Problems with hand-based input/output
The current hand input devices suffer from the same delays that plague the head mount display systems, but the user's over-compensation is even more noticeable. Because there is typically some interaction with the hand and other objects, absolute position control is much more important here
5 than it would be with head positioning, where relative motion is usually sufficient.
These devices are also extremely limited in their ability to generate any kind of tactile force or feedback to the user. Based on our research, even to
perform gross manipulation tasks with a DataGlove is extremely difficult
to without some kind of sensory feedback. Any kind of fine manipulation is impossible. Though tactile feedback of some kind may be possible, the quality of this will very likely be extremely low and the cost extremely high for the foreseeable future. •
Perhaps the major failing of the glove-based system is that it requires the
15 user to keep the hand and arm unsupported. This requires the user to employ both the agonist and antagonist muscle sets of the arm working against each other in order to perform any kind of complex task. The user actually is working harder at this than he would at pushing a real object because, in the case of a real object, at least one muscle group is at rest.
20 Further, because there is no true stable surface for the arm to rest against, any kind of control requires even more force between the muscle groups.. Our experience demonstrated that a user of such a system when faced
with any kind of gross manipulation tasks, could only be expected to use the system for five-minute periods with a large degree of exertion. Any
25 kind of extended activity was precluded.
As a consequence of these drawbacks, it is our expectation that the DataGlove and other similar interface devices will be replaced by more useful devices in the future. ■
OP- Vocabulary
agonist and antagonist muscle sets (1. 16) — two muscle groups which normally act in opposition to each other
Task 13
3
Read the text again and complete the table in note form.
Problem of hand input device Consequences
Task 14The following pairs of words are taken from the text. In each case, say whether
their meanings are similar (S) or opposite (0).
111 suffer from (1. 1) plague (1. 1) absolute (1. 4) true (1. 20) tactile (1.8) sensory (1. 10) force (1.8) exertion (1.24) gross (1.9) fine (1.10)
impossible (1. 11) precluded (1.25) ❑ failing (1. 14) drawback (1. 26) working (1.18) at rest (1. 19)
Word-play
Solve the crossword puzzle using the clues below.
Across
1The adjectival form of maths. (12)
6A piece of glass with a curved surface used to make things appear clearer, larger, or smaller. (4)
7The opposite of gross. (4)
9Short for 5down. (3)
10 The study of robots. (8)
11This is worn on the hand. (5)
12This sort of reality is not real. (7)
Down
1 Making (goods) on a large scale using machinery. (13)
2VR device worn on the head. (6)
3Present VR hand input devices are capable only of gross_____ tasks. (12)
4To work out or estimate. (9)
5A kind of display. (6,7)
8A device for finding direction, with a needle that points to magnetic north. (7)
Language focus L
Classifying
The term 'classifying' means arranging objects in classes or groups according to shared characteristics. For example, the Class of 'animals' includes all living things that can feel and move about, such as fish and birds. Each of these subgroups is also a class in its own right, having shared characteristics.
Classifying, then, is a process of bringing order out of confusion by organizing information in a logical fashion. There are often several ways of classifying the same information.
1 From general to specific: focusing on the large or high-level category and talking about its parts, that is from general to specific, the following expressions can be used:
is is made up of
can be divided into is composed of
is of comprises
has consists of
A general-to-specific classification will usually have singular main verbs, unless two or more things are being analysed simultaneously.
Examples:
1 The CPU is divided intothree parts: the control unit, the arithmetic-logic unit, and memory.
2 The CPU hasthree parts: the control unit, the arithmetic-logic unit, and memory.
3 The CPU is made up ofthree parts: the control unit, the arithmetic-logic unit, and memory.
4 The CPU is composed ofthree parts: the control unit, the arithmetic-logic unit, and memory.
5 The CPU consists ofthree parts: the control unit, the arithmetic-logic unit, and memory.
2 From specific to general: what the smaller (or lower-level) components make when they are put together. This kind of classification uses the following expressions:
make up may be
form can be
constitute are classified as
A specific-to-general classification will have plural verbs, because two or more lower-level categories are the focus of classification.
