Cowan University in Perth, Australia, puts human and computer players on the battlefields of the first-person shooter game UT2004. After a few rounds of combat, the humans have to decide which players are human and which are bots.

For as long as there have been exchanges for shares, currencies or commodities, investors have looked for ways to beat the markets and produce above-average returns. Highly-skilled traders and fund managers command premium salaries for their abilities to second-guess market moves and return profits to their clients. But developments in computer science mean that automated trading systems could well outperform even experienced traders across global financial markets. Researchers at HP's European labs in Bristol, England have found that international financial institutions are increasingly showing interest in their work on automated trading agents - despite the fact that the agents were not originally developed for financial markets. HP Labs' complex adaptive systems group first started working on trading algorithms in the mid-1990s.