During this third decade, the UGC/UFC started the process of assessing research quality. In 1989, and again in 1992, the Department shared a "5" rating with the other departments making up the University's Computing Science unit of assessment.
The Department's postgraduate teaching also expanded rapidly. A masters degree in Knowledge Based Systems, which offered specialist themes in Foundations of AI, Expert Systems, Intelligent Robotics and Natural Language Processing, was established in 1983, and for many years was the largest of the Faculty's taught postgraduate courses with 40-50 graduates annually. Many of the Department's complement of about 60 Ph.D. students were drawn from its ranks. At undergraduate level, the most significant development was the launch, in 1987/88, of the joint degree in Artificial Intelligence and Computer Science, with support from the UFC's Engineering and Technology initiative. Subsequently, the modular structure of the course material enabled the introduction of joint degrees in AI and Mathematics and AI and Psychology. At that time, the Department also shared an "Excellent" rating awarded by the SHEFC's quality assessment exercise for its teaching provision in the area of Computer Studies.
The start of the fourth decade of AI activity coincided with the publication in 1993 of "Realising our Potential", the Government's new strategy for harnessing the strengths of science and engineering to the wealth creation process. For many departments across the UK, the transfer of technology from academia to industry and commerce was uncharted territory. However, from a relatively early stage in the development of AI at Edinburgh, there was strong interest in putting AI technology to work outside the laboratory. With financial banking from ICFC, in 1969 Michie and Howe had established a small company, called Conversational Software Ltd (CSL), to develop and market the POP-2 symbolic programming language. Probably the first AI spin-off company in the world, CSL's POP-2 systems supported work in UK industry and academia for a decade or more, long after it ceased to trade. As is so often the case with small companies, the development costs had outstripped market demand. The next exercise in technology transfer was a more modest affair, and was concerned with broadcasting some of the computing tools developed for the Department's work with schoolchildren. In 1981 a small firm, Jessop Microelectronics, was licensed to manufacture and sell the Edinburgh Turtle, a small motorised cart that could be moved around under program control leaving a trace of its path. An excellent tool for introducing programming, spatial and mathematical concepts to young children, over 1000 were sold to UK schools (including 100 supplied to special schools under a DTI initiative). At the same time, with support from Research Machines, Peter Ross and Ken Johnson re-implemented the children's programming language, LOGO, on Research Machines microcomputers. Called RM Logo, for a decade or more it was supplied to educational establishments throughout the UK by Research Machines.
As commercial interest in IT in the early 1980s exploded into life, the Department was bombarded by requests from UK companies for various kinds of technical assistance. For a variety of reasons, not least the Department's modest size at that time, the most effective way of providing this was to set up a separate non-profit making organisation to support applications oriented R&D. In July 1983, with the agreement of the University Court, Howe launched the Artificial Intelligence Applications Institute. At the end of its first year of operations, Austin Tate succeeded Howe as Director. Its mission was to help its clients acquire know-how and skills in the construction and application of knowledge based systems technology, enabling them to support their own product or service developments and so gain a competitive edge. In practice, the Institute was a technology transfer experiment: there was no blueprint, no model to specify how the transfer of AI technology could best be achieved. So, much time and effort was given over to conceiving, developing and testing a variety of mechanisms through which knowledge and skills could be imparted to clients. A ten year snapshot of its activities revealed that it employed about twenty technical staff; it had an annual turnover just short of £1M, and it had broken even financially from the outset. Overseas, it had major clients in Japan and the US. Its work focused on three sub-areas of knowledge-based systems, planning and scheduling systems, decision support systems and information systems.
Formally, the Department of Artificial Intelligence disappeared in 1998 when the University conflated the three departments, Artificial Intelligence, Cognitive Science and Computer Science, to form the new School of Informatics.
Text 2
A gift of tongues
Troy Dreier
PC MAGAZINE July 2006.
1. Jokes about the uselessness of machine translation abound. The Central Intelligence Agency was said to have spent millions trying to program computers to translate Russian into English. The best it managed to do, so the tale goes, was to turn the Famous-Russian saying "The spirit is willing but the flesh is weak" into "The vodka is good but the meat is rotten." Sadly, this story is a myth. But machine translation has certainly produced its share of howlers. Since its earliest days, the subject has suffered from exaggerated claims and impossible expectations.