Examples:
1 The control unit, the arithmetic-logic unit, and memory are the three parts that make upthe CPU.
2 The control unit, the arithmetic-logic unit, and memory are the three parts that formthe CPU.
Exercise 1Using the diagram below, complete the paragraph on the following page.
Refer back to the text on C language (Unit 4, page 46) and complete the diagram.
Machine translation
Start-up
Decide whether the following sentences are true (T) or false (F):
1 _____ Some machine translation (MT) systems produce good translations.
2It is difficult to compare different MT systems.
3The easiest way to evaluate any machine translation of a given text is to
compare it to a human translation of the same text.
Reading
Task 2Read the text on the following page and check your answers to Task 1.
Lost in the machine translation
|
All machine-translated texts have to be extensively post-edited (and often pre-edited) by experienced translators. To offer a useful saving, the machine must make the time the translator spends significantly less than he or she would have taken by hand.
A few weeks ago, translators,
system developers, academics, and others from Europe, the US,
Canada, China, and Japan met for the first time in a Swiss hotel to mull over MT matters. A surprisingly large number of European governmental and corporate organizations are conducting expensive and elaborate
evaluations of MT, but they may not produce 'buy or don't buy' results.
Good human translators produce 8
good translations; all MT systems
produce bad translations. But just
what is a good translation? One
4 traditional assessment technique involves a bunch of people scoring translations on various scales for
intelligibility ('Does this translation into English make sense as a piece of English?'); accuracy ('Does this piece of English give the same information as the French
original?'); style, and so on.
However, such assessment is expensive, and designing the scales is something of a black art.
Take error analysis, a fancy name 5
for counting the various types of errors the MT system produces. You might spend five months working out a suitable scoring scheme — is correct gender agreement more important than correct number? —and totting up figures for a suitably large sample of text, but what do those figures mean? If one system produces vastly more errors than another, it is obviously inferior. But suppose they produce different types of error in the same overall numbers: which type of error is worse? Some errors are bound to cost translators more effort to correct, but it requires a lot more work to find out which.
Properly designed and integrated 9
MT systems really ought to enhance the translator's life, but few take this on trust. Of course, they do things differently in Japan. While
Europeans are dabbling their toes and most Americans deal only in English, the Japanese have gone in at the deep end. The Tokyo area already sports two or three independent MT training schools where, as the eminent Professor Nagao casually noted in his presentation, activities are
functioning with the efficiency of the Toyota production line. We're lucky they're only doing it
in Japanese.
Each of the sentences below (except one) summarizes an individual paragraph of the text. Order the sentences so that they form a summary of the text. One of the sentences contains information which is not in the text. Which one?
The developers of MT systems have also had problems evaluating their systems.
Many European organizations are evaluating MT, but the results may not be conclusive.
Assessing machine translations as good or bad is very difficult because such judgements cannot be made scientifically.
It is time-consuming for potential users to test the MT manufacturers' claims that their products double productivity.
Better tests are needed to monitor linguistic weaknesses in MT systems.
All machine translations need to be edited by a human translator. A reliable MT system is unlikely to be available this century.
The price of MT systems varies greatly and none actually translates.
The Japanese have a few independent MT training schools, which are said to be very efficient.
Analysing the errors made by MT systems is inconclusive because it may only show that different systems produce similar numbers of different error types.
Match each of the following verbs from the text with the expression that has a similar meaning:
1 churn out (para. 1) aadd up
2tie up (para. 3) bthink carefully about
3mull over (para. 4) cmanage successfully
4tot up (para. 5) dproduce large amounts of
5cope with (para. 7) e fail
6fall apart (para. 7) foccupy the time of
 |
| Task 5 | Using the paragraph reference given, find words or phrases in the text which have a similar meaning to: 1 ridiculous (para. 1) 2colour brochures (para. 3) 3casually (para. 3) 4sure to (para. 5) 5group (para. 8) 6mysterious ability (para. 8) 7experimenting in a small way (para. 9) 8invested heavily (para. 9) |
Speaking
| Task 6 | Look at these sentences. Discuss why a machine might find them difficult to translate. I bought a set of six chairs. The sun set at 9 p.m. He set a book on the table. We set of ffor London in the morning. She had her hair set for the party. The VCR is on the television set. Can you think of other examples where this kind of problem occurs? |
Al and expert systems
Listening
Task 7 [2]You are going to hear a conversation in which David, a graduate student doing
research in the field of artificial intelligence, explains to a friend, Kevin, what AI and expert systems are. Before you listen, try to write short definitions to explain:
1 artificial intelligence
2expert systems
Now listen to the conversation and modify your definitions as necessary.
Task 8 nListen again to the recording and answer these questions:
1 Does visual perception require intelligence when done by humans?
2What two categories of task are mentioned in relation to AI programs?
3Which category of task is AI more successful at?
4What is the relationship between AI and expert systems?
5What examples of existing expert systems are mentioned?
6In what way do expert systems imitate human experts?
7Why does the Japanese system have two parallel inference engines?
8What is the function of inference trees?
Task 9 ijRead this adapted extract from the tapescript and fill in the gaps with the
missing words.
KEVIN: What are e _______ 1 2 s used for?
DAVID: They're built for commercial a __________ 3. Up to now they've been
used for a variety of tasks — medical d _____________ 4, electronic fault finding,
machine translation, and so on. But the point about them is that you can 5 them about how they came to a particular c__________________________ 6
KEVIN: So, in that respect, they imitate human experts.
DAVID: Yes. I read recently about a Japanese system that can be used by 1 7 to draw conclusions about new legal cases. It refers to
d_______________ 8
of statutory laws and legal precedents and is able to see similarities in the r________________________ processes used to decide each case — exactly
as a s___________ 10lawyer would.
KEVIN: How can it do that?
DAVID: The system has two reasoning mechanisms, known as i__________
e_________ 12, which work in p_____________ 13. One operates on the written
laws, the other operates on the legal precedents. They draw all the possible
conclusions and then output them in the form of i 14
Now listen again to the recording and check your answers.
| Reading Read quickly through the text which follows and note down the answers to the following questions: What does the expert system ROI do? How did Scott French 'clone' Jacqueline Susann? What other applications of AI are mentioned in the text? | ||
| Task 10 |
One tough cookie
| T |
he software division of Mrs. Fields Cookies, Fields Software Group, has sold a version of its AI-based Retail Operations Intelligence
5 system to fast-food giant Burger King Corp. The expert system, called ROI, assists in the management of franchised or multiple-location retail operations
10 by creating work schedules, recommending marketing tactics, and assisting in personnel hiring. Fields has been successful with this package and has started
15 commercializing it. Now Burger King is developing its own expert system in an attempt to outperform its hamburger competitor McDonald's. Maybe it can clone
20 Ronald McDonald's expertise.
AI waxes poetic
Cloning a well-known figure is no joke. Just This Once is a new novel
making the rounds in the publishing world. It was written
25 by Scott French, who claims that 10% of the novel was written by him, 25% was created by an AI program he created to imitate novelist Jacqueline Susann, and
30 the remainder was a
collaborative effort between himself and the computer. Susann, who died in 1974, wrote the definitive trash novel -
35 and one of history's all-time bestsellers — Valley of the Dolls.
French used Nexpert Object and took development lessons from Bechtel AI Institute to
40 program his system with
hundreds of formulas he had developed regarding Susann's essential plots and charactertizations; it created a
45 350-page novel, which some in
the literary community are
-IIcalling 'computerized literary ghost-
writing'. While not all the reviews
on his methodology are positive
50 (some think it is a violation of Susann's intellectual property), French claims, 'I don't think you can copyright the way a person thinks.' If French gets a book deal,
55 this would be the first computer-generated novel ever published. Just this once, indeed.