2. Hype still exists. But Japanese researchers, perhaps spurred on by the linguistic barrier that often seems to separate their country's scientists and technicians from those in the rest of the world, have made great strides towards the goal of reliable machine translation—and now their efforts are being imitated in the West.
3. Until recently, the main commercial users of translation programs have been big Japanese manufacturers. They rely on machine translation to produce the initial drafts of their English manuals and sales material. (This may help to explain the bafflement many western consumers feel as they leaf through the instructions for their video recorders.) The most popular program for doing this is e-j bank, which was designed by Nobuaki Kamejima, a reclusive software wizard at AI Laboratories in Tokyo. Now, however, a bigger market beckons. The explosion of foreign languages (especially Japanese and German) on the Internet is turning machine translation into a mainstream business. The fraction of web sites posted in English has fallen from 98% to 82% over the past three years, and the trend is still downwards. Consumer software, some of it written by non-Japanese software houses, is now becoming available to interpret this electronic Babel to those who cannot read it.
4. Machines for translating from one language to another were first talked about in the 1930s. Nothing much happened, however, until 1940 when an American mathematician called Warren Weaver became intrigued with the way the British had used their pioneering Colossus computer to crack the military codes produced by Germany's Enigma encryption machines. In a memo to his employer, the Rockefeller Foundation, Weaver wrote: "I have a text in front of me which is written in Russian but I am going to pretend that it is really written in English and that it has been coded in some strange symbols. All I need to do is to strip off the code in order to retrieve the information contained in the text."
5. The earliest "translation engines" were all based on this direct, so-called "transformer", approach. Input sentences of the source language were transformed directly into output sentences of the target language, using a simple form of parsing. The parser did a rough/analysis of the source sentence, dividing it into subject, object, verb, etc. Source words were then replaced by target words selected from a dictionary, and their order rearranged so as to comply with the rules of the target language.
6. It sounds simple, but it wasn't. The problem with Weaver's approach was summarized succinctly by Yehoshua Bar-Hillel, a linguist and philosopher who wondered what kind of sense a machine would make of the sentence "The pen is in the box" (the writing instrument is in the container) and the sentence "The box is in the pen" (the container is in the[play]pen).
7. Humans resolve such ambiguities in one of two ways. Either they note the context of the preceding sentences or they infer the meaning in isolation by knowing certain rules about the real world—in this case, that boxes are bigger than pens (writing instruments) but smaller than pens (play-pens) and that bigger objects cannot fit inside smaller ones. The computers available to Weaver and his immediate successors could not possibly have managed that.
8. But modern computers, which have more processing power arid more memory, can. Their translation engines are able to adopt a less direct approach, using what is called "linguistic knowledge". It is this that has allowed Mr. Kamejima to produce e-j bank, and has also permitted NeocorTech of San Diego to come up with Tsunami and Typhoon - the first Japanese-language-translation software to run on the standard (English) version of Microsoft Windows.
9. Linguistic-knowledge translators have two sets of grammatical rules—one for the source language and one for the target. They also have a lot of information about the idiomatic differences between the languages, to stop them making silly mistakes.
10. The first set of grammatical rules is used by the parser to analyze an input sentence ("I read" The Economist "every week"). The sentence is resolved into a tree that describes the structural relationship between the sentence's components ("I" [subject], "read" (verb), "The Economist" (object) and "every week" [phrase modifying the verb). Thus far, the process is like that of a Weaver-style transformer engine. But then things get more complex. Instead of working to a pre-arranged formula, a generator (i.e., a parser in reverse) is brought into play to create a sentence structure in the target language. It does so using a dictionary and a comparative grammar—a set of rules that describes the difference between each sentence component in the source language and its counterpart in the target language. Thus a bridge to the second language is built on deep structural foundations.
11. Apart from being much more accurate, such linguistic-knowledge engines should, in theory, be reversible—you should be able to work backwards from the target language to the source language. In practice, there are a few catches which prevent this from happening as well as it might - but the architecture does at least make life easier for software designers trying to produce matching pairs of programs. Tsunami (English to Japanese) and Typhoon Japanese to English), for instance, share much of their underlying programming code.
12. Having been designed from the start for use on a personal computer rather than a powerful workstation or even a mainframe, Tsunami and Typhoon use memory extremely efficiently. As a result, they are blindingly fast on the latest PCs—translating either way at speeds of more than 300,000 words an hour. Do they produce perfect translations at the click of a mouse? Not by a long shot. But they do come up with surprisingly good first drafts for expert translators to get their teeth into. One mistake that the early researchers made was to imagine that nothing less than flawless, fully automated machine translation would suffice. With more realistic expectations, machine translation is, at last, beginning to thrive.