Hundreds of other places are employing AI; some of the
60 applications may seem mundane
while others are intriguing.
From expert systems that help
you plan your garden to voice
systems that help doctors treat
65 critically injured patients in
emergency rooms to natural language front-ends for multimedia systems that feature models, actors, and actresses, AI
70 is being accepted in arenas outside those traditional realms
of science and engineering. ■
Answer these questions about the text.
1 What does the acronym ROI stand for?
2Why is Burger King developing an expert system?
3 What kind of books did Susann write?
4 What percentage of his novel did French write jointly with his computer?
5Has French's novel been well-received?
6How does French justify his action?
7 Has French found a publisher for his book?
8Where has AI traditionally been accepted?
Choose the definition that best expresses the meaning of the word or phrase.
alicensed to sell another company's products
bindividual
cspecially selected
amanaging
bemploying
ctraining
ado better than
bremove from the top position
csurvive longer than
acopy
buse to one's own advantage
cmake people laugh at
acirculating
bmaking no progress
c making a bad impression
aprinted on cheap paper
bpopular
cof poor quality
awriting under someone else's name
bwriting stories intended to frighten
cwriting for someone who is dead
aordinary
bsimple
cworld-wide
Writing
Read this summary of the first paragraph of the text on page 149, then compare it to the original.
The software division of Mrs. Field's Cookies has sold a version of its Al-based system for assisting in the management of retail operations to Burger King, who are now developing their own system in an attempt to outperform McDonald's.
1 Note the information that has been omitted from the summary.
2Look carefully at how the remaining information has been re-ordered and condensed.
3Now complete the summary of the text, keeping it as concise as possible.
Word-play
The clues below contain anagrams of words from this unit. Enter the words in the grid, then solve the anagram in the boldboxes to find the hidden word.
1Expert systems have been used in medical_____ . (ossadiing)
2A set of instructions for making something. (lamurof)
3Modernize. (dapetu)
4A rival company. (torpetimoc)
5Used of lines which are the same distance apart at any point. (alerlapl)
6Assessment. (tualiaveno)
7Having formal permission to sell another company's goods in a particular geographical area. (danrifcesh)
8An exact copy. (ecnol)
9Machines are still not very good at doing this. (nartgslanti)
Hidden word clue
This kind of engine is one of two reasoning mechanisms in an expert system.
Language focus M
Cause and effect
Understanding the different ways of expressing the relationship between the causes and the effects of an action is very important when you are reading English. This cause—effect relationship is commonly used in texts about computing.
Before we look at some of the ways of expressing cause and effect, note carefully this important distinction.
We can mention the cause before the effect.
Example:
(cause) (effect)
Dust often causesthe recording condition of disks to deteriorate.
We can mention the effect before the cause.
Example:
(effect) (cause)
Deterioration in the recording condition of disks is often due todust.
There are many different ways of expressing cause and effect. 1 Verbs linking cause and effect:
result cause
produce result in
allow result from
prevent bring about
enable
Examples:
1 The introduction of computer technology brought aboutsignificant changes in office routines. (cause —> effect)
2 Computers can create artificial objects in their memories. This allowsdevelopers to test product design without actually creating a real prototype.
(cause —> effect)
3 The extensive use of computers in schools is resulting ina new generation of computer-literate students. (cause —> effect)
4 The problems were caused bythe volume of network traffic.* (effect 4— cause)
Note: * See Language focus H for an explanation of the passive used in example 4.