Text 3
David Stone
PC MAGAZINE March 8, 2005.
Biologists hail SI 00 million project to build a "petaflop" computer as likely to revolutionize our understanding of cellular biology. The computer, nicknamed 'Blue Genes', world be around 500 times faster than today's most powerful supercomputer.
Computer scientists say that the planned machine, details of which were revealed last: week, is the first large leap in computer architecture in decades.
IBM will build the programme around the challenge of modeling protein folding (see below), with much of the research costs going on designing software. It will involve 50 scientists from IBM Research's Deep Computing Institute and Computational Biology Group, and unnamed outside academics.
But Blue Gene's hardware will not he customized to the problem and, if IBM's blueprint works, it will offer all scientific disciplines petaflop computers. These will be capable of more than one quadrillion floating point operations ('flop') per second - around two million times more powerful than today's top desktops. Most experts have" predicted that fundamental technological difficulties would prevent a petaflop computer being built before around 2015.
"It is, fantastic that IBM is doing this," says George Lake, a scientist at the university of Washington and NASA project, scientist for high-performance computing in Earth and space science. IBM is showing leadership by ushering in a new generation of supercomputers, he says.
The biggest-technological constraints to building a petaflop machine have been latency - increasing the speed with which a chip addresses the memory - and reducing power-consumption. A petaflop computer build using conventional chips would consume almost one billion watts of power. IBM reckons Blue Gene will use just one million-watts.
Although processor speeds have increased exponentially, the time to fetch dm from the memory of a supercomputer, 300 nanoseconds, is only slightly less than half what it was 20 years ago. Putting more and more transistors on a chip is therefore unlikely to lead to much greater speed.
"We set out from scratch, completely ignoring history, and thought how can we get the highest performance out of silicon," says Monty Denneau, a scientist at IBM's Thomas J. Watson research center in Yorktown Heights, New York, who is assistant architect of Slue Gene.
Arvind, a professor of computer science at Mit who is considered one of the top authorities on computer architecture, applauds IBM's approach. "It has made very big steps in rethinking computer architecture to try to do without the components that consume power, it has taken all these research ideas and pulled them together."
Task III. Write précis of the following articles.
Text 1
Antiviruses. Principle of work. Examples of antiviruses.
Antivirus software consists of computer programs that attempt to identify, thwart and eliminate computer viruses and other malicious software (malware). Antivirus software typically uses two different techniques to accomplish this:
• Examining (scanning) files to look for known viruses matching definitions in a virus dictionary
• Identifying suspicious behavior from any computer program which might indicate infection. Such analysis may include data captures, port monitoring and other methods.
Most commercial antivirus software uses both of these approaches, with an emphasis on the virus dictionary approach.
Historically, the term antivirus has also been used for computer viruses that spread and combated malicious viruses. This was common on the Amiga computer platform.
Dictionary
In the virus dictionary approach, when the antivirus software looks at a file, it refers to a dictionary of known viruses that the authors of the antivirus software have identified. If a piece of code in the file matches any virus identified in the dictionary, then the antivirus software can take one of the following actions:
• attempt to repair the file by removing the virus itself from the file
• quarantine the file (such that the file remains inaccessible to other programs and its virus can no longer spread)
• delete the infected file
To achieve consistent success in the medium and long term, the virus dictionary approach requires periodic (generally online) downloads of updated virus dictionary entries. As civically minded and technically inclined users identify new viruses "in the wild", they can send their infected files to the authors of antivirus software, who then include information about the new viruses in their dictionaries.
Dictionary-based antivirus software typically examines files when the computer's operating system creates, opens, closes or e-mails them. In this way it can detect a known virus immediately upon receipt. Note too that a System Administrator can typically schedule the antivirus software to examine (scan) all files on the computer's hard disk on a regular basis. Although the dictionary approach can effectively contain virus outbreaks in the right circumstances, virus authors have tried to stay a step ahead of such software by writing "oligomorphic", "polymorphic" and more recently "metamorphic" viruses, which encrypt parts of themselves or otherwise modify themselves as a method of disguise, so as not to match the virus's signature in the dictionary.