2 Connectives introducing cause:
| due to as the/a result of since because in response to as |
Examples:
1Early computers developed quickly as a result of their use in military applications. (effect -I-cause)
2 Teachers must rethink their roles as computer technology is creating a revolution in the classroom. (effect 4-cause)
3 Because off-the-shelf programs do not always fit a company's needs, software often has to be specially developed. (effect 4— cause)
3 Connectives introducing result:
| with the result that so that thus therefore consequently hence for this reason thereby |
Examples:
1 Computers can remove many of the routine and boring tasks, thereby leaving us with more time for interesting, creative work. (cause —* effect)
2 Carpel tunnel syndrome is a serious medical condition. For this reason, computer users should be careful of their posture and take frequent breaks. (cause effect)
3 When using an online database service, you must pay for the time you use. Consequently, you should have a good idea of what you want before you log on. (cause effect)
4 Another way of showing causal relationship is by introducing the cause withif, Both the cause clause and the effect clause verbs are in the present tense.
Examples:
1 If your company has a LAN, you can share the use of a printer with your colleagues. (cause effect)
2 It is easy to transport your data to another location if it is stored on a disk. (effect 4— cause)
Exercise 1Read the following sentences and underline the part which expresses the cause.
1Because a modem can be used for inter-computer communication, many people can now do their office work on their computer at home and transfer the files to a computer at the office.
2 Many people do not explore new software because they are comfortable with what they already have.
3 When robots malfunction, it is usually due to mistakes in the programming or the design.
4Laser printers can be quite expensive and are therefore often shared through networks.
5Voice-recognition systems are becoming more sophisticated. Thus, keyboards may be unnecessary in the future.
_ .91.114•■■••
Read the following sentences and underline that part which expresses the
effect/result.
1 Computers can remove many of the routine and boring tasks from our lives, thereby leaving us with more time for interesting and creative work.
2 Because there are many different types of printers, you must analyse your needs before making a purchase.
3 Since anyone can consult your files on a computer, it is a good idea to protect sensitive files with a password.
4Fax boards are available to plug into your computer, so you do not have to buy a fax machine.
5Computers have been reduced in both size and cost as a result of advances in design and technology.
The sentences below have appeared in previous units. Read them again and circle the marker showing a cause—effect relationship and underline the part of the sentence that expresses the cause. The first one has been done for you.
1 By 1980, IBM decided there was a market for 250,000 PCs, !soy they set up a special team to develop the first IBM PC. (Unit 1)
2 Because of these and so many other different judgements, there can be no absolute. (Unit 3)
3 Global communication and computer networks will become more and more a part of professional and personal lives as the price of microcomputers and network access drops. (Unit 6)
4One of the features of a computer virus that separates it from other kinds of computer program is that it replicates itself, so that it can spread to other computers. (Unit 7)
5...Lehigh is waiting to infect other unsuspecting computers if you boot from one of those four infected floppies. (Unit 7)
6As they became more proficient on the computer, some showed gains in their overall self-confidence, as well. (Unit 10)
7Robots are better at this task, not because they are faster or cheaper than humans, but because they work in a place where humans cannot. (Unit 11)
8This automatic accuracy is particularly valuable in this kind of industry because locating and fixing mistakes is costly. (Unit 11)
9Artificial worlds are being built up in a computer memory so that people can walk through at will, look around, and even touch objects. (Unit 12)
Multimedia
Start-up
Task 1Discuss these questions.
1Can you think of any actual or potential applications of multimedia in industry?
2Do you think multimedia systems will ever become as popular as conventional audio-visual systems?
Listening
Task 2ij You are going to hear Nathan Ward, a multimedia applications developer,
answering questions on various aspects of multimedia. Before you listen, try to predict the answers to these questions:
1 Why is multimedia similar to graphics?
2How does Nathan Ward define multimedia?
3Which types of data are involved?
4Is it easy to adapt most PCs for multimedia applications?
5What does the term `full-motion video' refer to?
6Are there industry standards for multimedia?
7What is the best platform for multimedia, according to Ward?
8What is the most popular application of multimedia?
Now listen to the recording and check your guesses.
Task 3Listen again to the conversation and complete the table below.